From 39443092a91ebf392e64c231dbf09c4875e9c891 Mon Sep 17 00:00:00 2001
From: Mariska Wesseling <m.g.h.wesseling@tudelft.nl>
Date: Wed, 24 Jan 2024 16:38:43 +0100
Subject: [PATCH] restructure folder
---
LigamentInsertions/Analyses.py | 477 ---------
LigamentInsertions/AreaTest.py | 102 --
LigamentInsertions/AttechmentArea.py | 281 ------
LigamentInsertions/BlumensaatLine.py | 360 -------
LigamentInsertions/CheckMatchingPoints.py | 81 --
LigamentInsertions/CreateFibulaTransform.py | 35 -
LigamentInsertions/DICOMScalarVolumePlugin.py | 841 ----------------
LigamentInsertions/Elevation PlotLateral.py | 79 --
LigamentInsertions/HausdorffDistance.py | 166 ---
LigamentInsertions/OAIdownload.py | 119 ---
LigamentInsertions/ParaviewLoad.py | 126 ---
LigamentInsertions/ProjectCentroids.py | 213 ----
LigamentInsertions/ReadMe.md | 27 -
LigamentInsertions/Registration4DCT.py | 0
LigamentInsertions/SlicerEnableUndo.py | 27 -
LigamentInsertions/SlicerExportXray.py | 173 ----
LigamentInsertions/SlicerPositionBeam.py | 36 -
LigamentInsertions/SlicerXrayMeanSSM.py | 17 -
LigamentInsertions/TibiaGrid.py | 280 ------
LigamentInsertions/TransformWires.py | 82 --
LigamentInsertions/VisualiseSSM.py | 412 --------
LigamentInsertions/VisualizeCenter.py | 171 ----
LigamentInsertions/VisualizeMeanSSM.ipynb | 942 ------------------
.../VisualizeProjectedCentroids.py | 207 ----
LigamentInsertions/Visualize_modes.py | 244 -----
.../Visualize_modes_ligaments.py | 223 -----
LigamentInsertions/Xray.py | 101 --
LigamentInsertions/average_points_to_stls.py | 105 --
LigamentInsertions/close_mesh.py | 84 --
LigamentInsertions/extractSegmentations.py | 47 -
LigamentInsertions/fitErrorMRI.py | 95 --
LigamentInsertions/fitSSM.py | 348 -------
LigamentInsertions/fitSSM_mri.py | 127 ---
LigamentInsertions/plotHausdorffDistance.py | 32 -
LigamentInsertions/remesh.py | 65 --
LigamentInsertions/rotateMesh.py | 60 --
LigamentInsertions/scaleOsim.py | 261 -----
LigamentInsertions/showAxes.py | 178 ----
LigamentInsertions/stl2vtk.py | 35 -
LigamentInsertions/testVisualizeSSM.py | 441 --------
LigamentInsertions/vislualize_distances.py | 174 ----
LigamentInsertions/vtk2stl.py | 41 -
LigamentStudy/.idea/LigamentStudy.iml | 8 -
LigamentStudy/Analyses.py | 477 ---------
LigamentStudy/AreaTest.py | 102 --
LigamentStudy/AttechmentArea.py | 281 ------
LigamentStudy/BlumensaatLine.py | 360 -------
LigamentStudy/CheckMatchingPoints.py | 81 --
LigamentStudy/CreateFibulaTransform.py | 35 -
LigamentStudy/DICOMScalarVolumePlugin.py | 841 ----------------
LigamentStudy/Elevation PlotLateral.py | 79 --
LigamentStudy/HausdorffDistance.py | 166 ---
LigamentStudy/OAIdownload.py | 119 ---
LigamentStudy/ParaviewLoad.py | 126 ---
LigamentStudy/ProjectCentroids.py | 213 ----
LigamentStudy/ReadMe.md | 27 -
LigamentStudy/Registration4DCT.py | 0
LigamentStudy/SlicerEnableUndo.py | 27 -
LigamentStudy/SlicerExportXray.py | 173 ----
LigamentStudy/SlicerPositionBeam.py | 36 -
LigamentStudy/SlicerXrayMeanSSM.py | 17 -
LigamentStudy/TibiaGrid.py | 280 ------
LigamentStudy/TransformWires.py | 82 --
LigamentStudy/VisualiseSSM.py | 412 --------
LigamentStudy/VisualizeCenter.py | 171 ----
LigamentStudy/VisualizeMeanSSM.ipynb | 942 ------------------
LigamentStudy/VisualizeProjectedCentroids.py | 207 ----
LigamentStudy/Visualize_modes.py | 244 -----
LigamentStudy/Visualize_modes_ligaments.py | 223 -----
LigamentStudy/Xray.py | 101 --
LigamentStudy/average_points_to_stls.py | 105 --
LigamentStudy/close_mesh.py | 84 --
LigamentStudy/extractSegmentations.py | 47 -
LigamentStudy/fitErrorMRI.py | 95 --
LigamentStudy/fitSSM.py | 348 -------
LigamentStudy/fitSSM_mri.py | 127 ---
LigamentStudy/plotHausdorffDistance.py | 32 -
LigamentStudy/remesh.py | 65 --
LigamentStudy/rotateMesh.py | 60 --
LigamentStudy/scaleOsim.py | 261 -----
LigamentStudy/showAxes.py | 178 ----
LigamentStudy/stl2vtk.py | 35 -
LigamentStudy/testVisualizeSSM.py | 441 --------
LigamentStudy/vislualize_distances.py | 174 ----
LigamentStudy/vtk2stl.py | 41 -
85 files changed, 15838 deletions(-)
delete mode 100644 LigamentInsertions/Analyses.py
delete mode 100644 LigamentInsertions/AreaTest.py
delete mode 100644 LigamentInsertions/AttechmentArea.py
delete mode 100644 LigamentInsertions/BlumensaatLine.py
delete mode 100644 LigamentInsertions/CheckMatchingPoints.py
delete mode 100644 LigamentInsertions/CreateFibulaTransform.py
delete mode 100644 LigamentInsertions/DICOMScalarVolumePlugin.py
delete mode 100644 LigamentInsertions/Elevation PlotLateral.py
delete mode 100644 LigamentInsertions/HausdorffDistance.py
delete mode 100644 LigamentInsertions/OAIdownload.py
delete mode 100644 LigamentInsertions/ParaviewLoad.py
delete mode 100644 LigamentInsertions/ProjectCentroids.py
delete mode 100644 LigamentInsertions/ReadMe.md
delete mode 100644 LigamentInsertions/Registration4DCT.py
delete mode 100644 LigamentInsertions/SlicerEnableUndo.py
delete mode 100644 LigamentInsertions/SlicerExportXray.py
delete mode 100644 LigamentInsertions/SlicerPositionBeam.py
delete mode 100644 LigamentInsertions/SlicerXrayMeanSSM.py
delete mode 100644 LigamentInsertions/TibiaGrid.py
delete mode 100644 LigamentInsertions/TransformWires.py
delete mode 100644 LigamentInsertions/VisualiseSSM.py
delete mode 100644 LigamentInsertions/VisualizeCenter.py
delete mode 100644 LigamentInsertions/VisualizeMeanSSM.ipynb
delete mode 100644 LigamentInsertions/VisualizeProjectedCentroids.py
delete mode 100644 LigamentInsertions/Visualize_modes.py
delete mode 100644 LigamentInsertions/Visualize_modes_ligaments.py
delete mode 100644 LigamentInsertions/Xray.py
delete mode 100644 LigamentInsertions/average_points_to_stls.py
delete mode 100644 LigamentInsertions/close_mesh.py
delete mode 100644 LigamentInsertions/extractSegmentations.py
delete mode 100644 LigamentInsertions/fitErrorMRI.py
delete mode 100644 LigamentInsertions/fitSSM.py
delete mode 100644 LigamentInsertions/fitSSM_mri.py
delete mode 100644 LigamentInsertions/plotHausdorffDistance.py
delete mode 100644 LigamentInsertions/remesh.py
delete mode 100644 LigamentInsertions/rotateMesh.py
delete mode 100644 LigamentInsertions/scaleOsim.py
delete mode 100644 LigamentInsertions/showAxes.py
delete mode 100644 LigamentInsertions/stl2vtk.py
delete mode 100644 LigamentInsertions/testVisualizeSSM.py
delete mode 100644 LigamentInsertions/vislualize_distances.py
delete mode 100644 LigamentInsertions/vtk2stl.py
delete mode 100644 LigamentStudy/.idea/LigamentStudy.iml
delete mode 100644 LigamentStudy/Analyses.py
delete mode 100644 LigamentStudy/AreaTest.py
delete mode 100644 LigamentStudy/AttechmentArea.py
delete mode 100644 LigamentStudy/BlumensaatLine.py
delete mode 100644 LigamentStudy/CheckMatchingPoints.py
delete mode 100644 LigamentStudy/CreateFibulaTransform.py
delete mode 100644 LigamentStudy/DICOMScalarVolumePlugin.py
delete mode 100644 LigamentStudy/Elevation PlotLateral.py
delete mode 100644 LigamentStudy/HausdorffDistance.py
delete mode 100644 LigamentStudy/OAIdownload.py
delete mode 100644 LigamentStudy/ParaviewLoad.py
delete mode 100644 LigamentStudy/ProjectCentroids.py
delete mode 100644 LigamentStudy/ReadMe.md
delete mode 100644 LigamentStudy/Registration4DCT.py
delete mode 100644 LigamentStudy/SlicerEnableUndo.py
delete mode 100644 LigamentStudy/SlicerExportXray.py
delete mode 100644 LigamentStudy/SlicerPositionBeam.py
delete mode 100644 LigamentStudy/SlicerXrayMeanSSM.py
delete mode 100644 LigamentStudy/TibiaGrid.py
delete mode 100644 LigamentStudy/TransformWires.py
delete mode 100644 LigamentStudy/VisualiseSSM.py
delete mode 100644 LigamentStudy/VisualizeCenter.py
delete mode 100644 LigamentStudy/VisualizeMeanSSM.ipynb
delete mode 100644 LigamentStudy/VisualizeProjectedCentroids.py
delete mode 100644 LigamentStudy/Visualize_modes.py
delete mode 100644 LigamentStudy/Visualize_modes_ligaments.py
delete mode 100644 LigamentStudy/Xray.py
delete mode 100644 LigamentStudy/average_points_to_stls.py
delete mode 100644 LigamentStudy/close_mesh.py
delete mode 100644 LigamentStudy/extractSegmentations.py
delete mode 100644 LigamentStudy/fitErrorMRI.py
delete mode 100644 LigamentStudy/fitSSM.py
delete mode 100644 LigamentStudy/fitSSM_mri.py
delete mode 100644 LigamentStudy/plotHausdorffDistance.py
delete mode 100644 LigamentStudy/remesh.py
delete mode 100644 LigamentStudy/rotateMesh.py
delete mode 100644 LigamentStudy/scaleOsim.py
delete mode 100644 LigamentStudy/showAxes.py
delete mode 100644 LigamentStudy/stl2vtk.py
delete mode 100644 LigamentStudy/testVisualizeSSM.py
delete mode 100644 LigamentStudy/vislualize_distances.py
delete mode 100644 LigamentStudy/vtk2stl.py
diff --git a/LigamentInsertions/Analyses.py b/LigamentInsertions/Analyses.py
deleted file mode 100644
index cc1ed2e..0000000
--- a/LigamentInsertions/Analyses.py
+++ /dev/null
@@ -1,477 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-from openpyxl import load_workbook
-
-# femur
-# PCL: [1,1,1,1,1,1,1,1,1,1]
-# MCL-superficial: [6,5,6,6,6,6,4,4,5,5]
-# MCL-deep: [3,2+8,5,3,3,2,2,-,3,3]
-# posterior oblique: [7,3,7+8,7,7,5,7,6,7,-]
-# ACL: [4,6,3,5,4,-(4),-(5),3,4,4]
-# LCL (prox): [5,7,4,4,5,7,6,5,6,6]
-# popliteus (dist): [2,4,2,2,2,3,3,2,2,2]
-
-# tibia
-# PCL: [5,7,6,5,3,4,4,5,5,4]
-# MCL-superficial: [1,1,1,1,1,1,1,1,1,1]
-# MCL-deep: [3,3+4,8,3,5,3,5,-(4),3,3]
-# posterior oblique: [4,5+6,3+4+5+7,4,4,5,3,2,4,-]
-# ACL: [6,8,9,6,6,6,6,6,6,5]
-# LCL: [2,2,2,2,2,2,2,3,2,2]
-# popliteus: [-,-,-,-,-,-,-,-,-,-]
-
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #9,13,19,23,26,29,32,35,41
-segments = ['fibula'] #'femur', fibula, 'tibia','femur'
-short = 1
-# the mesh number related to the ligament for each specimen
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments_fib = [[0,0,0,0,0,0,0,0,0,0], # PCL
- [0,0,0,0,0,0,0,0,0,0], # MCLp
- [0,0,0,0,0,0,0,0,0,0], # MCLd
- [0,0,0,0,0,0,0,0,0,0], # MCLd2
- [0,0,0,0,0,0,0,0,0,0], # POL
- [0,0,0,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [0,0,0,0,0,0,0,0,0,0], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-book = load_workbook(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces4.xlsx"))
-writer = pd.ExcelWriter(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces4.xlsx"), engine='openpyxl')
-writer.book = book
-
-for segment in segments:
- surface = np.empty((13, 10))
- surface[:] = np.nan
- center = np.empty((13, 10))
- center[:] = np.nan
- ML_size = np.empty((1, 10))
- ML_size[:] = np.nan
- AP_size = np.empty((1, 10))
- AP_size[:] = np.nan
- SI_size = np.empty((1, 10))
- SI_size[:] = np.nan
- bb_max = np.empty((3, 10))
- bb_max[:] = np.nan
- bb_min = np.empty((3, 10))
- bb_min[:] = np.nan
- ML_size_med = np.empty((1, 10))
- ML_size_med[:] = np.nan
- AP_size_med = np.empty((1, 10))
- AP_size_med[:] = np.nan
- SI_size_med = np.empty((1, 10))
- SI_size_med[:] = np.nan
- bb_max_med = np.empty((3, 10))
- bb_max_med[:] = np.nan
- bb_min_med = np.empty((3, 10))
- bb_min_med[:] = np.nan
- ML_size_lat = np.empty((1, 10))
- ML_size_lat[:] = np.nan
- AP_size_lat = np.empty((1, 10))
- AP_size_lat[:] = np.nan
- SI_size_lat = np.empty((1, 10))
- SI_size_lat[:] = np.nan
- bb_max_lat = np.empty((3, 10))
- bb_max_lat[:] = np.nan
- bb_min_lat = np.empty((3, 10))
- bb_min_lat[:] = np.nan
- dist_to_edge = np.empty((13, 10, 3))
- dist_to_edge[:] = np.nan
- perc_of_len = np.empty((13, 10, 3))
- perc_of_len[:] = np.nan
- perc_of_len_med = np.empty((13, 10, 3))
- perc_of_len_med[:] = np.nan
- perc_of_len_lat = np.empty((13, 10, 3))
- perc_of_len_lat[:] = np.nan
- center = np.empty((13, 10, 3))
- center[:] = np.nan
- if segment == 'femur':
- ligaments = ligaments_fem
- elif segment == 'tibia':
- ligaments = ligaments_tib
- else:
- ligaments = ligaments_fib
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # transform femur to local coordinate system to get anatomical directions
- if segment=='fibula':
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_tibia_resample._ACS.txt'))
- else:
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- mesh2 = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/' + str(subject) + '\Segmentation_' + segment + '_sep.stl'
- ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- # ms5.save_current_mesh(path + '\Segmentation_' + segment + '_sep_transform.stl', binary=False)
- if segment=='tibia':
- ms5.load_new_mesh(path + '\Segmentation_' + segment + '_sep_transform.stl')
- else:
- ms5.load_new_mesh(path + '\Segmentation_' + segment + '_transform.stl')
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size[0, ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size[0, ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size[0, ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- if side == 'R':
- ms5.conditional_vertex_selection(condselect="x<0") #select lat (41=L)
- ms5.apply_filter('move_selected_vertices_to_another_layer') #0=med, 1=lat
- else:
- ms5.conditional_vertex_selection(condselect="x>0") # select lat (41=L)
- ms5.apply_filter('move_selected_vertices_to_another_layer') # 0=med, 1=lat
-
- ms5.set_current_mesh(0)
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size_med[0,ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size_med[0,ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size_med[0,ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max_med[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min_med[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- ms5.set_current_mesh(1)
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max_lat[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min_lat[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- # determine surface area attachments
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- ms4 = pymeshlab.MeshSet()
- if segment == 'fibula':
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '_transform.stl')
- else:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface[lig,ind] = geometric_measures['surface_area']
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center[lig,ind,:] = geometric_measures['shell_barycenter']
- if side == 'R':
- dist_to_edge[lig, ind, :] = center[lig, ind, :] - bb_min[:, ind]
- dist_to_edge[lig, ind, 0] = center[lig, ind, 0] - bb_max[0, ind]
- else:
- dist_to_edge[lig,ind,:] = center[lig, ind,:] - bb_min[:, ind]
-
- if segment == 'tibia' or segment == 'fibula':
- dist_to_edge[lig, ind, 2] = center[lig, ind, 2] - bb_max[2, ind]
- perc_of_len[lig,ind,:] = abs(dist_to_edge[lig,ind,:]/(ML_size[0,ind],AP_size[0,ind],AP_size[0,ind]))
- perc_of_len_med[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size_med[0, ind], AP_size_med[0, ind], AP_size_med[0, ind]))
- perc_of_len_lat[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size_lat[0, ind], AP_size_lat[0, ind], AP_size_lat[0, ind]))
- for lig_comb in [2, 4]:
- lig_no = ligaments[lig_comb][ind]
- if not lig_no == 0:
- if lig_comb == 2:
- lig = 11
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 1) + '.stl')
- except:
- print('')
- if lig_comb == 4:
- lig = 12
- ms4 = pymeshlab.MeshSet()
- if segment=='fibula':
- ms4.load_new_mesh(path + '\Segmentation_tibia_area' + str(lig_no) + '.stl')
- else:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 1) + '.stl')
- except:
- print('')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 2) + '.stl')
- except:
- print('')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 3) + '.stl')
- except:
- print('')
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface[lig, ind] = geometric_measures['surface_area']
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center[lig, ind, :] = geometric_measures['shell_barycenter']
- dist_to_edge[lig, ind, :] = center[lig, ind, :] - bb_min[:, ind]
- if segment == 'tibia' or segment == 'fibula':
- dist_to_edge[lig, ind, 2] = center[lig, ind, 2] - bb_max[2, ind]
- perc_of_len[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size[0, ind], AP_size[0, ind], AP_size[0, ind]))
- perc_of_len_med[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (
- ML_size_med[0, ind], AP_size_med[0, ind], AP_size_med[0, ind]))
- perc_of_len_lat[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (
- ML_size_lat[0, ind], AP_size_lat[0, ind], AP_size_lat[0, ind]))
-
- df = pd.DataFrame({'PCLx': 100-perc_of_len[0,:,0]*100,
- 'MCL-sx': 100-perc_of_len[1, :, 0]*100,
- 'MCL-d1x': 100-perc_of_len[2, :, 0]*100,
- 'MCL-d2x': 100-perc_of_len[3, :, 0]*100,
- 'MCL-d2': 100-perc_of_len[11, :, 0]*100,
- 'posterior oblique1x': 100-perc_of_len[4, :, 0]*100,
- 'posterior oblique2x': 100-perc_of_len[5, :, 0]*100,
- 'posterior oblique3x': 100-perc_of_len[6, :, 0]*100,
- 'posterior oblique4x': 100-perc_of_len[7, :, 0]*100,
- 'posterior obliquex': 100-perc_of_len[12, :, 0]*100,
- 'ACLx': 100-perc_of_len[8, :, 0]*100,
- 'LCLx': 100-perc_of_len[9, :, 0]*100,
- 'popliteusx': 100-perc_of_len[10, :, 0]*100,
- 'PCLy': 100-perc_of_len[0,:,1]*100,
- 'MCL-sy': 100-perc_of_len[1, :, 1]*100,
- 'MCL-d1y': 100-perc_of_len[2, :, 1]*100,
- 'MCL-d2y': 100-perc_of_len[3, :, 1]*100,
- 'MCL-dy': 100-perc_of_len[11, :, 1]*100,
- 'posterior oblique1y': 100-perc_of_len[4, :, 1]*100,
- 'posterior oblique2y': 100-perc_of_len[5, :, 1]*100,
- 'posterior oblique3y': 100-perc_of_len[6, :, 1]*100,
- 'posterior oblique4y': 100-perc_of_len[7, :, 1]*100,
- 'posterior obliquey': 100-perc_of_len[12, :, 1]*100,
- 'ACLy': 100-perc_of_len[8, :, 1]*100,
- 'LCLy': 100-perc_of_len[9, :, 1]*100,
- 'popliteusy': 100-perc_of_len[10, :, 1]*100,
- 'PCLz': perc_of_len[0,:,2]*100,
- 'MCL-sz': perc_of_len[1,:,2]*100,
- 'MCL-d1z': perc_of_len[2,:,2]*100,
- 'MCL-d2z': perc_of_len[3, :, 2]*100,
- 'MCL-dz': perc_of_len[11, :, 2]*100,
- 'posterior oblique1z': perc_of_len[4,:,2]*100,
- 'posterior oblique2z': perc_of_len[5, :, 2]*100,
- 'posterior oblique3z': perc_of_len[6, :, 2]*100,
- 'posterior oblique4z': perc_of_len[7, :, 2]*100,
- 'posterior obliquez': perc_of_len[12, :, 2]*100,
- 'ACLz': perc_of_len[8,:,2]*100,
- 'LCLz': perc_of_len[9,:,2]*100,
- 'popliteusz': perc_of_len[10,:,2]*100
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='perc_of_len ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(
- decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- # table_data = table_data.T
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- table_data.to_excel(writer, sheet_name='table perc_of_len ' + segment)
-
- df = pd.DataFrame({'PCLy': 100-perc_of_len_med[0, :, 1]*100,
- 'MCL-sy': 100-perc_of_len_med[1, :, 1]*100,
- 'MCL-d1y': 100-perc_of_len_med[2, :, 1]*100,
- 'MCL-d2y': 100-perc_of_len_med[3, :, 1]*100,
- 'MCL-dy': 100-perc_of_len_med[11, :, 1]*100,
- 'posterior oblique1y': 100-perc_of_len_med[4, :, 1]*100,
- 'posterior oblique2y': 100-perc_of_len_med[5, :, 1]*100,
- 'posterior oblique3y': 100-perc_of_len_med[6, :, 1]*100,
- 'posterior oblique4y': 100-perc_of_len_med[7, :, 1]*100,
- 'posterior obliquey': 100-perc_of_len_med[12, :, 1]*100,
- 'ACLy': 100-perc_of_len_lat[8, :, 1]*100,
- 'LCLy': 100-perc_of_len_lat[9, :, 1]*100,
- 'popliteusy': 100-perc_of_len_lat[10, :, 1]*100,
- 'PCLz': perc_of_len_med[0, :, 2]*100,
- 'MCL-sz': perc_of_len_med[1, :, 2]*100,
- 'MCL-d1z': perc_of_len_med[2, :, 2]*100,
- 'MCL-d2z': perc_of_len_med[3, :, 2]*100,
- 'MCL-dz': perc_of_len_med[11, :, 2]*100,
- 'posterior oblique1z': perc_of_len_med[4, :, 2]*100,
- 'posterior oblique2z': perc_of_len_med[5, :, 2]*100,
- 'posterior oblique3z': perc_of_len_med[6, :, 2]*100,
- 'posterior oblique4z': perc_of_len_med[7, :, 2]*100,
- 'posterior obliquez': perc_of_len_med[12, :, 2]*100,
- 'ACLz': perc_of_len_lat[8, :, 2]*100,
- 'LCLz': perc_of_len_lat[9, :, 2]*100,
- 'popliteusz': perc_of_len_lat[10, :, 2]*100
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='perc_of_len med_lat ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- # table_data = table_data.T
- table_data.to_excel(writer, sheet_name='table perc_of_len med_lat ' + segment)
-
- df = pd.DataFrame({'PCLx': 100-dist_to_edge[0,:,0],
- 'MCL-sx': 100-dist_to_edge[1, :, 0],
- 'MCL-d1x': 100-dist_to_edge[2, :, 0],
- 'MCL-d2x': 100-dist_to_edge[3, :, 0],
- 'MCL-dx': 100-dist_to_edge[11, :, 0],
- 'posterior oblique1x': 100-dist_to_edge[4, :, 0],
- 'posterior oblique2x': 100-dist_to_edge[5, :, 0],
- 'posterior oblique3x': 100-dist_to_edge[6, :, 0],
- 'posterior oblique4x': 100-dist_to_edge[7, :, 0],
- 'posterior obliquex': 100-dist_to_edge[12, :, 0],
- 'ACLx': 100-dist_to_edge[8, :, 0],
- 'LCLx': 100-dist_to_edge[9, :, 0],
- 'popliteusx': 100-dist_to_edge[10, :, 0],
- 'PCLy': 100-dist_to_edge[0,:,1],
- 'MCL-sy': 100-dist_to_edge[1, :, 1],
- 'MCL-d1y': 100-dist_to_edge[2, :, 1],
- 'MCL-d2y': 100-dist_to_edge[3, :, 1],
- 'MCL-dy': 100-dist_to_edge[11, :, 1],
- 'posterior oblique1y': 100-dist_to_edge[4, :, 1],
- 'posterior oblique2y': 100-dist_to_edge[5, :, 1],
- 'posterior oblique3y': 100-dist_to_edge[6, :, 1],
- 'posterior oblique4y': 100-dist_to_edge[7, :, 1],
- 'posterior obliquey': 100-dist_to_edge[12, :, 1],
- 'ACLy': 100-dist_to_edge[8, :, 1],
- 'LCLy': 100-dist_to_edge[9, :, 1],
- 'popliteusy': 100-dist_to_edge[10, :, 1],
- 'PCLz': dist_to_edge[0,:,2],
- 'MCL-sz': dist_to_edge[1,:,2],
- 'MCL-d1z': dist_to_edge[2,:,2],
- 'MCL-d2z': dist_to_edge[3, :, 2],
- 'MCL-dz': dist_to_edge[11, :, 2],
- 'posterior oblique1z': dist_to_edge[4,:,2],
- 'posterior oblique2z': dist_to_edge[5, :, 2],
- 'posterior oblique3z': dist_to_edge[6, :, 2],
- 'posterior oblique4z': dist_to_edge[7, :, 2],
- 'posterior obliquez': dist_to_edge[12, :, 2],
- 'ACLz': dist_to_edge[8,:,2],
- 'LCLz': dist_to_edge[9,:,2],
- 'popliteusz': dist_to_edge[10,:,2]
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
-
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='distance_to_edge ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(
- decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- # table_data = table_data.T
- table_data.to_excel(writer, sheet_name='table distance_to_edge ' + segment)
-
- MCLd = np.nansum([surface[2, :], surface[3, :]], 0)
- MCLd[MCLd == 0] = 'nan'
- pol = np.nansum([surface[4, :],surface[5, :],surface[6, :],surface[7, :]],0)
- pol[pol == 0] = 'nan'
- df = pd.DataFrame({'PCL': surface[0,:],
- 'MCL-s': surface[1,:],
- 'MCL-d1:': surface[2,:],
- 'MCL-d2:': surface[3, :],
- 'MCL-d:': MCLd,
- 'posterior oblique1': surface[4,:],
- 'posterior oblique2': surface[5, :],
- 'posterior oblique3': surface[6, :],
- 'posterior oblique4': surface[7, :],
- 'posterior oblique': pol,
- 'ACL': surface[8,:],
- 'LCL': surface[9,:],
- 'popliteus': surface[10,:],
- })
- means = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(decimals=1).astype(str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(decimals=1) .astype(str)+ ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means,ignore_index=True)
- # summary_ave_data = summary_ave_data.append(std,ignore_index=True)
- # summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- # summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'ATTACHMENT AREA (MEAN±STD, RANGE)'},axis='index')
-
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='surface ' + segment)
-
-writer.save()
-writer.close()
diff --git a/LigamentInsertions/AreaTest.py b/LigamentInsertions/AreaTest.py
deleted file mode 100644
index d940e83..0000000
--- a/LigamentInsertions/AreaTest.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import pandas as pd
-import os
-import trimesh
-import numpy as np
-import matplotlib.path as plt
-
-
-def heron(a, b, c):
- s = (a + b + c) / 2
- area = (s * (s - a) * (s - b) * (s - c)) ** 0.5
- return area
-
-
-def distance3d(x1, y1, z1, x2, y2, z2):
- a = (x1 - x2) ** 2 + (y1 - y2) ** 2 + (z1 - z2) ** 2
- d = a ** 0.5
- return d
-
-
-def area(x1, y1, z1, x2, y2, z2, x3, y3, z3):
- a = distance3d(x1, y1, z1, x2, y2, z2)
- b = distance3d(x2, y2, z2, x3, y3, z3)
- c = distance3d(x3, y3, z3, x1, y1, z1)
- A = heron(a, b, c)
- return A
-
-
-# print("area of triangle is %r " %A)
-
-# A utility function to calculate area
-# of triangle formed by (x1, y1),
-# (x2, y2) and (x3, y3)
-
-# def area(x1, y1, x2, y2, x3, y3):
-# return abs((x1 * (y2 - y3) + x2 * (y3 - y1)
-# + x3 * (y1 - y2)) / 2.0)
-
-
-# A function to check whether point P(x, y)
-# lies inside the triangle formed by
-# A(x1, y1), B(x2, y2) and C(x3, y3)
-def isInside(p1, p2, p3, p):
- x1 = p1[0]
- y1 = p1[1]
- z1 = p1[2]
- x2 = p2[0]
- y2 = p2[1]
- z2 = p2[2]
- x3 = p3[0]
- y3 = p3[1]
- z3 = p3[2]
- x = p[0]
- y = p[1]
- z = p[2]
-
- # Calculate area of triangle ABC
- A = area(x1, y1, z1, x2, y2, z2, x3, y3, z3)
-
- # Calculate area of triangle PBC
- A1 = area(x, y, z, x2, y2, z2, x3, y3, z3)
-
- # Calculate area of triangle PAC
- A2 = area(x1, y1, z1, x, y, z, x3, y3, z3)
-
- # Calculate area of triangle PAB
- A3 = area(x1, y1, z1, x2, y2, z2, x, y, z)
-
- # Check if sum of A1, A2 and A3
- # is same as A
- if abs(A - (A1 + A2 + A3) < 1e-6):
- return True
- else:
- return False
-
-
-def intersection(planeNormal, planePoint, rayDirection, rayPoint):
- epsilon = 1e-6
-
- # Define plane
- # planeNormal = np.array([0, 0, 1])
- # planePoint = np.array([0, 0, 5]) #Any point on the plane
-
- # Define ray
- # rayDirection = np.array([0, -1, -1])
- # rayPoint = np.array([0, 0, 10]) #Any point along the ray
-
- ndotu = planeNormal.dot(rayDirection)
-
- if abs(ndotu) < epsilon:
- intersect = 0
- else:
- w = rayPoint - planePoint[0, :]
- si = -planeNormal.dot(w) / ndotu
- Psi = w + si * rayDirection + planePoint[0, :]
- if isInside(planePoint[0], planePoint[1], planePoint[2], Psi) == False:
- intersect = 0
- else:
- intersect = Psi[0]
-
- return intersect
-
-intersection(np.array([0,0,1]), np.array([(1,1,0),(1,2,0),(2,1.5,0)]), np.array([0,0,1]), np.array((1.5,1.5,1)))
diff --git a/LigamentInsertions/AttechmentArea.py b/LigamentInsertions/AttechmentArea.py
deleted file mode 100644
index e89c618..0000000
--- a/LigamentInsertions/AttechmentArea.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import pymeshlab
-import os.path
-import trimesh
-import numpy as np
-
-
-def cylinder_between(p1, p2, r, path):
- dx = p2[0] - p1[0]
- dy = p2[1] - p1[1]
- dz = p2[2] - p1[2]
- dist = np.sqrt(dx**2 + dy**2 + dz**2)+0.5
-
- phi = np.arctan2(dy, dx)
- theta = np.arccos(dz/dist)
-
- T = trimesh.transformations.translation_matrix([dx/2 + p1[0], dy/2 + p1[1], dz/2 + p1[2]])
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(phi, zaxis)
- Ry = trimesh.transformations.rotation_matrix(theta, yaxis)
- R = trimesh.transformations.concatenate_matrices(T,Rz, Ry)
-
- cylinder = trimesh.creation.cylinder(r, height=dist, sections=None, segment=None, transform=R)
- cylinder.export(path)
-
-
-def cut_mesh(out2, path, i):
- # load wire mesh in new meshlab file
- ms2 = pymeshlab.MeshSet()
- ms2.load_new_mesh(mesh1)
- # translate wire to mesh in direction of the normal of the plane and copy to create thick wire
- ms2.transform_translate_center_set_origin(traslmethod=0, axisx=-plane_normal[0, 0] ,
- axisy=-plane_normal[0, 1] ,
- axisz=-plane_normal[0, 2] ,
- freeze=True, alllayers=False)
- factor = 0.1
- no = 0
- for ind in range(0, 24):
- ms2.load_new_mesh(mesh1)
- factor += 0.25
- ms2.transform_translate_center_set_origin(traslmethod=0, axisx=-plane_normal[0, 0] * out2['mean'] * factor,
- axisy=-plane_normal[0, 1] * out2['mean'] * factor,
- axisz=-plane_normal[0, 2] * out2['mean'] * factor,
- freeze=True, alllayers=False)
- ms2.apply_filter('mesh_boolean_union', first_mesh=no, second_mesh=no + 1)
- no += 2
-
- # save thick wire
- ms2.save_current_mesh(path + '\Segmentation_' + segment + '_wire' + str(i) + 'union.stl', binary=False)
-
- # load thick wire and area in new meshlab file
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_wire' + str(i) + 'union.stl')
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
-
- # compute signed distance
- # out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=False)
- out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=True)
-
- # select and delete vertices with negative distance
- # ms4.conditional_vertex_selection(condselect="q<0.15")
- ms4.conditional_vertex_selection(condselect="(q <0)") # "(q <0) && (q >-0.4)"
- ms4.delete_selected_vertices()
- # split mesh
- out4 = ms4.apply_filter('split_in_connected_components')
-
- return ms4
-
-
-subjects = [19] # 9,13,19,23,26,29,32,35,37,41
-segments = ['femur'] # ['femur', 'tibia'] # ['femur'] #
-no_subjects = len(subjects)
-no_segments = len(segments)
-
-for subject in subjects:
- for segment in segments:
-
- # # split wires to seperate files
- path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/" + str(subject) + '/'
- # mesh_all = path + 'Segmentation_' + segment + '_wires.stl'
- # ms3 = pymeshlab.MeshSet()
- # ms3.load_new_mesh(mesh_all)
- # ms3.apply_filter('split_in_connected_components')
- #
- # no_meshes = ms3.number_meshes()
- # for i in range(1, no_meshes):
- # ms3.set_current_mesh(i)
- # no_vertices = ms3.mesh(i).vertex_matrix().shape[0]
- # if no_vertices < 50:
- # ms3.delete_current_mesh()
- # else:
- # if not os.path.isfile(path + '\Segmentation_' + segment + '_wires' + str(i-1) + '.stl') and not i == 1:
- # no = i-1
- # else:
- # no = i
- # ms3.save_current_mesh(path + '\Segmentation_' + segment + '_wires' + str(no) + '.stl')
- #
- # no_meshes = no
- # # combine tibia and fibula
- # if segment == 'tibia':
- # ms1 = pymeshlab.MeshSet()
- # ms1.load_new_mesh(path + 'Segmentation_tibia_sep.stl')
- # ms1.load_new_mesh(path + 'Segmentation_fibula.stl')
- # ms1.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
- # ms1.save_current_mesh(path + 'Segmentation_tibia.stl', binary=False)
-
- # run over all wires
- error = []
- mesh2 = path + 'Segmentation_' + segment + '.stl'
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('uniform_mesh_resampling', cellsize=1)
- # # ms5.apply_filter('transform_rotate', rotaxis=2, angle=180)
- # ms5.save_current_mesh(path + '\Segmentation_' + segment + '_resample.stl', binary=False)
-
- for i in range(3,4): #range(1, no_meshes+1): #range(3,4): #range(5,no_meshes+1): #
- mesh1 = path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl'
-
- # load meshes in new meshlab file
- ms = pymeshlab.MeshSet()
- ms.load_new_mesh(mesh1)
- ms.load_new_mesh(mesh2)
-
- # calculate Hausdorff distance in both directions
- out2 = ms.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=False, maxdist=9)
- out1 = ms.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=False, maxdist=9)
-
- # select and delete all vertices far from the wire
- ms.conditional_vertex_selection(condselect="q>8.9")
- ms.delete_selected_vertices()
- # save section containing area
- ms.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
- #fit plane through section containing area
- # ms.set_current_mesh(new_curr_id=0)
- ms.select_all()
- ms.fit_a_plane_to_selection()
- plane_normal = ms.mesh(2).vertex_normal_matrix()
-
- ms4 = cut_mesh(out2, path, i)
-
- # check the number of components and remove the ones with few vertices
- no_meshes = ms4.number_meshes()
- meshes_to_remove = no_meshes-4
- if meshes_to_remove > 0:
- for ind in range(0,no_meshes):
- no_vertices = ms4.mesh(ind).vertex_matrix().shape[0]
- if no_vertices < 50:
- ms4.set_current_mesh(ind)
- ms4.delete_current_mesh()
- else:
- last_mesh = ind
- else:
- last_mesh = 3
- # check the number of meshes
- # if there are less than 4, split is not done in 2 large surfaces and surface needs to be closed
- no_meshes = ms4.number_meshes()
- if no_meshes < 4: #no_vertices < 10:
- # load wire in new meshset
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl')
- # find for each point the largest distance on mesh
- dist_matrix = []
- dist_matrix_ind = []
- start_ind = []
- verts = ms6.mesh(0).vertex_matrix()
- for ind in range(0, len(verts)):
- ms6.apply_filter('colorize_by_geodesic_distance_from_a_given_point', startpoint=verts[ind], maxdistance=100)
- dist_matrix.append(np.max(ms6.mesh(0).vertex_quality_array()))
- dist_matrix_ind.append(np.argmax(ms6.mesh(0).vertex_quality_array()))
- start_ind.append(ind)
- # find which point has largest distance
- max1 = np.argmax(dist_matrix)
- end_point = verts[dist_matrix_ind[max1]]
- start_point = verts[start_ind[max1]]
- # create cylinder between these points
- r = 0.5
- path_cylinder = path + '\Segmentation_' + segment + '_wires' + str(i) + 'cylinder.stl'
- cylinder_between(start_point, end_point, r, path_cylinder)
- # combine wire and cylinder
- ms6.load_new_mesh(path_cylinder)
- ms6.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
- ms6.save_current_mesh(path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl', binary=False)
- # split mesh again with closed wire
- ms4 = cut_mesh(out2, path, i)
- # remove meshes with few vertices
- no_meshes = ms4.number_meshes()
- for ind in range(0,no_meshes):
- no_vertices = ms4.mesh(ind).vertex_matrix().shape[0]
- if no_vertices < 50:
- ms4.set_current_mesh(ind)
- ms4.delete_current_mesh()
- else:
- last_mesh = ind
- # select last mesh to save
- no_meshes = ms4.number_meshes()
- # save only mesh part inside wire
- # ms4.set_current_mesh(new_curr_id=last_mesh)
- to_del = [0,1,2]
- for removes in range(len(to_del)):
- ms4.set_current_mesh(new_curr_id=to_del[removes])
- ms4.delete_current_mesh()
- print(ms4.number_meshes())
- ms4.apply_filter('flatten_visible_layers')
- try:
- ms4.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl', binary=False)
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface = geometric_measures['surface_area']
- print('Surface area ' + segment + ' ligament' + str(i) + ': ' + str(surface) + ' mm2')
- ms4.save_project(path + '\Segmentation_' + segment + '_area' + str(i) + '.mlp')
- except:
- error.append(i)
-
-
-
-
-# ms.select_all()
-# ms.fit_a_plane_to_selection()
-# plane_normal = ms.mesh(1).face_normal_matrix()
-
-# vert_matrix_connect = ms.mesh(2).vertex_matrix()
-# matrix = ms.mesh(1).vertex_matrix()
-# points = []
-# val = []
-# for i in range(0,len(vert_matrix_connect)):
-# points.append(np.argmin(np.abs(np.sum(matrix-vert_matrix_connect[i,:],axis=1))))
-# val.append(np.amin(np.abs(np.sum(matrix-vert_matrix_connect[i,:],axis=1))))
-
-# ms.conditional_vertex_selection(condselect="vi=="+points[0])
-# ms.delete_selected_vertices()
-
-# out4 = ms2.apply_filter('mesh_boolean_intersection', first_mesh=1, second_mesh=0)
-
-
-# no_verts = ms2.mesh(1).selected_vertex_number()
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-# while no_verts/2 < no_verts_new:
-# ms2.select_border()
-# ms2.delete_selected_vertices()
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-
-# matrix = ms2.mesh(1).face_matrix()
-# unique, counts = np.unique(matrix, return_counts=True)
-# # dict(zip(unique, counts))
-# bla2 = np.where(counts < 5)
-#
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-
-# while no_verts_new>0:
-# for i in range(0,len(bla2[0])):
-# ms2.conditional_vertex_selection(condselect=("vi=="+str(bla2[0][i])))
-# ms2.delete_selected_faces_and_vertices()
-#
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-# matrix = ms2.mesh(1).face_matrix()
-# unique, counts = np.unique(matrix, return_counts=True)
-# # dict(zip(unique, counts))
-# bla2 = np.where(counts < 5)
-
-#
-# ms2.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True, maxdist=5)
-
-# ms2.conditional_vertex_selection(condselect="q>2")
-# ms2.delete_selected_vertices()
-
-
-
-#
-
-# ms.apply_filter('select_faces_from_vertices', points, inclusive=True)
-# ms.apply_filter('delete_selected_faces_and_vertices')
-
-# vert_matrix_connect.apply_filter('surface_reconstruction_ball_pivoting')
-
-# file = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\26\femur_wires.xyz'
-# np.savetxt(file, vert_matrix_connect)
-
-
-# ms2.save_project(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\26\Segmentation.mlp')
\ No newline at end of file
diff --git a/LigamentInsertions/BlumensaatLine.py b/LigamentInsertions/BlumensaatLine.py
deleted file mode 100644
index 4e1473f..0000000
--- a/LigamentInsertions/BlumensaatLine.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# Find most anterior edge of the femoral notch roof - representation Blumensaat line for 3D shapes
-# https://journals.lww.com/jbjsjournal/Fulltext/2010/06000/The_Location_of_Femoral_and_Tibial_Tunnels_in.10.aspx?__hstc=215929672.82af9c9a98fa600b1bb630f9cde2cb5f.1528502400314.1528502400315.1528502400316.1&__hssc=215929672.1.1528502400317&__hsfp=1773666937&casa_token=BT765BcrC3sAAAAA:Vu9rn-q5ng4c8339KQuq2mGZDgrAgBStwvn4lvYEbvCgvKQZkbJL24hWbKFdnHTc8VBmAIXA3HVvuWg22-9Mvwv1sw
-# https://www.dropbox.com/sh/l7pd43t7c4hrjdl/AABkncBbleifnpLDKSDDc0dCa/D3%20-%20Dimitriou%202020%20-%20Anterior%20cruciate%20ligament%20bundle%20insertions%20vary.pdf?dl=0
-
-import trimesh
-import numpy as np
-import os
-import math
-import pandas as pd
-# import pymeshlab
-import seaborn as sns
-
-
-def findIntersection(x1, y1, x2, y2, x3, y3, x4, y4):
- px = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
- py = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
-
- ang = math.atan2(py - y3, px - x3) - math.atan2(y1 - y3, x1 - x3)
-
- l = math.cos(ang)*np.linalg.norm(np.asarray((x3,y3))-np.asarray((x4,y4)))
-
- return l, px, py
-
-def split(start, end, segments):
- x_delta = (end[0] - start[0]) / float(segments)
- y_delta = (end[1] - start[1]) / float(segments)
- z_delta = (end[2] - start[2]) / float(segments)
- points = []
- for i in range(1, segments):
- points.append([start[0] + i * x_delta, start[1] + i * y_delta, start[2] + i * z_delta])
- return [start] + points + [end]
-
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments = ligaments_fem
-
-# find most ant point in yz plane
-subjects = [100] # [9,13,19,23,26,29,32,35,37,41] #
-lig = 'ACL'
-segment = 'femur'
-
-d = []
-h = []
-h_centriods = []
-d_centriods = []
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_rot.stl')
- path_col = r'C:\\Users\\mariskawesseli\\Documents\\GitLab\\knee_ssm\\OAI\\Output/tibia_bone\\new_bone\\shape_models'
- side = 'R'
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), 'Segmentation_femur_transform.STL')
-
- mesh = trimesh.load_mesh(path)
- verts = mesh.vertices
- AP = mesh.bounding_box.bounds[1, 1] - mesh.bounding_box.bounds[0, 1]
- ML = mesh.bounding_box.bounds[1, 0] - mesh.bounding_box.bounds[0, 0]
- bbox = mesh.bounding_box.bounds
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, (0,-1,0), (0,20,0), cached_dots=None, return_both=False)
- # posterior_mesh.show()
-
- # find blumensaat line
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(mesh, (0,0,0), (1,0,0), heights=np.linspace(-10, 10, 21))
- dist_point = []
- prox_point = []
- for i in range(0,len(face_index)):
- plane_verts = np.unique(mesh.faces[face_index[i]])
- plane_points = mesh.vertices[plane_verts]
-
- goon = 1
- tel = 2
- while goon == 1:
- min_z = np.where(plane_points[:,2] == np.partition(plane_points[:,2], tel)[tel])
- y_min = plane_points[min_z][0][1]
- min_z2 = np.where(plane_points[:,2] == np.partition(plane_points[:,2], tel+1)[tel+1])
- y_min2 = plane_points[min_z2][0][1]
- if y_min-y_min2 > -15:
- goon = 1
- tel += 1
- else:
- goon = 0
- dist_point.append(plane_points[min_z][0])
- min_y = np.where(plane_points[:,1] == plane_points[:,1].min())
- prox_point.append(plane_points[min_y][0])
-
- most_ant_ind1 = np.asarray(dist_point)[:, 1].argmax()
- most_ant_ind2 = np.asarray(prox_point)[:, 1].argmax()
-
- p1 = []
- p2 = []
- if most_ant_ind1 == most_ant_ind2:
- p1.append(dist_point[most_ant_ind1])
- p2.append(prox_point[most_ant_ind1])
- print('equal')
- else:
- p1.append(dist_point[most_ant_ind2])
- p2.append(prox_point[most_ant_ind2])
- print('not equal')
-
- if side == 'R':
- if lig == 'ACL':
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (1,0,0), (0, 0, 0), cached_dots=None, return_both=False)
- else:
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (-1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
- else:
- if lig == 'ACL':
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (-1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
- else:
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
-
- # find height
- vec1 = (p1[0][0] - p2[0][0], p1[0][1] - p2[0][1], p1[0][2] - p2[0][2])
- norm = np.sqrt(vec1[0] ** 2 + vec1[1] ** 2 + vec1[2] ** 2)
- direction = [vec1[0] / norm, vec1[1] / norm, vec1[2] / norm]
-
-
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
-
- # trimesh.path.segments.parameters_to_segments(p1[-1], -1*direction, ((0,0,0),(0,1,0)))
- # trimesh.path.segments.segments_to_parameters(np.asarray(segments))
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,0,10), cached_dots=None, return_both=False)
-
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, (0,0,0), direction, heights=np.linspace(-10, 10, 21))
-
- dist = []
- p3 = []
- p4 = []
- p1_2d = p1[-1][1:3]
- p2_2d = p2[-1][1:3]
- for i in range(0,len(face_index)):
- plane_verts = np.unique(lateral_mesh.faces[face_index[i]])
- plane_points = lateral_mesh.vertices[plane_verts]
-
- min_y = np.where(plane_points[:,1] == np.partition(plane_points[:,1], 0)[0])
- max_y = np.where(plane_points[:,1] == np.partition(plane_points[:,1], -1)[-1])
-
- p3.append(plane_points[min_y][0])
- p4.append(plane_points[max_y][0])
- dist.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3[i][1:3]))/np.linalg.norm(p2_2d-p1_2d))
- # dist.append(np.linalg.norm(plane_points[min_y][0]-plane_points[max_y][0]))
-
- # segments = np.asarray([p3[np.asarray(dist).argmax()], p4[np.asarray(dist).argmax()]])
- # p_dist = trimesh.load_path(segments)
- dist1 = dist
- p3_2d = p3[np.asarray(dist1).argmax()][1:3]
- h.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3_2d))/np.linalg.norm(p2_2d-p1_2d))
-
- # find depth
- # lateral_mesh.show()
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, (0, 0, 0), direction,
- heights=np.linspace(-30, -5, 41))
-
- dist = []
- p6 = []
- p4 = []
- p1_2d = p1[-1][1:3]
- p2_2d = p2[-1][1:3]
- for i in range(0, len(face_index)):
- plane_verts = np.unique(lateral_mesh.faces[face_index[i]])
- plane_points = lateral_mesh.vertices[plane_verts]
-
- min_y = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], 0)[0])
- max_y = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], -1)[-1])
-
- p6.append(plane_points[min_y][0])
- p4.append(plane_points[max_y][0])
- dist.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - p6[i][1:3])) / np.linalg.norm(p2_2d - p1_2d))
- # dist.append(np.linalg.norm(plane_points[min_y][0]-plane_points[max_y][0]))
-
- jump_ind = np.where(np.diff(np.asarray(p6), axis=0)[:,1] == np.min(np.diff(np.asarray(p6), axis=0)[:,1]))[0][0]
-
- # segments = np.asarray([p6[jump_ind+1], p4[jump_ind+1]])
- # p_dist = trimesh.load_path(segments)
-
- p6_2d = p6[jump_ind+1][1:3]
- # min_z = lateral_mesh.vertices[np.argmin(lateral_mesh.vertices[:,2])]
- # p5 = np.asarray(min_z)
- # p5_2d = p5[1:3]
-
- direction = np.asarray(direction) * -1
- direction_perp = np.array((direction[0], -direction[2], direction[1]))
-
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, p1[0], direction_perp,
- heights=np.linspace(0, 1, 1))
- plane_verts = np.unique(lateral_mesh.faces[face_index[0]])
- plane_points = lateral_mesh.vertices[plane_verts]
- min_z = np.where(plane_points[:, 2] == np.partition(plane_points[:, 2], 0)[0])
- p5 = plane_points[min_z][0]
- p5_2d = p5[1:3]
-
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], p6_2d[0], p6_2d[1], p5_2d[0], p5_2d[1])
- d.append(l)
-
- # visualization
- # p1[0][0] = 0
- # p2[0][0] = 0
- # p3[np.asarray(dist1).argmax()][0] = 0
- # p4[jump_ind + 1][0] = 0
- # p5[0] = 0
- # p6[jump_ind + 1][0] = 0
-
- points = trimesh.points.PointCloud(np.asarray((p1[0],p2[0],p6[jump_ind+1],p5, p3[np.asarray(dist1).argmax()])), colors=None, metadata=None)
- segments = np.asarray([p1[-1], p2[-1]])
- p = trimesh.load_path(segments)
- segments = np.asarray([p6[jump_ind+1], p5])
- p_dist = trimesh.load_path(segments)
-
- mesh.visual.face_colors[:] = np.array([227, 218, 201, 150])
- mesh.visual.vertex_colors[:] = np.array([227, 218, 201, 150])
- if lig == 'ACL':
- line = trimesh.path.segments.parameters_to_segments([p1[-1],p6[jump_ind+1],p3[np.asarray(dist1).argmax()],p5], [direction,direction_perp,direction,direction_perp],
- np.array(((d[-1]-5,-14),(-12,h[-1]-10),(d[-1]-25.5,-23.5),(-1.5,h[-1]+1))).astype(float)) #ACL
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[4], 4)
- grid_points2 = split(box_points[0], box_points[3], 4)
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1], grid_points1[2], grid_points1[3]],
- [direction_perp], np.array(
- ((h[-1] + 2.5, -0), (h[-1] + 2.5, 0), (h[-1] + 2, -0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] - 1.5, 0), (d[-1] - 1.5, 0),
- (d[-1] - 2, 0))).astype(
- float))
- else:
- line = trimesh.path.segments.parameters_to_segments([p1[-1], p6[jump_ind + 1], p3[np.asarray(dist1).argmax()], p5],
- [direction, direction_perp, direction, direction_perp],
- np.array(((d[-1] -8, -16), (h[-1] - 11, -13.5),
- (d[-1] - 27, -25.5), (h[-1],-3))).astype(float)) #PCL
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[7], 4)
- grid_points2 = split(box_points[0], box_points[5], 4)
-
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1],grid_points1[2],grid_points1[3]],[direction_perp],np.array(((h[-1]+3,-0),(h[-1]+3,0),(h[-1]+2.5,-0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] -1,0), (d[-1]-1,0), (d[-1]-1.5,0))).astype(
- float))
- grid_line_path = trimesh.load_path(np.squeeze(grid_line), colors=((0.5,0.5,0.5,),(0.5,0.5,0.5),(0.5,0.5,0.5)))
- grid_line2_path = trimesh.load_path(np.squeeze(grid_line2),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
- scene = trimesh.Scene([mesh, trimesh.load_path(np.squeeze(line)),grid_line_path,grid_line2_path]) #, points
- origin, xaxis, yaxis, zaxis = scene.camera_transform[0:3,3], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- if lig == 'ACL':
- Rx = trimesh.transformations.rotation_matrix(np.radians(-90), xaxis)
- Ry = trimesh.transformations.rotation_matrix(np.radians(-90), yaxis)
- else:
- Rx = trimesh.transformations.rotation_matrix(np.radians(-90), xaxis)
- Ry = trimesh.transformations.rotation_matrix(np.radians(90), yaxis)
- R = trimesh.transformations.concatenate_matrices(Ry,Rx)
- scene.apply_transform(R)
- # scene.camera_transform = camera_trans
- scene.show()
- # mesh.vertices[:, 0] = 0
- # trimesh.Scene([mesh, points, trimesh.load_path(np.squeeze(line))]).show()
-
-# posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,-30,0), cached_dots=None, return_both=False)
-# posterior_mesh.show()
- if subject == 100:
- points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_rot.xyz')
- if lig == 'ACL':
- center = np.arange(341 - 263) + 263 # ACL
- mean = np.array((61.2, -78.9, 39.3)) / 100 * np.array((ML, AP, AP)) + np.array( #
- (bbox[0, 0], bbox[1, 1], bbox[0, 2]))
- else:
- center = np.arange(112) # PCL
- mean = np.array((39.5, -63.4, 23.8)) / 100 * np.array((ML, AP, AP)) + np.array(
- (bbox[0, 2], bbox[1, 1], bbox[0, 2]))
-
- points_lig = points_lig[center]
- # origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- # Rz = trimesh.transformations.rotation_matrix(180/np.pi, zaxis)
- # points_lig.apply_transform(Rz)
- color_file = np.loadtxt(path_col + '\meanshape_ligs_color.xyz')[:, 3]
- color_file = color_file[center]
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
-
- color = []
- for ind_col, point in enumerate(points_lig):
- center_2d = point[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - center_2d)) / np.linalg.norm(p2_2d - p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0],
- p5_2d[1])
- d_centriods.append(l)
- vcolors = [c[int(color_file[ind_col] - 1)][0] * 255, c[int(color_file[ind_col] - 1)][1] * 255,
- c[int(color_file[ind_col] - 1)][2] * 255]
- color.append(vcolors)
- p_lig = trimesh.points.PointCloud(points_lig, colors=color)
- p_mean = trimesh.primitives.Sphere(radius=1, center=mean, subdivisions=3, color=[255, 0, 0]) # trimesh.points.PointCloud([mean, mean], colors=[[255, 0, 0], [255, 0, 0]])
- p_mean.visual.face_colors = np.array([255, 0, 0, 255])
- # scene2 = trimesh.Scene([mesh, points, p_lig, trimesh.load_path(np.squeeze(line))])
- # scene2.apply_transform(R)
- # scene2.camera_transform = camera_trans
- # scene2.show()
- scene.add_geometry([p_lig, p_mean],transform=R)
- scene.show()
- else:
- if lig == 'ACL':
- lig_no = ligaments[8][ind]
- elif lig == 'PCL':
- lig_no = ligaments[0][ind]
- if not lig_no == 0:
- segment = 'femur'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
-
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center = geometric_measures['shell_barycenter']
- center_2d = center[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-center_2d))/np.linalg.norm(p2_2d-p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0], p5_2d[1])
- d_centriods.append(l)
- else:
- h_centriods.append(0)
- d_centriods.append(0)
-
-[1-abs(i / j) for i, j in zip(d_centriods, d)]
-[i / j for i, j in zip(h_centriods, h)]
-
-d_centriods/np.asarray(d)
-h_centriods/np.asarray(h)
-
-np.mean(abs(np.asarray(d_centriods))/np.asarray(d))
-np.mean(h_centriods/np.asarray(h))
-
-
-
diff --git a/LigamentInsertions/CheckMatchingPoints.py b/LigamentInsertions/CheckMatchingPoints.py
deleted file mode 100644
index f659209..0000000
--- a/LigamentInsertions/CheckMatchingPoints.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import trimesh
-import os
-import numpy as np
-
-segments = ['femur']
-subjects = ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-lig = 'pop'
-center_only = 1
-
-if lig == 'PCL':
- center_tibia = np.arange(131) # np.arange(470-341)+341 #np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.arange(112) # np.arange(341-263)+263 #np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
-
-if lig == 'LCL':
- center_femur = np.arange(706-641)+641 # np.arange(415-379)+379 # np.arange(370-341)+341 = 4096
- center_tibia = np.arange(242)
-if lig == 'pop':
- center_femur = np.arange(776-706)+706 #np.arange(454-415)+415 # np.arange(401-370)+370 = 4096
- center_tibia = 0
-
-no_points=[]
-points_in_attachment = []
-perc_points=[]
-total_points=[]
-no_all_points=[]
-perc_points_in_area=[]
-
-for segment in segments:
- if segment == 'tibia' or segment == 'fibula':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
- if segment == 'fibula':
- short = '_short'
- else:
- short = ''
-
- for ind, subject in enumerate(subjects):
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points_color_8192.xyz')
- points_area = trimesh.load_mesh(path + '\8192\SSM_' + segment + short + '_areas_test.xyz')
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color_8192.xyz')[:, 3]
- if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- print(color)
- corresponding_points = np.where(color>=7)
- all_points = np.where(color >= 0)
- all_points_lig = points_lig[all_points]
- points_lig = points_lig[corresponding_points]
- result = []
- result2 = []
- for i in range(0,len(points_area.vertices)):
- rows = np.where(points_lig[:, 0] == points_area.vertices[i,0])
- if len(rows[0])>=1:
- # print(points_lig[rows])
- # print(points_area.vertices[i,:])
- result.append(points_lig[rows])
-
- rows = np.where(all_points_lig[:, 0] == points_area.vertices[i, 0])
- if len(rows[0]) >= 1:
- # print(all_points_lig[rows])
- # print(points_area.vertices[i, :])
- result2.append(all_points_lig[rows])
-
- no_points.append(len(result)) #SSM_points_predicted
- no_all_points.append(len(result2)) #all points in area
- perc_points.append(len(result)/len(result2)) #perc points wrt all points
-
- total_points.append(len(points_area.vertices))
- points_in_attachment.append(result)
-
-perc_points_in_area.append(np.asarray(no_points)/len(corresponding_points[0])) # perc points inside area
-
-print(str(np.average(no_all_points)) + ' (' + str(np.min(no_all_points)) + '-' + str(np.max(no_all_points)) + ')')
-print(len(corresponding_points[0]))
-print(str(np.average(no_points)) + ' (' + str(np.min(no_points)) + '-' + str(np.max(no_points)) + ')')
-print(str(round(np.average(perc_points_in_area)*100,1)) + ' (' + str(round(np.min(perc_points_in_area)*100,1)) + '-' + str(round(np.max(perc_points_in_area)*100,1)) + ')')
-print(str(round(np.average(perc_points)*100,1)) + ' (' + str(round(np.min(perc_points)*100,1)) + '-' + str(round(np.max(perc_points)*100,1)) + ')')
-
diff --git a/LigamentInsertions/CreateFibulaTransform.py b/LigamentInsertions/CreateFibulaTransform.py
deleted file mode 100644
index 1ca64fc..0000000
--- a/LigamentInsertions/CreateFibulaTransform.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import trimesh
-import numpy as np
-import pymeshlab
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-ligaments_fib = [[2,2,2,2,2,2,2,3,2,2]] # LCL
-
-for ind, subject in enumerate(subjects):
- # path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- #
- # rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_tibia_resample._ACS.txt'))
- # mesh2 = path + '\Segmentation_tibia_fib.stl'
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- # ms5.save_current_mesh(path + '\Segmentation_fibula_tib_frame.stl', binary=False)
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- fibula = trimesh.load_mesh(path + '\Segmentation_fibula_tib_frame.stl')
- most_prox_point = fibula.vertices[np.argmax(fibula.vertices[:,2]),:]
- T = trimesh.transformations.translation_matrix(-most_prox_point)
- fibula_area = trimesh.load_mesh(path + '\Segmentation_tibia_area' + str(ligaments_fib[0][ind]) + '_transform.stl')
- # fibula_wire = trimesh.load_mesh(path + '\Segmentation_fibula_wires_transform_lateral.stl')
- # center = np.array([-48.399971,-14.163541,-15.73211])
-
- fibula.apply_transform(T)
- # fibula.export(path + '\Segmentation_fibula_transform.stl')
- fibula_area.apply_transform(T)
- fibula_area.export(path + '\Segmentation_fibula_area' + str(ligaments_fib[0][ind]) + '_transform.stl')
- # fibula_wire.apply_transform(T)
- # fibula_wire.export(path + '\Segmentation_fibula_wire_transform_lateral.stl')
- # points = center-most_prox_point
- # print(points)
diff --git a/LigamentInsertions/DICOMScalarVolumePlugin.py b/LigamentInsertions/DICOMScalarVolumePlugin.py
deleted file mode 100644
index 3dd13e6..0000000
--- a/LigamentInsertions/DICOMScalarVolumePlugin.py
+++ /dev/null
@@ -1,841 +0,0 @@
-import numpy
-import os
-import vtk, qt, ctk, slicer, vtkITK
-from DICOMLib import DICOMPlugin
-from DICOMLib import DICOMLoadable
-from DICOMLib import DICOMUtils
-from DICOMLib import DICOMExportScalarVolume
-import logging
-from functools import cmp_to_key
-
-#
-# This is the plugin to handle translation of scalar volumes
-# from DICOM files into MRML nodes. It follows the DICOM module's
-# plugin architecture.
-#
-
-class DICOMScalarVolumePluginClass(DICOMPlugin):
- """ ScalarVolume specific interpretation code
- """
-
- def __init__(self,epsilon=0.01):
- super().__init__()
- self.loadType = "Scalar Volume"
- self.epsilon = epsilon
- self.acquisitionModeling = None
- self.defaultStudyID = 'SLICER10001' #TODO: What should be the new study ID?
-
- self.tags['sopClassUID'] = "0008,0016"
- self.tags['photometricInterpretation'] = "0028,0004"
- self.tags['seriesDescription'] = "0008,103e"
- self.tags['seriesUID'] = "0020,000E"
- self.tags['seriesNumber'] = "0020,0011"
- self.tags['position'] = "0020,0032"
- self.tags['orientation'] = "0020,0037"
- self.tags['pixelData'] = "7fe0,0010"
- self.tags['seriesInstanceUID'] = "0020,000E"
- self.tags['acquisitionNumber'] = "0020,0012"
- self.tags['imageType'] = "0008,0008"
- self.tags['contentTime'] = "0008,0033"
- self.tags['triggerTime'] = "0018,1060"
- self.tags['diffusionGradientOrientation'] = "0018,9089"
- self.tags['imageOrientationPatient'] = "0020,0037"
- self.tags['numberOfFrames'] = "0028,0008"
- self.tags['instanceUID'] = "0008,0018"
- self.tags['windowCenter'] = "0028,1050"
- self.tags['windowWidth'] = "0028,1051"
- self.tags['rows'] = "0028,0010"
- self.tags['columns'] = "0028,0011"
-
- @staticmethod
- def readerApproaches():
- """Available reader implementations. First entry is initial default.
- Note: the settings file stores the index of the user's selected reader
- approach, so if new approaches are added the should go at the
- end of the list.
- """
- return ["GDCM with DCMTK fallback", "DCMTK", "GDCM", "Archetype"]
-
- @staticmethod
- def settingsPanelEntry(panel, parent):
- """Create a settings panel entry for this plugin class.
- It is added to the DICOM panel of the application settings
- by the DICOM module.
- """
- formLayout = qt.QFormLayout(parent)
-
- readersComboBox = qt.QComboBox()
- for approach in DICOMScalarVolumePluginClass.readerApproaches():
- readersComboBox.addItem(approach)
- readersComboBox.toolTip = ("Preferred back end. Archetype was used by default in Slicer before June of 2017."
- "Change this setting if data that previously loaded stops working (and report an issue).")
- formLayout.addRow("DICOM reader approach:", readersComboBox)
- panel.registerProperty(
- "DICOM/ScalarVolume/ReaderApproach", readersComboBox,
- "currentIndex", str(qt.SIGNAL("currentIndexChanged(int)")))
-
- importFormatsComboBox = ctk.ctkComboBox()
- importFormatsComboBox.toolTip = ("Enable adding non-linear transform to regularize images acquired irregular geometry:"
- " non-rectilinear grid (such as tilted gantry CT acquisitions) and non-uniform slice spacing."
- " If no regularization is applied then image may appear distorted if it was acquired with irregular geometry.")
- importFormatsComboBox.addItem("default (none)", "default")
- importFormatsComboBox.addItem("none", "none")
- importFormatsComboBox.addItem("apply regularization transform", "transform")
- # in the future additional option, such as "resample" may be added
- importFormatsComboBox.currentIndex = 0
- formLayout.addRow("Acquisition geometry regularization:", importFormatsComboBox)
- panel.registerProperty(
- "DICOM/ScalarVolume/AcquisitionGeometryRegularization", importFormatsComboBox,
- "currentUserDataAsString", str(qt.SIGNAL("currentIndexChanged(int)")),
- "DICOM examination settings", ctk.ctkSettingsPanel.OptionRequireRestart)
- # DICOM examination settings are cached so we need to restart to make sure changes take effect
-
- allowLoadingByTimeCheckBox = qt.QCheckBox()
- allowLoadingByTimeCheckBox.toolTip = ("Offer loading of individual slices or group of slices"
- " that were acquired at a specific time (content or trigger time)."
- " If this option is enabled then a large number of loadable items may be displayed in the Advanced section of DICOM browser.")
- formLayout.addRow("Allow loading subseries by time:", allowLoadingByTimeCheckBox)
- allowLoadingByTimeMapper = ctk.ctkBooleanMapper(allowLoadingByTimeCheckBox, "checked", str(qt.SIGNAL("toggled(bool)")))
- panel.registerProperty(
- "DICOM/ScalarVolume/AllowLoadingByTime", allowLoadingByTimeMapper,
- "valueAsInt", str(qt.SIGNAL("valueAsIntChanged(int)")),
- "DICOM examination settings", ctk.ctkSettingsPanel.OptionRequireRestart)
- # DICOM examination settings are cached so we need to restart to make sure changes take effect
-
- @staticmethod
- def compareVolumeNodes(volumeNode1,volumeNode2):
- """
- Given two mrml volume nodes, return true of the numpy arrays have identical data
- and other metadata matches. Returns empty string on match, otherwise
- a string with a list of differences separated by newlines.
- """
- volumesLogic = slicer.modules.volumes.logic()
- comparison = ""
- comparison += volumesLogic.CompareVolumeGeometry(volumeNode1, volumeNode2)
- image1 = volumeNode1.GetImageData()
- image2 = volumeNode2.GetImageData()
- if image1.GetScalarType() != image2.GetScalarType():
- comparison += f"First volume is {image1.GetScalarTypeAsString()}, but second is {image2.GetScalarTypeAsString()}"
- array1 = slicer.util.array(volumeNode1.GetID())
- array2 = slicer.util.array(volumeNode2.GetID())
- if not numpy.all(array1 == array2):
- comparison += "Pixel data mismatch\n"
- return comparison
-
- def acquisitionGeometryRegularizationEnabled(self):
- settings = qt.QSettings()
- return (settings.value("DICOM/ScalarVolume/AcquisitionGeometryRegularization", "default") == "transform")
-
- def allowLoadingByTime(self):
- settings = qt.QSettings()
- return (int(settings.value("DICOM/ScalarVolume/AllowLoadingByTime", "0")) != 0)
-
- def examineForImport(self,fileLists):
- """ Returns a sorted list of DICOMLoadable instances
- corresponding to ways of interpreting the
- fileLists parameter (list of file lists).
- """
- loadables = []
- for files in fileLists:
- cachedLoadables = self.getCachedLoadables(files)
- if cachedLoadables:
- loadables += cachedLoadables
- else:
- loadablesForFiles = self.examineFiles(files)
- loadables += loadablesForFiles
- self.cacheLoadables(files,loadablesForFiles)
-
- # sort the loadables by series number if possible
- loadables.sort(key=cmp_to_key(lambda x,y: self.seriesSorter(x,y)))
-
- return loadables
-
- def cleanNodeName(self, value):
- cleanValue = value
- cleanValue = cleanValue.replace("|", "-")
- cleanValue = cleanValue.replace("/", "-")
- cleanValue = cleanValue.replace("\\", "-")
- cleanValue = cleanValue.replace("*", "(star)")
- cleanValue = cleanValue.replace("\\", "-")
- return cleanValue
-
- def examineFiles(self,files):
- """ Returns a list of DICOMLoadable instances
- corresponding to ways of interpreting the
- files parameter.
- """
-
- seriesUID = slicer.dicomDatabase.fileValue(files[0],self.tags['seriesUID'])
- seriesName = self.defaultSeriesNodeName(seriesUID)
-
- # default loadable includes all files for series
- allFilesLoadable = DICOMLoadable()
- allFilesLoadable.files = files
- allFilesLoadable.name = self.cleanNodeName(seriesName)
- allFilesLoadable.tooltip = "%d files, first file: %s" % (len(allFilesLoadable.files), allFilesLoadable.files[0])
- allFilesLoadable.selected = True
- # add it to the list of loadables later, if pixel data is available in at least one file
-
- # make subseries volumes based on tag differences
- subseriesTags = [
- "seriesInstanceUID",
- "acquisitionNumber",
- # GE volume viewer and Siemens Axiom CBCT systems put an overview (localizer) slice and all the reconstructed slices
- # in one series, using two different image types. Splitting based on image type allows loading of these volumes
- # (loading the series without localizer).
- "imageType",
- "imageOrientationPatient",
- "diffusionGradientOrientation",
- ]
-
- if self.allowLoadingByTime():
- subseriesTags.append("contentTime")
- subseriesTags.append("triggerTime")
-
- # Values for these tags will only be enumerated (value itself will not be part of the loadable name)
- # because the vale itself is usually too long and complicated to be displayed to users
- subseriesTagsToEnumerateValues = [
- "seriesInstanceUID",
- "imageOrientationPatient",
- "diffusionGradientOrientation",
- ]
-
- #
- # first, look for subseries within this series
- # - build a list of files for each unique value
- # of each tag
- #
- subseriesFiles = {}
- subseriesValues = {}
- for file in allFilesLoadable.files:
- # check for subseries values
- for tag in subseriesTags:
- value = slicer.dicomDatabase.fileValue(file,self.tags[tag])
- value = value.replace(",","_") # remove commas so it can be used as an index
- if tag not in subseriesValues:
- subseriesValues[tag] = []
- if not subseriesValues[tag].__contains__(value):
- subseriesValues[tag].append(value)
- if (tag,value) not in subseriesFiles:
- subseriesFiles[tag,value] = []
- subseriesFiles[tag,value].append(file)
-
- loadables = []
-
- # Pixel data is available, so add the default loadable to the output
- loadables.append(allFilesLoadable)
-
- #
- # second, for any tags that have more than one value, create a new
- # virtual series
- #
- subseriesCount = 0
- # List of loadables that look like subseries that contain the full series except a single frame
- probableLocalizerFreeLoadables = []
- for tag in subseriesTags:
- if len(subseriesValues[tag]) > 1:
- subseriesCount += 1
- for valueIndex, value in enumerate(subseriesValues[tag]):
- # default loadable includes all files for series
- loadable = DICOMLoadable()
- loadable.files = subseriesFiles[tag,value]
- # value can be a long string (and it will be used for generating node name)
- # therefore use just an index instead
- if tag in subseriesTagsToEnumerateValues:
- loadable.name = seriesName + " - %s %d" % (tag, valueIndex+1)
- else:
- loadable.name = seriesName + f" - {tag} {value}"
- loadable.name = self.cleanNodeName(loadable.name)
- loadable.tooltip = "%d files, grouped by %s = %s. First file: %s. %s = %s" % (len(loadable.files), tag, value, loadable.files[0], tag, value)
- loadable.selected = False
- loadables.append(loadable)
- if len(subseriesValues[tag]) == 2:
- otherValue = subseriesValues[tag][1-valueIndex]
- if len(subseriesFiles[tag,value]) > 1 and len(subseriesFiles[tag, otherValue]) == 1:
- # this looks like a subseries without a localizer image
- probableLocalizerFreeLoadables.append(loadable)
-
- # remove any files from loadables that don't have pixel data (no point sending them to ITK for reading)
- # also remove DICOM SEG, since it is not handled by ITK readers
- newLoadables = []
- for loadable in loadables:
- newFiles = []
- excludedLoadable = False
- for file in loadable.files:
- if slicer.dicomDatabase.fileValueExists(file,self.tags['pixelData']):
- newFiles.append(file)
- if slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])=='1.2.840.10008.5.1.4.1.1.66.4':
- excludedLoadable = True
- if 'DICOMSegmentationPlugin' not in slicer.modules.dicomPlugins:
- logging.warning('Please install Quantitative Reporting extension to enable loading of DICOM Segmentation objects')
- elif slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])=='1.2.840.10008.5.1.4.1.1.481.3':
- excludedLoadable = True
- if 'DicomRtImportExportPlugin' not in slicer.modules.dicomPlugins:
- logging.warning('Please install SlicerRT extension to enable loading of DICOM RT Structure Set objects')
- if len(newFiles) > 0 and not excludedLoadable:
- loadable.files = newFiles
- loadable.grayscale = ('MONOCHROME' in slicer.dicomDatabase.fileValue(newFiles[0],self.tags['photometricInterpretation']))
- newLoadables.append(loadable)
- elif excludedLoadable:
- continue
- else:
- # here all files in have no pixel data, so they might be
- # secondary capture images which will read, so let's pass
- # them through with a warning and low confidence
- loadable.warning += "There is no pixel data attribute for the DICOM objects, but they might be readable as secondary capture images. "
- loadable.confidence = 0.2
- loadable.grayscale = ('MONOCHROME' in slicer.dicomDatabase.fileValue(loadable.files[0],self.tags['photometricInterpretation']))
- newLoadables.append(loadable)
- loadables = newLoadables
-
- #
- # now for each series and subseries, sort the images
- # by position and check for consistency
- # then adjust confidence values based on warnings
- #
- for loadable in loadables:
- loadable.files, distances, loadable.warning = DICOMUtils.getSortedImageFiles(loadable.files, self.epsilon)
-
- loadablesBetterThanAllFiles = []
- if allFilesLoadable.warning != "":
- for probableLocalizerFreeLoadable in probableLocalizerFreeLoadables:
- if probableLocalizerFreeLoadable.warning == "":
- # localizer-free loadables are better then all files, if they don't have warning
- loadablesBetterThanAllFiles.append(probableLocalizerFreeLoadable)
- if not loadablesBetterThanAllFiles and subseriesCount == 1:
- # there was a sorting warning and
- # only one kind of subseries, so it's probably correct
- # to have lower confidence in the default all-files version.
- for loadable in loadables:
- if loadable != allFilesLoadable and loadable.warning == "":
- loadablesBetterThanAllFiles.append(loadable)
-
- # if there are loadables that are clearly better then all files, then use those (otherwise use all files loadable)
- preferredLoadables = loadablesBetterThanAllFiles if loadablesBetterThanAllFiles else [allFilesLoadable]
- # reduce confidence and deselect all non-preferred loadables
- for loadable in loadables:
- if loadable in preferredLoadables:
- loadable.selected = True
- else:
- loadable.selected = False
- if loadable.confidence > .45:
- loadable.confidence = .45
-
- return loadables
-
- def seriesSorter(self,x,y):
- """ returns -1, 0, 1 for sorting of strings like: "400: series description"
- Works for DICOMLoadable or other objects with name attribute
- """
- if not (hasattr(x,'name') and hasattr(y,'name')):
- return 0
- xName = x.name
- yName = y.name
- try:
- xNumber = int(xName[:xName.index(':')])
- yNumber = int(yName[:yName.index(':')])
- except ValueError:
- return 0
- cmp = xNumber - yNumber
- return cmp
-
- #
- # different ways to load a set of dicom files:
- # - Logic: relies on the same loading mechanism used
- # by the File->Add Data dialog in the Slicer GUI.
- # This uses vtkITK under the hood with GDCM as
- # the default loader.
- # - DCMTK: explicitly uses the DCMTKImageIO
- # - GDCM: explicitly uses the GDCMImageIO
- #
-
- def loadFilesWithArchetype(self,files,name):
- """Load files in the traditional Slicer manner
- using the volume logic helper class
- and the vtkITK archetype helper code
- """
- fileList = vtk.vtkStringArray()
- for f in files:
- fileList.InsertNextValue(f)
- volumesLogic = slicer.modules.volumes.logic()
- return(volumesLogic.AddArchetypeScalarVolume(files[0],name,0,fileList))
-
- def loadFilesWithSeriesReader(self,imageIOName,files,name,grayscale=True):
- """ Explicitly use the named imageIO to perform the loading
- """
-
- if grayscale:
- reader = vtkITK.vtkITKArchetypeImageSeriesScalarReader()
- else:
- reader = vtkITK.vtkITKArchetypeImageSeriesVectorReaderFile()
- reader.SetArchetype(files[0])
- for f in files:
- reader.AddFileName(f)
- reader.SetSingleFile(0)
- reader.SetOutputScalarTypeToNative()
- reader.SetDesiredCoordinateOrientationToNative()
- reader.SetUseNativeOriginOn()
- if imageIOName == "GDCM":
- reader.SetDICOMImageIOApproachToGDCM()
- elif imageIOName == "DCMTK":
- reader.SetDICOMImageIOApproachToDCMTK()
- else:
- raise Exception("Invalid imageIOName of %s" % imageIOName)
- logging.info("Loading with imageIOName: %s" % imageIOName)
- reader.Update()
-
- slicer.modules.reader = reader
- if reader.GetErrorCode() != vtk.vtkErrorCode.NoError:
- errorStrings = (imageIOName, vtk.vtkErrorCode.GetStringFromErrorCode(reader.GetErrorCode()))
- logging.error("Could not read scalar volume using %s approach. Error is: %s" % errorStrings)
- return
-
- imageChangeInformation = vtk.vtkImageChangeInformation()
- imageChangeInformation.SetInputConnection(reader.GetOutputPort())
- imageChangeInformation.SetOutputSpacing( 1, 1, 1 )
- imageChangeInformation.SetOutputOrigin( 0, 0, 0 )
- imageChangeInformation.Update()
-
- name = slicer.mrmlScene.GenerateUniqueName(name)
- if grayscale:
- volumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLScalarVolumeNode", name)
- else:
- volumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLVectorVolumeNode", name)
- volumeNode.SetAndObserveImageData(imageChangeInformation.GetOutputDataObject(0))
- slicer.vtkMRMLVolumeArchetypeStorageNode.SetMetaDataDictionaryFromReader(volumeNode, reader)
- volumeNode.SetRASToIJKMatrix(reader.GetRasToIjkMatrix())
- volumeNode.CreateDefaultDisplayNodes()
-
- slicer.modules.DICOMInstance.reader = reader
- slicer.modules.DICOMInstance.imageChangeInformation = imageChangeInformation
-
- return(volumeNode)
-
- def setVolumeNodeProperties(self,volumeNode,loadable):
- """After the scalar volume has been loaded, populate the node
- attributes and display node with values extracted from the dicom instances
- """
- if volumeNode:
- #
- # create subject hierarchy items for the loaded series
- #
- self.addSeriesInSubjectHierarchy(loadable,volumeNode)
-
- #
- # add list of DICOM instance UIDs to the volume node
- # corresponding to the loaded files
- #
- instanceUIDs = ""
- for file in loadable.files:
- uid = slicer.dicomDatabase.fileValue(file,self.tags['instanceUID'])
- if uid == "":
- uid = "Unknown"
- instanceUIDs += uid + " "
- instanceUIDs = instanceUIDs[:-1] # strip last space
- volumeNode.SetAttribute("DICOM.instanceUIDs", instanceUIDs)
-
- # Choose a file in the middle of the series as representative frame,
- # because that is more likely to contain the object of interest than the first or last frame.
- # This is important for example for getting a relevant window/center value for the series.
- file = loadable.files[int(len(loadable.files)/2)]
-
- #
- # automatically select the volume to display
- #
- appLogic = slicer.app.applicationLogic()
- selNode = appLogic.GetSelectionNode()
- selNode.SetActiveVolumeID(volumeNode.GetID())
- appLogic.PropagateVolumeSelection()
-
- #
- # apply window/level from DICOM if available (the first pair that is found)
- # Note: There can be multiple presets (multiplicity 1-n) in the standard [1]. We have
- # a way to put these into the display node [2], so they can be selected in the Volumes
- # module.
- # [1] http://medical.nema.org/medical/dicom/current/output/html/part06.html
- # [2] https://github.com/Slicer/Slicer/blob/3bfa2fc2b310d41c09b7a9e8f8f6c4f43d3bd1e2/Libs/MRML/Core/vtkMRMLScalarVolumeDisplayNode.h#L172
- #
- try:
- windowCenter = float( slicer.dicomDatabase.fileValue(file,self.tags['windowCenter']) )
- windowWidth = float( slicer.dicomDatabase.fileValue(file,self.tags['windowWidth']) )
- displayNode = volumeNode.GetDisplayNode()
- if displayNode:
- logging.info('Window/level found in DICOM tags (center=' + str(windowCenter) + ', width=' + str(windowWidth) + ') has been applied to volume ' + volumeNode.GetName())
- displayNode.AddWindowLevelPreset(windowWidth, windowCenter)
- displayNode.SetWindowLevelFromPreset(0)
- else:
- logging.info('No display node: cannot use window/level found in DICOM tags')
- except ValueError:
- pass # DICOM tags cannot be parsed to floating point numbers
-
- sopClassUID = slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])
-
- # initialize color lookup table
- modality = self.mapSOPClassUIDToModality(sopClassUID)
- if modality == "PT":
- displayNode = volumeNode.GetDisplayNode()
- if displayNode:
- displayNode.SetAndObserveColorNodeID(slicer.modules.colors.logic().GetPETColorNodeID(slicer.vtkMRMLPETProceduralColorNode.PETheat))
-
- # initialize quantity and units codes
- (quantity,units) = self.mapSOPClassUIDToDICOMQuantityAndUnits(sopClassUID)
- if quantity is not None:
- volumeNode.SetVoxelValueQuantity(quantity)
- if units is not None:
- volumeNode.SetVoxelValueUnits(units)
-
- def loadWithMultipleLoaders(self,loadable):
- """Load using multiple paths (for testing)
- """
- volumeNode = self.loadFilesWithArchetype(loadable.files, loadable.name+"-archetype")
- self.setVolumeNodeProperties(volumeNode, loadable)
- volumeNode = self.loadFilesWithSeriesReader("GDCM", loadable.files, loadable.name+"-gdcm", loadable.grayscale)
- self.setVolumeNodeProperties(volumeNode, loadable)
- volumeNode = self.loadFilesWithSeriesReader("DCMTK", loadable.files, loadable.name+"-dcmtk", loadable.grayscale)
- self.setVolumeNodeProperties(volumeNode, loadable)
-
- return volumeNode
-
- def load(self,loadable,readerApproach=None):
- """Load the select as a scalar volume using desired approach
- """
- # first, determine which reader approach the user prefers
- if not readerApproach:
- readerIndex = slicer.util.settingsValue('DICOM/ScalarVolume/ReaderApproach', 0, converter=int)
- readerApproach = DICOMScalarVolumePluginClass.readerApproaches()[readerIndex]
- # second, try to load with the selected approach
- if readerApproach == "Archetype":
- volumeNode = self.loadFilesWithArchetype(loadable.files, loadable.name)
- elif readerApproach == "GDCM with DCMTK fallback":
- volumeNode = self.loadFilesWithSeriesReader("GDCM", loadable.files, loadable.name, loadable.grayscale)
- if not volumeNode:
- volumeNode = self.loadFilesWithSeriesReader("DCMTK", loadable.files, loadable.name, loadable.grayscale)
- else:
- volumeNode = self.loadFilesWithSeriesReader(readerApproach, loadable.files, loadable.name, loadable.grayscale)
- # third, transfer data from the dicom instances into the appropriate Slicer data containers
- self.setVolumeNodeProperties(volumeNode, loadable)
-
- # examine the loaded volume and if needed create a new transform
- # that makes the loaded volume match the DICOM coordinates of
- # the individual frames. Save the class instance so external
- # code such as the DICOMReaders test can introspect to validate.
-
- if volumeNode:
- self.acquisitionModeling = self.AcquisitionModeling()
- self.acquisitionModeling.createAcquisitionTransform(volumeNode,
- addAcquisitionTransformIfNeeded=self.acquisitionGeometryRegularizationEnabled())
-
- return volumeNode
-
- def examineForExport(self,subjectHierarchyItemID):
- """Return a list of DICOMExportable instances that describe the
- available techniques that this plugin offers to convert MRML
- data into DICOM data
- """
- # cannot export if there is no data node or the data node is not a volume
- shn = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- dataNode = shn.GetItemDataNode(subjectHierarchyItemID)
- if dataNode is None or not dataNode.IsA('vtkMRMLScalarVolumeNode'):
- return []
-
- # Define basic properties of the exportable
- exportable = slicer.qSlicerDICOMExportable()
- exportable.name = self.loadType
- exportable.tooltip = "Creates a series of DICOM files from scalar volumes"
- exportable.subjectHierarchyItemID = subjectHierarchyItemID
- exportable.pluginClass = self.__module__
- exportable.confidence = 0.5 # There could be more specialized volume types
-
- # Define required tags and default values
- exportable.setTag('SeriesDescription', 'No series description')
- exportable.setTag('Modality', 'CT')
- exportable.setTag('Manufacturer', 'Unknown manufacturer')
- exportable.setTag('Model', 'Unknown model')
- exportable.setTag('StudyDate', '')
- exportable.setTag('StudyTime', '')
- exportable.setTag('StudyInstanceUID', '')
- exportable.setTag('SeriesDate', '')
- exportable.setTag('SeriesTime', '')
- exportable.setTag('ContentDate', '')
- exportable.setTag('ContentTime', '')
- exportable.setTag('SeriesNumber', '1')
- exportable.setTag('SeriesInstanceUID', '')
- exportable.setTag('FrameOfReferenceInstanceUID', '')
-
- return [exportable]
-
- def export(self,exportables):
- for exportable in exportables:
- # Get volume node to export
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- if shNode is None:
- error = "Invalid subject hierarchy"
- logging.error(error)
- return error
- volumeNode = shNode.GetItemDataNode(exportable.subjectHierarchyItemID)
- if volumeNode is None or not volumeNode.IsA('vtkMRMLScalarVolumeNode'):
- error = "Series '" + shNode.GetItemName(exportable.subjectHierarchyItemID) + "' cannot be exported"
- logging.error(error)
- return error
-
- # Get output directory and create a subdirectory. This is necessary
- # to avoid overwriting the files in case of multiple exportables, as
- # naming of the DICOM files is static
- directoryName = 'ScalarVolume_' + str(exportable.subjectHierarchyItemID)
- directoryDir = qt.QDir(exportable.directory)
- directoryDir.mkpath(directoryName)
- directoryDir.cd(directoryName)
- directory = directoryDir.absolutePath()
- logging.info("Export scalar volume '" + volumeNode.GetName() + "' to directory " + directory)
-
- # Get study and patient items
- studyItemID = shNode.GetItemParent(exportable.subjectHierarchyItemID)
- if not studyItemID:
- error = "Unable to get study for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
- patientItemID = shNode.GetItemParent(studyItemID)
- if not patientItemID:
- error = "Unable to get patient for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
-
- # Assemble tags dictionary for volume export
- tags = {}
- tags['Patient Name'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientNameTagName())
- tags['Patient ID'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientIDTagName())
- tags['Patient Birth Date'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientBirthDateTagName())
- tags['Patient Sex'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientSexTagName())
- tags['Patient Comments'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientCommentsTagName())
- tags['Study ID'] = self.defaultStudyID
- tags['Study Date'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyDateTagName())
- tags['Study Time'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyTimeTagName())
- tags['Study Description'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyDescriptionTagName())
- tags['Modality'] = exportable.tag('Modality')
- tags['Manufacturer'] = exportable.tag('Manufacturer')
- tags['Model'] = exportable.tag('Model')
- tags['Series Description'] = exportable.tag('SeriesDescription')
- tags['Series Number'] = exportable.tag('SeriesNumber')
- tags['Series Date'] = exportable.tag('SeriesDate')
- tags['Series Time'] = exportable.tag('SeriesTime')
- tags['Content Date'] = exportable.tag('ContentDate')
- tags['Content Time'] = exportable.tag('ContentTime')
-
- tags['Study Instance UID'] = exportable.tag('StudyInstanceUID')
- tags['Series Instance UID'] = exportable.tag('SeriesInstanceUID')
- tags['Frame of Reference Instance UID'] = exportable.tag('FrameOfReferenceInstanceUID')
-
- # Validate tags
- if tags['Modality'] == "":
- error = "Empty modality for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
- #TODO: more tag checks
-
- # Perform export
- exporter = DICOMExportScalarVolume(tags['Study ID'], volumeNode, tags, directory)
- if not exporter.export():
- return "Creating DICOM files from scalar volume failed"
-
- # Success
- return ""
-
- class AcquisitionModeling:
- """Code for representing and analyzing acquisition properties in slicer
- This is an internal class of the DICOMScalarVolumePluginClass so that
- it can be used here and from within the DICOMReaders test.
- TODO: This code work on legacy single frame DICOM images that have position and orientation
- flags in each instance (not on multiframe with per-frame positions).
- """
-
- def __init__(self,cornerEpsilon=1e-3,zeroEpsilon=1e-6):
- """cornerEpsilon sets the threshold for the amount of difference between the
- vtkITK generated volume geometry vs the DICOM geometry. Any spatial dimension with
- a difference larger than cornerEpsilon will trigger the addition of a grid transform.
- Any difference less than zeroEpsilon is assumed to be numerical error.
- """
- self.cornerEpsilon = cornerEpsilon
- self.zeroEpsilon = zeroEpsilon
-
- def gridTransformFromCorners(self,volumeNode,sourceCorners,targetCorners):
- """Create a grid transform that maps between the current and the desired corners.
- """
- # sanity check
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- cornerShape = (slices, 2, 2, 3)
- if not (sourceCorners.shape == cornerShape and targetCorners.shape == cornerShape):
- raise Exception("Corner shapes do not match volume dimensions %s, %s, %s" %
- (sourceCorners.shape, targetCorners.shape, cornerShape))
-
- # create the grid transform node
- gridTransform = slicer.vtkMRMLGridTransformNode()
- gridTransform.SetName(slicer.mrmlScene.GenerateUniqueName(volumeNode.GetName()+' acquisition transform'))
- slicer.mrmlScene.AddNode(gridTransform)
-
- # place grid transform in the same subject hierarchy folder as the volume node
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- volumeParentItemId = shNode.GetItemParent(shNode.GetItemByDataNode(volumeNode))
- shNode.SetItemParent(shNode.GetItemByDataNode(gridTransform), volumeParentItemId)
-
- # create a grid transform with one vector at the corner of each slice
- # the transform is in the same space and orientation as the volume node
- gridImage = vtk.vtkImageData()
- gridImage.SetOrigin(*volumeNode.GetOrigin())
- gridImage.SetDimensions(2, 2, slices)
- sourceSpacing = volumeNode.GetSpacing()
- gridImage.SetSpacing(sourceSpacing[0] * columns, sourceSpacing[1] * rows, sourceSpacing[2])
- gridImage.AllocateScalars(vtk.VTK_DOUBLE, 3)
- transform = slicer.vtkOrientedGridTransform()
- directionMatrix = vtk.vtkMatrix4x4()
- volumeNode.GetIJKToRASDirectionMatrix(directionMatrix)
- transform.SetGridDirectionMatrix(directionMatrix)
- transform.SetDisplacementGridData(gridImage)
- gridTransform.SetAndObserveTransformToParent(transform)
- volumeNode.SetAndObserveTransformNodeID(gridTransform.GetID())
-
- # populate the grid so that each corner of each slice
- # is mapped from the source corner to the target corner
- displacements = slicer.util.arrayFromGridTransform(gridTransform)
- for sliceIndex in range(slices):
- for row in range(2):
- for column in range(2):
- displacements[sliceIndex][row][column] = targetCorners[sliceIndex][row][column] - sourceCorners[sliceIndex][row][column]
-
- def sliceCornersFromDICOM(self,volumeNode):
- """Calculate the RAS position of each of the four corners of each
- slice of a volume node based on the dicom headers
- Note: PixelSpacing is row spacing followed by column spacing [1] (i.e. vertical then horizontal)
- while ImageOrientationPatient is row cosines then column cosines [2] (i.e. horizontal then vertical).
- [1] http://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_10.7.1.1
- [2] http://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_C.7.6.2
- """
- spacingTag = "0028,0030"
- positionTag = "0020,0032"
- orientationTag = "0020,0037"
-
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- corners = numpy.zeros(shape=[slices,2,2,3])
- uids = volumeNode.GetAttribute('DICOM.instanceUIDs').split()
- if len(uids) != slices:
- # There is no uid for each slice, so most likely all frames are in a single file
- # or maybe there is a problem with the sequence
- logging.warning("Cannot get DICOM slice positions for volume "+volumeNode.GetName())
- return None
- for sliceIndex in range(slices):
- uid = uids[sliceIndex]
- # get slice geometry from instance
- positionString = slicer.dicomDatabase.instanceValue(uid, positionTag)
- orientationString = slicer.dicomDatabase.instanceValue(uid, orientationTag)
- spacingString = slicer.dicomDatabase.instanceValue(uid, spacingTag)
- if positionString == "" or orientationString == "" or spacingString == "":
- logging.warning('No geometry information available for DICOM data, skipping corner calculations')
- return None
-
- position = numpy.array(list(map(float, positionString.split('\\'))))
- orientation = list(map(float, orientationString.split('\\')))
- rowOrientation = numpy.array(orientation[:3])
- columnOrientation = numpy.array(orientation[3:])
- spacing = numpy.array(list(map(float, spacingString.split('\\'))))
- # map from LPS to RAS
- lpsToRAS = numpy.array([-1,-1,1])
- position *= lpsToRAS
- rowOrientation *= lpsToRAS
- columnOrientation *= lpsToRAS
- rowVector = columns * spacing[1] * rowOrientation # dicom PixelSpacing is between rows first, then columns
- columnVector = rows * spacing[0] * columnOrientation
- # apply the transform to the four corners
- for column in range(2):
- for row in range(2):
- corners[sliceIndex][row][column] = position
- corners[sliceIndex][row][column] += column * rowVector
- corners[sliceIndex][row][column] += row * columnVector
- return corners
-
- def sliceCornersFromIJKToRAS(self,volumeNode):
- """Calculate the RAS position of each of the four corners of each
- slice of a volume node based on the ijkToRAS matrix of the volume node
- """
- ijkToRAS = vtk.vtkMatrix4x4()
- volumeNode.GetIJKToRASMatrix(ijkToRAS)
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- corners = numpy.zeros(shape=[slices,2,2,3])
- for sliceIndex in range(slices):
- for column in range(2):
- for row in range(2):
- corners[sliceIndex][row][column] = numpy.array(ijkToRAS.MultiplyPoint([column * columns, row * rows, sliceIndex, 1])[:3])
- return corners
-
- def cornersToWorld(self,volumeNode,corners):
- """Map corners through the volumeNodes transform to world
- This can be used to confirm that an acquisition transform has correctly
- mapped the slice corners to match the dicom acquisition.
- """
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- worldCorners = numpy.zeros(shape=[slices,2,2,3])
- for slice in range(slices):
- for row in range(2):
- for column in range(2):
- volumeNode.TransformPointToWorld(corners[slice,row,column], worldCorners[slice,row,column])
- return worldCorners
-
- def createAcquisitionTransform(self, volumeNode, addAcquisitionTransformIfNeeded = True):
- """Creates the actual transform if needed.
- Slice corners are cached for inpection by tests
- """
- self.originalCorners = self.sliceCornersFromIJKToRAS(volumeNode)
- self.targetCorners = self.sliceCornersFromDICOM(volumeNode)
- if self.originalCorners is None or self.targetCorners is None:
- # can't create transform without corner information
- return
- maxError = (abs(self.originalCorners - self.targetCorners)).max()
-
- if maxError > self.cornerEpsilon:
- warningText = f"Irregular volume geometry detected (maximum error of {maxError:g} mm is above tolerance threshold of {self.cornerEpsilon:g} mm)."
- if addAcquisitionTransformIfNeeded:
- logging.warning(warningText + " Adding acquisition transform to regularize geometry.")
- self.gridTransformFromCorners(volumeNode, self.originalCorners, self.targetCorners)
- self.fixedCorners = self.cornersToWorld(volumeNode, self.originalCorners)
- if not numpy.allclose(self.fixedCorners, self.targetCorners):
- raise Exception("Acquisition transform didn't fix slice corners!")
- else:
- logging.warning(warningText + " Regularization transform is not added, as the option is disabled.")
- elif maxError > 0 and maxError > self.zeroEpsilon:
- logging.debug("Irregular volume geometry detected, but maximum error is within tolerance"+
- f" (maximum error of {maxError:g} mm, tolerance threshold is {self.cornerEpsilon:g} mm).")
-
-
-#
-# DICOMScalarVolumePlugin
-#
-
-class DICOMScalarVolumePlugin:
- """
- This class is the 'hook' for slicer to detect and recognize the plugin
- as a loadable scripted module
- """
- def __init__(self, parent):
- parent.title = "DICOM Scalar Volume Plugin"
- parent.categories = ["Developer Tools.DICOM Plugins"]
- parent.contributors = ["Steve Pieper (Isomics Inc.), Csaba Pinter (Queen's)"]
- parent.helpText = """
- Plugin to the DICOM Module to parse and load scalar volumes
- from DICOM files.
- No module interface here, only in the DICOM module
- """
- parent.acknowledgementText = """
- This DICOM Plugin was developed by
- Steve Pieper, Isomics, Inc.
- and was partially funded by NIH grant 3P41RR013218.
- """
-
- # don't show this module - it only appears in the DICOM module
- parent.hidden = True
-
- # Add this extension to the DICOM module's list for discovery when the module
- # is created. Since this module may be discovered before DICOM itself,
- # create the list if it doesn't already exist.
- try:
- slicer.modules.dicomPlugins
- except AttributeError:
- slicer.modules.dicomPlugins = {}
- slicer.modules.dicomPlugins['DICOMScalarVolumePlugin'] = DICOMScalarVolumePluginClass
\ No newline at end of file
diff --git a/LigamentInsertions/Elevation PlotLateral.py b/LigamentInsertions/Elevation PlotLateral.py
deleted file mode 100644
index 81b9d30..0000000
--- a/LigamentInsertions/Elevation PlotLateral.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import numpy as np
-import pyvista as pv
-import json
-import os
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-segment = 'femur'
-
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- file_resample = os.path.join(path, 'Segmentation_femur_transform.stl')
- file_wires = os.path.join(path, 'Segmentation_femur_wires_transform.stl')
- epicondyle_mw = os.path.join(path, 'Femur', 'FemurLateralCondyle', 'MarkupsFiducial.mrk.json')
- epicondyle_tv = os.path.join(path, 'Femur', 'FemurLateralCondyle', 'MarkupsFiducial' + str(subject) + 'TV.mrk.json')
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
- mesh = pv.read(file_resample)
- if side == 'R':
- clipped = mesh.clip(normal=[-1, 0, 0], origin=[30, 0, 0])
- else:
- clipped = mesh.clip(normal=[1, 0, 0], origin=[-30, 0, 0])
- clipped = clipped.clip(normal=[0, 0, 1], origin=[0, 0, 40])
- z = clipped.points[:,0]
- mi, ma = round(min(z)), round(max(z))
- step = 1
- cntrs = np.arange(mi, ma + step, step)
- contours = clipped.contour(cntrs, scalars=clipped.points[:,0])
-
- wires = pv.read(file_wires)
-
- f = open(epicondyle_mw, "r")
- data = json.loads(f.read())
- position_mw = np.asarray(data['markups'][0]['controlPoints'][0]['position'])
- f.close()
- pos_mw = pv.wrap(position_mw).transform(rot_mat)
- pos_mw = pos_mw.glyph(scale=1000, geom=pv.Sphere())
-
- f = open(epicondyle_tv, "r")
- data = json.loads(f.read())
- position_tv = np.asarray(data['markups'][0]['controlPoints'][0]['position'])
- f.close()
- pos_tv = pv.wrap(position_tv).transform(rot_mat)
- pos_tv = pos_tv.glyph(scale=1000, geom=pv.Sphere())
-
- pv.set_plot_theme("document")
- pv.global_theme.auto_close = True
- p = pv.Plotter()
- p.add_mesh(contours, line_width=5, color="black")
- if side == 'R':
- p.add_mesh(clipped, colormap='terrain_r')
- p.camera_position = 'yz'
- p.camera.roll += 0
- else:
- p.add_mesh(clipped, colormap='terrain')
- p.camera_position = 'zy'
- p.camera.roll += 90
- p.show(screenshot=path+r'\Femur\elevation_map.png')
-
- p2 = pv.Plotter()
- p2.add_mesh(contours, line_width=5, color="black")
- if side == 'R':
- p2.add_mesh(clipped, colormap='terrain_r')
- p2.camera_position = 'yz'
- p2.camera.roll += 0
- else:
- p2.add_mesh(clipped, colormap='terrain')
- p2.camera_position = 'zy'
- p2.camera.roll += 90
- p2.add_mesh(pos_mw,color='tomato')
- p2.add_mesh(pos_tv,color='springgreen')
- p2.add_mesh(wires, opacity=0.50,color='cyan')
- p2.show(screenshot=path+r'\Femur\elevation_map_all.png', auto_close=True)
\ No newline at end of file
diff --git a/LigamentInsertions/HausdorffDistance.py b/LigamentInsertions/HausdorffDistance.py
deleted file mode 100644
index b229802..0000000
--- a/LigamentInsertions/HausdorffDistance.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import pymeshlab
-# from plyfile import PlyData, PlyElement
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-# from vtk import *
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-import trimesh
-
-def writeply(surface,filename):
- """Write mesh as ply file."""
- writer = vtkPLYWriter()
- writer.SetInputData(surface)
- writer.SetFileTypeToASCII()
- writer.SetFileName(filename)
- writer.Write()
-
-def readVTK(file):
- reader = vtkDataSetReader()
- reader.SetFileName(file)
- reader.ReadAllVectorsOn()
- reader.ReadAllScalarsOn()
- reader.Update()
-
- data = reader.GetOutput()
- return data
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #
-segments = ['femur'] #'femur','tibia' ','tibia','fibula'
-
-for segment in segments:
- RMS = []
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
- if segment == 'fibula':
- remesh = '_remesh'
- else:
- remesh = ''
-
- # xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(
- # subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # points1 = trimesh.load_mesh(xyz_file)
- # # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- # points2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\reconstructed\shape9.xyz')
- # kwargs = {"scale": True}
- # icp = trimesh.registration.icp(points2.vertices, points1.vertices, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- # points2.apply_transform(icp[0])
- # # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\reconstructed\9_reconstruct_transform_icp_test.xyz', points2.vertices, delimiter=" ")
-
- # files from SSM workflow shapeworks
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.nrrd'
- # particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\4096\Segmentation_' + segment + '_' + side + '_short_' + str(
- # subject) + remesh + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- reconstructed_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\reconstructed\mesh' + str(subject) + 'dt.stl'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input'
- if segment == 'fibula':
- org_mesh = path_bones + '/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_remesh2.stl'
- else:
- org_mesh = path_bones + '/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.stl'
- # get change in position from nrrd header files
- header = nrrd.read_header(pad_file)
- pad_position = header['space origin']
- header = nrrd.read_header(com_file)
- com_position = header['space origin']
-
- # get translation from align from rotation matrix
- rot_ssm = np.loadtxt(file_align)
-
- # translate reconstructed SSM instance to align with original mesh
- translate = pad_position - com_position + rot_ssm[3, :]
- mesh3 = reconstructed_file # =local.particle file
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(mesh3)
-
- max_val = 200
- iters = 1
- while translate[2] + max_val * iters < -max_val:
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-max_val)
- iters = iters + 1
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-222)
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0,
- axisz=translate[2] + max_val * iters)
- ms6.apply_filter('invert_faces_orientation')
- ms6.apply_filter('simplification_quadric_edge_collapse_decimation', targetfacenum=7500)
- # ms6.save_current_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=-15,
- axisy=-90, axisz=-180)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=translate[0],
- # axisy=translate[1], axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0,
- # axisz=translate[2] + 1350)
- ms6.save_current_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- # run ICP to get final position SSM point cloud on original mesh
- ms1 = pymeshlab.MeshSet()
- ms1.load_new_mesh(org_mesh)
- ms1.apply_filter('simplification_quadric_edge_collapse_decimation', targetfacenum=10000)
- ms1.save_current_mesh(path + '\8192\Segmentation_' + segment + '_resample.stl')
-
- mesh = trimesh.load_mesh(path + '\8192\Segmentation_' + segment + '_resample.stl')
- # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- points.apply_transform(M)
- kwargs = {"scale": False}
- icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- points.apply_transform(icp[0])
- icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20, **kwargs)
- points.apply_transform(icp[0])
- points.export(path + '\8192\SSM_' + segment + '_reconstruct_transform_icp.stl')
-
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform_icp.stl')
- ms5.load_new_mesh(org_mesh)
- out2 = ms5.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True)
- out1 = ms5.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=True)
-
- RMS.append(max(out1['RMS'], out2['RMS']))
-
- print('max: ' + str(max(out1['max'], out2['max'])))
- print('min: ' + str(max(out1['min'], out2['min'])))
- print('mean: ' + str(max(out1['mean'], out2['mean'])))
- print('RMS: ' + str(max(out1['RMS'], out2['RMS'])))
-
- # dist_to_use = np.argmax([out1['max'], out2['max']])
- #
- # vq1 = ms5.mesh(2+dist_to_use*2).vertex_quality_array()
- #
- # samples = [sum(vq1 < 0.5), sum((vq1 > 0.5) & (vq1 < 1)), sum((vq1 > 1) & (vq1 < 1.5)),
- # sum((vq1 > 1.5) & (vq1 < 2)), sum(vq1 > 2)]
- #
- # x = np.arange(5) # the label locations
- # width = 0.35 # the width of the bars
- # fig, ax = plt.subplots()
- # rects1 = ax.bar(x, samples, width, label='femoral cartilage')
-
- ms5.save_current_mesh(path + r'/8192/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_HD.ply', binary=False,
- save_vertex_quality=True)
- np.save(path + r'/8192/' + segment + '_HD.np',[out1,out2])
-
- print('RMS ' + segment + str(np.average(RMS)))
diff --git a/LigamentInsertions/OAIdownload.py b/LigamentInsertions/OAIdownload.py
deleted file mode 100644
index 4957e53..0000000
--- a/LigamentInsertions/OAIdownload.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import base64
-import requests
-import json
-import urllib.request
-import shutil
-from pathlib import Path
-
-# Encode our credentials then convert it to a string.
-credentials = base64.b64encode(b'mariskawesseling:p1SM3csN5xXsGFfo').decode('utf-8')
-
-# Create the headers we will be using for all requests.
-headers = {
- 'Authorization': 'Basic ' + credentials,
- 'User-Agent': 'Example Client',
- 'Accept': 'application/json'
-}
-
-# Send Http request
-response = requests.get('https://nda.nih.gov/api/package/auth', headers=headers)
-
-# Business Logic.
-
-# If the response status code does not equal 200
-# throw an exception up.
-if response.status_code != requests.codes.ok:
- print('failed to authenticate')
- response.raise_for_status()
-
-# The auth endpoint does no return any data to parse
-# only a Http response code is returned.
-
-# Assume code in authentication section is present.
-
-packageId = 1190875
-
-# Construct the request to get the files of package 1234
-# URL structure is: https://nda.nih.gov/api/package/{packageId}/files
-response = requests.get('https://nda.nih.gov/api/package/' + str(packageId) + '/files', headers=headers)
-
-# Get the results array from the json response.
-results = response.json()['results']
-
-# Business Logic.
-
-files = {}
-
-# Add important file data to the files dictionary.
-for f in results:
- files[f['package_file_id']] = {'name': f['download_alias']}
-
-# Assume code in authentication section is present.
-# Assume that one of the retrieving files implementations is present too
-
-# Create a post request to the batch generate presigned urls endpoint.
-# Use keys from files dictionary to form a list, which is converted to
-# a json array which is posted.
-response = requests.post('https://nda.nih.gov/api/package/' + str(packageId) + '/files/batchGeneratePresignedUrls',
- json=list(files.keys()), headers=headers)
-
-# Get the presigned urls from the response.
-results = response.json()['presignedUrls']
-
-# Business Logic.
-
-# Add a download key to the file's data.
-for url in results:
- files[url['package_file_id']]['download'] = url['downloadURL']
-
-# Iterate on file id and it's data to perform the downloads.
-# for id, data in files:
-# name = data['name']
-# downloadUrl = data['download']
-# # Create a downloads directory
-# file = 'downloads/' + name
-# # Strip out the file's name for creating non-existent directories
-# directory = file[:file.rfind('/')]
-#
-# # Create non-existent directories, package files have their
-# # own directory structure, and this will ensure that it is
-# # kept in tact when downloading.
-# Path(directory).mkdir(parents=True, exist_ok=True)
-#
-# # Initiate the download.
-# with urllib.request.urlopen(downloadUrl) as dl, open(file, 'wb') as out_file:
-# shutil.copyfileobj(dl, out_file)
-
-import csv
-
-# Assume code in authentication section is present.
-
-# packageId = 1234
-
-s3Files = []
-
-# Load in and process the manifest file.
-# Not all manifest files are structured like this, all you require is
-# an S3 url and a package that has the files associated with it.
-# with open('datastructure_manifest.txt', 'r') as manifest:
-# for rows in csv.reader(manifest, dialect='excel-tab'):
-# for row in rows:
-# if row.startsWith('s3://'):
-# s3Files.append(row)
-
-# The manifest files have their column declarations listed twice, trim those out
-# s3Files = s3Files[2:]
-s3Files = ['s3://NDAR_Central_1/submission_13364/00m/0.E.1/9005075/20050926/10593811.tar.gz']
-
-# Construct the request to get the files of package 1234
-# URL structure is: https://nda.nih.gov/api/package/{packageId}/files
-response = requests.post('https://nda.nih.gov/api/package/' + str(packageId) + '/files', json=s3Files, headers=headers)
-
-# Business Logic.
-
-files = {}
-
-# Add important file data to the files dictionary.
-# We can skip having to transform the json because a json array is returned.
-for f in response.json():
- files[f['package_file_id']] = {'name': f['download_alias']}
\ No newline at end of file
diff --git a/LigamentInsertions/ParaviewLoad.py b/LigamentInsertions/ParaviewLoad.py
deleted file mode 100644
index ee87a40..0000000
--- a/LigamentInsertions/ParaviewLoad.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import glob
-
-# subject = 9,13,19,23,26,29,32,35,37,41
-subject = 41
-segments = ['femur'] #['femur'] #
-renderView1 = GetActiveViewOrCreate('RenderView')
-
-for segment in segments:
- path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/" + str(subject) + '/'
- Counter = len(glob.glob1(path, 'Segmentation_' + segment + '_area*.stl'))
-
- for count in range(1,Counter+1):
-
- segmentation_femur_area1stl = STLReader(registrationName='Segmentation_' + segment + '_area' + str(count) + '.stl', #_transform
- FileNames=[os.path.join(path,'Segmentation_' + segment + '_area' + str(count) + '.stl')]) #_transform
- # show data in view
- segmentation_femur_area1stlDisplay = Show(segmentation_femur_area1stl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femur_area1stlDisplay.Representation = 'Surface'
- segmentation_femur_area1stlDisplay.ColorArrayName = ['CELLS', 'STLSolidLabeling']
- segmentation_femur_area1stlDisplay.SelectTCoordArray = 'None'
- segmentation_femur_area1stlDisplay.SelectNormalArray = 'None'
- segmentation_femur_area1stlDisplay.SelectTangentArray = 'None'
- segmentation_femur_area1stlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.SelectOrientationVectors = 'None'
- segmentation_femur_area1stlDisplay.ScaleFactor = 3.404553985595703
- segmentation_femur_area1stlDisplay.SelectScaleArray = 'None'
- segmentation_femur_area1stlDisplay.GlyphType = 'Arrow'
- segmentation_femur_area1stlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femur_area1stlDisplay.GaussianRadius = 0.17022769927978515
- segmentation_femur_area1stlDisplay.SetScaleArray = [None, '']
- segmentation_femur_area1stlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.OpacityArray = [None, '']
- segmentation_femur_area1stlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femur_area1stlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
-
- segmentation_femurstl = STLReader(registrationName='Segmentation_' + segment + '.stl', FileNames=[os.path.join(path,'Segmentation_' + segment + '.stl')]) #_transform
- segmentation_femur_wiresstl = STLReader(registrationName='Segmentation_' + segment + '_wires.stl', FileNames=[os.path.join(path,'Segmentation_' + segment + '_wires.stl')]) #_transform
-
- # show data in view
- segmentation_femurstlDisplay = Show(segmentation_femurstl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femurstlDisplay.Representation = 'Surface'
- segmentation_femurstlDisplay.ColorArrayName = [None, '']
- segmentation_femurstlDisplay.SelectTCoordArray = 'None'
- segmentation_femurstlDisplay.SelectNormalArray = 'None'
- segmentation_femurstlDisplay.SelectTangentArray = 'None'
- segmentation_femurstlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.SelectOrientationVectors = 'None'
- segmentation_femurstlDisplay.ScaleFactor = 10.438916015625
- segmentation_femurstlDisplay.SelectScaleArray = 'None'
- segmentation_femurstlDisplay.GlyphType = 'Arrow'
- segmentation_femurstlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femurstlDisplay.GaussianRadius = 0.52194580078125
- segmentation_femurstlDisplay.SetScaleArray = [None, '']
- segmentation_femurstlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.OpacityArray = [None, '']
- segmentation_femurstlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femurstlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
- # show data in view
- segmentation_femur_wiresstlDisplay = Show(segmentation_femur_wiresstl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femur_wiresstlDisplay.Representation = 'Surface'
- segmentation_femur_wiresstlDisplay.ColorArrayName = [None, '']
- segmentation_femur_wiresstlDisplay.SelectTCoordArray = 'None'
- segmentation_femur_wiresstlDisplay.SelectNormalArray = 'None'
- segmentation_femur_wiresstlDisplay.SelectTangentArray = 'None'
- segmentation_femur_wiresstlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.SelectOrientationVectors = 'None'
- segmentation_femur_wiresstlDisplay.ScaleFactor = 9.296994972229005
- segmentation_femur_wiresstlDisplay.SelectScaleArray = 'None'
- segmentation_femur_wiresstlDisplay.GlyphType = 'Arrow'
- segmentation_femur_wiresstlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femur_wiresstlDisplay.GaussianRadius = 0.46484974861145023
- segmentation_femur_wiresstlDisplay.SetScaleArray = [None, '']
- segmentation_femur_wiresstlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.OpacityArray = [None, '']
- segmentation_femur_wiresstlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femur_wiresstlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
- # update the view to ensure updated data information
- renderView1.Update()
-
- # change solid color
- segmentation_femur_wiresstlDisplay.AmbientColor = [1.0, 1.0, 0.0]
- segmentation_femur_wiresstlDisplay.DiffuseColor = [1.0, 1.0, 0.0]
-
- # ================================================================
- # addendum: following script captures some of the application
- # state to faithfully reproduce the visualization during playback
- # ================================================================
-
- # get layout
- layout1 = GetLayout()
-
- # --------------------------------
- # saving layout sizes for layouts
-
- # layout/tab size in pixels
- layout1.SetSize(866, 780)
-
- # -----------------------------------
- # saving camera placements for views
-
- # current camera placement for renderView1
- renderView1.CameraPosition = [230.80556325282282, -162.27554399127564, -1805.7694344571757]
- renderView1.CameraFocalPoint = [203.418271000369, -148.44984503432718, -1793.8450018543017]
- renderView1.CameraViewUp = [0.12379209123259044, -0.4960295986051203, 0.8594359519219018]
- renderView1.CameraParallelScale = 8.519062667601332
-
-ResetCamera()
-# --------------------------------------------
-# uncomment the following to render all views
-# RenderAllViews()
-# alternatively, if you want to write images, you can use SaveScreenshot(...).
-
-# paraview.simple.Box(Center=(-0.3667695,10.1671895,95.5673735),XLength = 91.910263, YLength = 71.482658, ZLength = 71.482658)
\ No newline at end of file
diff --git a/LigamentInsertions/ProjectCentroids.py b/LigamentInsertions/ProjectCentroids.py
deleted file mode 100644
index 79d7079..0000000
--- a/LigamentInsertions/ProjectCentroids.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import pandas as pd
-import os
-import trimesh
-import numpy as np
-import matplotlib.path as plt
-import copy
-import time
-
-def heron(a,b,c):
- s = (a + b + c) / 2
- area = (s*(s-a) * (s-b)*(s-c)) ** 0.5
- return area
-
-def distance3d(x1,y1,z1,x2,y2,z2):
- a=(x1-x2)**2+(y1-y2)**2 + (z1-z2)**2
- d= a ** 0.5
- return d
-
-def area(x1,y1,z1,x2,y2,z2,x3,y3,z3):
- a=distance3d(x1,y1,z1,x2,y2,z2)
- b=distance3d(x2,y2,z2,x3,y3,z3)
- c=distance3d(x3,y3,z3,x1,y1,z1)
- A = heron(a,b,c)
- return A
- # print("area of triangle is %r " %A)
-
-# A utility function to calculate area
-# of triangle formed by (x1, y1),
-# (x2, y2) and (x3, y3)
-
-# def area(x1, y1, x2, y2, x3, y3):
-# return abs((x1 * (y2 - y3) + x2 * (y3 - y1)
-# + x3 * (y1 - y2)) / 2.0)
-
-
-# A function to check whether point P(x, y)
-# lies inside the triangle formed by
-# A(x1, y1), B(x2, y2) and C(x3, y3)
-def isInside(p1, p2, p3, p):
- x1 = p1[0]
- y1 = p1[1]
- z1 = p1[2]
- x2 = p2[0]
- y2 = p2[1]
- z2 = p2[2]
- x3 = p3[0]
- y3 = p3[1]
- z3 = p3[2]
- x = p[0]
- y = p[1]
- z = p[2]
-
- # Calculate area of triangle ABC
- A = area(x1, y1,z1, x2, y2,z2, x3, y3,z3)
-
- # Calculate area of triangle PBC
- A1 = area(x, y,z, x2, y2,z2, x3, y3,z3)
-
- # Calculate area of triangle PAC
- A2 = area(x1, y1,z1, x, y, z,x3, y3,z3)
-
- # Calculate area of triangle PAB
- A3 = area(x1, y1,z1, x2, y2,z2, x, y,z)
-
- # Check if sum of A1, A2 and A3
- # is same as A
- if abs(A - (A1 + A2 + A3)) < 1e-6:
- return True
- else:
- return False
-
-def intersection(planeNormal,planePoint,rayDirection,rayPoint):
- epsilon=1e-6
-
- #Define plane
- # planeNormal = np.array([0, 0, 1])
- # planePoint = np.array([0, 0, 5]) #Any point on the plane
-
- #Define ray
- # rayDirection = np.array([0, -1, -1])
- # rayPoint = np.array([0, 0, 10]) #Any point along the ray
-
- ndotu = planeNormal.dot(rayDirection)
-
- if abs(ndotu) < epsilon:
- intersect = 0
- else:
- w = rayPoint - planePoint[0,:]
- si = -planeNormal.dot(w) / ndotu
- Psi = w + si * rayDirection + planePoint[0,:]
- if isInside(planePoint[0], planePoint[1], planePoint[2], Psi) == False:
- intersect = 0
- else:
- intersect = Psi[0]
-
- return intersect
-
-
-subjects = [9] #[9,13,19,] #23,26,29,32,35,37,
-segment = 'femur'
-
-df = pd.read_excel(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces2.xlsx"),
- sheet_name='perc_of_len femur')
-lig_names = ['PCL'] #, 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)'
-
-for subject in subjects:
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- else:
- side = 'L'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- mesh = path + '/Segmentation_' + segment + '_transform.stl'
- bone = trimesh.load_mesh(mesh)
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
- AP_size = np.max(bone.vertices[:,1])-np.min(bone.vertices[:,1]) # AP length
-
- Rx = trimesh.transformations.rotation_matrix(1.57, [0, 1, 0])
- for name in lig_names:
- if side == 'R':
- most_med_point = np.min(bone.vertices[:, 0]) # med
- most_lat_point = np.max(bone.vertices[:, 0]) # lat
- med_section_dir = + 15
- lat_section_dir = - 15
- else:
- most_med_point = np.max(bone.vertices[:, 0]) # med
- most_lat_point = np.min(bone.vertices[:, 0]) # lat
- med_section_dir = - 10
- lat_section_dir = + 10
- if name == 'PCL' or name == 'ACL':
- most_med_point = most_med_point*0.10
- most_lat_point = most_lat_point*0.10
- med_section = most_med_point #- med_section_dir
- lat_section = most_lat_point #- lat_section_dir
- else:
- most_med_point = most_med_point
- most_lat_point = most_lat_point
- med_section = most_med_point + med_section_dir
- lat_section = most_lat_point + lat_section_dir
-
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- LCL_points = [most_med_point * np.ones(10),
- np.min(bone.vertices[:, 1]) + np.asarray(AP_size * df[name + 'y'][0:10]),
- np.min(bone.vertices[:, 2]) + np.asarray(AP_size * df[name + 'z'][0:10])]
- LCL_points = np.transpose(np.asarray(LCL_points))
- else:
- LCL_points = [most_lat_point*np.ones(10), np.min(bone.vertices[:,1])+np.asarray(AP_size*df[name+'y'][0:10]), np.min(bone.vertices[:,2])+np.asarray(AP_size*df[name+'z'][0:10])]
- LCL_points = np.transpose(np.asarray(LCL_points))
-
- for pts in range(0,10):
- if not np.isnan(LCL_points[pts,:]).any():
- intersect = []
- bone_part = copy.deepcopy(bone)
- top = max(LCL_points[:,2])+2.5
- far_verts = bone_part.vertices[:, 2] < top
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- if side == 'R':
- far_verts = bone_part.vertices[:, 0] < med_section
- else:
- far_verts = bone_part.vertices[:, 0] > med_section
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- # trimesh.Scene(bone_part).show()
- else:
- if side == 'R':
- far_verts = bone_part.vertices[:, 0] > lat_section
- else:
- far_verts = bone_part.vertices[:, 0] < lat_section
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- # trimesh.Scene(bone_part).show()
- # tic = time.perf_counter()
- for count, tr in enumerate(bone_part.face_normals):
- intersect.append(intersection(tr, bone_part.vertices[bone_part.faces[count,:]], np.array([1,0,0]), LCL_points[pts,:]))
- # toc = time.perf_counter()
- # print(f"Downloaded the tutorial in {toc - tic:0.4f} seconds")
-
- # T = trimesh.transformations.translation_matrix(LCL_points[pts])
- # point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=T)
- # trimesh.Scene([bone_part, point]).show()
-
- x_coord = [i for i in intersect if i != 0]
- if not len(x_coord) == 0:
- if name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- to_use = np.argmin(abs(x_coord - most_med_point))
- elif name == 'PCL':
- to_use = np.argmin(abs(x_coord - most_med_point))
- elif name == 'ACL':
- to_use = np.argmin(abs(x_coord - most_lat_point))
- else:
- to_use = np.argmax(abs(x_coord - most_lat_point))
- if not abs(x_coord[to_use]-LCL_points[pts, 0]) > 20:
- LCL_points[pts, 0] = x_coord[to_use]
-
-
- # points = trimesh.PointCloud(LCL_points, colors=None, metadata=None) # create point cloud
-
- points = []
- for ind in range(0,10):
- T = trimesh.transformations.translation_matrix(LCL_points[ind])
- R = np.linalg.inv(rot_mat)
- M = trimesh.transformations.concatenate_matrices(R, T, Rx)
- point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=M)
- # point = trimesh.creation.icosphere(subdivisions=3, radius=1.0, color=None, transform=T)
- if ind == 0:
- points = point
- else:
- points = trimesh.boolean.union([points,point])
-
- points.export(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),name+'centroids.stl'))
diff --git a/LigamentInsertions/ReadMe.md b/LigamentInsertions/ReadMe.md
deleted file mode 100644
index 86d06f7..0000000
--- a/LigamentInsertions/ReadMe.md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-X. Fit SSM to new bone
-Make sure you have a SSM of the specific bone in Shapeworks and know which points represent the ligament attachments.
-
-1. Segment bone from images
-2. Groom pipeline for new bone
-Reflect bone if left (put _L in the name) as SSM is for right bones.
-Make sure the bone is correctly aligned and cropped as the input bones of the original SSM.
-For cropping use the median bone of the original SSM
-If needed adapt the padding
-3. Create the mean shape particle file of the original SSM (getMeanShape.py)
-4. Run the SSM with fixed domains, where the number of fixed domains is the number of original input bones used for the SSM.
-If needed increase the narrow band
-5. Position the particle file (SSM point cloud) at the original bone location (FitSSM_mri.py)
-Make sure the original bone is not too large, to avoid very slow ICP. Aim for a file size below 10MB (about 20,000 faces) (Meshlab - Simplificaion: Quadric Edge Collapse Decimation)
-Required variables:
-subjects => name of the subjects you want to process
-sides => for each subject the side that is being analyzed
-segments => segments you want to analyze
-short_ssm => for each segment, is the shorter SSM needed (0=false, 1=true)
-no_particles => for each segment, number of particles in the SSM
-6. Get the points associated with all ligament locations on the original bone mesh location
-7. For each ligament, determine the SSM points associated to the ligament attachments (adaptLigaments.py)
-Interpolate the points to obtain the number of points needed for the OpenSim model
-Write points to osim file
-8. Scale the ligament parameter based on the length of the ligament (still in Matlab)
-
diff --git a/LigamentInsertions/Registration4DCT.py b/LigamentInsertions/Registration4DCT.py
deleted file mode 100644
index e69de29..0000000
diff --git a/LigamentInsertions/SlicerEnableUndo.py b/LigamentInsertions/SlicerEnableUndo.py
deleted file mode 100644
index 826a0d6..0000000
--- a/LigamentInsertions/SlicerEnableUndo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Enable undo for the scene
-# exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerEnableUndo.py').read())
-
-slicer.mrmlScene.SetUndoOn()
-
-# Enable undo for markups fiducial nodes
-
-defaultMarkupsNode = slicer.mrmlScene.GetDefaultNodeByClass("vtkMRMLMarkupsFiducialNode")
-if not defaultMarkupsNode:
- defaultMarkupsNode = slicer.vtkMRMLMarkupsFiducialNode()
- slicer.mrmlScene.AddDefaultNode(defaultMarkupsNode)
-
-defaultMarkupsNode.UndoEnabledOn()
-
-# Add standard keyboard shortcuts for scene undo/redo
-
-redoKeyBindings = qt.QKeySequence.keyBindings(qt.QKeySequence.Redo)
-for redoBinding in redoKeyBindings:
- redoShortcut = qt.QShortcut(slicer.util.mainWindow())
- redoShortcut.setKey(redoBinding)
- redoShortcut.connect("activated()", slicer.mrmlScene.Redo)
-
-undoKeyBindings = qt.QKeySequence.keyBindings(qt.QKeySequence.Undo)
-for undoBinding in undoKeyBindings:
- undoShortcut = qt.QShortcut(slicer.util.mainWindow())
- undoShortcut.setKey(undoBinding)
- undoShortcut.connect("activated()", slicer.mrmlScene.Undo)
\ No newline at end of file
diff --git a/LigamentInsertions/SlicerExportXray.py b/LigamentInsertions/SlicerExportXray.py
deleted file mode 100644
index f6604d9..0000000
--- a/LigamentInsertions/SlicerExportXray.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import glob
-import shutil
-import os
-import DICOMScalarVolumePlugin
-import slicer
-import vtk
-#exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerExportXray.py').read())
-
-subjects = [13,19,23,26,29,32,35,37,41] # 9
-for subject in subjects:
- lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),'DRR')
- slicer.mrmlScene.Clear(0)
- slicer.util.loadScene(glob.glob(os.path.join(path,"*.mrml"))[0])
- no_med=-1
- no_lat=-1
- for name in lig_names:
- slicer.util.loadSegmentation(os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData',str(subject),name+'centroids.stl'))
-
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- segmentationNode = slicer.util.getNode('Segmentation_med')
- else:
- segmentationNode = slicer.util.getNode('Segmentation_lat')
- segmentationNode.GetSegmentation().CopySegmentFromSegmentation(slicer.util.getNode(name+'centroids').GetSegmentation(),name+'centroids')
-
- labelmapVolumeNode = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLabelMapVolumeNode')
- # slicer.modules.segmentations.logic().ExportVisibleSegmentsToLabelmapNode(segmentationNode, labelmapVolumeNode)
- segmentIds = vtk.vtkStringArray()
- segmentIds.InsertNextValue(name + 'centroids')
- slicer.vtkSlicerSegmentationsModuleLogic.ExportSegmentsToLabelmapNode(segmentationNode, segmentIds, labelmapVolumeNode)
-
- outputvolumenode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLScalarVolumeNode", 'Labelmap'+name)
- sef = slicer.modules.volumes.logic().CreateScalarVolumeFromVolume(slicer.mrmlScene, outputvolumenode, labelmapVolumeNode)
- volumeNode = slicer.util.getNode("Labelmap"+name)
- voxels = slicer.util.arrayFromVolume(volumeNode)
- voxels[voxels==1] = 8000
- voxels[voxels==2] = 8000
- voxels[voxels==3] = 8000
- voxels[voxels==4] = 8000
- voxels[voxels==0] = -8000
-
- rtImagePlan = slicer.util.getNode("RTPlan")
- if name=='PCL' or name=='MCL-p' or name == 'MCL-d' or name=='posterior oblique':
- beam_name = "NewBeam_med"
- no_med +=1
- no=no_med
- else:
- beam_name = "NewBeam_lat"
- no_lat +=1
- no=no_lat
- rtImageBeam = rtImagePlan.GetBeamByName(beam_name)
- Volume = slicer.util.getNode("Labelmap"+name)
- # Create DRR image computation node for user imager parameters
- drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
- # Set and observe RTImage beam by the DRR node
- drrParameters.SetAndObserveBeamNode(rtImageBeam)
- # Get DRR computation logic
- drrLogic = slicer.modules.drrimagecomputation.logic()
- # Update imager markups for the 3D view and slice views (optional)
- drrLogic.UpdateMarkupsNodes(drrParameters)
- # Update imager normal and view-up vectors (mandatory)
- drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
- # Compute DRR image
- drr_image = drrLogic.ComputePlastimatchDRR(drrParameters, Volume)
- # slicer.mrmlScene.Clear(0)
- if no == 0:
- volumeNode = slicer.util.getNode("DRR : " + beam_name)
- else:
- volumeNode = slicer.util.getNode("DRR : " + beam_name + "_" + str(no))
- outputFolder = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), "DRR")
- # Create patient and study and put the volume under the study
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- patientItemID = shNode.CreateSubjectItem(shNode.GetSceneItemID(), "test patient")
- studyItemID = shNode.CreateStudyItem(patientItemID, "test study")
- volumeShItemID = shNode.GetItemByDataNode(volumeNode)
- shNode.SetItemParent(volumeShItemID, studyItemID)
- exporter = DICOMScalarVolumePlugin.DICOMScalarVolumePluginClass()
- exportables = exporter.examineForExport(volumeShItemID)
- for exp in exportables:
- exp.directory = outputFolder
-
- exporter.export(exportables)
- folders = [x[0] for x in os.walk(outputFolder)]
- im_folder = [s for s in folders if str(volumeShItemID) in s]
- shutil.move(im_folder[0] + '\IMG0001.dcm', outputFolder + '/' + name + '0001.dcm')
- os.rmdir(im_folder[0])
-
- names = ['all','med','lat']
- for name in names:
- volumeNode = slicer.util.getNode("Segmentation_"+name+'-label')
- voxels = slicer.util.arrayFromVolume(volumeNode)
- voxels[voxels==1] = 8000
- voxels[voxels==2] = 8000
- voxels[voxels==3] = 8000
- voxels[voxels==4] = 8000
- voxels[voxels == 5] = 8000
- voxels[voxels == 6] = 8000
- voxels[voxels == 7] = 8000
- voxels[voxels == 8] = 8000
- voxels[voxels==0] = -8000
-
- names = ["med_fem", "lat_fem", "med_wires", "lat_wires", "med_all_wires", "lat_all_wires"]
- for name in names:
- rtImagePlan = slicer.util.getNode("RTPlan")
- if 'lat' in name:
- beam_name = "NewBeam_lat"
- no_lat += 1
- no = no_lat
- else:
- beam_name = "NewBeam_med"
- no_med += 1
- no = no_med
- rtImageBeam = rtImagePlan.GetBeamByName(beam_name)
- if 'fem' in name:
- Volume = slicer.util.getNode("resampled06")
- elif 'med_wires' in name:
- Volume = slicer.util.getNode("Segmentation_med-label")
- elif 'lat_wires' in name:
- Volume = slicer.util.getNode("Segmentation_lat-label")
- elif 'all' in name:
- Volume = slicer.util.getNode("Segmentation_all-label")
- # Create DRR image computation node for user imager parameters
- drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
- # Set and observe RTImage beam by the DRR node
- drrParameters.SetAndObserveBeamNode(rtImageBeam)
- # Get DRR computation logic
- drrLogic = slicer.modules.drrimagecomputation.logic()
- # Update imager markups for the 3D view and slice views (optional)
- drrLogic.UpdateMarkupsNodes(drrParameters)
- # Update imager normal and view-up vectors (mandatory)
- drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
- # Compute DRR image
- drr_image = drrLogic.ComputePlastimatchDRR(drrParameters, Volume)
- # slicer.mrmlScene.Clear(0)
-
- if no == 0:
- volumeNode = slicer.util.getNode("DRR : " + beam_name) #getNode("DRR : Beam_" + name)
- else:
- volumeNode = slicer.util.getNode("DRR : " + beam_name + '_' + str(no))
- outputFolder = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), "DRR")
- # Create patient and study and put the volume under the study
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- patientItemID = shNode.CreateSubjectItem(shNode.GetSceneItemID(), "test patient")
- studyItemID = shNode.CreateStudyItem(patientItemID, "test study")
- volumeShItemID = shNode.GetItemByDataNode(volumeNode)
- shNode.SetItemParent(volumeShItemID, studyItemID)
- exporter = DICOMScalarVolumePlugin.DICOMScalarVolumePluginClass()
- exportables = exporter.examineForExport(volumeShItemID)
- for exp in exportables:
- exp.directory = outputFolder
-
- exporter.export(exportables)
- folders = [x[0] for x in os.walk(outputFolder)]
- im_folder = [s for s in folders if str(volumeShItemID) in s]
- shutil.move(im_folder[0] + '\IMG0001.dcm', outputFolder+'/' + name + '0001.dcm')
- os.rmdir(im_folder[0])
-
-# in slicer
-# import resampled data
-# import segmented seperate wires as segmentation
-# create 3 new segmentations (all, med, lat) with resampled image as master volume
-# in segmentations - copy wires segmentation to segmentation resampled volume
-# add correct wires to med/lat/all
-# export visible segments to label map
-# in volumes - convert to scalar volume
-
-# import resampled femur
-# external beam planning
-# Ref volume: resampled06
-# Gantry: 101/281
-# Structure set: segmentsation all
-# DRR image computation
-# export to DICOM - crate dicom series
diff --git a/LigamentInsertions/SlicerPositionBeam.py b/LigamentInsertions/SlicerPositionBeam.py
deleted file mode 100644
index 5a903be..0000000
--- a/LigamentInsertions/SlicerPositionBeam.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerPositionBeam.py').read())
-import os,glob
-subject = 23
-
-path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),'DRR')
-slicer.mrmlScene.Clear(0)
-slicer.util.loadScene(glob.glob(os.path.join(path,"*.mrml"))[0])
-# Create dummy RTPlan
-rtImagePlan = getNode("RTPlan")
-rtImageBeam = rtImagePlan.GetBeamByName("NewBeam_lat")
-# Set required beam parameters
-current_angle = rtImageBeam.GetGantryAngle()
-rtImageBeam.SetGantryAngle(current_angle-7)
-rtImageBeam.SetCouchAngle(355)
-
-rtImageBeam2 = rtImagePlan.GetBeamByName("NewBeam_med")
-rtImageBeam2.SetGantryAngle(current_angle-7+180)
-rtImageBeam2.SetCouchAngle(355)
-
-# # Get CT volume
-# ctVolume = getNode('resampled06')
-# # Create DRR image computation node for user imager parameters
-# drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
-# # Set and observe RTImage beam by the DRR node
-# drrParameters.SetAndObserveBeamNode(rtImageBeam)
-# # Get DRR computation logic
-# drrLogic = slicer.modules.drrimagecomputation.logic()
-# # Update imager markups for the 3D view and slice views (optional)
-# drrLogic.UpdateMarkupsNodes(drrParameters)
-# # Update imager normal and view-up vectors (mandatory)
-# drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
-# # Compute DRR image
-# drrLogic.ComputePlastimatchDRR(drrParameters, ctVolume)
-
-# save scene
-slicer.util.saveScene(glob.glob(os.path.join(path,"*.mrml"))[0])
diff --git a/LigamentInsertions/SlicerXrayMeanSSM.py b/LigamentInsertions/SlicerXrayMeanSSM.py
deleted file mode 100644
index 27c0315..0000000
--- a/LigamentInsertions/SlicerXrayMeanSSM.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import DICOMScalarVolumePlugin
-import slicer
-import vtk
-
-volumeNode = slicer.util.getNode("Volume")
-voxels = slicer.util.arrayFromVolume(volumeNode)
-voxels[voxels==0] = -1000
-voxels[voxels==1] = 1000
-voxels[voxels==2] = 2000
-
-volumeNode = slicer.util.getNode("LCLpoints7_1-LCLpoints7-label")
-voxels = slicer.util.arrayFromVolume(volumeNode)
-voxels[voxels==1] = 8000
-voxels[voxels==0] = -8000
-
-import seaborn as sns
-c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
diff --git a/LigamentInsertions/TibiaGrid.py b/LigamentInsertions/TibiaGrid.py
deleted file mode 100644
index ed1d2eb..0000000
--- a/LigamentInsertions/TibiaGrid.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Find most anterior edge of the femoral notch roof - representation Blumensaat line for 3D shapes
-# https://journals.lww.com/jbjsjournal/Fulltext/2010/06000/The_Location_of_Femoral_and_Tibial_Tunnels_in.10.aspx?__hstc=215929672.82af9c9a98fa600b1bb630f9cde2cb5f.1528502400314.1528502400315.1528502400316.1&__hssc=215929672.1.1528502400317&__hsfp=1773666937&casa_token=BT765BcrC3sAAAAA:Vu9rn-q5ng4c8339KQuq2mGZDgrAgBStwvn4lvYEbvCgvKQZkbJL24hWbKFdnHTc8VBmAIXA3HVvuWg22-9Mvwv1sw
-# https://www.dropbox.com/sh/l7pd43t7c4hrjdl/AABkncBbleifnpLDKSDDc0dCa/D3%20-%20Dimitriou%202020%20-%20Anterior%20cruciate%20ligament%20bundle%20insertions%20vary.pdf?dl=0
-
-import trimesh
-import numpy as np
-import os
-import math
-import pandas as pd
-import pymeshlab
-import seaborn as sns
-
-
-def findIntersection(x1, y1, x2, y2, x3, y3, x4, y4):
- px = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
- py = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
-
- ang = math.atan2(py - y3, px - x3) - math.atan2(y1 - y3, x1 - x3)
-
- l = math.cos(ang)*np.linalg.norm(np.asarray((x3,y3))-np.asarray((x4,y4)))
-
- return l, px, py
-
-def split(start, end, segments):
- x_delta = (end[0] - start[0]) / float(segments)
- y_delta = (end[1] - start[1]) / float(segments)
- z_delta = (end[2] - start[2]) / float(segments)
- points = []
- for i in range(1, segments):
- points.append([start[0] + i * x_delta, start[1] + i * y_delta, start[2] + i * z_delta])
- return [start] + points + [end]
-
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments = ligaments_tib
-
-# find most ant point in yz plane
-subjects = [100] # [9,13,19,23,26,29,32,35,37,41] #
-lig = 'ACL'
-segment = 'tibia'
-
-d = []
-h = []
-h_centriods = []
-d_centriods = []
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_rot.stl')
- path_col = r'C:\\Users\\mariskawesseli\\Documents\\GitLab\\knee_ssm\\OAI\\Output/tibia_bone\\new_bone\\shape_models'
- side = 'R'
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), 'Segmentation_tibia_transform.STL')
-
- mesh = trimesh.load_mesh(path)
- verts = mesh.vertices
- AP = mesh.bounding_box.bounds[1,1] - mesh.bounding_box.bounds[0,1]
- ML = mesh.bounding_box.bounds[1,0] - mesh.bounding_box.bounds[0,0]
- bbox = mesh.bounding_box.bounds
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, (0,-1,0), (0,20,0), cached_dots=None, return_both=False)
- # posterior_mesh.show()
-
- # find anterior line
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(mesh, (0, 0, 0), (0, 0, 1),
- heights=np.linspace(-15, 5, 21))
- ant_point = []
- prox_point = []
- for i in range(0, len(face_index)):
- plane_verts = np.unique(mesh.faces[face_index[i]])
- plane_points = mesh.vertices[plane_verts]
-
- # goon = 1
- # tel = 2
- # while goon == 1:
- min_y = np.where(plane_points[:, 1] == plane_points[:, 1].max())
- ant_point.append(plane_points[min_y])
- # min_y2 = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], tel + 1)[tel + 1])
- # z_min2 = plane_points[min_y2][0][1]
- # if z_min - z_min2 > -15:
- # goon = 1
- # tel += 1
- # else:
- # goon = 0
- # dist_point.append(plane_points[min_y][0])
- # min_y = np.where(plane_points[:, 1] == plane_points[:, 1].min())
- # prox_point.append(plane_points[min_y][0])
-
- # most_ant_ind1 = np.asarray(dist_point)[:, 1].argmax()
- # most_ant_ind2 = np.asarray(prox_point)[:, 1].argmax()
- p1=ant_point[np.argmin(np.squeeze(np.array(ant_point))[:,1])][0]
-
- # min_y = mesh.vertices[np.argmin(mesh.vertices[:, 1])]
- # p1 = min_y
- p2 = np.array((0,p1[1],p1[2]))
-
- # find posterior line
- min_y = mesh.vertices[np.argmin(mesh.vertices[:, 1])]
- p3 = min_y
- p4 = np.array((0, min_y[1], min_y[2]))
-
- # find medial line
- min_x = mesh.vertices[np.argmin(mesh.vertices[:, 0])]
- p5 = min_x
- p6 = np.array((min_x[0], 0, min_x[2]))
-
- # find lateral line
- max_x = mesh.vertices[np.argmax(mesh.vertices[:, 0])]
- p7 = max_x
- p8 = np.array((max_x[0], 0, max_x[2]))
-
-
- # find height
- # vec1 = (p1[0][0] - p2[0][0], p1[0][1] - p2[0][1], p1[0][2] - p2[0][2])
- # norm = np.sqrt(vec1[0] ** 2 + vec1[1] ** 2 + vec1[2] ** 2)
- # direction = [vec1[0] / norm, vec1[1] / norm, vec1[2] / norm]
-
-
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
-
- # trimesh.path.segments.parameters_to_segments(p1[-1], -1*direction, ((0,0,0),(0,1,0)))
- # trimesh.path.segments.segments_to_parameters(np.asarray(segments))
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,0,10), cached_dots=None, return_both=False)
-
-
-
- # segments = np.asarray([p3[np.asarray(dist).argmax()], p4[np.asarray(dist).argmax()]])
- # p_dist = trimesh.load_path(segments)
- p1_2d = p1[0:2]
- p2_2d = p2[0:2]
- p3_2d = p3[0:2]
- # d.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3_2d))/np.linalg.norm(p2_2d-p1_2d))
- d.append(p3[1]-p1[1])
-
- # find depth
- p5_2d = p5[0:2]
- p6_2d = p6[0:2]
- p7_2d = p7[0:2]
- # h.append(np.linalg.norm(np.cross(p6_2d - p5_2d, p5_2d - p7_2d)) / np.linalg.norm(p6_2d - p7_2d))
- h.append(p7[0] - p5[0])
-
- # visualization
- # p1[0][0] = 0
- # p2[0][0] = 0
- # p3[np.asarray(dist1).argmax()][0] = 0
- # p4[jump_ind + 1][0] = 0
- # p5[0] = 0
- # p6[jump_ind + 1][0] = 0
-
- points = trimesh.points.PointCloud(np.asarray((p1,p2,p3,p4,p5,p6,p7,p8)), colors=None, metadata=None)
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
- # segments = np.asarray([p6[jump_ind+1], p5])
- # p_dist = trimesh.load_path(segments)
-
- mesh.visual.face_colors[:] = np.array([227, 218, 201, 100])
- mesh.visual.vertex_colors[:] = np.array([227, 218, 201, 100])
- direction = (0,1,0)
- direction_perp = (1,0,0)
- line = trimesh.path.segments.parameters_to_segments([p5,p7,p1,p3], [direction,direction,direction_perp,direction_perp],
- np.array(((27,d[-1]+30),(-26,-d[-1]-27),(-42,h[-1]-37),(48,-h[-1]+46))).astype(float))
-
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[5], 4)
- grid_points2 = split(box_points[0], box_points[2], 4)
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1], grid_points1[2], grid_points1[3]],
- [direction_perp], np.array(
- ((h[-1] + 4, -0), (h[-1] + 2.5, 0), (h[-1] + 0.5, -0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] - 1.5, 0), (d[-1] - 1.5, 0),
- (d[-1] - 1.5, 0))).astype(
- float))
- grid_line_path = trimesh.load_path(np.squeeze(grid_line),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
- grid_line2_path = trimesh.load_path(np.squeeze(grid_line2),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
-
- scene = trimesh.Scene([mesh, trimesh.load_path(np.squeeze(line)),grid_line_path,grid_line2_path]) #, points
- scene.show()
- # mesh.vertices[:, 0] = 0
- # trimesh.Scene([mesh, points, trimesh.load_path(np.squeeze(line))]).show()
-
-# posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,-30,0), cached_dots=None, return_both=False)
-# posterior_mesh.show()
- if subject == 100:
- points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_rot.xyz')
- if lig == 'ACL':
- center = np.arange(470 - 341) + 341 # ACL
- mean = np.array((48.2, -45.1, -9.4))/100 * np.array((ML,AP,AP)) + np.array((bbox[0,0],bbox[1,1],bbox[1,2]))
- else:
- center = np.arange(131) # PCL np.array((0,0,0)) #
- mean = np.array((50.8, -87.6, -23.9))/100 * np.array((ML,AP,AP)) + np.array((bbox[0,0],bbox[1,1],bbox[1,2]))
- points_lig = points_lig[center]
- # origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- # Rz = trimesh.transformations.rotation_matrix(180/np.pi, zaxis)
- # points_lig.apply_transform(Rz)
- color_file = np.loadtxt(path_col + '\meanshape_ligs_color.xyz')[:, 3]
- color_file = color_file[center]
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
-
- color = []
- for ind_col, point in enumerate(points_lig):
- center_2d = point[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - center_2d)) / np.linalg.norm(p2_2d - p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0],
- p5_2d[1])
- d_centriods.append(l)
- vcolors=[c[int(color_file[ind_col] - 1)][0] * 255, c[int(color_file[ind_col] - 1)][1] * 255,
- c[int(color_file[ind_col] - 1)][2] * 255]
- color.append(vcolors)
- p_lig = trimesh.points.PointCloud(points_lig, colors=color)
- p_mean = trimesh.primitives.Sphere(radius=1, center=mean, subdivisions=3, color=[255, 0, 0]) # trimesh.points.PointCloud([mean,mean], colors=[[255,0,0],[255,0,0]])
- p_mean.visual.face_colors = np.array([255, 0, 0, 255])
- # scene2 = trimesh.Scene([mesh, points, p_lig, trimesh.load_path(np.squeeze(line))])
- # scene2.apply_transform(R)
- # scene2.camera_transform = camera_trans
- # scene2.show()
- scene.add_geometry([p_lig, p_mean]) #p_lig ,transform=R
- scene.show()
- else:
- if lig == 'ACL':
- lig_no = ligaments[8][ind]
- elif lig == 'PCL':
- lig_no = ligaments[0][ind]
- if not lig_no == 0:
- segment = 'femur'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
-
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center = geometric_measures['shell_barycenter']
- center_2d = center[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-center_2d))/np.linalg.norm(p2_2d-p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0], p5_2d[1])
- d_centriods.append(l)
- else:
- h_centriods.append(0)
- d_centriods.append(0)
-
-[1-abs(i / j) for i, j in zip(d_centriods, d)]
-[i / j for i, j in zip(h_centriods, h)]
-
-d_centriods/np.asarray(d)
-h_centriods/np.asarray(h)
-
-np.mean(abs(np.asarray(d_centriods))/np.asarray(d))
-np.mean(h_centriods/np.asarray(h))
-
-
-
diff --git a/LigamentInsertions/TransformWires.py b/LigamentInsertions/TransformWires.py
deleted file mode 100644
index 35ab2ae..0000000
--- a/LigamentInsertions/TransformWires.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-from openpyxl import load_workbook
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #9,13,19,23,26,29,32,35,41
-segments = ['tibia','femur'] #'femur',
-short = 1
-ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
-ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
-ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
-for segment in segments:
- if segment == 'femur':
- ligaments = ligaments_fem
- else:
- ligaments = ligaments_tib
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- mesh2 = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/' + str(
- subject) + '\Segmentation_' + segment + '_wires.stl'
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(mesh2)
- ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- ms5.save_current_mesh(path + '\Segmentation_' + segment + '_wires_transform.stl', binary=False)
-
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- mesh2 = path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl'
- # transform femur to local coordinate system to get anatomical directions
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(mesh2)
- ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- ms5.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '_transform.stl', binary=False)
diff --git a/LigamentInsertions/VisualiseSSM.py b/LigamentInsertions/VisualiseSSM.py
deleted file mode 100644
index df356a1..0000000
--- a/LigamentInsertions/VisualiseSSM.py
+++ /dev/null
@@ -1,412 +0,0 @@
-import sys
-import os
-import vtk
-from numpy import random
-import trimesh
-import numpy as np
-import seaborn as sns
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-def create_pointcloud_polydata(points, colors=None, seg=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- # vpoints.SetMarkerStyle(vtk.vtkPlotPoints.CIRCLE)
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
- rgb_col = []
- if not colors is None:
- # if seg == 'femur':
- # max_val=8
- # color[112:len(color)] = (color[112:len(color)]/max_val)*10
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- rgb_col = []
- for i in range(points.shape[0]):
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- vcolors.SetTuple3(i, c[int(colors[i] *10)][0]*255, c[int(colors[i] *10)][1]*255, c[int(colors[i] *10)][2]*255)
- rgb_col.append([c[int(colors[i] *10)][0] * 255, c[int(colors[i] *10)][1] * 255, c[int(colors[i] *10)][2] * 255])
- # print(i, c[int(colors[i] - 1)][0], c[int(colors[i] - 1)][1], c[int(colors[i] - 1)][2])
- # c = rgb(1,10,colors[i])
- # vcolors.SetTuple3(i, c[0], c[1], c[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
-
- return vpoly, rgb_col
-
-
-def rgb(minimum, maximum, value):
- minimum, maximum = float(minimum), float(maximum)
- ratio = (value-minimum) / (maximum - minimum) #2 *
- g = int(max(0, 255*(1 - ratio)))
- r = int(max(0, 255*(ratio - 0)))
- b = 0 #255 - b - r
- return r, g, b
-
-
-def createSpline(points):
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
-
- spline = vtk.vtkParametricSpline()
- spline.SetPoints(vpoints)
-
- functionSource = vtk.vtkParametricFunctionSource()
- functionSource.SetParametricFunction(spline)
- functionSource.Update()
-
- # Create a mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputConnection(functionSource.GetOutputPort())
-
- # Create an actor
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-if __name__ == '__main__':
- center_only = 0
- lateral_only = 0
-
- if center_only == 1:
- center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
- elif lateral_only == 1:
- center_femur = np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # LCL+pop
- center_tibia = np.arange(242) # LCL
-
- subjects = [100] #[100] # ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-
- segments = ['tibia'] #'femur',
- ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
- ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
- ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
- for segment in segments:
- SSMpoints = [[] for i in range(11)]
- if center_only == 1 or lateral_only == 1:
- if segment == 'tibia':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
-
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject == 100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject == 100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs_color.xyz') # _8192
- color = np.loadtxt(path + r'\meanshape_ligs_color.xyz')[:, 3] # _8192
-
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)
- bone_actor = load_stl(path + '/mean_shape.stl') # _8192
- bone_actor.GetProperty().SetOpacity(1.0)
-
- mesh = trimesh.load_mesh(path + '/mean_shape.stl') # _8192
- # dist = trimesh.proximity.nearby_faces(mesh, np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- dist3 = trimesh.proximity.closest_point_naive(mesh, np.squeeze(
- np.asarray(points_lig[np.argwhere(color >= 7)])), tol=1.0)
-
- # faces = np.unique(np.asarray([item for sublist in dist for item in sublist]))
- faces = np.unique(np.asarray([item for sublist in dist3[3] for item in sublist]))
- mesh.update_faces(faces)
- mesh.export(path + '/mean_shape_80percsurf.stl') # _8192
- surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') # _8192
- else:
- # points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_areas.xyz') #_pred_points_color
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points_color.xyz') # _pred_points_color
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color.xyz')[:,3] #_areas _short_areas _pred_points
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- # color = color[center]
- point_cloud_lig = create_pointcloud_polydata(points_lig, seg=segment) #,color colors=color,
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- # bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_fibula_1_tissue_rot.stl')
- else:
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_resample.stl') # '/SSM_' + segment + '_reconstruct_transform_icp.stl'
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- # if center_only == 1:
- # wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires1.stl')
- # wire_actor2 = load_stl(path + '/Segmentation_' + segment_temp + '_wires3.stl')
- # wire_actor2.GetProperty().SetColor(1, 1, 0)
- # else:
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.75)
-
- points_bone = trimesh.load_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
- point_cloud_bone = create_pointcloud_polydata(points_bone)
-
- # orders = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_order.npy')
-
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(point_cloud_bone)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- actor.GetProperty().SetColor(0,0,0)
- actor.GetProperty().SetPointSize(2)
- # actor.GetProperty().SetOpacity(1.0)
-
- # spline_actor = createSpline(np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().RenderPointsAsSpheresOn()
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(7.5)
-
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(11)
- lut.SetTableRange(1, 11)
- for j in range(0,11):
- lut.SetTableValue(int(j*1), c[j*10][0], c[j*10][1], c[j*10][2])
- # print(int(j*1), c[j*10-1][0], c[j*10-1][1], c[j*10-1][2])
-
- j = 10-1
- surf_col = [c[j][0], c[j][1], c[j][2]]
- surf_col = [169/255, 169/255, 169/255]
- surf_actor.GetProperty().SetColor(surf_col)
- surf_actor.GetProperty().SetOpacity(1.0)
-
- legend = vtk.vtkScalarBarActor()
- legend.SetOrientationToHorizontal()
- labelFormat = vtk.vtkTextProperty()
- labelFormat.SetFontSize(16)
- titleFormat = vtk.vtkTextProperty()
- titleFormat.SetFontSize(8)
- legend.SetLabelTextProperty(labelFormat)
- # legend.SetTitleTextProperty(titleFormat)
-
- legend.SetNumberOfLabels(11)
- lut.SetTableRange(0, 100)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
-
- legend.SetTitle("% of specimens \n")
- legend.SetLabelFormat("%1.0f")
- legend.SetUnconstrainedFontSize(1)
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0,0,0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- # legend.SetMaximumWidthInPixels(75)
- # legend.SetMaximumHeightInPixels(300)
- legend.SetMaximumWidthInPixels(300)
- legend.SetMaximumHeightInPixels(75)
- legend.SetTitleTextProperty(text_prop_cb)
- # legend.SetPosition(0.85,0.5)
- legend.SetPosition(0.5, 0.85)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(spline_actor)
- renderer.AddActor(surf_actor)
- if not subject == 100 and not subject == 'S0':
- renderer.AddActor(wire_actor)
- # renderer.AddActor(wire_actor2)
- renderer.AddActor(legend)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
-
-
- polyData = vtk.vtkPolyData()
- polyData.DeepCopy(actor2.GetMapper().GetInput())
- transform = vtk.vtkTransform()
- transform.SetMatrix(actor2.GetMatrix())
- fil = vtk.vtkTransformPolyDataFilter()
- fil.SetTransform(transform)
- fil.SetInputDataObject(polyData)
- fil.Update()
- polyData.DeepCopy(fil.GetOutput())
-
- writer = vtk.vtkPLYWriter()
- writer.SetFileTypeToASCII()
- writer.SetColorModeToDefault()
- filename = r'C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col2.ply'
- writer.SetFileName(filename)
- writer.SetInputData(polyData)
- writer.Write()
-
- # import pandas as pd
- # pd.DataFrame(color).to_clipboard()
\ No newline at end of file
diff --git a/LigamentInsertions/VisualizeCenter.py b/LigamentInsertions/VisualizeCenter.py
deleted file mode 100644
index bb85692..0000000
--- a/LigamentInsertions/VisualizeCenter.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import pymeshlab
-import os
-import vtk
-from VisualiseSSM import create_pointcloud_polydata
-import numpy as np
-import glob
-
-
-def load_stl(filename, rot_mat):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.SetMatrix([item for sublist in rot_mat for item in sublist])
- # transform.Translate(10, 0, 0)
-
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(transformFilter.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-class MyInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):
-
- def __init__(self,parent=None):
- self.parent = renderWindowInteractor
-
- self.AddObserver("KeyPressEvent",self.keyPressEvent)
-
- def keyPressEvent(self,obj,event):
- key = self.parent.GetKeySym()
- if key == 'b':
- vis = outlineActor.GetVisibility()
- if vis:
- outlineActor.SetVisibility(False)
- else:
- outlineActor.SetVisibility(True)
-
- return
-
-
-subject = 35 # [9,13,19,23,26,29,32,35,37,41]
-segment = 'femur'
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
-if segment == 'femur':
- ligaments = ligaments_fem
-else:
- ligaments = ligaments_tib
-
-ind = np.where(np.asarray([9,13,19,23,26,29,32,35,37,41]) == subject)
-# Renderer
-renderer = vtk.vtkRenderer()
-
-path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
-for lig in range(0, 8):
- lig_no = ligaments[lig][ind[0][0]]
- if not lig_no == 0:
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface = geometric_measures['surface_area']
- center= geometric_measures['shell_barycenter']
-
- point_cloud_lig = create_pointcloud_polydata(np.asarray([center,center]))
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.SetMatrix([item for sublist in rot_mat for item in sublist])
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetTransform(transform)
- transformFilter.SetInputData(point_cloud_lig)
- transformFilter.Update()
-
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputConnection(transformFilter.GetOutputPort())
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(10)
-
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
-
- Counter = len(glob.glob1(path, 'Segmentation_' + segment + '_area*.stl'))
- for count in range(1, Counter + 1):
- bone_actor = load_stl(path + '\Segmentation_' + segment + '_area' + str(count) + '.stl', rot_mat)
- bone_actor.GetProperty().SetOpacity(0.75)
- bone_actor.GetProperty().SetColor(0, 0, 1)
- renderer.AddActor(bone_actor)
-
-wire_actor = load_stl(path + '/Segmentation_' + segment + '_wires.stl', rot_mat)
-wire_actor.GetProperty().SetOpacity(1.0)
-wire_actor.GetProperty().SetColor(1, 1, 0)
-renderer.AddActor(wire_actor)
-
-reader = vtk.vtkSTLReader()
-reader.SetFileName(path + '/Segmentation_' + segment + '_resample.stl')
-transform = vtk.vtkTransform()
-transform.Identity()
-transform.SetMatrix([item for sublist in rot_mat for item in sublist])
-transformFilter = vtk.vtkTransformPolyDataFilter()
-transformFilter.SetInputConnection(reader.GetOutputPort())
-transformFilter.SetTransform(transform)
-transformFilter.Update()
-mapper = vtk.vtkPolyDataMapper()
-mapper.SetInputConnection(transformFilter.GetOutputPort())
-bone_actor = vtk.vtkActor()
-bone_actor.SetMapper(mapper)
-bone_actor.GetProperty().SetOpacity(0.75)
-
-renderer.AddActor(bone_actor)
-
-outline = vtk.vtkOutlineFilter()
-outline.SetInputConnection(transformFilter.GetOutputPort())
-outlineMapper = vtk.vtkPolyDataMapper()
-outlineMapper.SetInputConnection(outline.GetOutputPort())
-outlineActor = vtk.vtkActor()
-outlineActor.SetMapper(outlineMapper)
-outlineActor.GetProperty().SetColor(0,0,0)
-outlineActor.SetVisibility(False)
-
-renderer.AddActor(outlineActor)
-
-renderer.SetBackground(1.0, 1.0, 1.0)
-renderer.ResetCamera()
-
-# Render Window
-renderWindow = vtk.vtkRenderWindow()
-renderWindow.AddRenderer(renderer)
-
-# Interactor
-renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-renderWindowInteractor.SetRenderWindow(renderWindow)
-renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-renderWindowInteractor.SetInteractorStyle(MyInteractorStyle())
-
-# Begin Interaction
-renderWindow.Render()
-renderWindow.SetWindowName("XYZ Data Viewer")
-renderWindowInteractor.Start()
\ No newline at end of file
diff --git a/LigamentInsertions/VisualizeMeanSSM.ipynb b/LigamentInsertions/VisualizeMeanSSM.ipynb
deleted file mode 100644
index 153a725..0000000
--- a/LigamentInsertions/VisualizeMeanSSM.ipynb
+++ /dev/null
@@ -1,942 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "85e5e2e4-aac7-4e29-beb2-725fd4934c4c",
- "metadata": {},
- "source": [
- "# Interactive figure ligament attachment locations anterior and posterior cruciate ligaments\n",
- "3D figures showing the ligament attachment locations of the ACL and PCL ligaments on the mean SSM shape of the femur and tibia.\n",
- "Interactive figure for paper:\n",
- "Voskuijl, T., Wesseling, M., Pennings, M., Piscaer, T., Hanff, D., Meuffels, D.E. \"The adaption of anterior and posterior cruciate ligament attachment sites to the variance of three dimensional bony knee shapes\". Submitted to "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65b03f42-0f5e-4433-bbc1-9d9479691852",
- "metadata": {},
- "source": [
- "Install required packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0a85e4e1-b480-4f37-a5de-4d2098d01e66",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: vtk in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (9.2.6)\n",
- "Requirement already satisfied: matplotlib>=2.0.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from vtk) (3.7.1)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (1.0.5)\n",
- "Requirement already satisfied: cycler>=0.10 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (0.11.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (4.25.0)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (1.4.4)\n",
- "Requirement already satisfied: numpy>=1.20 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (1.24.3)\n",
- "Requirement already satisfied: packaging>=20.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (23.0)\n",
- "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (9.4.0)\n",
- "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (3.0.9)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (2.8.2)\n",
- "Requirement already satisfied: six>=1.5 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.0.0->vtk) (1.16.0)\n",
- "Collecting trimesh\n",
- " Obtaining dependency information for trimesh from https://files.pythonhosted.org/packages/c9/10/c5925a556ae5eebca155524443cb94d84ba5715b56085fbbdd8438eb5509/trimesh-3.23.5-py3-none-any.whl.metadata\n",
- " Using cached trimesh-3.23.5-py3-none-any.whl.metadata (17 kB)\n",
- "Requirement already satisfied: numpy in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from trimesh) (1.24.3)\n",
- "Using cached trimesh-3.23.5-py3-none-any.whl (685 kB)\n",
- "Installing collected packages: trimesh\n",
- "Successfully installed trimesh-3.23.5\n",
- "Requirement already satisfied: seaborn in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (0.12.2)\n",
- "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from seaborn) (1.24.3)\n",
- "Requirement already satisfied: pandas>=0.25 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from seaborn) (1.5.3)\n",
- "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from seaborn) (3.7.1)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n",
- "Requirement already satisfied: cycler>=0.10 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
- "Requirement already satisfied: packaging>=20.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
- "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
- "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
- "Requirement already satisfied: pytz>=2020.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from pandas>=0.25->seaborn) (2022.7)\n",
- "Requirement already satisfied: six>=1.5 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
- ]
- }
- ],
- "source": [
- "# ! pip install vtk\n",
- "# ! pip install trimesh\n",
- "# ! pip install seaborn\n",
- "# ! pip install pyvista\n",
- "## ! pip install pythreejs\n",
- "# ! pip install trame\n",
- "# ! pip install trame-vtk\n",
- "# ! pip install trame-vuetify"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "54ddf022-251d-427d-a33a-6305fe69aa57",
- "metadata": {},
- "source": [
- "Import required libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "9ad2e1fd-d4a4-48d2-8966-671b64ce093d",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import vtk\n",
- "import trimesh\n",
- "import numpy as np\n",
- "import seaborn as sns\n",
- "import pyvista as pv"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "176e33a2-d0a1-4124-9f75-a993904edf03",
- "metadata": {},
- "source": [
- "Function to create pointcloud that represents attachment regions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "4efddfba-c04e-4023-8f6c-fdca0a5f25de",
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_pointcloud_polydata(points, colors=None, seg=None):\n",
- "\n",
- " vpoints = vtk.vtkPoints()\n",
- " vpoints.SetNumberOfPoints(points.shape[0])\n",
- " for i in range(points.shape[0]):\n",
- " vpoints.SetPoint(i, points[i])\n",
- "\n",
- " vpoly = vtk.vtkPolyData()\n",
- " vpoly.SetPoints(vpoints)\n",
- " rgb_col = []\n",
- " if not colors is None:\n",
- " if seg == 'femur':\n",
- " max_val=8\n",
- " color[112:len(color)] = (color[112:len(color)]/max_val)*10\n",
- " vcolors = vtk.vtkUnsignedCharArray()\n",
- " vcolors.SetNumberOfComponents(3)\n",
- " vcolors.SetName(\"Colors\")\n",
- " vcolors.SetNumberOfTuples(points.shape[0])\n",
- " rgb_col = []\n",
- " for i in range(points.shape[0]):\n",
- " c = sns.color_palette(\"viridis_r\", n_colors=101, as_cmap=False)\n",
- " vcolors.SetTuple3(i, c[int(colors[i] *10)][0]*255, c[int(colors[i] *10)][1]*255, c[int(colors[i] *10)][2]*255)\n",
- " rgb_col.append([c[int(colors[i] *10)][0] * 255, c[int(colors[i] *10)][1] * 255, c[int(colors[i] *10)][2] * 255])\n",
- " vpoly.GetPointData().SetScalars(vcolors)\n",
- "\n",
- " vcells = vtk.vtkCellArray()\n",
- "\n",
- " for i in range(points.shape[0]):\n",
- " vcells.InsertNextCell(1)\n",
- " vcells.InsertCellPoint(i)\n",
- "\n",
- " vpoly.SetVerts(vcells)\n",
- "\n",
- " return vpoly, rgb_col"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1b90cf6-d3c5-40d5-9299-c60dc1aa510b",
- "metadata": {},
- "source": [
- "Function to load STL file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "8f40bba1-dcd8-4d37-9682-d7d19e21bea0",
- "metadata": {},
- "outputs": [],
- "source": [
- "def load_stl(filename):\n",
- " reader = vtk.vtkSTLReader()\n",
- " reader.SetFileName(filename)\n",
- "\n",
- " mapper = vtk.vtkPolyDataMapper()\n",
- " if vtk.VTK_MAJOR_VERSION <= 5:\n",
- " mapper.SetInput(reader.GetOutput())\n",
- " else:\n",
- " mapper.SetInputConnection(reader.GetOutputPort())\n",
- "\n",
- " actor = vtk.vtkActor()\n",
- " actor.SetMapper(mapper)\n",
- "\n",
- " return actor"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c5029e33-bd8b-4653-a785-f06f980fb543",
- "metadata": {},
- "source": [
- "### Femur attachments"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "56ae25b4-d99b-4ddf-81dd-26285834f9fd",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "77dfff23-beb1-413e-b73b-c20a9e00245f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# segment = 'femur'\n",
- "# center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL\n",
- "# center = center_femur"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "371b2f84",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'femur'\n",
- "# center_femur = np.concatenate((np.arange(706-641)+641,np.arange(776-706)+706)) # np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # LCL+pop\n",
- "center_femur = np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # ACL+PCL\n",
- "\n",
- "center = center_femur"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "14e03598-c3ac-4706-a503-b18a06d7dada",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "e418af21-2c51-441b-8e2c-94d800a1fd73",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment + '8192')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "70f93a0e-6d37-4c52-ae78-cc346d2e082c",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "154780b1-e011-4a8c-8935-03900ef064d2",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)\n",
- "\n",
- "surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ea25079c-f397-4661-a92e-e59128ab29c4",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "d587c057-3e90-4632-9c92-f54a34f9aa7f",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)\n",
- "\n",
- "surf_col = [169/255, 169/255, 169/255]\n",
- "surf_actor.GetProperty().SetColor(surf_col)\n",
- "surf_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "018a9286-c797-47fb-a793-407ec1c7c7c9",
- "metadata": {},
- "source": [
- "Set colors for ligament attachment points depending on the number of specimens in which each point was identified as attachment region"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "4b4318f7-1dea-4975-a368-f4cfe9840485",
- "metadata": {},
- "outputs": [],
- "source": [
- "c = sns.color_palette(\"viridis_r\", n_colors=101, as_cmap=False)\n",
- "lut = vtk.vtkLookupTable()\n",
- "lut.SetNumberOfColors(11)\n",
- "lut.SetTableRange(1, 11)\n",
- "for j in range(0,11):\n",
- " lut.SetTableValue(int(j*1), c[j*10][0], c[j*10][1], c[j*10][2])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8c62a61-76b1-4ae0-b8f9-3684a41d2d68",
- "metadata": {},
- "source": [
- "Create legend"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "8b2c983b-43b9-4e52-b489-821eb41d9e1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "legend = vtk.vtkScalarBarActor()\n",
- "labelFormat = vtk.vtkTextProperty()\n",
- "labelFormat.SetFontSize(16)\n",
- "titleFormat = vtk.vtkTextProperty()\n",
- "titleFormat.SetFontSize(8)\n",
- "legend.SetLabelTextProperty(labelFormat)\n",
- "\n",
- "legend.SetNumberOfLabels(11)\n",
- "lut.SetTableRange(0, 100)\n",
- "legend.SetLookupTable(lut)\n",
- "\n",
- "legend.SetTitle(\"% of specimens \\n\")\n",
- "legend.SetLabelFormat(\"%1.0f\")\n",
- "legend.SetUnconstrainedFontSize(1)\n",
- "\n",
- "text_prop_cb = legend.GetLabelTextProperty()\n",
- "text_prop_cb.SetFontFamilyAsString('Arial')\n",
- "text_prop_cb.SetFontFamilyToArial()\n",
- "text_prop_cb.SetColor(0,0,0)\n",
- "text_prop_cb.ShadowOff()\n",
- "legend.SetLabelTextProperty(text_prop_cb)\n",
- "legend.SetMaximumWidthInPixels(75)\n",
- "legend.SetMaximumHeightInPixels(300)\n",
- "legend.SetTitleTextProperty(text_prop_cb)\n",
- "legend.SetPosition(0.85,0.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21820712-33a4-4fe4-b97d-0e6381d975b6",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "b9032586",
- "metadata": {},
- "outputs": [],
- "source": [
- "# ! pip install --upgrade trame-vuetify"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "92cf7566",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9d4cec15d4b9477eb10107895221037b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe4dbcc910_3&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "p = pv.Plotter()\n",
- "p.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "# p.add_mesh(edges, color=\"red\", line_width=5)\n",
- "# p.camera_position = [(-0.2, -0.13, 0.12), (-0.015, 0.10, -0.0), (0.28, 0.26, 0.9)]\n",
- "p.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "c98f226f-a28a-4e44-a342-5771f4984d63",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cdb8734269af433ab11dd6fe581eeebf",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe4dc46c90_4&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "\n",
- "# bla=pv.PolyData(point_cloud_lig)\n",
- "# bla.plot()\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "#plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "cd376163",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "147eb2c24de74f62afdf323fe5aa3f13",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe40f4f4d0_2&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show() # show the two spheres from two PolyData\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5324c624",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "74e52268",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur_lateral.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "990d654f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\femur_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9ae0ddf1-b716-4e73-ab28-06015b2a8bc7",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1ce09a3c-0311-4047-884e-9033530412c0",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dedd30e3-4598-4b88-9d0d-e8b4b6fe5f26",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'tibia'\n",
- "center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL\n",
- "center = center_tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "075b81cd-8176-4b48-87f8-9c151951a7a8",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eeb6abea-1e7c-42a3-be1c-6a0d1730ff58",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "11ca3571-06e8-49eb-a610-bef66f8057a3",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2445f518-0319-4075-800b-d35bdabe434a",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "59c3faa7-6783-4203-bb08-141891633172",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "606f3104-8996-4529-b101-796ac53e00c2",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c336cfc1-921c-49fc-afee-9d437eba70dd",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd8b342b-39bd-4bcb-b0f6-f25d6d3edc2e",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "944d007a",
- "metadata": {},
- "outputs": [],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show() # show the two spheres from two PolyData\n",
- "\n",
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0b196960-88d0-471c-9bec-a7231a969c85",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\tibia_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "17302db6",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Fibula"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4a222656",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "818cb614",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'fibula'\n",
- "center_tibia = np.arange(242) # LCL\n",
- "center = center_tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21a123ba",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5ab4903",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7e125bc0",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "24ea09d5",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)\n",
- "\n",
- "surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7efad518",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ab0dab8b",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)\n",
- "\n",
- "surf_col = [169/255, 169/255, 169/255]\n",
- "surf_actor.GetProperty().SetColor(surf_col)\n",
- "surf_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a9c84df6",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c69f7d93",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter = pv.Plotter(window_size=(600, 600),notebook=True)\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aec5eda7",
- "metadata": {},
- "outputs": [],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show()\n",
- "\n",
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfibula_lateralxx.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c4b0e4f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\tibia_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c364012",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e3cb15e",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.4"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autoclose": false,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 1,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
- "toc_cell": false,
- "toc_position": {},
- "toc_section_display": true,
- "toc_window_display": false
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/LigamentInsertions/VisualizeProjectedCentroids.py b/LigamentInsertions/VisualizeProjectedCentroids.py
deleted file mode 100644
index 28aa072..0000000
--- a/LigamentInsertions/VisualizeProjectedCentroids.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import vtk
-import sys
-import os
-import vtk
-from numpy import random, genfromtxt, size
-import trimesh
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def create_pointcloud_polydata(points, colors=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
-
- if not colors is None:
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- for i in range(points.shape[0]):
- vcolors.SetTuple3(i, colors[0], colors[1], colors[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly
-
-
-lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
-color = ((0.75,1,0.5),
- (0,0.5,0),
- (1,0,1),
- (0.5,1,1),
- (1,1,0),
- (1,0.5,0.75),
- (1,0.5,0))
-
-if __name__ == '__main__':
- subjects = [35] #9,13,19,23,26,29,32,35,37,41
-
- segments = ['femur'] #'femur',
- ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
- ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
- for segment in segments:
- SSMpoints = [[] for i in range(8)]
- for ind in range(0,8):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject==100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject==100:
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(0.75)
- else:
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points.xyz') # _areas
- point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_resample.stl')
- bone_actor.GetProperty().SetOpacity(0.75)
- wire_actor = load_stl(path + '/Segmentation_' + segment + '_wires.stl')
- wire_actor.GetProperty().SetColor(0, 0, 1)
- lig_actor = []
- for count, lig in enumerate(lig_names):
- lig_actor.append(load_stl(os.path.join(path,lig+'centroids.stl')))
- lig_actor[count].GetProperty().SetColor(color[count])
-
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(5)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- renderer.AddActor(bone_actor)
- if not subject==100:
- renderer.AddActor(wire_actor)
- for count, lig in enumerate(lig_names):
- renderer.AddActor(lig_actor[count])
- # renderer.AddActor(actor2)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer")
- renderWindowInteractor.Start()
-
diff --git a/LigamentInsertions/Visualize_modes.py b/LigamentInsertions/Visualize_modes.py
deleted file mode 100644
index 4ce19dc..0000000
--- a/LigamentInsertions/Visualize_modes.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import vtk
-import sys
-import os
-import vtk
-from numpy import random, genfromtxt, size
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-def load_stl(filename,signed_distance):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- # mapper = vtk.vtkPolyDataMapper()
- # if vtk.VTK_MAJOR_VERSION <= 5:
- # mapper.SetInput(reader.GetOutput())
- # else:
- # mapper.SetInputConnection(reader.GetOutputPort())
-
- # vcolors = vtk.vtkUnsignedCharArray()
- # vcolors.SetNumberOfComponents(3)
- # vcolors.SetName("Colors")
- # vcolors.SetNumberOfTuples(signed_distance.shape[0])
-
- c = sns.color_palette("viridis_r", n_colors=round(max(signed_distance) - min(signed_distance)), as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfTableValues(int(round(max(signed_distance) - min(signed_distance))))
- lut.SetTableRange(min(signed_distance),max(signed_distance))
- lut.Build()
- # Fill in a few known colors, the rest will be generated if needed
- for j in range(0,round(max(signed_distance) - min(signed_distance))):
- lut.SetTableValue(j, c[j][0]*255,c[j][1]*255,c[j][2]*255)
-
- heights = vtk.vtkDoubleArray()
- for i in range(obj.GetNumberOfPoints()):
- z = signed_distance[i]
- heights.InsertNextValue(z)
- obj.GetPointData().SetScalars(heights)
- # for i in range(signed_distance.shape[0]):
- # ind = round(signed_distance[i]-min(signed_distance))
- # # print(ind)
- # vcolors.SetTuple3(i, c[ind-1][0] * 255,
- # c[ind-1][1] * 255,
- # c[ind-1][2] * 255)
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper.SetScalarRange(min(signed_distance),max(signed_distance))
- mapper.SetLookupTable(lut)
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def load_vtk(filename,tx=0):
- reader = vtk.vtkPolyDataReader()
- reader.SetFileName(filename)
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.Translate(tx, 0, 0)
-
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(transformFilter.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def create_pointcloud_polydata(points, colors=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
-
- if not colors is None:
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- for i in range(points.shape[0]):
- vcolors.SetTuple3(i, colors[0], colors[1], colors[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly
-
-segments = ['femur'] #'femur',
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
-for segment in segments:
-
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode1_+2SD.stl'
- mode_min = 'mode1_-2SD.stl'
- plus2sd = trimesh.load_mesh(path + mode_plus)
- min2sd = trimesh.load_mesh(path + mode_min)
- signed_distance = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices)
-
- colors = np.array(((241,163,64),
- (247,247,247),
- (153,142,195)))/255
-
- mode_plus = 'mode1_+2SD.vtk'
- mode_min = 'mode1_-2SD.vtk'
-
- bone_actor = load_stl(path + mean_shape,signed_distance)
- bone_actor.GetProperty().SetOpacity(1)
-
- # bone_actor.GetProperty().SetColor(colors[1])
-
- # plus_actor = load_vtk(path + mode_plus)
- # plus_actor.GetProperty().SetOpacity(1)
- # plus_actor.GetProperty().SetColor(colors[2])
- #
- # min_actor = load_vtk(path + mode_min)
- # min_actor.GetProperty().SetOpacity(0.8)
- # min_actor.GetProperty().SetColor(colors[0])
-
- # mapper = vtk.vtkPolyDataMapper()
- # mapper.SetInputData(point_cloud)
- # actor = vtk.vtkActor()
- # actor.SetMapper(mapper)
- # actor.GetProperty().SetColor(0,0,0)
- # actor.GetProperty().SetOpacity(1.0)
-
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(min_actor)
- # renderer.AddActor(plus_actor)
-
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer")
- renderWindowInteractor.Start()
-
diff --git a/LigamentInsertions/Visualize_modes_ligaments.py b/LigamentInsertions/Visualize_modes_ligaments.py
deleted file mode 100644
index ae75a1d..0000000
--- a/LigamentInsertions/Visualize_modes_ligaments.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-from VisualiseSSM import create_pointcloud_polydata, load_stl
-import math as m
-
-
-def Rx(theta):
- return np.matrix([[1, 0, 0],
- [0, m.cos(theta), -m.sin(theta)],
- [0, m.sin(theta), m.cos(theta)]])
-
-
-def Ry(theta):
- return np.matrix([[m.cos(theta), 0, m.sin(theta)],
- [0, 1, 0],
- [-m.sin(theta), 0, m.cos(theta)]])
-
-
-def Rz(theta):
- return np.matrix([[m.cos(theta), -m.sin(theta), 0],
- [m.sin(theta), m.cos(theta), 0],
- [0, 0, 1]])
-
-
-segment = 'femur'
-
-rw = vtk.vtkRenderWindow()
-# xmins = [0, .5, 0, .5, 0, .5]
-# xmaxs = [0.5, 1, 0.5, 1, .5, 1]
-# ymins = [.66, .66, .33, .33, 0, 0, ]
-# ymaxs = [1, 1, .66, .66, 0.33, 0.33]
-
-xmins = [0, 0, .33, .33, .66, .66]
-xmaxs = [.33, .33, .66, .66, 1, 1]
-ymins = [0, .5, 0, .5, 0, .5]
-ymaxs = [0.5, 1, 0.5, 1, .5, 1]
-iren = vtk.vtkRenderWindowInteractor()
-iren.SetRenderWindow(rw)
-
-renderer = vtk.vtkRenderer()
-
-center_only = 0
-lateral_only = 1
-if center_only == 1:
- center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
-elif lateral_only == 1:
- center_femur = np.concatenate((np.arange(706-641)+641,np.arange(776-706)+706)) # np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) = 4096 # LCL+pop
- center_tibia = np.arange(242) # LCL
-
-tel=0
-
-if segment == 'tibia':
- center = center_tibia
-elif segment == 'femur':
- center = center_femur
-
-for modes in range(1,4):
-
- if segment == 'fibula':
- d = -40
- else:
- d = -100
-
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode' + str(modes) + '_+2SD_8192.stl'
- mode_min = 'mode' + str(modes) + '_-2SD_8192.stl'
-
- # ligament points
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_+2sd_8192.xyz')
- color = np.loadtxt(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_+2sd_8192.xyz')[:, 3]
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- R = Rx(90 * np.pi / 180)*Ry(180 * np.pi / 180) * Rz(0 * np.pi / 180)
- points_lig2 = []
- for point in points_lig:
- points_lig2.append(np.asarray(R * point[np.newaxis].T))
- points_lig2 = np.squeeze(np.asarray(points_lig2))
- # points_lig2 = points_lig2 + np.array((0, modes * d, 0))
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig2, color)
-
- points_lig_neg = trimesh.load_mesh(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_-2sd_8192.xyz')
- color_neg = np.loadtxt(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_-2sd_8192.xyz')[:, 3]
- if center_only == 1 or lateral_only == 1:
- points_lig_neg = points_lig_neg[center]
- color_neg = color_neg[center]
- R = Rx(90 * np.pi / 180) * Ry(180 * np.pi / 180) * Rz(0 * np.pi / 180)
- points_lig_neg2 = []
- for point in points_lig_neg:
- points_lig_neg2.append(np.asarray(R * point[np.newaxis].T))
- points_lig_neg2 = np.squeeze(np.asarray(points_lig_neg2))
- # points_lig_neg2 = points_lig_neg2 + np.array((d*-1, modes*d, 0))
- point_cloud_lig_neg, rgb_col_neg = create_pointcloud_polydata(points_lig_neg2, color_neg)
-
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(1.0)
-
- # load mesh via trimesh to get the correct order for distance transform
- reader = vtk.vtkSTLReader()
- reader.SetFileName(path + mode_plus)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- reader2 = vtk.vtkSTLReader()
- reader2.SetFileName(path + mode_min)
- reader2.Update()
- obj2 = reader2.GetOutputDataObject(0)
-
- # mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputDataObject(obj2)
-
- # translation
- transform = vtk.vtkTransform()
- transform.Identity()
- # transform.Translate(0,modes * d, 0)
- transform.RotateX(90)
- transform.RotateY(180)
- transform.RotateZ(0)
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- transform2 = vtk.vtkTransform()
- transform2.Identity()
- # transform2.Translate(d*-1, modes*d, 0)
- transform2.RotateX(90)
- transform2.RotateY(180)
- transform2.RotateZ(0)
- transformFilter2 = vtk.vtkTransformPolyDataFilter()
- transformFilter2.SetInputConnection(reader2.GetOutputPort())
- transformFilter2.SetTransform(transform2)
- transformFilter2.Update()
-
- # actors
- bone_actor = vtk.vtkActor()
- bone_actor.SetMapper(mapper)
- mapper.SetInputConnection(transformFilter.GetOutputPort())
- bone_actor.SetMapper(mapper)
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- bone_actor2 = vtk.vtkActor()
- mapper2.SetInputConnection(transformFilter2.GetOutputPort())
- bone_actor2.SetMapper(mapper2)
- bone_actor2.GetProperty().SetColor(0.89, 0.85, 0.79)
-
- mapper2lig = vtk.vtkPolyDataMapper()
- mapper2lig.SetInputData(point_cloud_lig)
- actor2lig = vtk.vtkActor()
- actor2lig.SetMapper(mapper2lig)
- actor2lig.GetProperty().SetColor(1, 0, 0)
- actor2lig.GetProperty().SetPointSize(7.5)
-
- mapper3 = vtk.vtkPolyDataMapper()
- mapper3.SetInputData(point_cloud_lig_neg)
- actor3 = vtk.vtkActor()
- actor3.SetMapper(mapper3)
- actor3.GetProperty().SetColor(1, 0, 0)
- actor3.GetProperty().SetPointSize(7.5)
-
- for ind in range(2):
- ren = vtk.vtkRenderer()
- rw.AddRenderer(ren)
- ren.SetViewport(xmins[tel], ymins[tel], xmaxs[tel], ymaxs[tel])
-
- # Share the camera between viewports.
- if tel == 0:
- camera = ren.GetActiveCamera()
- else:
- ren.SetActiveCamera(camera)
-
- # Create a mapper and actor
- if tel == 0 or tel == 2 or tel == 4:
- ren.AddActor(bone_actor)
- ren.AddActor(actor2lig)
- else:
- ren.AddActor(bone_actor2)
- ren.AddActor(actor3)
-
- ren.SetBackground(1.0, 1.0, 1.0)
-
- ren.ResetCamera()
-
- tel+=1
-
- # Renderer
- renderer.AddActor(bone_actor)
- renderer.AddActor(bone_actor2)
- renderer.AddActor(actor2lig)
- renderer.AddActor(actor3)
- # renderer.AddActor(legend)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
-# Render Window
-renderWindow = vtk.vtkRenderWindow()
-renderWindow.AddRenderer(renderer)
-renderWindow.SetSize(750, 750)
-
-# Interactor
-renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-renderWindowInteractor.SetRenderWindow(renderWindow)
-renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
-# Begin Interaction
-renderWindow.Render()
-renderWindow.SetWindowName("SSM distances")
-renderWindowInteractor.Start()
-
-rw.Render()
-rw.SetWindowName('MultipleViewPorts')
-rw.SetSize(1500, 650)
-iren.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-iren.Start()
\ No newline at end of file
diff --git a/LigamentInsertions/Xray.py b/LigamentInsertions/Xray.py
deleted file mode 100644
index 182c281..0000000
--- a/LigamentInsertions/Xray.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import cv2
-import numpy as np
-import SimpleITK as sitk
-import pydicom as dicom
-import os
-import glob
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-for subject in subjects:
- path_drr = r'C:/Users/mariskawesseli/Documents/LigamentStudy/ImageData/'+str(subject)+'/DRR/'
- images = ['med_fem0001.dcm','med_wires0001.dcm','lat_fem0001.dcm','lat_wires0001.dcm',
- 'med_fem0001.dcm','med_all_wires0001.dcm','lat_fem0001.dcm','lat_all_wires0001.dcm']
- lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
-
- for file in glob.glob(os.path.join(path_drr,"*.dcm")):
- image = file
- ds = dicom.dcmread(image)
- pixel_array_numpy = ds.pixel_array
- image = image.replace('.dcm', '.jpg')
- cv2.imwrite(os.path.join(path_drr, image), pixel_array_numpy)
-
-
- for ind in range(0,4):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- else:
- side = 'L'
-
- mask2 = []
- src = cv2.imread(os.path.join(path_drr,images[0+2*ind].replace('.dcm', '.jpg')))
- mask = cv2.imread(os.path.join(path_drr, images[1+2*ind].replace('.dcm', '.jpg')))
- if 'med' in images[0+2*ind]:
- for ind2 in range(0,4):
- mask2.append(cv2.imread(os.path.join(path_drr, lig_names[ind2] + '0001.dcm'.replace('.dcm', '.jpg'))))
- else:
- for ind2 in range(4, 7):
- mask2.append(cv2.imread(os.path.join(path_drr, lig_names[ind2] + '0001.dcm'.replace('.dcm', '.jpg'))))
-
- # convert mask to gray and then threshold it to convert it to binary
- gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
- gray2 = []
- for new_mask in mask2:
- gray2.append(cv2.cvtColor(new_mask, cv2.COLOR_BGR2GRAY))
- # ret, binary = cv2.threshold(gray, 40, 255, cv2.THRESH_BINARY)
- blur = cv2.GaussianBlur(gray, (3,3), 0)
- blur2 = []
- for new_gray in gray2:
- blur2.append(cv2.GaussianBlur(new_gray, (3, 3), 0))
- binary = cv2.threshold(blur, 250, 255, cv2.THRESH_BINARY_INV)[1] # + cv2.THRESH_OTSU
- binary2 = []
- for new_blur in blur2:
- binary2.append(cv2.threshold(new_blur, 250, 255, cv2.THRESH_BINARY_INV)[1]) # + cv2.THRESH_OTSU
-
- # find contours of two major blobs present in the mask
- contours,hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
- contours2 = []
- for new_binary in binary2:
- contoursX, hierarchyX = cv2.findContours(new_binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
- contours2.append(contoursX)
-
- # draw the found contours on to source image
- for contour in contours:
- cv2.drawContours(src, contour, -1, (255,0,0), thickness = 1)
- colors = [(0,0,255),(0,255,0),(255,0,255),(0,255,255)]
- count = -1
- for new_contours in contours2:
- count += 1
- c = colors[count]
- for contour in new_contours:
- cv2.drawContours(src, contour, -1, c, thickness = 1)
-
- # split source to B,G,R channels
- b,g,r = cv2.split(src)
- b2, g2, r2 = cv2.split(src)
-
- # add a constant to R channel to highlight the selected area in red
- r = cv2.add(b, 30, dst = b, mask = binary, dtype = cv2.CV_8U)
- #r2 = []
- # for new_binary in binary2:
- # r2 = cv2.add(b, 30, dst=b, mask=new_binary, dtype=cv2.CV_8U)
-
- # merge the channels back together
- img_overlay = cv2.merge((b,g,r), src)
- # for new_r in r2:
- img_overlay = cv2.merge((b2, g2, r2), img_overlay)
- # cv2.imshow('overlay', img_overlay)
- if side == 'R':
- if (ind == 1) or (ind == 3):
- img_rot = cv2.rotate(img_overlay, cv2.ROTATE_180)
- img_rot = cv2.flip(img_rot, 1)
- else:
- img_rot = img_overlay
- else:
- if (ind == 0) or (ind == 2):
- img_rot = cv2.rotate(img_overlay, cv2.ROTATE_180)
- img_rot = cv2.flip(img_rot, 1)
- else:
- img_rot = img_overlay
- cv2.imwrite(os.path.join(path_drr, images[1+2*ind].replace('.dcm', '_combine.jpg')),img_rot)
-
-
diff --git a/LigamentInsertions/average_points_to_stls.py b/LigamentInsertions/average_points_to_stls.py
deleted file mode 100644
index 9727565..0000000
--- a/LigamentInsertions/average_points_to_stls.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import trimesh
-import numpy as np
-import os
-
-points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz')
-color = np.loadtxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz')[:, 3]
-
-segment = 'femur'
-subjects = ['100'] #, S0 [100]
-lig = 'pop'
-center_only = 1
-
-if lig == 'LCL':
- center_femur = np.arange(706-641)+641 # np.arange(415-379)+379 # np.arange(370-341)+341 = 4096
- center_tibia = np.arange(242)
-if lig == 'pop':
- center_femur = np.arange(776-706)+706 #np.arange(454-415)+415 # np.arange(401-370)+370 = 4096
- center_tibia = 0
-
-if segment == 'tibia' or segment == 'fibula':
- center = center_tibia
-elif segment == 'femur':
- center = center_femur
-
-points10 = []
-points9 = []
-points8 = []
-points7 = []
-points6 = []
-points5 = []
-points4 = []
-points3 = []
-points2 = []
-points1 = []
-
-if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
-print(color)
-for ind in range(0,len(color)):
- T = trimesh.transformations.translation_matrix(points_lig[ind])
- point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=T)
- # point = trimesh.creation.icosphere(subdivisions=3, radius=1.0, color=None, transform=T)
-
- if color[ind] == 10:
- if bool(points10):
- points10 = trimesh.boolean.union([points10, point])
- else:
- points10 = point
- elif color[ind] == 9:
- if bool(points9):
- points9 = trimesh.boolean.union([points9, point])
- else:
- points9 = point
- elif color[ind] == 8:
- if bool(points8):
- points8 = trimesh.boolean.union([points8, point])
- else:
- points8 = point
- elif color[ind] == 7:
- if bool(points7):
- points7 = trimesh.boolean.union([points7, point])
- else:
- points7 = point
- elif color[ind] == 6:
- if bool(points6):
- points6 = trimesh.boolean.union([points6, point])
- else:
- points6 = point
- elif color[ind] == 5:
- if bool(points5):
- points5 = trimesh.boolean.union([points5, point])
- else:
- points5 = point
- elif color[ind] == 4:
- if bool(points4):
- points4 = trimesh.boolean.union([points4, point])
- else:
- points4 = point
- elif color[ind] == 3:
- if bool(points3):
- points3 = trimesh.boolean.union([points3, point])
- else:
- points3 = point
- elif color[ind] == 2:
- if bool(points2):
- points2 = trimesh.boolean.union([points2, point])
- else:
- points2 = point
- elif color[ind] == 1:
- if bool(points1):
- points1 = trimesh.boolean.union([points1, point])
- else:
- points1 = point
-
-points10.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points10.stl'))
-points9.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points9.stl'))
-points8.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points8.stl'))
-points7.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points7.stl'))
-points6.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points6.stl'))
-points5.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points5.stl'))
-points4.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points4.stl'))
-points3.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points3.stl'))
-points2.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points2.stl'))
-points1.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points1.stl'))
\ No newline at end of file
diff --git a/LigamentInsertions/close_mesh.py b/LigamentInsertions/close_mesh.py
deleted file mode 100644
index 5f9cebc..0000000
--- a/LigamentInsertions/close_mesh.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-
-def cylinder_between(p1, p2, r):
- dx = p2[0] - p1[0]
- dy = p2[1] - p1[1]
- dz = p2[2] - p1[2]
- dist = np.sqrt(dx**2 + dy**2 + dz**2)+0.5
-
- phi = np.arctan2(dy, dx)
- theta = np.arccos(dz/dist)
-
- T = trimesh.transformations.translation_matrix([dx/2 + p1[0], dy/2 + p1[1], dz/2 + p1[2]])
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(phi, zaxis)
- Ry = trimesh.transformations.rotation_matrix(theta, yaxis)
- R = trimesh.transformations.concatenate_matrices(T,Rz, Ry)
-
- cylinder = trimesh.creation.cylinder(r, height=dist, sections=None, segment=None, transform=R)
- cylinder.export(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19\cylinder.stl")
-
-path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19"
-i=2
-
-ms3= pymeshlab.MeshSet()
-ms3.load_new_mesh(path + '\Segmentation_femur_wires' + str(i) + '.stl')
-dist_matrix = []
-dist_matrix_ind = []
-start_ind = []
-verts = ms3.mesh(0).vertex_matrix()
-for ind in range(0,len(verts)):
- ms3.apply_filter('colorize_by_geodesic_distance_from_a_given_point', startpoint=verts[ind],maxdistance=100)
- dist_matrix.append(np.max(ms3.mesh(0).vertex_quality_array()))
- dist_matrix_ind.append(np.argmax(ms3.mesh(0).vertex_quality_array()))
- start_ind.append(ind)
-
-max1 = np.argmax(dist_matrix)
-end_point = verts[dist_matrix_ind[max1]]
-start_point = verts[start_ind[max1]]
-r = 0.5
-cylinder_between(start_point, end_point, r)
-
-path_cylinder = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19\cylinder.stl"
-ms3.load_new_mesh(path_cylinder)
-ms3.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
-ms3.save_current_mesh(path + '\Segmentation_femur_wires' + str(i) + 'close.stl', binary=False)
-
-# ms4 = pymeshlab.MeshSet()
-# ms4.load_new_mesh(path + '\Segmentation_femur_wire' + str(i) + 'union.stl')
-# ms4.load_new_mesh(path + '\Segmentation_femur_area' + str(i) + '.stl')
-#
-# # compute signed distance
-# out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=True)
-#
-# # select and delete vertices with negative distance
-# ms4.conditional_vertex_selection(condselect="q<0")
-# ms4.delete_selected_vertices()
-# # split mesh
-# out4 = ms4.apply_filter('split_in_connected_components')
-#
-# no_meshes = ms4.number_meshes()
-# meshes_to_remove = no_meshes-4
-# for ind in range(0,meshes_to_remove):
-# no_vertices = ms4.mesh(ind+4).vertex_matrix().shape[0]
-# ms4.set_current_mesh(ind+4)
-# ms4.delete_current_mesh()
-#
-# no_vertices = ms4.mesh(3).vertex_matrix().shape[0]
-# if no_vertices < 10:
-# ms4.set_current_mesh(3)
-# ms4.delete_current_mesh()
-#
-# ms4.set_current_mesh(2)
-# ms4.apply_filter('select_border')
-# ms4.mesh(2).selected_face_number()
-# ms4.apply_filter('dilate_selection')
-# ms4.mesh(2).selected_face_number()
-# ms4.apply_filter('dilate_selection')
-#
-# geometric_measures = ms4.apply_filter('compute_geometric_measures')
-# surface = geometric_measures['surface_area']
-# print('Surface area femur ligament' + str(i) + ': ' + str(surface) + ' mm2')
-# # ms4.save_project(path + '\Segmentation_femur_area' + str(i) + 'test.mlp')
\ No newline at end of file
diff --git a/LigamentInsertions/extractSegmentations.py b/LigamentInsertions/extractSegmentations.py
deleted file mode 100644
index 79301ae..0000000
--- a/LigamentInsertions/extractSegmentations.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\extractSegmentations.py').read())
-
-import os,glob
-import shutil
-
-dir = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\Fibula"
-
-for name in os.listdir(dir):
- print(name)
- slicer.mrmlScene.Clear(0)
- path = os.path.join(dir, name)
-
- slicer.util.loadSegmentation(glob.glob(os.path.join(path, "Segmentation.seg.nrrd"))[0])
- slicer.util.loadVolume(glob.glob(os.path.join(path, "*RIGHT.nrrd"))[0])
- volumeNode = slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode")
- segmentationNode = getNode("Segmentation")
-
- # use islands to get noise out
- segmentEditorNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLSegmentEditorNode")
-
- segmentEditorWidget = slicer.qMRMLSegmentEditorWidget()
- segmentEditorWidget.setMRMLScene(slicer.mrmlScene)
- segmentEditorWidget.setMRMLSegmentEditorNode(segmentEditorNode)
- segmentEditorWidget.setSegmentationNode(segmentationNode)
- segmentEditorWidget.setMasterVolumeNode(volumeNode)
-
- segmentEditorWidget.setActiveEffectByName("Islands")
- effect = segmentEditorWidget.activeEffect()
- effect.setParameter("Operation", 'KEEP_LARGEST_ISLAND')
- effect.self().onApply()
-
- # export segmentation to volume
- shNode = slicer.mrmlScene.GetSubjectHierarchyNode()
-
- exportFolderItemId = shNode.CreateFolderItem(shNode.GetSceneItemID(), "Segments")
- slicer.modules.segmentations.logic().ExportAllSegmentsToModels(segmentationNode, exportFolderItemId)
-
- outputFolder = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\fibula_bone\mesh"
- segmentationNode.SetName(str(name)+'_R')
- slicer.vtkSlicerSegmentationsModuleLogic.ExportSegmentsClosedSurfaceRepresentationToFiles(outputFolder,
- segmentationNode,
- None, "STL",
- True, 1.0, False)
-
- outputFolder_seg = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\fibula_bone\segmentation"
- slicer.util.saveNode(segmentationNode, outputFolder_seg + "/" + str(name) + "_R.nrrd")
- # shutil.copy(glob.glob(os.path.join(path, "Segmentation.seg.nrrd"))[0], outputFolder_seg + "/" + str(name) + "_R.nrrd")
diff --git a/LigamentInsertions/fitErrorMRI.py b/LigamentInsertions/fitErrorMRI.py
deleted file mode 100644
index 3d90545..0000000
--- a/LigamentInsertions/fitErrorMRI.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import numpy as np
-import os
-import trimesh
-import pymeshlab
-import glob
-
-subjects = ['1'] #['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['R']
-segments = ['femur','tibia', 'fibula'] #
-short_ssm = [0, 1, 0] #
-data_folder = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/'
-run_fit = 1
-
-for subj_ind, subject in enumerate(subjects):
-
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- path = data_folder
- ssm_path = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- input_path = path + segment + '_bone' + short + r'\new_bone_mri\input/'
- ssm_files = glob.glob(ssm_path + "*.stl")
- input_files = glob.glob(input_path + "*.stl")
-
- # get ligament locations
- if segment == 'femur':
- no_pathpoint = 0
- else:
- no_pathpoint = 1
- if run_fit == 1:
- # run ICP to get final position SSM point cloud on original mesh
- # mesh OpenSim model - make sure this is high quality
- mesh1 = trimesh.load_mesh(ssm_files[subj_ind]) # SSM mesh
- # mesh segmented from MRI
- mesh2 = trimesh.load_mesh(input_files[subj_ind]) # mesh in position MRI
-
- # Mirror if needed (only for left as SSM/model is right? - check how to deal with left model)
- if side == 'L':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- else:
- M = trimesh.transformations.scale_and_translate((1, 1, 1))
- # mesh2.apply_transform(M)
- # Rotate segmented bone (check why needed?)
- origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rx = trimesh.transformations.rotation_matrix(-90 / (180 / np.pi), xaxis)
- Ry = trimesh.transformations.rotation_matrix(90 / (180 / np.pi), yaxis)
- # Rz = trimesh.transformations.rotation_matrix(180 / (180 / np.pi), zaxis)
- R = trimesh.transformations.concatenate_matrices(Ry, Rx)
- mesh1.apply_transform(M)
- # Translate segmented mesh to OpenSim bone location
- T = trimesh.transformations.translation_matrix(mesh2.center_mass - mesh1.center_mass)
- mesh1.apply_transform(T)
-
- new_path = ssm_path + '/fit/'
- if not os.path.exists(new_path):
- # If the path does not exist, create it
- os.makedirs(new_path)
- mesh1.export(new_path + os.path.split(ssm_files[subj_ind])[1])
-
- # ICP to fit segmented bone to OpenSim mesh
- kwargs = {"scale": False}
- # icp = trimesh.registration.icp(mesh1.vertices, mesh2, initial=np.identity(4), threshold=1e-5,
- # max_iterations=20, **kwargs)
-
- icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=np.identity(4), threshold=1e-5, max_iterations=20,
- **kwargs)
- mesh1.apply_transform(icp[0])
- mesh1.export(new_path + 'icp_' + os.path.split(ssm_files[subj_ind])[1])
-
- # hausdorff distance
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(new_path + 'icp_' + os.path.split(ssm_files[subj_ind])[1])
- ms5.load_new_mesh(input_files[subj_ind])
- out1 = ms5.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=True)
- out2 = ms5.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True)
-
- print(segment + ' max: ' + str(max(out1['max'], out2['max'])))
- print(segment + ' min: ' + str(max(out1['min'], out2['min'])))
- print(segment + ' mean: ' + str(max(out1['mean'], out2['mean'])))
- print(segment + ' RMS: ' + str(max(out1['RMS'], out2['RMS'])))
-
- print(segment + ' max: ' + str(out1['max']))
- print(segment + ' min: ' + str(out1['min']))
- print(segment + ' mean: ' + str(out1['mean']))
- print(segment + ' RMS: ' + str(out1['RMS']))
\ No newline at end of file
diff --git a/LigamentInsertions/fitSSM.py b/LigamentInsertions/fitSSM.py
deleted file mode 100644
index e63ce15..0000000
--- a/LigamentInsertions/fitSSM.py
+++ /dev/null
@@ -1,348 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #[9,13,19,23,26,29,32,35,37,41]
-segments = ['femur'] #'femur',
-short = 0
-run = 1
-
-occurances=[]
-all_occ = []
-orders=[]
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments_fib = [[0,0,0,0,0,0,0,0,0,0], # PCL
- [0,0,0,0,0,0,0,0,0,0], # MCLp
- [0,0,0,0,0,0,0,0,0,0], # MCLd
- [0,0,0,0,0,0,0,0,0,0], # MCLd2
- [0,0,0,0,0,0,0,0,0,0], # POL
- [0,0,0,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [0,0,0,0,0,0,0,0,0,0], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-for segment in segments:
- if segment == 'femur':
- ligaments = ligaments_fem
- elif segment == 'fibula':
- ligaments = ligaments_fib
- else:
- ligaments = ligaments_tib
-
- SSMpoints = [[] for i in range(11)]
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if run == 1:
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
- if segment == 'fibula':
- points = str(2048)
- elif segment == 'femur':
- points = str(8192) # 4096
- else:
- points = str(4096)
- """SSM part"""
- # files from SSM workflow shapeworks
- if short == 1:
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.nrrd'
- particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\shape_models/' + points + '\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\input'
- else:
- file_resample = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\resampled\segmentations\Segmentation_' + segment + '_' + side + '_short_' +str(
- subject) + reflect + '.isores.nrrd'
- file_crop = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\cropped\segmentations\Segmentation_' + segment + '_' + side + '_short_' +str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.nrrd'
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_short_' + side + '_' + str(subject) + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.nrrd'
- particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r"_bone\new_bone\shape_models/" + points + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input'
-
- # get change in position from nrrd header files
- # header = nrrd.read_header(pad_file)
- # pad_position = header['space origin']
- header = nrrd.read_header(com_file)
- com_position = header['space origin']
- header = nrrd.read_header(file_resample)
- resample_position = header['space origin']
- header = nrrd.read_header(file_crop)
- crop_position = header['space origin']
-
- # with open(file_com) as fobj:
- # for line in fobj:
- # line = line.replace('[',']')
- # line_data = re.split("]|,", line)
- #
- # NA = np.asarray(line_data[1:4])
- # trans_mat = NA.astype(float)
-
- # get translation from align from rotation matrix
- rot_ssm = np.loadtxt(file_align)
-
- # rot_mat_ssm = np.transpose(rot_ssm)
- # rot_mat_ssm = np.vstack((rot_mat_ssm, [0,0,0,1]))
-
- # translate points cloud SSM instance to align with original mesh
- # com_position[0] = com_position[0]*-1
-
- diff = resample_position -rot_ssm[3,:] -crop_position
- translate = diff
- translate[0] = diff[1]
- translate[1] = diff[0]
- translate[2] = diff[2]
- if subject == 32:
- translate[0] = translate[0]+40.5
- translate[1] = translate[1]-8
- # translate[1] = translate[1] - 2*rot_ssm[3, 1]
- # if reflect == '.reflect':
- # translate[0] = 0# -46 #-resample_position[0] -rot_ssm[3,0] +com_position[0]
-
- # r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\Segmentation_femur_R_short_ssm_reconstruct3.stl' #
- pre, ext = os.path.splitext(xyz_file)
- copyfile(particle_file, pre + '.paticles')
- if not os.path.isfile(pre + '.xyz'):
- os.rename(pre + '.paticles', pre + '.xyz')
- mesh3 = xyz_file # =local.particle file
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(mesh3)
- max_val = 200
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=translate[0], axisy=translate[1], axisz=-max_val)
-
- iters = 1
- while translate[2]+max_val*iters < -max_val:
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod =0, axisx=0, axisy=0, axisz=-max_val)
- iters=iters+1
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-222)
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod =0, axisx=0, axisy=0, axisz=translate[2]+max_val*iters)
- ms6.save_current_mesh(path + '\SSM_' + segment + '_transform.xyz')
-
- # run ICP to get final position SSM point cloud on original mesh
- mesh = trimesh.load_mesh(path + '\Segmentation_' + segment + '_resample.stl')
- # mesh = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_remesh.stl')
- # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1,1,1))
- points.apply_transform(M)
- # np.savetxt(path + '\SSM_' + segment + '_short_transform_mirror.xyz', points.vertices, delimiter=" ")
- kwargs = {"scale": False}
- top_points = np.asarray(points.vertices)
- exclude_bottom = top_points[top_points[:, 2] > min(top_points[:, 2])+1]
- # icp = trimesh.registration.mesh_other(mesh, exclude_bottom, samples=2000, scale=False, icp_first=10, icp_final=50)
- # points.apply_transform(np.linalg.inv(icp[0]))
- icp = trimesh.registration.icp(exclude_bottom,mesh,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
- points.apply_transform(icp[0])
-
- # icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- # points.apply_transform(icp[0])
-
- np.savetxt(path + '\SSM_' + segment + '_transform_icp.xyz', points.vertices, delimiter=" ")
-
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
-
- if short == 1:
- short_name = '_short'
- else:
- short_name = ''
- if run == 2:
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + short_name + '_transform_icp.xyz') #_short
- if segment == 'fibula':
- segment_temp = 'tibia'
- Counter = len(glob.glob1(path, 'Segmentation_' + segment_temp + '_area' + str(ligaments_fib[9][ind]) + '*.stl'))
- else:
- segment_temp = segment
- Counter = len(glob.glob1(path, 'Segmentation_' + segment_temp + '_area*.stl'))
- close_verts = []
- close_verts_verts = np.empty([0,3])
- for count in range(1, int(np.ceil(Counter/2)) + 1):
- if segment == 'fibula':
- count_n = count + ligaments_fib[9][ind] - 1
- else:
- count_n = count
- mesh = trimesh.load_mesh(os.path.join(path,'Segmentation_' + segment_temp + '_area' + str(count_n) + '.stl'))
- # [closest, distance, id] = trimesh.proximity.closest_point(mesh, points.vertices)
- distance = trimesh.proximity.signed_distance(mesh, points.vertices)
- if segment == 'fibula':
- max_dist = 1.5
- elif segment == 'tibia':
- max_dist = 1
- else:
- max_dist = 1
- close_verts.append(np.where(abs(distance)<max_dist))
- # close_verts = np.vstack([close_verts, np.where(abs(distance)<2)])
- # close_verts_verts.append(points.vertices[np.where(abs(distance)<2)])
- close_verts_verts = np.vstack([close_verts_verts, points.vertices[np.where(abs(distance)<max_dist)]])
- # np.savetxt(path + '\SSM_' + segment + '_areas_test.xyz', np.asarray(close_verts_verts), delimiter=" ")
-
- if segment == 'fibula':
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- SSMpoints[lig][ind] = close_verts[0][0]
- else:
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- SSMpoints[lig][ind] = close_verts[lig_no-1][0]
-
- # dupes = [x for n, x in enumerate(np.concatenate(SSMpoints[0])) if x in np.concatenate(SSMpoints[0])[:n]]
- from collections import Counter
- if run == 2:
- occurances = []
- all_occ = []
- orders = []
- for ind in range(0,11):
- count=0
- occur = []
- if ind == 2:
- occur = Counter(np.concatenate(SSMpoints[ind]+SSMpoints[ind+1], axis=0))
- elif ind == 4:
- occur = Counter(np.concatenate(SSMpoints[ind]+ SSMpoints[ind + 1]+ SSMpoints[ind + 2]+ SSMpoints[ind + 3], axis=0))
- elif ind == 3 or ind == 5 or ind == 6 or ind == 7:
- continue
- else:
- occur = Counter(np.concatenate(SSMpoints[ind], axis=0))
- order = occur.most_common()
- for j in range(0,10):
- if len(SSMpoints[ind][j]):
- count=count+1
- if ind == 4:
- print(count)
- try:
- index = [x for x, y in enumerate(order) if y[1] == int(count/2)]
- most_occ = order[0:index[-1]]
- except:
- most_occ = order[0:1]
- all_occ.append(np.asarray([i[0] for i in order]))
- occurances.append(np.asarray([i[0] for i in most_occ]))
- orders.append(np.asarray([i[1] for i in order]))
- elif run == 0:
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment + '.npz')
- occurances.append(bla['PCL']); occurances.append(bla['MCLp']); occurances.append(bla['MCLd']); occurances.append(bla['post_obl']); occurances.append(bla['ACL']);\
- occurances.append(bla['LCL']); occurances.append(bla['pop'])
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_8192' + segment + '.npz')
- all_occ.append(bla['PCL']); all_occ.append(bla['MCLp']); all_occ.append(bla['MCLd']); all_occ.append(bla['post_obl']); all_occ.append(bla['ACL']);
- all_occ.append(bla['LCL']); all_occ.append(bla['pop'])
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_8192' + segment + '.npz')
- orders.append(bla['PCL']); orders.append(bla['MCLp']); orders.append(bla['MCLd']); orders.append(bla['post_obl']); orders.append(bla['ACL']);
- orders.append(bla['LCL']); orders.append(bla['pop'])
-
- points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_8192.xyz')
- np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_8192.xyz', points.vertices[np.hstack(occurances).astype(int)], delimiter=" ")
-
- pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- np.savetxt(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz',
- pred_lig_points_color, delimiter=" ")
-
- mask = np.ones(len(points.vertices), dtype=bool)
- mask[np.hstack(occurances).astype(int)] = False
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz',
- # points.vertices[np.where(mask)], delimiter=" ")
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment + '_order.npy', PCL=orders[0],MCLp=orders[1],
- MCLd=orders[2],post_obl=orders[3],ACL=orders[4],LCL=orders[5],pop=orders[6])
-
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- # ms5.load_new_mesh(os.path.join(path,'Segmentation_femur_area' + str(count) + '.stl'))
- # no_meshes = ms5.number_meshes()
- # ms5.apply_filter('distance_from_reference_mesh', measuremesh=0, refmesh=no_meshes-1, signeddist=False)
- # ms5.set_current_mesh(0)
- # ms5.conditional_vertex_selection(condselect="q<1")
- # m = ms5.current_mesh()
- subjects = [9,13,19,23,26,29,32,35,37,41]
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + short_name +'_transform_icp.xyz') #_short
- pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- np.savetxt(path + '\8192\SSM_' + segment + '_pred_points_8192.xyz', np.asarray(pred_lig_points), delimiter=" ")
- pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)],np.hstack(orders).astype(int)]
- np.savetxt(path + '\8192\SSM_' + segment + '_pred_points_color_8192.xyz', np.asarray(pred_lig_points_color), delimiter=" ")
-
- # for modes in range(1,4):
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\mode' + str(modes) + '_+2sd.xyz')
- # pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_mode' + str(modes) + '_+2sd.xyz', np.asarray(pred_lig_points), delimiter=" ")
- # pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_color_mode' + str(modes) + '_+2sd.xyz',
- # np.asarray(pred_lig_points_color), delimiter=" ")
- #
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\mode' + str(modes) + '_-2sd2.xyz')
- # pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_mode' + str(modes) + '_-2sd.xyz',
- # np.asarray(pred_lig_points), delimiter=" ")
- # pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_color_mode' + str(modes) + '_-2sd.xyz',
- # np.asarray(pred_lig_points_color), delimiter=" ")
-
-
-
-
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment ,PCL=occurances[0],MCLp=occurances[1],
- MCLd=occurances[2],post_obl=occurances[3],ACL=occurances[4],LCL=occurances[5],pop=occurances[6])
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_8192' + segment, PCL=all_occ[0],MCLp=all_occ[1],
- MCLd=all_occ[2],post_obl=all_occ[3],ACL=all_occ[4],LCL=all_occ[5],pop=all_occ[6])
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_8192' + segment, PCL=orders[0],
- MCLp=orders[1], MCLd=orders[2], post_obl=orders[3], ACL=orders[4], LCL=orders[5], pop=orders[6])
diff --git a/LigamentInsertions/fitSSM_mri.py b/LigamentInsertions/fitSSM_mri.py
deleted file mode 100644
index 1026396..0000000
--- a/LigamentInsertions/fitSSM_mri.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-
-
-def csv2xyz(csv_path):
- import csv
-
- # Define the paths to the input and output files
- xyz_path = os.path.splitext(csv_path)[0] + ".xyz"
-
- # Load the CSV file and extract the relevant data
- data = []
- with open(csv_path, "r") as csvfile:
- reader = np.genfromtxt(csvfile) # csv.reader(csvfile)
- data = reader
- # for i, row in enumerate(reader):
- # if i == 0:
- # continue # Skip the first row
- # x, y, *z = row[1:-1] # Extract x, y, and any additional columns as z
- # data.append([x, y, *z])
-
- # Write the data to the output file in XYZ format
- with open(xyz_path, "w") as xyzfile:
- for row in data:
- x, y, *z = row
- xyzfile.write(f"{x} {y} {z[0]}\n")
-
- return xyz_path
-
-
-subjects = ['1L','2L','3L','4L','5L','6L','8L','9L','1R','2R','3R','4R','5R','6R','8R','9R'] # ['1'] # ['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['L','L','L','L','L','L','L','L', 'R','R','R','R','R','R','R','R']
-segments = ['femur','tibia', 'fibula'] #['tibia'] #
-short_ssm = [0,1,0] #[1] #
-no_particles = [4096,4096,2048] #[4096] #
-data_folder = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/"
-
-for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- # Load which SSM points are related to which ligaments
- occ = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_' + segment + short + '.npz')
- occurances = [occ['PCL'], occ['MCLp'], occ['MCLd'], occ['post_obl'], occ['ACL'], occ['LCL'], occ['pop']]
-
- all_occ = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_' + segment + '.npz')
- all_occ = [all_occ['PCL'], all_occ['MCLp'], all_occ['MCLd'], all_occ['post_obl'], all_occ['ACL'], all_occ['LCL'], all_occ['pop']]
-
- order = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_' + segment + '.npz')
- orders = [order['PCL'], order['MCLp'], order['MCLd'], order['post_obl'], order['ACL'], order['LCL'],
- order['pop']]
-
- for subj_ind, subject in enumerate(subjects):
- path = data_folder
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- # files from SSM workflow shapeworks
- ssm_path = path + segment + '_bone' + short + r'\new_bone_4DCT/'
- ssm_files = glob.glob(ssm_path + "*.particles")
- # ssm_files = glob.glob(ssm_path + "*.csv")
- particle_file_name = ssm_files[subj_ind]
- shape_model_folder = ssm_path
-
- new_path = ssm_path + '/fit/'
- if not os.path.exists(new_path):
- # If the path does not exist, create it
- os.makedirs(new_path)
-
- # path_bones = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- input_files = glob.glob(ssm_path + "*.stl")
- mesh_inp = input_files[subj_ind]
-
- # Create xyz file from particles file
- xyz_file = csv2xyz(particle_file_name)
-
- # pre, ext = os.path.splitext(particle_file_name)
- # particle_file = os.path.join(shape_model_folder, str(no_particles[seg_ind]), particle_file_name)
- # xyz_file = os.path.join(shape_model_folder, pre + '.xyz')
- # copyfile(particle_file, xyz_file)
-
- # Reflect (mirror) points if needed and translate to the position of the original mesh
- mesh_inp = trimesh.load_mesh(mesh_inp)
- points_xyz = trimesh.load_mesh(xyz_file)
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- points_xyz.apply_transform(M)
- mesh_inp.apply_transform(M)
- translate = mesh_inp.center_mass-points_xyz.centroid
- points_xyz.apply_transform(trimesh.transformations.translation_matrix(translate))
- np.savetxt(new_path + '\SSM_' + segment + str(subject) + '_transform.xyz', points_xyz.vertices, delimiter=" ") # save intermediate translation to check
-
- # run ICP to get final position SSM point cloud on original mesh
- kwargs = {"scale": False}
- icp = trimesh.registration.icp(points_xyz.vertices,mesh_inp,initial=np.identity(4),threshold=1e-5,max_iterations=40,**kwargs)
- # icp = trimesh.registration.icp(points_xyz.vertices, mesh_inp, initial=icp[0], threshold=1e-5, max_iterations=20,**kwargs) # run icp twice to improve fit
- points_xyz.apply_transform(icp[0])
- np.savetxt(new_path + r'\SSM_' + segment + str(subject) + '_transform_icp.xyz', points_xyz.vertices, delimiter=" ") # save position SSM points on original mesh
-
- mesh_inp.apply_transform(trimesh.transformations.translation_matrix(translate))
- mesh_inp.apply_transform(icp[0])
- mesh_inp.export(new_path + r'\SSM_' + segment + short + str(subject) + '.stl')
-
- # link which SSM points are related to which ligaments to new point cloud
- pred_lig_points = points_xyz.vertices[np.hstack(occurances).astype(int)]
- np.savetxt(new_path + r'\SSM_' + segment + short + str(subject) + '_pred_points.xyz', np.asarray(pred_lig_points), delimiter=" ")
- pred_lig_points_color = np.c_[points_xyz.vertices[np.hstack(all_occ).astype(int)],np.hstack(orders).astype(int)]
- np.savetxt(new_path + '\SSM_' + segment + str(subject) + '_pred_points_color.xyz', np.asarray(pred_lig_points_color), delimiter=" ")
-
- print('processing ' + segment + ' done for ' + str(subject))
diff --git a/LigamentInsertions/plotHausdorffDistance.py b/LigamentInsertions/plotHausdorffDistance.py
deleted file mode 100644
index 514713b..0000000
--- a/LigamentInsertions/plotHausdorffDistance.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-import trimesh
-import os
-from openpyxl import load_workbook
-import pandas as pd
-
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-segments = ['femur','tibia','fibula']
-
-data = []
-for segment in segments:
- RMS = []
- for subject in subjects:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if segment=='femur':
- HD = np.load(path + r'/8192/' + segment + '_HD.np.npy', allow_pickle=True)
- else:
- HD = np.load(path + r'/' + segment + '_HD.np.npy',allow_pickle=True)
- RMS.append(HD[0]['RMS'])
- # RMS.append(max(HD[0]['RMS'], HD[1]['RMS']))
-
- data.append(np.mean(RMS).round(decimals=2).astype(str) + ' ±' + np.std(RMS).round(decimals=2).astype(str))
-
-df = pd.DataFrame(data, index=['femur','tibia','fibula'], columns=['Hausdorff distance RMS (mm)'])
-
-book = load_workbook(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","HausdorffDistance.xlsx"))
-writer = pd.ExcelWriter(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","HausdorffDistance.xlsx"), engine='openpyxl')
-writer.book = book
-df.to_excel(writer, sheet_name='HD')
-writer.save()
-writer.close()
diff --git a/LigamentInsertions/remesh.py b/LigamentInsertions/remesh.py
deleted file mode 100644
index 4ba854c..0000000
--- a/LigamentInsertions/remesh.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import trimesh
-import pymeshlab
-import os
-import numpy as np
-
-# mesh = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\femur_cartilage\mesh\9005075.segmentation_masks_femoral_cartilage_R.ply')
-# trimesh.remesh.subdivide_to_size(mesh.vertices, mesh.faces, 5, max_iter=10, return_index=False)
-# trimesh.exchange.export.export_mesh(mesh,r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\femur_cartilage\mesh_resample\9005075.segmentation_masks_femoral_cartilage_R.ply', 'ply')
-
-inputDir = r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation'
-datasetName = "tibia_cartilage"
-mesh_dir = inputDir + r'/segmentation_meshes/' + datasetName + '/mesh/med/'
-mesh_dir_out = inputDir + r'/segmentation_meshes/' + datasetName + '/mesh_resample/med/'
-
-files_mesh = []
-for file in sorted(os.listdir(mesh_dir)):
- files_mesh.append(mesh_dir + file)
-
-pt_to_use = r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\healthyKL_pts.txt'
-with open(pt_to_use) as f:
- pts = f.readlines()
-pts = [i.split('\n')[0] for i in pts]
-pts_use = pts
-
-matches_mesh = []
-for pt in pts_use:
- if any(pt in s for s in files_mesh):
- matches_mesh.append([match for match in files_mesh if pt in match])
-
-files_mesh = [item for sublist in matches_mesh for item in sublist]
-
-#
-# for file in files_mesh:
-# ms6 = pymeshlab.MeshSet()
-# ms6.load_new_mesh(file)
-# ms6.apply_filter('uniform_mesh_resampling', cellsize=1.05, offset=0.5, mergeclosevert=False)
-# ms6.save_current_mesh(file_name=mesh_dir_out + file.split('/')[-1], save_textures=False)
-
- # for femur
- # m = ms6.current_mesh()
- # fm = m.face_matrix()
- #
- # # ms6 = pymeshlab.MeshSet()
- # # ms6.load_new_mesh(mesh_dir_out + file.split('/')[-1])
- # ms6.apply_filter('simplification_quadric_edge_collapse_decimation',targetfacenum=np.max(fm),targetperc=0,qualitythr=0.3,preserveboundary=True,preservenormal=True,preservetopology=True)
- # ms6.save_current_mesh(file_name=mesh_dir_out + file.split('/')[-1], save_textures=False)
-
-datasetName = "tibia_cartilage"
-side = 'med' # 'lat' #
-outputDirectory = r'C:/Users/mariskawesseli/Documents/GitLab/knee_ssm/OAI/Output/tibia_cartilage_' + side + '/groomed/'
-
-origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-Rx = trimesh.transformations.rotation_matrix(np.pi/4, xaxis)
-
-mesh_reg = trimesh.load_mesh(outputDirectory + 'reference.ply')
-for file in files_mesh:
- mesh1 = trimesh.load_mesh(mesh_dir_out + file.split('/')[-1])
-
- T, cost = trimesh.registration.mesh_other(mesh1, mesh_reg, samples=500, scale=False, icp_first=10, icp_final=50)
- mesh1.apply_transform(T)
- mesh1.apply_transform(Rx)
-
- mesh1.export(outputDirectory + 'meshes/' + file.split('/')[-1])
-
-
diff --git a/LigamentInsertions/rotateMesh.py b/LigamentInsertions/rotateMesh.py
deleted file mode 100644
index c4607b5..0000000
--- a/LigamentInsertions/rotateMesh.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import trimesh
-import os
-import numpy as np
-import glob
-
-
-subjects = ['1'] #['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['R']
-segments = ['femur','tibia', 'fibula']
-short_ssm = [0, 1, 0]
-no_particles = [4096, 4096, 2048]
-opensim_meshes = ['smith2019-L-femur-bone_remesh.stl','smith2019-L-tibia-bone_remesh.stl',
- 'smith2019-L-fibula-bone_remesh.stl']
-run_fit = 1
-run_find_points = 1
-create_cmd = 0
-add_contact = 1
-for_linux = 0
-
-opensim_geometry_folder = r'C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry'
-input_file_folder = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output'
-gen_model = r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\4DCT\1\lenhart2015_nocontact.osim' # generic scaled model without contact
-
-data_folder = r"C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\4DCT/"
-path = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/'
-cadaver_folder = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/'
-
-for sample in range(0,1): #100
-
- for subj_ind, subject in enumerate(subjects):
-
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- ssm_path = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- ssm_files = glob.glob(ssm_path + "*.stl")
- mesh_inp = ssm_files[subj_ind]
- new_path = ssm_path + '/fit/'
-
- out_file = os.path.join(data_folder, str(subject), 'input_mesh_' + segment + short + '_translated.stl')
- out_file2 = os.path.join(data_folder, str(subject), 'input_mesh_' + segment + short + '_icp.stl')
-
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(0 / (180 / np.pi), xaxis)
- Ry = trimesh.transformations.rotation_matrix(-90 / (180 / np.pi), zaxis)
- R = trimesh.transformations.concatenate_matrices(Rz, Ry)
-
- mesh = trimesh.load_mesh(mesh_inp)
- mesh.apply_transform(R)
- mesh.export(os.path.join(args.inputDir,mesh_path))
diff --git a/LigamentInsertions/scaleOsim.py b/LigamentInsertions/scaleOsim.py
deleted file mode 100644
index 689e65c..0000000
--- a/LigamentInsertions/scaleOsim.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import trimesh
-import numpy as np
-from scipy import interpolate
-import matplotlib.pyplot as plt
-
-
-def interpolate_lig_points(lig_points, no_points, plot=1):
- x = lig_points[:, 0]
- y = lig_points[:, 1]
- z = lig_points[:, 2]
- goon = 1
- n=0
- # Create a uniformly spaced grid
- while goon == 1:
- steps = no_points/2+n # number of rows and columns for the grid
- grid_steps = complex(str(steps) + 'j')
-
- interp = interpolate.Rbf(x, y, z, function='thin_plate')
- yi, xi = np.mgrid[min(lig_points[:, 1]):max(lig_points[:, 1]):grid_steps,
- min(lig_points[:, 0]):max(lig_points[:, 0]):grid_steps]
- zi = interp(xi, yi)
- inds_remove = []
- inds_nan = []
- diff_val = []
- xi_nan = xi
- yi_nan = yi
- zi_nan = zi
- for i in range(0, len(xi)):
- for j in range(0, len(xi)):
- diff = np.linalg.norm(lig_points[:, :] - np.asarray([xi[i, j], yi[i, j], zi[i, j]]), axis=1)
- diff_val.append(np.abs(np.amin(diff)))
- if np.amin(diff) > 1.5:
- inds_remove.append([i * steps + j])
- inds_nan.append([i, j])
- xi_nan[i, j] = np.nan
- yi_nan[i, j] = np.nan
- zi_nan[i, j] = np.nan
- n=n+1
- if np.count_nonzero(~np.isnan(xi_nan)) >= no_points or n==10:
- # print(str(np.count_nonzero(~np.isnan(xi_nan))) + ' ' + str(no_points))
- goon = 0
- diff_val = np.zeros([len(xi_nan),len(xi_nan)])
- if np.count_nonzero(~np.isnan(xi_nan)) > no_points:
- to_remove = np.count_nonzero(~np.isnan(xi_nan))-no_points
- for i in range(0, len(xi_nan)):
- for j in range(0, len(xi_nan)):
- diff = np.linalg.norm(lig_points[:, :] - np.asarray([xi_nan[i, j], yi_nan[i, j], zi_nan[i, j]]), axis=1)
- diff_val[i,j] = np.abs(np.amin(diff))
- for k in range(0,to_remove):
- i,j = np.unravel_index(np.nanargmax(diff_val),diff_val.shape)
- diff_val[i,j] = np.nan
- xi_nan[i,j] = np.nan
- yi_nan[i,j] = np.nan
- zi_nan[i,j] = np.nan
- # print('-1 ' + str(np.count_nonzero(~np.isnan(xi_nan))) + ' ' + str(no_points))
-
- if len(lig_points) == 2:
- tck, u = interpolate.splprep([lig_points[:, 0], lig_points[:, 1], lig_points[:, 2]],s=10,k=1)
- x_knots, y_knots, z_knots = interpolate.splev(tck[0], tck)
- u_fine = np.linspace(0, 1, no_points)
- x_fine, y_fine, z_fine = interpolate.splev(u_fine, tck, der=0)
- # lig_points_osim = np.transpose(np.asarray([x_fine, y_fine, z_fine]))
- xi_nan, yi_nan, zi_nan = x_fine, y_fine, z_fine
-
- lig_points_osim = xi_nan[np.logical_not(np.isnan(xi_nan))]/1000, yi_nan[np.logical_not(np.isnan(yi_nan))]/1000, zi_nan[np.logical_not(np.isnan(zi_nan))]/1000
-
- if plot == 1:
- fig2 = plt.figure()
- ax3d = fig2.add_subplot(111, projection='3d')
- ax3d.plot(lig_points[:, 0], lig_points[:, 1], lig_points[:, 2], 'r*')
- # ax3d.plot(x_knots, y_knots, z_knots, 'bo')
- # ax3d.plot(x_fine, y_fine, z_fine, 'go')
- ax3d.scatter(xi_nan[:],yi_nan[:],zi_nan[:],c='g')
- fig2.show()
- plt.show()
-
- return lig_points_osim
-
-osim_model = r'C:\Users\mariskawesseli\Documents\MOBI\data\S0_2_meniscus_lig.osim'
-"""femur"""
-# # run ICP to get final position SSM point cloud on original mesh
-# mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-femur-bone_remesh.stl')
-# mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_remesh2.STL')
-# # points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
-#
-# M = trimesh.transformations.scale_and_translate((1,1,-1))
-# mesh1.apply_transform(M)
-# T = trimesh.transformations.translation_matrix([64.724205, -26.297621, -95.929390])
-# origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-# Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-# Ry = trimesh.transformations.rotation_matrix(90/(180/np.pi), yaxis)
-# R = trimesh.transformations.concatenate_matrices(T, Ry, Rx)
-# mesh2.apply_transform(R)
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_test.STL')
-#
-# s, fi = trimesh.sample.sample_surface_even(mesh2, 32015, radius=None)
-# mesh3 = trimesh.icp[0]imesh(vertices=s, faces=mesh2.faces[fi])
-# mesh3.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_test.STL')
-# sort_index = np.argsort(fi) # sort and group the face indices
-# points = s[sort_index, :]
-# faceIndices = fi[sort_index]
-# uniqueFaceIndices = np.unique(faceIndices)
-# allMeshPatches = trimesh.icp[0]imesh()
-# pointGroups = [points[faceIndices == i] for i in uniqueFaceIndices]
-# for faceIndex, pointsOnFace in zip(uniqueFaceIndices, pointGroups):
-# meshpatch = trimesh.icp[0]imesh(mesh2.vertices[mesh2.faces[faceIndex, :]].reshape(3, 3),
-# np.array([0, 1, 2]).reshape(1, 3))
-# allMeshPatches += meshpatch
-# allMeshPatches.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_remesh_test.STL')
-#
-# kwargs = {"scale": False}
-# icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-#
-# kwargs = {"scale": True}
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_icp.STL')
-# icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-# mesh2.apply_transform(icp[0])
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_icp.STL')
-# scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-# # mesh1.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\test.STL')
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points.xyz')
-# ligs.apply_transform(R)
-# ligs.apply_transform(icp[0])
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim.xyz')
-# PCL = ligs.vertices[0:61]
-# al = [0,1,2,3,4,5,7,10,11,13,29,30,31,34,35,39,40,42,43,47,49,50,51,55,56,57,58,59,24,27,28,48]
-# pm = [item for item in list(range(61)) if item not in al]
-# PCLal_osim = interpolate_lig_points(PCL[al,:],5)
-# PCLpm_osim = interpolate_lig_points(PCL[pm,:],5)
-# MCLs = ligs.vertices[61:71]
-# MCLs_osim = interpolate_lig_points(MCLs,6)
-# MCLd = ligs.vertices[71:73]
-# MCLd_osim = interpolate_lig_points(MCLd,5)
-# post_obl = ligs.vertices[73:81]
-# post_obl_osim = interpolate_lig_points(post_obl,5)
-# ACL = ligs.vertices[81:100]
-# al = [0,1,2,4,7,9,12,15,18]
-# pm = [item for item in list(range(19)) if item not in al]
-# ACLal_osim = interpolate_lig_points(ACL[al,:],6)
-# ACLpm_osim = interpolate_lig_points(ACL[pm,:],6)
-# LCL = ligs.vertices[100:105]
-# LCL_osim = interpolate_lig_points(LCL,4)
-#
-# osim_points_fem = np.concatenate([np.asarray(PCLal_osim),np.asarray(PCLpm_osim), np.asarray(MCLs_osim),
-# np.asarray(MCLd_osim), np.asarray(post_obl_osim), np.asarray(ACLal_osim),
-# np.asarray(ACLpm_osim), np.asarray(LCL_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim_interp.xyz', osim_points_fem, delimiter=" ")
-#
-# # fig2 = plt.figure()
-# # ax3d = fig2.add_subplot(111, projection='3d')
-# # ax3d.plot(ACL[al, 0], ACL[al, 1], ACL[al, 2], 'r*')
-# # ax3d.plot(ACL[pm, 0], ACL[pm, 1], ACL[pm, 2], 'bo')
-# # fig2.show()
-# # plt.show()
-
-"""tibia"""
-# # run ICP to get final position SSM point cloud on original mesh
-# mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-tibia-bone_remesh.stl')
-# mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_remesh.STL')
-# # points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
-#
-# M = trimesh.transformations.scale_and_translate((1,1,-1))
-# mesh1.apply_transform(M)
-# T = trimesh.transformations.translation_matrix([101.562462, -72.768566, -17.893391])
-# origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-# Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-# Ry = trimesh.transformations.rotation_matrix(90/(180/np.pi), yaxis)
-# R = trimesh.transformations.concatenate_matrices(Ry, Rx)
-# mesh2.apply_transform(T)
-# mesh2.apply_transform(R)
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_remesh_test.STL')
-#
-# kwargs = {"scale": False}
-# icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-#
-# kwargs = {"scale": True}
-# # mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_icp.STL')
-# icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-# mesh2.apply_transform(icp[0])
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_icp.STL')
-# scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-# mesh1.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\test_tib.STL')
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\SSM_tibia_short_pred_points.xyz')
-# ligs.apply_transform(T)
-# ligs.apply_transform(R)
-# ligs.apply_transform(icp[0])
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_short_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_short_pred_points_osim.xyz')
-PCL = ligs.vertices[0:71]
-al = [0,1,2,4,6,7,10,12,13,19,21,22,23,24,25,26,27,31,32,34,35,40,45,46,47,50,52,56,59,60,61,62,63,65,70]
-pm = [item for item in list(range(71)) if item not in al]
-PCLal_osim = interpolate_lig_points(PCL[al,:],5)
-PCLpm_osim = interpolate_lig_points(PCL[pm,:],5)
-MCLd = ligs.vertices[71:82]
-MCLd_osim = interpolate_lig_points(MCLd,5)
-post_obl = ligs.vertices[82:86]
-post_obl_osim = interpolate_lig_points(post_obl,5)
-ACL = ligs.vertices[86:150]
-al = [0,1,2,4,6,7,10,12,13,19,21,22,23,24,25,26,27,31,32,34,35,40,45,46,47,50,52,56,59,60,61,62,63]
-pm = [item for item in list(range(64)) if item not in al]
-ACLal_osim = interpolate_lig_points(ACL[al,:],6)
-ACLpm_osim = interpolate_lig_points(ACL[pm,:],6)
-
-osim_points_tib = np.concatenate([np.asarray(PCLal_osim),np.asarray(PCLpm_osim),
- np.asarray(MCLd_osim), np.asarray(post_obl_osim), np.asarray(ACLal_osim),
- np.asarray(ACLpm_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_pred_points_osim_interp.xyz', osim_points_tib, delimiter=" ")
-
-
-"""fibula"""
-# run ICP to get final position SSM point cloud on original mesh
-# mesh OpenSim model - make sure this is high quality
-mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-fibula-bone_remesh.stl')
-# mesh segmented from MRI
-mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh.STL')
-
-# Mirror if needed (only for left as SSM/model is right? - check how to deal with left model)
-M = trimesh.transformations.scale_and_translate((1,-1,1))
-mesh2.apply_transform(M)
-# Rotate segmented bone (check why needed?)
-origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-Ry = trimesh.transformations.rotation_matrix(-90/(180/np.pi), yaxis)
-Rz = trimesh.transformations.rotation_matrix(180/(180/np.pi), zaxis)
-R = trimesh.transformations.concatenate_matrices(Ry, Rx, Rz)
-mesh2.apply_transform(R)
-# Translate segmented mesh to OpenSim bone location
-T = trimesh.transformations.translation_matrix(mesh1.center_mass-mesh2.center_mass)
-mesh2.apply_transform(T)
-mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh_test.STL')
-
-# ICP to fit segmented bone to OpenSim mesh
-kwargs = {"scale": False}
-icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-kwargs = {"scale": True}
-icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-mesh2.apply_transform(icp[0])
-mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh_icp.STL')
-scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\SSM_fibula_short_pred_points.xyz')
-ligs.apply_transform(M)
-ligs.apply_transform(R)
-ligs.apply_transform(T)
-ligs.apply_transform(icp[0])
-np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim.xyz')
-
-LCL = ligs.vertices[0:79]
-LCL_osim = interpolate_lig_points(LCL,4)
-
-osim_points_fib = np.concatenate([np.asarray(LCL_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim_interp.xyz', osim_points_tib, delimiter=" ")
diff --git a/LigamentInsertions/showAxes.py b/LigamentInsertions/showAxes.py
deleted file mode 100644
index 526f6f9..0000000
--- a/LigamentInsertions/showAxes.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-import seaborn as sns
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-subjects = ['9'] #['9','13','19','23','26','29','32','35','37','41'] #, S0 [100] #
-
-segments = ['tibia'] #'femur',
-ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
-ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
-ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
-for segment in segments:
- SSMpoints = [[] for i in range(11)]
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject==100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject==100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/mean_shape.stl')
- # bone_actor.GetProperty().SetOpacity(0.75)
- else:
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- else:
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_transform.stl')
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.85)
-
- # actor.GetProperty().SetOpacity(1.0)
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
-
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(10)
- lut.SetTableRange(1, 10)
- for j in range(0,10):
- lut.SetTableValue(int(j), c[j][0], c[j][1], c[j][2])
-
- legend = vtk.vtkScalarBarActor()
- legend.SetNumberOfLabels(10)
- lut.SetTableRange(1, 10)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
- legend.SetTitle("Specimens \n")
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0,0,0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- legend.SetMaximumWidthInPixels(75)
- legend.SetMaximumHeightInPixels(300)
- legend.SetTitleTextProperty(text_prop_cb)
- legend.SetPosition(0.85,0.6)
-
- axes = vtk.vtkAxesActor()
- axes.SetTotalLength(75,75,100)
- axes.SetXAxisLabelText('M-L')
- axes.SetYAxisLabelText('A-P')
- axes.SetZAxisLabelText('S-I')
- axes.GetXAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetXAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
- axes.GetYAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetYAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
- axes.GetZAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetZAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(bone_actor)
- # if not subject == 100 and not subject == 'S0':
- # renderer.AddActor(wire_actor)
- # renderer.AddActor(legend)
- renderer.AddActor(axes)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
diff --git a/LigamentInsertions/stl2vtk.py b/LigamentInsertions/stl2vtk.py
deleted file mode 100644
index 9a8b81f..0000000
--- a/LigamentInsertions/stl2vtk.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import vtk
-
-# Define the input and output directories
-input_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\tibia_bone_short\new_bone\shape_models"
-output_dir = input_dir
-
-# Create a VTK STL reader
-stl_reader = vtk.vtkSTLReader()
-
-# Loop through all STL files in the input directory
-for filename in os.listdir(input_dir):
- if filename.endswith("mean_shape.stl"):
- # Load the STL file
- stl_path = os.path.join(input_dir, filename)
- stl_reader.SetFileName(stl_path)
- stl_reader.Update()
-
- # Convert the STL data to polydata
- polydata_filter = vtk.vtkDataSetSurfaceFilter()
- polydata_filter.SetInputConnection(stl_reader.GetOutputPort())
- polydata_filter.Update()
- polydata = polydata_filter.GetOutput()
-
- # Modify the VTK file header to version 4.2
- header = "# vtk DataFile Version 4.2\n"
-
- # Save the polydata as a VTK file in format 4.2
- vtk_path = os.path.join(output_dir, filename.replace(".stl", ".vtk"))
- vtk_writer = vtk.vtkPolyDataWriter()
- vtk_writer.SetFileName(vtk_path)
- vtk_writer.SetInputData(polydata)
- vtk_writer.SetFileTypeToBinary()
- vtk_writer.SetHeader(header)
- vtk_writer.Update()
diff --git a/LigamentInsertions/testVisualizeSSM.py b/LigamentInsertions/testVisualizeSSM.py
deleted file mode 100644
index d3273f9..0000000
--- a/LigamentInsertions/testVisualizeSSM.py
+++ /dev/null
@@ -1,441 +0,0 @@
-# import pyvista as pv
-# mesh= pv.read(r"C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col.ply")
-# mesh.plot()
-
-# import pyvista as pv
-# import numpy as np
-# # Re cast PolyData because file was not properly saved
-# bad = pv.read(r"C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col.ply")
-# bad.plot()
-# mesh = pv.PolyData(bad.points)
-# # Plot it
-# scalars = bad['RGBA']
-# # mesh.plot(scalars=scalars)
-# mesh.plot(scalars=scalars[:,0:3])
-# mesh.plot(scalars=scalars)
-# mesh.plot(scalars=scalars, rgba=True)
-
-import sys
-import os
-import vtk
-from numpy import random
-import trimesh
-import numpy as np
-import seaborn as sns
-
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-def create_pointcloud_polydata(points, colors=None, seg=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- # vpoints.SetMarkerStyle(vtk.vtkPlotPoints.CIRCLE)
-
- vpoly = vtk.vtkPolyData()
-
- appendFilter = vtk.vtkAppendPolyData()
- for i in range(points.shape[0]):
- sphereSource = vtk.vtkSphereSource()
- # spheres.SetThetaResolution(1)
- # spheres.SetPhiResolution(1)
- sphereSource.SetRadius(1)
- sphereSource.SetCenter(vpoints.GetPoint(i))
- sphereSource.Update()
-
- appendFilter.AddInputData(sphereSource.GetOutput())
-
- # vpoly.SetPoints(vpoints)
- rgb_col = []
- if not colors is None:
- if seg == 'femur':
- max_val = 8
- color[112:len(color)] = (color[112:len(color)] / max_val) * 10
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- rgb_col = []
- for i in range(points.shape[0]):
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- vcolors.SetTuple3(i, c[int(colors[i] * 10)][0] * 255, c[int(colors[i] * 10)][1] * 255,
- c[int(colors[i] * 10)][2] * 255)
- rgb_col.append(
- [c[int(colors[i] * 10)][0] * 255, c[int(colors[i] * 10)][1] * 255, c[int(colors[i] * 10)][2] * 255])
- # print(i, c[int(colors[i] - 1)][0], c[int(colors[i] - 1)][1], c[int(colors[i] - 1)][2])
- # c = rgb(1,10,colors[i])
- # vcolors.SetTuple3(i, c[0], c[1], c[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- actor.GetProperty().SetColor(color)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly, rgb_col
-
-
-def rgb(minimum, maximum, value):
- minimum, maximum = float(minimum), float(maximum)
- ratio = (value - minimum) / (maximum - minimum) # 2 *
- g = int(max(0, 255 * (1 - ratio)))
- r = int(max(0, 255 * (ratio - 0)))
- b = 0 # 255 - b - r
- return r, g, b
-
-
-def createSpline(points):
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
-
- spline = vtk.vtkParametricSpline()
- spline.SetPoints(vpoints)
-
- functionSource = vtk.vtkParametricFunctionSource()
- functionSource.SetParametricFunction(spline)
- functionSource.Update()
-
- # Create a mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputConnection(functionSource.GetOutputPort())
-
- # Create an actor
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-if __name__ == '__main__':
- center_tibia = np.concatenate((np.arange(131), np.arange(470 - 341) + 341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112), np.arange(341 - 263) + 263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
- center_only = 1
- subjects = [100] # [100] # ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-
- segments = ['femur'] # 'femur',
- ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
- ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
- ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
- for segment in segments:
- SSMpoints = [[] for i in range(11)]
- if segment == 'tibia':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
-
- for ind in range(0, 11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject == 100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs_color.xyz')
- color = np.loadtxt(path + r'\meanshape_ligs_color.xyz')[:, 3]
-
- if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(1.0)
-
- mesh = trimesh.load_mesh(path + '/mean_shape.stl')
- # dist = trimesh.proximity.nearby_faces(mesh, np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- dist3 = trimesh.proximity.closest_point_naive(mesh, np.squeeze(
- np.asarray(points_lig[np.argwhere(color >= 7)])), tol=1.0)
-
- # faces = np.unique(np.asarray([item for sublist in dist for item in sublist]))
- faces = np.unique(np.asarray([item for sublist in dist3[3] for item in sublist]))
- mesh.update_faces(faces)
- mesh.export(path + '/mean_shape_80percsurf.stl')
- surf_actor = load_stl(path + '/mean_shape_80percsurf.stl')
- else:
- # points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_areas.xyz') #_pred_points_color
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(
- path + '\SSM_' + segment + '_pred_points_color.xyz') # _pred_points_color
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color.xyz')[:,
- 3] # _areas _short_areas _pred_points
- if center_only == 1:
- points_lig = points_lig[center]
- # color = color[center]
- point_cloud_lig = create_pointcloud_polydata(points_lig, seg=segment) # ,color colors=color,
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- # bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_fibula_1_tissue_rot.stl')
- else:
- bone_actor = load_stl(
- path + '/Segmentation_' + segment + '_resample.stl') # '/SSM_' + segment + '_reconstruct_transform_icp.stl'
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- # if center_only == 1:
- # wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires1.stl')
- # wire_actor2 = load_stl(path + '/Segmentation_' + segment_temp + '_wires3.stl')
- # wire_actor2.GetProperty().SetColor(1, 1, 0)
- # else:
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.75)
-
- points_bone = trimesh.load_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
- point_cloud_bone = create_pointcloud_polydata(points_bone)
-
- # orders = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_order.npy')
-
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(point_cloud_bone)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- actor.GetProperty().SetColor(0, 0, 0)
- actor.GetProperty().SetPointSize(2)
- # actor.GetProperty().SetOpacity(1.0)
-
- # spline_actor = createSpline(np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().RenderPointsAsSpheresOn()
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(7.5)
-
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(11)
- lut.SetTableRange(1, 11)
- for j in range(0, 11):
- lut.SetTableValue(int(j * 1), c[j * 10][0], c[j * 10][1], c[j * 10][2])
- # print(int(j*1), c[j*10-1][0], c[j*10-1][1], c[j*10-1][2])
-
- j = 10 - 1
- surf_col = [c[j][0], c[j][1], c[j][2]]
- surf_col = [169 / 255, 169 / 255, 169 / 255]
- surf_actor.GetProperty().SetColor(surf_col)
- surf_actor.GetProperty().SetOpacity(1.0)
-
- legend = vtk.vtkScalarBarActor()
- legend.SetOrientationToHorizontal()
- labelFormat = vtk.vtkTextProperty()
- labelFormat.SetFontSize(16)
- titleFormat = vtk.vtkTextProperty()
- titleFormat.SetFontSize(8)
- legend.SetLabelTextProperty(labelFormat)
- # legend.SetTitleTextProperty(titleFormat)
-
- legend.SetNumberOfLabels(11)
- lut.SetTableRange(0, 100)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
-
- legend.SetTitle("% of specimens \n")
- legend.SetLabelFormat("%1.0f")
- legend.SetUnconstrainedFontSize(1)
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0, 0, 0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- # legend.SetMaximumWidthInPixels(75)
- # legend.SetMaximumHeightInPixels(300)
- legend.SetMaximumWidthInPixels(300)
- legend.SetMaximumHeightInPixels(75)
- legend.SetTitleTextProperty(text_prop_cb)
- # legend.SetPosition(0.85,0.5)
- legend.SetPosition(0.5, 0.85)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(spline_actor)
- # renderer.AddActor(surf_actor)
- if not subject == 100 and not subject == 'S0':
- renderer.AddActor(wire_actor)
- # renderer.AddActor(wire_actor2)
- renderer.AddActor(legend)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
-
-polyData = vtk.vtkPolyData()
-polyData.DeepCopy(actor2.GetMapper().GetInput())
-transform = vtk.vtkTransform()
-transform.SetMatrix(actor2.GetMatrix())
-fil = vtk.vtkTransformPolyDataFilter()
-fil.SetTransform(transform)
-fil.SetInputDataObject(polyData)
-fil.Update()
-polyData.DeepCopy(fil.GetOutput())
-
-writer = vtk.vtkPLYWriter()
-writer.SetFileTypeToASCII()
-writer.SetColorModeToDefault()
-filename = r'C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col2.ply'
-writer.SetFileName(filename)
-writer.SetInputData(polyData)
-writer.Write()
-
-# import pandas as pd
-# pd.DataFrame(color).to_clipboard()
\ No newline at end of file
diff --git a/LigamentInsertions/vislualize_distances.py b/LigamentInsertions/vislualize_distances.py
deleted file mode 100644
index 6b167ab..0000000
--- a/LigamentInsertions/vislualize_distances.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-
-segment = 'femur'
-renderer = vtk.vtkRenderer()
-
-rw = vtk.vtkRenderWindow()
-# xmins = [0, .5, 0, .5, 0, .5]
-# xmaxs = [0.5, 1, 0.5, 1, .5, 1]
-# ymins = [.66, .66, .33, .33, 0, 0, ]
-# ymaxs = [1, 1, .66, .66, 0.33, 0.33]
-
-xmins = [0, 0, .33, .33, .66, .66]
-xmaxs = [.33, .33, .66, .66, 1, 1]
-ymins = [0, .5, 0, .5, 0, .5]
-ymaxs = [0.5, 1, 0.5, 1, .5, 1]
-iren = vtk.vtkRenderWindowInteractor()
-iren.SetRenderWindow(rw)
-
-tel=0
-
-for modes in range(1,4):
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode' + str(modes) + '_+2SD_8192.stl'
- mode_min = 'mode' + str(modes) + '_-2SD_8192.stl'
-
- # determine signed distance
- plus2sd = trimesh.load_mesh(path + mode_plus)
- min2sd = trimesh.load_mesh(path + mode_min)
- mean = trimesh.load_mesh(path + mean_shape)
- # signed_distance = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices) # improve this?
- # signed_distance2 = trimesh.proximity.signed_distance(plus2sd, mean.vertices)
- # signed_distance3 = trimesh.proximity.signed_distance(min2sd, mean.vertices)
- # signed_distance = signed_distance2 + -signed_distance3
- signed_distance4 = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices)
- signed_distance = signed_distance4
-
- # load mesh via trimesh to get the correct order for distance transform
- reader = vtk.vtkSTLReader()
- reader.SetFileName(path + mode_min)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- # create lookup table
- c = sns.diverging_palette(25, 262, s=60, n=100, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(100)
- lut.SetTableRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- for j in range(0,100):
- lut.SetTableValue(int(j), c[j][0], c[j][1], c[j][2])
- lut.Build()
-
- # fix order signed distance transform as these are from trimesh
- vtk_nodes = vtk_to_numpy(obj.GetPoints().GetData())
- trimesh_nodes = min2sd.vertices
- dist = np.zeros((obj.GetNumberOfPoints(),1))
- for i in range(obj.GetNumberOfPoints()):
- # np.linalg.norm(vtk_nodes - trimesh_nodes)
- # idx = (np.hypot(*(vtk_nodes - trimesh_nodes[i]).T)).argmin()
- result = np.where((vtk_nodes[:,0] == trimesh_nodes[i][0]) & (vtk_nodes[:,1] == trimesh_nodes[i][1]) & (vtk_nodes[:,2] == trimesh_nodes[i][2]))
- # result = idx
- dist[result[0][0]] = signed_distance[i]
-
- vtk_dist = vtk.vtkDoubleArray()
- # z = np.zeros((obj.GetNumberOfPoints(),1))
- for i in range(obj.GetNumberOfPoints()):
- vtk_dist.InsertNextValue(dist[i])
- obj.GetPointData().SetScalars(vtk_dist)
-
- # mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper.SetScalarRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- mapper.SetLookupTable(lut)
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputDataObject(obj)
- mapper2.SetScalarRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- mapper2.SetLookupTable(lut)
-
- if segment == 'fibula':
- d = -40
- else:
- d = -100
- # translation
- transform = vtk.vtkTransform()
- transform.Identity()
- # transform.Translate(0,modes * d, 0)
- transform.RotateX(90)
- transform.RotateY(180)
- transform.RotateZ(0)
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- transform2 = vtk.vtkTransform()
- transform2.Identity()
- # transform2.Translate(d*-1, modes*d, 0)
- transform2.RotateX(90)
- transform2.RotateY(180)
- transform2.RotateZ(-90)
- transformFilter2 = vtk.vtkTransformPolyDataFilter()
- transformFilter2.SetInputConnection(reader.GetOutputPort())
- transformFilter2.SetTransform(transform2)
- transformFilter2.Update()
-
- # actors
- bone_actor = vtk.vtkActor()
- bone_actor.SetMapper(mapper)
- mapper.SetInputConnection(transformFilter.GetOutputPort())
- bone_actor.SetMapper(mapper)
- legend = vtk.vtkScalarBarActor()
- legend.SetLookupTable(lut)
- bone_actor2 = vtk.vtkActor()
- mapper2.SetInputConnection(transformFilter2.GetOutputPort())
- bone_actor2.SetMapper(mapper2)
-
- for ind in range(2):
- ren = vtk.vtkRenderer()
- rw.AddRenderer(ren)
- ren.SetViewport(xmins[tel], ymins[tel], xmaxs[tel], ymaxs[tel])
-
- # Share the camera between viewports.
- if tel == 0:
- camera = ren.GetActiveCamera()
- else:
- ren.SetActiveCamera(camera)
-
- # Create a mapper and actor
- if tel == 0 or tel == 2 or tel == 4:
- ren.AddActor(bone_actor)
- # ren.AddActor(actor2lig)
- else:
- ren.AddActor(bone_actor2)
- # ren.AddActor(actor3)
-
- ren.SetBackground(1.0, 1.0, 1.0)
-
- ren.ResetCamera()
-
- tel+=1
-
- # # Renderer
- # renderer.AddActor(bone_actor)
- # renderer.AddActor(bone_actor2)
- # # renderer.AddActor(legend)
- # renderer.SetBackground(1.0, 1.0, 1.0)
- # renderer.ResetCamera()
-
-# # Render Window
-# renderWindow = vtk.vtkRenderWindow()
-# renderWindow.AddRenderer(renderer)
-# renderWindow.SetSize(750, 750)
-#
-# # Interactor
-# renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-# renderWindowInteractor.SetRenderWindow(renderWindow)
-# renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-#
-# # Begin Interaction
-# renderWindow.Render()
-# renderWindow.SetWindowName("SSM distances")
-# renderWindowInteractor.Start()
-
-rw.Render()
-rw.SetWindowName('MultipleViewPorts')
-rw.SetSize(850, 400)
-iren.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-iren.Start()
\ No newline at end of file
diff --git a/LigamentInsertions/vtk2stl.py b/LigamentInsertions/vtk2stl.py
deleted file mode 100644
index 8323819..0000000
--- a/LigamentInsertions/vtk2stl.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import os
-import vtk
-
-# Define the input and output directories
-input_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\tibia_bone_short"
-output_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\tibia_bone_short"
-
-# Create a renderer and a render window
-renderer = vtk.vtkRenderer()
-render_window = vtk.vtkRenderWindow()
-render_window.AddRenderer(renderer)
-
-# Loop through all VTK files in the input directory
-for filename in os.listdir(input_dir):
- if filename.endswith(".vtk"):
- # Load the VTK file
- vtk_path = os.path.join(input_dir, filename)
- reader = vtk.vtkDataSetReader()
- reader.SetFileName(vtk_path)
- reader.Update()
- polydata = reader.GetOutput()
-
- # Convert the polydata to a stl file
- stl_path = os.path.join(output_dir, filename.replace(".vtk", ".stl"))
- writer = vtk.vtkSTLWriter()
- writer.SetFileName(stl_path)
- writer.SetInputData(polydata)
- writer.Write()
-
- # Add the polydata to the renderer for visualization
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(polydata)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- renderer.AddActor(actor)
-
-# Set up the interactor and start the rendering loop
-interactor = vtk.vtkRenderWindowInteractor()
-interactor.SetRenderWindow(render_window)
-render_window.Render()
-interactor.Start()
diff --git a/LigamentStudy/.idea/LigamentStudy.iml b/LigamentStudy/.idea/LigamentStudy.iml
deleted file mode 100644
index d0876a7..0000000
--- a/LigamentStudy/.idea/LigamentStudy.iml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
- <component name="NewModuleRootManager">
- <content url="file://$MODULE_DIR$" />
- <orderEntry type="inheritedJdk" />
- <orderEntry type="sourceFolder" forTests="false" />
- </component>
-</module>
\ No newline at end of file
diff --git a/LigamentStudy/Analyses.py b/LigamentStudy/Analyses.py
deleted file mode 100644
index cc1ed2e..0000000
--- a/LigamentStudy/Analyses.py
+++ /dev/null
@@ -1,477 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-from openpyxl import load_workbook
-
-# femur
-# PCL: [1,1,1,1,1,1,1,1,1,1]
-# MCL-superficial: [6,5,6,6,6,6,4,4,5,5]
-# MCL-deep: [3,2+8,5,3,3,2,2,-,3,3]
-# posterior oblique: [7,3,7+8,7,7,5,7,6,7,-]
-# ACL: [4,6,3,5,4,-(4),-(5),3,4,4]
-# LCL (prox): [5,7,4,4,5,7,6,5,6,6]
-# popliteus (dist): [2,4,2,2,2,3,3,2,2,2]
-
-# tibia
-# PCL: [5,7,6,5,3,4,4,5,5,4]
-# MCL-superficial: [1,1,1,1,1,1,1,1,1,1]
-# MCL-deep: [3,3+4,8,3,5,3,5,-(4),3,3]
-# posterior oblique: [4,5+6,3+4+5+7,4,4,5,3,2,4,-]
-# ACL: [6,8,9,6,6,6,6,6,6,5]
-# LCL: [2,2,2,2,2,2,2,3,2,2]
-# popliteus: [-,-,-,-,-,-,-,-,-,-]
-
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #9,13,19,23,26,29,32,35,41
-segments = ['fibula'] #'femur', fibula, 'tibia','femur'
-short = 1
-# the mesh number related to the ligament for each specimen
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments_fib = [[0,0,0,0,0,0,0,0,0,0], # PCL
- [0,0,0,0,0,0,0,0,0,0], # MCLp
- [0,0,0,0,0,0,0,0,0,0], # MCLd
- [0,0,0,0,0,0,0,0,0,0], # MCLd2
- [0,0,0,0,0,0,0,0,0,0], # POL
- [0,0,0,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [0,0,0,0,0,0,0,0,0,0], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-book = load_workbook(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces4.xlsx"))
-writer = pd.ExcelWriter(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces4.xlsx"), engine='openpyxl')
-writer.book = book
-
-for segment in segments:
- surface = np.empty((13, 10))
- surface[:] = np.nan
- center = np.empty((13, 10))
- center[:] = np.nan
- ML_size = np.empty((1, 10))
- ML_size[:] = np.nan
- AP_size = np.empty((1, 10))
- AP_size[:] = np.nan
- SI_size = np.empty((1, 10))
- SI_size[:] = np.nan
- bb_max = np.empty((3, 10))
- bb_max[:] = np.nan
- bb_min = np.empty((3, 10))
- bb_min[:] = np.nan
- ML_size_med = np.empty((1, 10))
- ML_size_med[:] = np.nan
- AP_size_med = np.empty((1, 10))
- AP_size_med[:] = np.nan
- SI_size_med = np.empty((1, 10))
- SI_size_med[:] = np.nan
- bb_max_med = np.empty((3, 10))
- bb_max_med[:] = np.nan
- bb_min_med = np.empty((3, 10))
- bb_min_med[:] = np.nan
- ML_size_lat = np.empty((1, 10))
- ML_size_lat[:] = np.nan
- AP_size_lat = np.empty((1, 10))
- AP_size_lat[:] = np.nan
- SI_size_lat = np.empty((1, 10))
- SI_size_lat[:] = np.nan
- bb_max_lat = np.empty((3, 10))
- bb_max_lat[:] = np.nan
- bb_min_lat = np.empty((3, 10))
- bb_min_lat[:] = np.nan
- dist_to_edge = np.empty((13, 10, 3))
- dist_to_edge[:] = np.nan
- perc_of_len = np.empty((13, 10, 3))
- perc_of_len[:] = np.nan
- perc_of_len_med = np.empty((13, 10, 3))
- perc_of_len_med[:] = np.nan
- perc_of_len_lat = np.empty((13, 10, 3))
- perc_of_len_lat[:] = np.nan
- center = np.empty((13, 10, 3))
- center[:] = np.nan
- if segment == 'femur':
- ligaments = ligaments_fem
- elif segment == 'tibia':
- ligaments = ligaments_tib
- else:
- ligaments = ligaments_fib
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # transform femur to local coordinate system to get anatomical directions
- if segment=='fibula':
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_tibia_resample._ACS.txt'))
- else:
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- mesh2 = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/' + str(subject) + '\Segmentation_' + segment + '_sep.stl'
- ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- # ms5.save_current_mesh(path + '\Segmentation_' + segment + '_sep_transform.stl', binary=False)
- if segment=='tibia':
- ms5.load_new_mesh(path + '\Segmentation_' + segment + '_sep_transform.stl')
- else:
- ms5.load_new_mesh(path + '\Segmentation_' + segment + '_transform.stl')
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size[0, ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size[0, ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size[0, ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- if side == 'R':
- ms5.conditional_vertex_selection(condselect="x<0") #select lat (41=L)
- ms5.apply_filter('move_selected_vertices_to_another_layer') #0=med, 1=lat
- else:
- ms5.conditional_vertex_selection(condselect="x>0") # select lat (41=L)
- ms5.apply_filter('move_selected_vertices_to_another_layer') # 0=med, 1=lat
-
- ms5.set_current_mesh(0)
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size_med[0,ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size_med[0,ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size_med[0,ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max_med[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min_med[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- ms5.set_current_mesh(1)
- geometric_measures_femur = ms5.apply_filter('compute_geometric_measures')
- ML_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_x()
- AP_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_y()
- SI_size_lat[0, ind] = geometric_measures_femur['bbox'].dim_z()
- # print('ML width femur: ' + str(ML_size[ind]) + ' mm')
- # print('AP width femur: ' + str(AP_size[ind]) + ' mm')
- bb_max_lat[:, ind] = np.max(ms5.current_mesh().vertex_matrix(), 0)
- bb_min_lat[:, ind] = np.min(ms5.current_mesh().vertex_matrix(), 0)
-
- # determine surface area attachments
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- ms4 = pymeshlab.MeshSet()
- if segment == 'fibula':
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '_transform.stl')
- else:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface[lig,ind] = geometric_measures['surface_area']
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center[lig,ind,:] = geometric_measures['shell_barycenter']
- if side == 'R':
- dist_to_edge[lig, ind, :] = center[lig, ind, :] - bb_min[:, ind]
- dist_to_edge[lig, ind, 0] = center[lig, ind, 0] - bb_max[0, ind]
- else:
- dist_to_edge[lig,ind,:] = center[lig, ind,:] - bb_min[:, ind]
-
- if segment == 'tibia' or segment == 'fibula':
- dist_to_edge[lig, ind, 2] = center[lig, ind, 2] - bb_max[2, ind]
- perc_of_len[lig,ind,:] = abs(dist_to_edge[lig,ind,:]/(ML_size[0,ind],AP_size[0,ind],AP_size[0,ind]))
- perc_of_len_med[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size_med[0, ind], AP_size_med[0, ind], AP_size_med[0, ind]))
- perc_of_len_lat[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size_lat[0, ind], AP_size_lat[0, ind], AP_size_lat[0, ind]))
- for lig_comb in [2, 4]:
- lig_no = ligaments[lig_comb][ind]
- if not lig_no == 0:
- if lig_comb == 2:
- lig = 11
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 1) + '.stl')
- except:
- print('')
- if lig_comb == 4:
- lig = 12
- ms4 = pymeshlab.MeshSet()
- if segment=='fibula':
- ms4.load_new_mesh(path + '\Segmentation_tibia_area' + str(lig_no) + '.stl')
- else:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 1) + '.stl')
- except:
- print('')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 2) + '.stl')
- except:
- print('')
- try:
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no + 3) + '.stl')
- except:
- print('')
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface[lig, ind] = geometric_measures['surface_area']
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center[lig, ind, :] = geometric_measures['shell_barycenter']
- dist_to_edge[lig, ind, :] = center[lig, ind, :] - bb_min[:, ind]
- if segment == 'tibia' or segment == 'fibula':
- dist_to_edge[lig, ind, 2] = center[lig, ind, 2] - bb_max[2, ind]
- perc_of_len[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (ML_size[0, ind], AP_size[0, ind], AP_size[0, ind]))
- perc_of_len_med[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (
- ML_size_med[0, ind], AP_size_med[0, ind], AP_size_med[0, ind]))
- perc_of_len_lat[lig, ind, :] = abs(dist_to_edge[lig, ind, :] / (
- ML_size_lat[0, ind], AP_size_lat[0, ind], AP_size_lat[0, ind]))
-
- df = pd.DataFrame({'PCLx': 100-perc_of_len[0,:,0]*100,
- 'MCL-sx': 100-perc_of_len[1, :, 0]*100,
- 'MCL-d1x': 100-perc_of_len[2, :, 0]*100,
- 'MCL-d2x': 100-perc_of_len[3, :, 0]*100,
- 'MCL-d2': 100-perc_of_len[11, :, 0]*100,
- 'posterior oblique1x': 100-perc_of_len[4, :, 0]*100,
- 'posterior oblique2x': 100-perc_of_len[5, :, 0]*100,
- 'posterior oblique3x': 100-perc_of_len[6, :, 0]*100,
- 'posterior oblique4x': 100-perc_of_len[7, :, 0]*100,
- 'posterior obliquex': 100-perc_of_len[12, :, 0]*100,
- 'ACLx': 100-perc_of_len[8, :, 0]*100,
- 'LCLx': 100-perc_of_len[9, :, 0]*100,
- 'popliteusx': 100-perc_of_len[10, :, 0]*100,
- 'PCLy': 100-perc_of_len[0,:,1]*100,
- 'MCL-sy': 100-perc_of_len[1, :, 1]*100,
- 'MCL-d1y': 100-perc_of_len[2, :, 1]*100,
- 'MCL-d2y': 100-perc_of_len[3, :, 1]*100,
- 'MCL-dy': 100-perc_of_len[11, :, 1]*100,
- 'posterior oblique1y': 100-perc_of_len[4, :, 1]*100,
- 'posterior oblique2y': 100-perc_of_len[5, :, 1]*100,
- 'posterior oblique3y': 100-perc_of_len[6, :, 1]*100,
- 'posterior oblique4y': 100-perc_of_len[7, :, 1]*100,
- 'posterior obliquey': 100-perc_of_len[12, :, 1]*100,
- 'ACLy': 100-perc_of_len[8, :, 1]*100,
- 'LCLy': 100-perc_of_len[9, :, 1]*100,
- 'popliteusy': 100-perc_of_len[10, :, 1]*100,
- 'PCLz': perc_of_len[0,:,2]*100,
- 'MCL-sz': perc_of_len[1,:,2]*100,
- 'MCL-d1z': perc_of_len[2,:,2]*100,
- 'MCL-d2z': perc_of_len[3, :, 2]*100,
- 'MCL-dz': perc_of_len[11, :, 2]*100,
- 'posterior oblique1z': perc_of_len[4,:,2]*100,
- 'posterior oblique2z': perc_of_len[5, :, 2]*100,
- 'posterior oblique3z': perc_of_len[6, :, 2]*100,
- 'posterior oblique4z': perc_of_len[7, :, 2]*100,
- 'posterior obliquez': perc_of_len[12, :, 2]*100,
- 'ACLz': perc_of_len[8,:,2]*100,
- 'LCLz': perc_of_len[9,:,2]*100,
- 'popliteusz': perc_of_len[10,:,2]*100
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='perc_of_len ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(
- decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- # table_data = table_data.T
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- table_data.to_excel(writer, sheet_name='table perc_of_len ' + segment)
-
- df = pd.DataFrame({'PCLy': 100-perc_of_len_med[0, :, 1]*100,
- 'MCL-sy': 100-perc_of_len_med[1, :, 1]*100,
- 'MCL-d1y': 100-perc_of_len_med[2, :, 1]*100,
- 'MCL-d2y': 100-perc_of_len_med[3, :, 1]*100,
- 'MCL-dy': 100-perc_of_len_med[11, :, 1]*100,
- 'posterior oblique1y': 100-perc_of_len_med[4, :, 1]*100,
- 'posterior oblique2y': 100-perc_of_len_med[5, :, 1]*100,
- 'posterior oblique3y': 100-perc_of_len_med[6, :, 1]*100,
- 'posterior oblique4y': 100-perc_of_len_med[7, :, 1]*100,
- 'posterior obliquey': 100-perc_of_len_med[12, :, 1]*100,
- 'ACLy': 100-perc_of_len_lat[8, :, 1]*100,
- 'LCLy': 100-perc_of_len_lat[9, :, 1]*100,
- 'popliteusy': 100-perc_of_len_lat[10, :, 1]*100,
- 'PCLz': perc_of_len_med[0, :, 2]*100,
- 'MCL-sz': perc_of_len_med[1, :, 2]*100,
- 'MCL-d1z': perc_of_len_med[2, :, 2]*100,
- 'MCL-d2z': perc_of_len_med[3, :, 2]*100,
- 'MCL-dz': perc_of_len_med[11, :, 2]*100,
- 'posterior oblique1z': perc_of_len_med[4, :, 2]*100,
- 'posterior oblique2z': perc_of_len_med[5, :, 2]*100,
- 'posterior oblique3z': perc_of_len_med[6, :, 2]*100,
- 'posterior oblique4z': perc_of_len_med[7, :, 2]*100,
- 'posterior obliquez': perc_of_len_med[12, :, 2]*100,
- 'ACLz': perc_of_len_lat[8, :, 2]*100,
- 'LCLz': perc_of_len_lat[9, :, 2]*100,
- 'popliteusz': perc_of_len_lat[10, :, 2]*100
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='perc_of_len med_lat ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- # table_data = table_data.T
- table_data.to_excel(writer, sheet_name='table perc_of_len med_lat ' + segment)
-
- df = pd.DataFrame({'PCLx': 100-dist_to_edge[0,:,0],
- 'MCL-sx': 100-dist_to_edge[1, :, 0],
- 'MCL-d1x': 100-dist_to_edge[2, :, 0],
- 'MCL-d2x': 100-dist_to_edge[3, :, 0],
- 'MCL-dx': 100-dist_to_edge[11, :, 0],
- 'posterior oblique1x': 100-dist_to_edge[4, :, 0],
- 'posterior oblique2x': 100-dist_to_edge[5, :, 0],
- 'posterior oblique3x': 100-dist_to_edge[6, :, 0],
- 'posterior oblique4x': 100-dist_to_edge[7, :, 0],
- 'posterior obliquex': 100-dist_to_edge[12, :, 0],
- 'ACLx': 100-dist_to_edge[8, :, 0],
- 'LCLx': 100-dist_to_edge[9, :, 0],
- 'popliteusx': 100-dist_to_edge[10, :, 0],
- 'PCLy': 100-dist_to_edge[0,:,1],
- 'MCL-sy': 100-dist_to_edge[1, :, 1],
- 'MCL-d1y': 100-dist_to_edge[2, :, 1],
- 'MCL-d2y': 100-dist_to_edge[3, :, 1],
- 'MCL-dy': 100-dist_to_edge[11, :, 1],
- 'posterior oblique1y': 100-dist_to_edge[4, :, 1],
- 'posterior oblique2y': 100-dist_to_edge[5, :, 1],
- 'posterior oblique3y': 100-dist_to_edge[6, :, 1],
- 'posterior oblique4y': 100-dist_to_edge[7, :, 1],
- 'posterior obliquey': 100-dist_to_edge[12, :, 1],
- 'ACLy': 100-dist_to_edge[8, :, 1],
- 'LCLy': 100-dist_to_edge[9, :, 1],
- 'popliteusy': 100-dist_to_edge[10, :, 1],
- 'PCLz': dist_to_edge[0,:,2],
- 'MCL-sz': dist_to_edge[1,:,2],
- 'MCL-d1z': dist_to_edge[2,:,2],
- 'MCL-d2z': dist_to_edge[3, :, 2],
- 'MCL-dz': dist_to_edge[11, :, 2],
- 'posterior oblique1z': dist_to_edge[4,:,2],
- 'posterior oblique2z': dist_to_edge[5, :, 2],
- 'posterior oblique3z': dist_to_edge[6, :, 2],
- 'posterior oblique4z': dist_to_edge[7, :, 2],
- 'posterior obliquez': dist_to_edge[12, :, 2],
- 'ACLz': dist_to_edge[8,:,2],
- 'LCLz': dist_to_edge[9,:,2],
- 'popliteusz': dist_to_edge[10,:,2]
- })
- means = df.mean(skipna=True).round(decimals=1)
- std = '±' + df.std(skipna=True).round(decimals=1).astype(str)
- r1 = '(' + df.max(skipna=True).round(decimals=1).astype(str) + '-'
- r2 = df.min(skipna=True).round(decimals=1).astype(str) + ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means, ignore_index=True)
- summary_ave_data = summary_ave_data.append(std, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'mean', 11: 'std', 12: 'range1', 13: 'range2'}, axis='index')
-
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='distance_to_edge ' + segment)
-
- means_table = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(
- decimals=1).astype(
- str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(
- decimals=1).astype(str) + ')'
-
- table_data = pd.DataFrame(means_table)
- # table_data = table_data.append(means_table, ignore_index=True)
- table_data = table_data.rename({0: 'POSITION (MEAN±STD, RANGE)'}, axis='columns')
- # table_data = table_data.T
- table_data.to_excel(writer, sheet_name='table distance_to_edge ' + segment)
-
- MCLd = np.nansum([surface[2, :], surface[3, :]], 0)
- MCLd[MCLd == 0] = 'nan'
- pol = np.nansum([surface[4, :],surface[5, :],surface[6, :],surface[7, :]],0)
- pol[pol == 0] = 'nan'
- df = pd.DataFrame({'PCL': surface[0,:],
- 'MCL-s': surface[1,:],
- 'MCL-d1:': surface[2,:],
- 'MCL-d2:': surface[3, :],
- 'MCL-d:': MCLd,
- 'posterior oblique1': surface[4,:],
- 'posterior oblique2': surface[5, :],
- 'posterior oblique3': surface[6, :],
- 'posterior oblique4': surface[7, :],
- 'posterior oblique': pol,
- 'ACL': surface[8,:],
- 'LCL': surface[9,:],
- 'popliteus': surface[10,:],
- })
- means = df.mean(skipna=True).round(decimals=1).astype(str) + ' ±' + df.std(skipna=True).round(decimals=1).astype(str) + \
- ' (' + df.min(skipna=True).round(decimals=1).astype(str) + '-' + df.max(skipna=True).round(decimals=1) .astype(str)+ ')'
- # print(tabulate(df, headers='keys', tablefmt='psql'))
-
- summary_ave_data = df.copy()
- summary_ave_data = summary_ave_data.append(means,ignore_index=True)
- # summary_ave_data = summary_ave_data.append(std,ignore_index=True)
- # summary_ave_data = summary_ave_data.append(r1, ignore_index=True)
- # summary_ave_data = summary_ave_data.append(r2, ignore_index=True)
- summary_ave_data = summary_ave_data.rename({10: 'ATTACHMENT AREA (MEAN±STD, RANGE)'},axis='index')
-
- summary_ave_data = summary_ave_data.T
- summary_ave_data.to_excel(writer, sheet_name='surface ' + segment)
-
-writer.save()
-writer.close()
diff --git a/LigamentStudy/AreaTest.py b/LigamentStudy/AreaTest.py
deleted file mode 100644
index d940e83..0000000
--- a/LigamentStudy/AreaTest.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import pandas as pd
-import os
-import trimesh
-import numpy as np
-import matplotlib.path as plt
-
-
-def heron(a, b, c):
- s = (a + b + c) / 2
- area = (s * (s - a) * (s - b) * (s - c)) ** 0.5
- return area
-
-
-def distance3d(x1, y1, z1, x2, y2, z2):
- a = (x1 - x2) ** 2 + (y1 - y2) ** 2 + (z1 - z2) ** 2
- d = a ** 0.5
- return d
-
-
-def area(x1, y1, z1, x2, y2, z2, x3, y3, z3):
- a = distance3d(x1, y1, z1, x2, y2, z2)
- b = distance3d(x2, y2, z2, x3, y3, z3)
- c = distance3d(x3, y3, z3, x1, y1, z1)
- A = heron(a, b, c)
- return A
-
-
-# print("area of triangle is %r " %A)
-
-# A utility function to calculate area
-# of triangle formed by (x1, y1),
-# (x2, y2) and (x3, y3)
-
-# def area(x1, y1, x2, y2, x3, y3):
-# return abs((x1 * (y2 - y3) + x2 * (y3 - y1)
-# + x3 * (y1 - y2)) / 2.0)
-
-
-# A function to check whether point P(x, y)
-# lies inside the triangle formed by
-# A(x1, y1), B(x2, y2) and C(x3, y3)
-def isInside(p1, p2, p3, p):
- x1 = p1[0]
- y1 = p1[1]
- z1 = p1[2]
- x2 = p2[0]
- y2 = p2[1]
- z2 = p2[2]
- x3 = p3[0]
- y3 = p3[1]
- z3 = p3[2]
- x = p[0]
- y = p[1]
- z = p[2]
-
- # Calculate area of triangle ABC
- A = area(x1, y1, z1, x2, y2, z2, x3, y3, z3)
-
- # Calculate area of triangle PBC
- A1 = area(x, y, z, x2, y2, z2, x3, y3, z3)
-
- # Calculate area of triangle PAC
- A2 = area(x1, y1, z1, x, y, z, x3, y3, z3)
-
- # Calculate area of triangle PAB
- A3 = area(x1, y1, z1, x2, y2, z2, x, y, z)
-
- # Check if sum of A1, A2 and A3
- # is same as A
- if abs(A - (A1 + A2 + A3) < 1e-6):
- return True
- else:
- return False
-
-
-def intersection(planeNormal, planePoint, rayDirection, rayPoint):
- epsilon = 1e-6
-
- # Define plane
- # planeNormal = np.array([0, 0, 1])
- # planePoint = np.array([0, 0, 5]) #Any point on the plane
-
- # Define ray
- # rayDirection = np.array([0, -1, -1])
- # rayPoint = np.array([0, 0, 10]) #Any point along the ray
-
- ndotu = planeNormal.dot(rayDirection)
-
- if abs(ndotu) < epsilon:
- intersect = 0
- else:
- w = rayPoint - planePoint[0, :]
- si = -planeNormal.dot(w) / ndotu
- Psi = w + si * rayDirection + planePoint[0, :]
- if isInside(planePoint[0], planePoint[1], planePoint[2], Psi) == False:
- intersect = 0
- else:
- intersect = Psi[0]
-
- return intersect
-
-intersection(np.array([0,0,1]), np.array([(1,1,0),(1,2,0),(2,1.5,0)]), np.array([0,0,1]), np.array((1.5,1.5,1)))
diff --git a/LigamentStudy/AttechmentArea.py b/LigamentStudy/AttechmentArea.py
deleted file mode 100644
index e89c618..0000000
--- a/LigamentStudy/AttechmentArea.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import pymeshlab
-import os.path
-import trimesh
-import numpy as np
-
-
-def cylinder_between(p1, p2, r, path):
- dx = p2[0] - p1[0]
- dy = p2[1] - p1[1]
- dz = p2[2] - p1[2]
- dist = np.sqrt(dx**2 + dy**2 + dz**2)+0.5
-
- phi = np.arctan2(dy, dx)
- theta = np.arccos(dz/dist)
-
- T = trimesh.transformations.translation_matrix([dx/2 + p1[0], dy/2 + p1[1], dz/2 + p1[2]])
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(phi, zaxis)
- Ry = trimesh.transformations.rotation_matrix(theta, yaxis)
- R = trimesh.transformations.concatenate_matrices(T,Rz, Ry)
-
- cylinder = trimesh.creation.cylinder(r, height=dist, sections=None, segment=None, transform=R)
- cylinder.export(path)
-
-
-def cut_mesh(out2, path, i):
- # load wire mesh in new meshlab file
- ms2 = pymeshlab.MeshSet()
- ms2.load_new_mesh(mesh1)
- # translate wire to mesh in direction of the normal of the plane and copy to create thick wire
- ms2.transform_translate_center_set_origin(traslmethod=0, axisx=-plane_normal[0, 0] ,
- axisy=-plane_normal[0, 1] ,
- axisz=-plane_normal[0, 2] ,
- freeze=True, alllayers=False)
- factor = 0.1
- no = 0
- for ind in range(0, 24):
- ms2.load_new_mesh(mesh1)
- factor += 0.25
- ms2.transform_translate_center_set_origin(traslmethod=0, axisx=-plane_normal[0, 0] * out2['mean'] * factor,
- axisy=-plane_normal[0, 1] * out2['mean'] * factor,
- axisz=-plane_normal[0, 2] * out2['mean'] * factor,
- freeze=True, alllayers=False)
- ms2.apply_filter('mesh_boolean_union', first_mesh=no, second_mesh=no + 1)
- no += 2
-
- # save thick wire
- ms2.save_current_mesh(path + '\Segmentation_' + segment + '_wire' + str(i) + 'union.stl', binary=False)
-
- # load thick wire and area in new meshlab file
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_wire' + str(i) + 'union.stl')
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
-
- # compute signed distance
- # out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=False)
- out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=True)
-
- # select and delete vertices with negative distance
- # ms4.conditional_vertex_selection(condselect="q<0.15")
- ms4.conditional_vertex_selection(condselect="(q <0)") # "(q <0) && (q >-0.4)"
- ms4.delete_selected_vertices()
- # split mesh
- out4 = ms4.apply_filter('split_in_connected_components')
-
- return ms4
-
-
-subjects = [19] # 9,13,19,23,26,29,32,35,37,41
-segments = ['femur'] # ['femur', 'tibia'] # ['femur'] #
-no_subjects = len(subjects)
-no_segments = len(segments)
-
-for subject in subjects:
- for segment in segments:
-
- # # split wires to seperate files
- path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/" + str(subject) + '/'
- # mesh_all = path + 'Segmentation_' + segment + '_wires.stl'
- # ms3 = pymeshlab.MeshSet()
- # ms3.load_new_mesh(mesh_all)
- # ms3.apply_filter('split_in_connected_components')
- #
- # no_meshes = ms3.number_meshes()
- # for i in range(1, no_meshes):
- # ms3.set_current_mesh(i)
- # no_vertices = ms3.mesh(i).vertex_matrix().shape[0]
- # if no_vertices < 50:
- # ms3.delete_current_mesh()
- # else:
- # if not os.path.isfile(path + '\Segmentation_' + segment + '_wires' + str(i-1) + '.stl') and not i == 1:
- # no = i-1
- # else:
- # no = i
- # ms3.save_current_mesh(path + '\Segmentation_' + segment + '_wires' + str(no) + '.stl')
- #
- # no_meshes = no
- # # combine tibia and fibula
- # if segment == 'tibia':
- # ms1 = pymeshlab.MeshSet()
- # ms1.load_new_mesh(path + 'Segmentation_tibia_sep.stl')
- # ms1.load_new_mesh(path + 'Segmentation_fibula.stl')
- # ms1.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
- # ms1.save_current_mesh(path + 'Segmentation_tibia.stl', binary=False)
-
- # run over all wires
- error = []
- mesh2 = path + 'Segmentation_' + segment + '.stl'
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('uniform_mesh_resampling', cellsize=1)
- # # ms5.apply_filter('transform_rotate', rotaxis=2, angle=180)
- # ms5.save_current_mesh(path + '\Segmentation_' + segment + '_resample.stl', binary=False)
-
- for i in range(3,4): #range(1, no_meshes+1): #range(3,4): #range(5,no_meshes+1): #
- mesh1 = path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl'
-
- # load meshes in new meshlab file
- ms = pymeshlab.MeshSet()
- ms.load_new_mesh(mesh1)
- ms.load_new_mesh(mesh2)
-
- # calculate Hausdorff distance in both directions
- out2 = ms.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=False, maxdist=9)
- out1 = ms.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=False, maxdist=9)
-
- # select and delete all vertices far from the wire
- ms.conditional_vertex_selection(condselect="q>8.9")
- ms.delete_selected_vertices()
- # save section containing area
- ms.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
- #fit plane through section containing area
- # ms.set_current_mesh(new_curr_id=0)
- ms.select_all()
- ms.fit_a_plane_to_selection()
- plane_normal = ms.mesh(2).vertex_normal_matrix()
-
- ms4 = cut_mesh(out2, path, i)
-
- # check the number of components and remove the ones with few vertices
- no_meshes = ms4.number_meshes()
- meshes_to_remove = no_meshes-4
- if meshes_to_remove > 0:
- for ind in range(0,no_meshes):
- no_vertices = ms4.mesh(ind).vertex_matrix().shape[0]
- if no_vertices < 50:
- ms4.set_current_mesh(ind)
- ms4.delete_current_mesh()
- else:
- last_mesh = ind
- else:
- last_mesh = 3
- # check the number of meshes
- # if there are less than 4, split is not done in 2 large surfaces and surface needs to be closed
- no_meshes = ms4.number_meshes()
- if no_meshes < 4: #no_vertices < 10:
- # load wire in new meshset
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl')
- # find for each point the largest distance on mesh
- dist_matrix = []
- dist_matrix_ind = []
- start_ind = []
- verts = ms6.mesh(0).vertex_matrix()
- for ind in range(0, len(verts)):
- ms6.apply_filter('colorize_by_geodesic_distance_from_a_given_point', startpoint=verts[ind], maxdistance=100)
- dist_matrix.append(np.max(ms6.mesh(0).vertex_quality_array()))
- dist_matrix_ind.append(np.argmax(ms6.mesh(0).vertex_quality_array()))
- start_ind.append(ind)
- # find which point has largest distance
- max1 = np.argmax(dist_matrix)
- end_point = verts[dist_matrix_ind[max1]]
- start_point = verts[start_ind[max1]]
- # create cylinder between these points
- r = 0.5
- path_cylinder = path + '\Segmentation_' + segment + '_wires' + str(i) + 'cylinder.stl'
- cylinder_between(start_point, end_point, r, path_cylinder)
- # combine wire and cylinder
- ms6.load_new_mesh(path_cylinder)
- ms6.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
- ms6.save_current_mesh(path + '\Segmentation_' + segment + '_wires' + str(i) + '.stl', binary=False)
- # split mesh again with closed wire
- ms4 = cut_mesh(out2, path, i)
- # remove meshes with few vertices
- no_meshes = ms4.number_meshes()
- for ind in range(0,no_meshes):
- no_vertices = ms4.mesh(ind).vertex_matrix().shape[0]
- if no_vertices < 50:
- ms4.set_current_mesh(ind)
- ms4.delete_current_mesh()
- else:
- last_mesh = ind
- # select last mesh to save
- no_meshes = ms4.number_meshes()
- # save only mesh part inside wire
- # ms4.set_current_mesh(new_curr_id=last_mesh)
- to_del = [0,1,2]
- for removes in range(len(to_del)):
- ms4.set_current_mesh(new_curr_id=to_del[removes])
- ms4.delete_current_mesh()
- print(ms4.number_meshes())
- ms4.apply_filter('flatten_visible_layers')
- try:
- ms4.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl', binary=False)
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(i) + '.stl')
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface = geometric_measures['surface_area']
- print('Surface area ' + segment + ' ligament' + str(i) + ': ' + str(surface) + ' mm2')
- ms4.save_project(path + '\Segmentation_' + segment + '_area' + str(i) + '.mlp')
- except:
- error.append(i)
-
-
-
-
-# ms.select_all()
-# ms.fit_a_plane_to_selection()
-# plane_normal = ms.mesh(1).face_normal_matrix()
-
-# vert_matrix_connect = ms.mesh(2).vertex_matrix()
-# matrix = ms.mesh(1).vertex_matrix()
-# points = []
-# val = []
-# for i in range(0,len(vert_matrix_connect)):
-# points.append(np.argmin(np.abs(np.sum(matrix-vert_matrix_connect[i,:],axis=1))))
-# val.append(np.amin(np.abs(np.sum(matrix-vert_matrix_connect[i,:],axis=1))))
-
-# ms.conditional_vertex_selection(condselect="vi=="+points[0])
-# ms.delete_selected_vertices()
-
-# out4 = ms2.apply_filter('mesh_boolean_intersection', first_mesh=1, second_mesh=0)
-
-
-# no_verts = ms2.mesh(1).selected_vertex_number()
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-# while no_verts/2 < no_verts_new:
-# ms2.select_border()
-# ms2.delete_selected_vertices()
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-
-# matrix = ms2.mesh(1).face_matrix()
-# unique, counts = np.unique(matrix, return_counts=True)
-# # dict(zip(unique, counts))
-# bla2 = np.where(counts < 5)
-#
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-
-# while no_verts_new>0:
-# for i in range(0,len(bla2[0])):
-# ms2.conditional_vertex_selection(condselect=("vi=="+str(bla2[0][i])))
-# ms2.delete_selected_faces_and_vertices()
-#
-# ms2.conditional_vertex_selection(condselect="q<0")
-# no_verts_new = ms2.mesh(1).selected_vertex_number()
-# matrix = ms2.mesh(1).face_matrix()
-# unique, counts = np.unique(matrix, return_counts=True)
-# # dict(zip(unique, counts))
-# bla2 = np.where(counts < 5)
-
-#
-# ms2.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True, maxdist=5)
-
-# ms2.conditional_vertex_selection(condselect="q>2")
-# ms2.delete_selected_vertices()
-
-
-
-#
-
-# ms.apply_filter('select_faces_from_vertices', points, inclusive=True)
-# ms.apply_filter('delete_selected_faces_and_vertices')
-
-# vert_matrix_connect.apply_filter('surface_reconstruction_ball_pivoting')
-
-# file = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\26\femur_wires.xyz'
-# np.savetxt(file, vert_matrix_connect)
-
-
-# ms2.save_project(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\26\Segmentation.mlp')
\ No newline at end of file
diff --git a/LigamentStudy/BlumensaatLine.py b/LigamentStudy/BlumensaatLine.py
deleted file mode 100644
index 4e1473f..0000000
--- a/LigamentStudy/BlumensaatLine.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# Find most anterior edge of the femoral notch roof - representation Blumensaat line for 3D shapes
-# https://journals.lww.com/jbjsjournal/Fulltext/2010/06000/The_Location_of_Femoral_and_Tibial_Tunnels_in.10.aspx?__hstc=215929672.82af9c9a98fa600b1bb630f9cde2cb5f.1528502400314.1528502400315.1528502400316.1&__hssc=215929672.1.1528502400317&__hsfp=1773666937&casa_token=BT765BcrC3sAAAAA:Vu9rn-q5ng4c8339KQuq2mGZDgrAgBStwvn4lvYEbvCgvKQZkbJL24hWbKFdnHTc8VBmAIXA3HVvuWg22-9Mvwv1sw
-# https://www.dropbox.com/sh/l7pd43t7c4hrjdl/AABkncBbleifnpLDKSDDc0dCa/D3%20-%20Dimitriou%202020%20-%20Anterior%20cruciate%20ligament%20bundle%20insertions%20vary.pdf?dl=0
-
-import trimesh
-import numpy as np
-import os
-import math
-import pandas as pd
-# import pymeshlab
-import seaborn as sns
-
-
-def findIntersection(x1, y1, x2, y2, x3, y3, x4, y4):
- px = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
- py = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
-
- ang = math.atan2(py - y3, px - x3) - math.atan2(y1 - y3, x1 - x3)
-
- l = math.cos(ang)*np.linalg.norm(np.asarray((x3,y3))-np.asarray((x4,y4)))
-
- return l, px, py
-
-def split(start, end, segments):
- x_delta = (end[0] - start[0]) / float(segments)
- y_delta = (end[1] - start[1]) / float(segments)
- z_delta = (end[2] - start[2]) / float(segments)
- points = []
- for i in range(1, segments):
- points.append([start[0] + i * x_delta, start[1] + i * y_delta, start[2] + i * z_delta])
- return [start] + points + [end]
-
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments = ligaments_fem
-
-# find most ant point in yz plane
-subjects = [100] # [9,13,19,23,26,29,32,35,37,41] #
-lig = 'ACL'
-segment = 'femur'
-
-d = []
-h = []
-h_centriods = []
-d_centriods = []
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_rot.stl')
- path_col = r'C:\\Users\\mariskawesseli\\Documents\\GitLab\\knee_ssm\\OAI\\Output/tibia_bone\\new_bone\\shape_models'
- side = 'R'
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), 'Segmentation_femur_transform.STL')
-
- mesh = trimesh.load_mesh(path)
- verts = mesh.vertices
- AP = mesh.bounding_box.bounds[1, 1] - mesh.bounding_box.bounds[0, 1]
- ML = mesh.bounding_box.bounds[1, 0] - mesh.bounding_box.bounds[0, 0]
- bbox = mesh.bounding_box.bounds
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, (0,-1,0), (0,20,0), cached_dots=None, return_both=False)
- # posterior_mesh.show()
-
- # find blumensaat line
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(mesh, (0,0,0), (1,0,0), heights=np.linspace(-10, 10, 21))
- dist_point = []
- prox_point = []
- for i in range(0,len(face_index)):
- plane_verts = np.unique(mesh.faces[face_index[i]])
- plane_points = mesh.vertices[plane_verts]
-
- goon = 1
- tel = 2
- while goon == 1:
- min_z = np.where(plane_points[:,2] == np.partition(plane_points[:,2], tel)[tel])
- y_min = plane_points[min_z][0][1]
- min_z2 = np.where(plane_points[:,2] == np.partition(plane_points[:,2], tel+1)[tel+1])
- y_min2 = plane_points[min_z2][0][1]
- if y_min-y_min2 > -15:
- goon = 1
- tel += 1
- else:
- goon = 0
- dist_point.append(plane_points[min_z][0])
- min_y = np.where(plane_points[:,1] == plane_points[:,1].min())
- prox_point.append(plane_points[min_y][0])
-
- most_ant_ind1 = np.asarray(dist_point)[:, 1].argmax()
- most_ant_ind2 = np.asarray(prox_point)[:, 1].argmax()
-
- p1 = []
- p2 = []
- if most_ant_ind1 == most_ant_ind2:
- p1.append(dist_point[most_ant_ind1])
- p2.append(prox_point[most_ant_ind1])
- print('equal')
- else:
- p1.append(dist_point[most_ant_ind2])
- p2.append(prox_point[most_ant_ind2])
- print('not equal')
-
- if side == 'R':
- if lig == 'ACL':
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (1,0,0), (0, 0, 0), cached_dots=None, return_both=False)
- else:
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (-1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
- else:
- if lig == 'ACL':
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (-1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
- else:
- lateral_mesh = trimesh.intersections.slice_mesh_plane(mesh, (1, 0, 0), (0, 0, 0), cached_dots=None,
- return_both=False)
-
- # find height
- vec1 = (p1[0][0] - p2[0][0], p1[0][1] - p2[0][1], p1[0][2] - p2[0][2])
- norm = np.sqrt(vec1[0] ** 2 + vec1[1] ** 2 + vec1[2] ** 2)
- direction = [vec1[0] / norm, vec1[1] / norm, vec1[2] / norm]
-
-
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
-
- # trimesh.path.segments.parameters_to_segments(p1[-1], -1*direction, ((0,0,0),(0,1,0)))
- # trimesh.path.segments.segments_to_parameters(np.asarray(segments))
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,0,10), cached_dots=None, return_both=False)
-
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, (0,0,0), direction, heights=np.linspace(-10, 10, 21))
-
- dist = []
- p3 = []
- p4 = []
- p1_2d = p1[-1][1:3]
- p2_2d = p2[-1][1:3]
- for i in range(0,len(face_index)):
- plane_verts = np.unique(lateral_mesh.faces[face_index[i]])
- plane_points = lateral_mesh.vertices[plane_verts]
-
- min_y = np.where(plane_points[:,1] == np.partition(plane_points[:,1], 0)[0])
- max_y = np.where(plane_points[:,1] == np.partition(plane_points[:,1], -1)[-1])
-
- p3.append(plane_points[min_y][0])
- p4.append(plane_points[max_y][0])
- dist.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3[i][1:3]))/np.linalg.norm(p2_2d-p1_2d))
- # dist.append(np.linalg.norm(plane_points[min_y][0]-plane_points[max_y][0]))
-
- # segments = np.asarray([p3[np.asarray(dist).argmax()], p4[np.asarray(dist).argmax()]])
- # p_dist = trimesh.load_path(segments)
- dist1 = dist
- p3_2d = p3[np.asarray(dist1).argmax()][1:3]
- h.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3_2d))/np.linalg.norm(p2_2d-p1_2d))
-
- # find depth
- # lateral_mesh.show()
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, (0, 0, 0), direction,
- heights=np.linspace(-30, -5, 41))
-
- dist = []
- p6 = []
- p4 = []
- p1_2d = p1[-1][1:3]
- p2_2d = p2[-1][1:3]
- for i in range(0, len(face_index)):
- plane_verts = np.unique(lateral_mesh.faces[face_index[i]])
- plane_points = lateral_mesh.vertices[plane_verts]
-
- min_y = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], 0)[0])
- max_y = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], -1)[-1])
-
- p6.append(plane_points[min_y][0])
- p4.append(plane_points[max_y][0])
- dist.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - p6[i][1:3])) / np.linalg.norm(p2_2d - p1_2d))
- # dist.append(np.linalg.norm(plane_points[min_y][0]-plane_points[max_y][0]))
-
- jump_ind = np.where(np.diff(np.asarray(p6), axis=0)[:,1] == np.min(np.diff(np.asarray(p6), axis=0)[:,1]))[0][0]
-
- # segments = np.asarray([p6[jump_ind+1], p4[jump_ind+1]])
- # p_dist = trimesh.load_path(segments)
-
- p6_2d = p6[jump_ind+1][1:3]
- # min_z = lateral_mesh.vertices[np.argmin(lateral_mesh.vertices[:,2])]
- # p5 = np.asarray(min_z)
- # p5_2d = p5[1:3]
-
- direction = np.asarray(direction) * -1
- direction_perp = np.array((direction[0], -direction[2], direction[1]))
-
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(lateral_mesh, p1[0], direction_perp,
- heights=np.linspace(0, 1, 1))
- plane_verts = np.unique(lateral_mesh.faces[face_index[0]])
- plane_points = lateral_mesh.vertices[plane_verts]
- min_z = np.where(plane_points[:, 2] == np.partition(plane_points[:, 2], 0)[0])
- p5 = plane_points[min_z][0]
- p5_2d = p5[1:3]
-
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], p6_2d[0], p6_2d[1], p5_2d[0], p5_2d[1])
- d.append(l)
-
- # visualization
- # p1[0][0] = 0
- # p2[0][0] = 0
- # p3[np.asarray(dist1).argmax()][0] = 0
- # p4[jump_ind + 1][0] = 0
- # p5[0] = 0
- # p6[jump_ind + 1][0] = 0
-
- points = trimesh.points.PointCloud(np.asarray((p1[0],p2[0],p6[jump_ind+1],p5, p3[np.asarray(dist1).argmax()])), colors=None, metadata=None)
- segments = np.asarray([p1[-1], p2[-1]])
- p = trimesh.load_path(segments)
- segments = np.asarray([p6[jump_ind+1], p5])
- p_dist = trimesh.load_path(segments)
-
- mesh.visual.face_colors[:] = np.array([227, 218, 201, 150])
- mesh.visual.vertex_colors[:] = np.array([227, 218, 201, 150])
- if lig == 'ACL':
- line = trimesh.path.segments.parameters_to_segments([p1[-1],p6[jump_ind+1],p3[np.asarray(dist1).argmax()],p5], [direction,direction_perp,direction,direction_perp],
- np.array(((d[-1]-5,-14),(-12,h[-1]-10),(d[-1]-25.5,-23.5),(-1.5,h[-1]+1))).astype(float)) #ACL
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[4], 4)
- grid_points2 = split(box_points[0], box_points[3], 4)
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1], grid_points1[2], grid_points1[3]],
- [direction_perp], np.array(
- ((h[-1] + 2.5, -0), (h[-1] + 2.5, 0), (h[-1] + 2, -0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] - 1.5, 0), (d[-1] - 1.5, 0),
- (d[-1] - 2, 0))).astype(
- float))
- else:
- line = trimesh.path.segments.parameters_to_segments([p1[-1], p6[jump_ind + 1], p3[np.asarray(dist1).argmax()], p5],
- [direction, direction_perp, direction, direction_perp],
- np.array(((d[-1] -8, -16), (h[-1] - 11, -13.5),
- (d[-1] - 27, -25.5), (h[-1],-3))).astype(float)) #PCL
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[7], 4)
- grid_points2 = split(box_points[0], box_points[5], 4)
-
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1],grid_points1[2],grid_points1[3]],[direction_perp],np.array(((h[-1]+3,-0),(h[-1]+3,0),(h[-1]+2.5,-0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] -1,0), (d[-1]-1,0), (d[-1]-1.5,0))).astype(
- float))
- grid_line_path = trimesh.load_path(np.squeeze(grid_line), colors=((0.5,0.5,0.5,),(0.5,0.5,0.5),(0.5,0.5,0.5)))
- grid_line2_path = trimesh.load_path(np.squeeze(grid_line2),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
- scene = trimesh.Scene([mesh, trimesh.load_path(np.squeeze(line)),grid_line_path,grid_line2_path]) #, points
- origin, xaxis, yaxis, zaxis = scene.camera_transform[0:3,3], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- if lig == 'ACL':
- Rx = trimesh.transformations.rotation_matrix(np.radians(-90), xaxis)
- Ry = trimesh.transformations.rotation_matrix(np.radians(-90), yaxis)
- else:
- Rx = trimesh.transformations.rotation_matrix(np.radians(-90), xaxis)
- Ry = trimesh.transformations.rotation_matrix(np.radians(90), yaxis)
- R = trimesh.transformations.concatenate_matrices(Ry,Rx)
- scene.apply_transform(R)
- # scene.camera_transform = camera_trans
- scene.show()
- # mesh.vertices[:, 0] = 0
- # trimesh.Scene([mesh, points, trimesh.load_path(np.squeeze(line))]).show()
-
-# posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,-30,0), cached_dots=None, return_both=False)
-# posterior_mesh.show()
- if subject == 100:
- points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_rot.xyz')
- if lig == 'ACL':
- center = np.arange(341 - 263) + 263 # ACL
- mean = np.array((61.2, -78.9, 39.3)) / 100 * np.array((ML, AP, AP)) + np.array( #
- (bbox[0, 0], bbox[1, 1], bbox[0, 2]))
- else:
- center = np.arange(112) # PCL
- mean = np.array((39.5, -63.4, 23.8)) / 100 * np.array((ML, AP, AP)) + np.array(
- (bbox[0, 2], bbox[1, 1], bbox[0, 2]))
-
- points_lig = points_lig[center]
- # origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- # Rz = trimesh.transformations.rotation_matrix(180/np.pi, zaxis)
- # points_lig.apply_transform(Rz)
- color_file = np.loadtxt(path_col + '\meanshape_ligs_color.xyz')[:, 3]
- color_file = color_file[center]
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
-
- color = []
- for ind_col, point in enumerate(points_lig):
- center_2d = point[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - center_2d)) / np.linalg.norm(p2_2d - p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0],
- p5_2d[1])
- d_centriods.append(l)
- vcolors = [c[int(color_file[ind_col] - 1)][0] * 255, c[int(color_file[ind_col] - 1)][1] * 255,
- c[int(color_file[ind_col] - 1)][2] * 255]
- color.append(vcolors)
- p_lig = trimesh.points.PointCloud(points_lig, colors=color)
- p_mean = trimesh.primitives.Sphere(radius=1, center=mean, subdivisions=3, color=[255, 0, 0]) # trimesh.points.PointCloud([mean, mean], colors=[[255, 0, 0], [255, 0, 0]])
- p_mean.visual.face_colors = np.array([255, 0, 0, 255])
- # scene2 = trimesh.Scene([mesh, points, p_lig, trimesh.load_path(np.squeeze(line))])
- # scene2.apply_transform(R)
- # scene2.camera_transform = camera_trans
- # scene2.show()
- scene.add_geometry([p_lig, p_mean],transform=R)
- scene.show()
- else:
- if lig == 'ACL':
- lig_no = ligaments[8][ind]
- elif lig == 'PCL':
- lig_no = ligaments[0][ind]
- if not lig_no == 0:
- segment = 'femur'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
-
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center = geometric_measures['shell_barycenter']
- center_2d = center[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-center_2d))/np.linalg.norm(p2_2d-p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0], p5_2d[1])
- d_centriods.append(l)
- else:
- h_centriods.append(0)
- d_centriods.append(0)
-
-[1-abs(i / j) for i, j in zip(d_centriods, d)]
-[i / j for i, j in zip(h_centriods, h)]
-
-d_centriods/np.asarray(d)
-h_centriods/np.asarray(h)
-
-np.mean(abs(np.asarray(d_centriods))/np.asarray(d))
-np.mean(h_centriods/np.asarray(h))
-
-
-
diff --git a/LigamentStudy/CheckMatchingPoints.py b/LigamentStudy/CheckMatchingPoints.py
deleted file mode 100644
index f659209..0000000
--- a/LigamentStudy/CheckMatchingPoints.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import trimesh
-import os
-import numpy as np
-
-segments = ['femur']
-subjects = ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-lig = 'pop'
-center_only = 1
-
-if lig == 'PCL':
- center_tibia = np.arange(131) # np.arange(470-341)+341 #np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.arange(112) # np.arange(341-263)+263 #np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
-
-if lig == 'LCL':
- center_femur = np.arange(706-641)+641 # np.arange(415-379)+379 # np.arange(370-341)+341 = 4096
- center_tibia = np.arange(242)
-if lig == 'pop':
- center_femur = np.arange(776-706)+706 #np.arange(454-415)+415 # np.arange(401-370)+370 = 4096
- center_tibia = 0
-
-no_points=[]
-points_in_attachment = []
-perc_points=[]
-total_points=[]
-no_all_points=[]
-perc_points_in_area=[]
-
-for segment in segments:
- if segment == 'tibia' or segment == 'fibula':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
- if segment == 'fibula':
- short = '_short'
- else:
- short = ''
-
- for ind, subject in enumerate(subjects):
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points_color_8192.xyz')
- points_area = trimesh.load_mesh(path + '\8192\SSM_' + segment + short + '_areas_test.xyz')
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color_8192.xyz')[:, 3]
- if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- print(color)
- corresponding_points = np.where(color>=7)
- all_points = np.where(color >= 0)
- all_points_lig = points_lig[all_points]
- points_lig = points_lig[corresponding_points]
- result = []
- result2 = []
- for i in range(0,len(points_area.vertices)):
- rows = np.where(points_lig[:, 0] == points_area.vertices[i,0])
- if len(rows[0])>=1:
- # print(points_lig[rows])
- # print(points_area.vertices[i,:])
- result.append(points_lig[rows])
-
- rows = np.where(all_points_lig[:, 0] == points_area.vertices[i, 0])
- if len(rows[0]) >= 1:
- # print(all_points_lig[rows])
- # print(points_area.vertices[i, :])
- result2.append(all_points_lig[rows])
-
- no_points.append(len(result)) #SSM_points_predicted
- no_all_points.append(len(result2)) #all points in area
- perc_points.append(len(result)/len(result2)) #perc points wrt all points
-
- total_points.append(len(points_area.vertices))
- points_in_attachment.append(result)
-
-perc_points_in_area.append(np.asarray(no_points)/len(corresponding_points[0])) # perc points inside area
-
-print(str(np.average(no_all_points)) + ' (' + str(np.min(no_all_points)) + '-' + str(np.max(no_all_points)) + ')')
-print(len(corresponding_points[0]))
-print(str(np.average(no_points)) + ' (' + str(np.min(no_points)) + '-' + str(np.max(no_points)) + ')')
-print(str(round(np.average(perc_points_in_area)*100,1)) + ' (' + str(round(np.min(perc_points_in_area)*100,1)) + '-' + str(round(np.max(perc_points_in_area)*100,1)) + ')')
-print(str(round(np.average(perc_points)*100,1)) + ' (' + str(round(np.min(perc_points)*100,1)) + '-' + str(round(np.max(perc_points)*100,1)) + ')')
-
diff --git a/LigamentStudy/CreateFibulaTransform.py b/LigamentStudy/CreateFibulaTransform.py
deleted file mode 100644
index 1ca64fc..0000000
--- a/LigamentStudy/CreateFibulaTransform.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import trimesh
-import numpy as np
-import pymeshlab
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-ligaments_fib = [[2,2,2,2,2,2,2,3,2,2]] # LCL
-
-for ind, subject in enumerate(subjects):
- # path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- #
- # rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_tibia_resample._ACS.txt'))
- # mesh2 = path + '\Segmentation_tibia_fib.stl'
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(mesh2)
- # ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- # ms5.save_current_mesh(path + '\Segmentation_fibula_tib_frame.stl', binary=False)
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- fibula = trimesh.load_mesh(path + '\Segmentation_fibula_tib_frame.stl')
- most_prox_point = fibula.vertices[np.argmax(fibula.vertices[:,2]),:]
- T = trimesh.transformations.translation_matrix(-most_prox_point)
- fibula_area = trimesh.load_mesh(path + '\Segmentation_tibia_area' + str(ligaments_fib[0][ind]) + '_transform.stl')
- # fibula_wire = trimesh.load_mesh(path + '\Segmentation_fibula_wires_transform_lateral.stl')
- # center = np.array([-48.399971,-14.163541,-15.73211])
-
- fibula.apply_transform(T)
- # fibula.export(path + '\Segmentation_fibula_transform.stl')
- fibula_area.apply_transform(T)
- fibula_area.export(path + '\Segmentation_fibula_area' + str(ligaments_fib[0][ind]) + '_transform.stl')
- # fibula_wire.apply_transform(T)
- # fibula_wire.export(path + '\Segmentation_fibula_wire_transform_lateral.stl')
- # points = center-most_prox_point
- # print(points)
diff --git a/LigamentStudy/DICOMScalarVolumePlugin.py b/LigamentStudy/DICOMScalarVolumePlugin.py
deleted file mode 100644
index 3dd13e6..0000000
--- a/LigamentStudy/DICOMScalarVolumePlugin.py
+++ /dev/null
@@ -1,841 +0,0 @@
-import numpy
-import os
-import vtk, qt, ctk, slicer, vtkITK
-from DICOMLib import DICOMPlugin
-from DICOMLib import DICOMLoadable
-from DICOMLib import DICOMUtils
-from DICOMLib import DICOMExportScalarVolume
-import logging
-from functools import cmp_to_key
-
-#
-# This is the plugin to handle translation of scalar volumes
-# from DICOM files into MRML nodes. It follows the DICOM module's
-# plugin architecture.
-#
-
-class DICOMScalarVolumePluginClass(DICOMPlugin):
- """ ScalarVolume specific interpretation code
- """
-
- def __init__(self,epsilon=0.01):
- super().__init__()
- self.loadType = "Scalar Volume"
- self.epsilon = epsilon
- self.acquisitionModeling = None
- self.defaultStudyID = 'SLICER10001' #TODO: What should be the new study ID?
-
- self.tags['sopClassUID'] = "0008,0016"
- self.tags['photometricInterpretation'] = "0028,0004"
- self.tags['seriesDescription'] = "0008,103e"
- self.tags['seriesUID'] = "0020,000E"
- self.tags['seriesNumber'] = "0020,0011"
- self.tags['position'] = "0020,0032"
- self.tags['orientation'] = "0020,0037"
- self.tags['pixelData'] = "7fe0,0010"
- self.tags['seriesInstanceUID'] = "0020,000E"
- self.tags['acquisitionNumber'] = "0020,0012"
- self.tags['imageType'] = "0008,0008"
- self.tags['contentTime'] = "0008,0033"
- self.tags['triggerTime'] = "0018,1060"
- self.tags['diffusionGradientOrientation'] = "0018,9089"
- self.tags['imageOrientationPatient'] = "0020,0037"
- self.tags['numberOfFrames'] = "0028,0008"
- self.tags['instanceUID'] = "0008,0018"
- self.tags['windowCenter'] = "0028,1050"
- self.tags['windowWidth'] = "0028,1051"
- self.tags['rows'] = "0028,0010"
- self.tags['columns'] = "0028,0011"
-
- @staticmethod
- def readerApproaches():
- """Available reader implementations. First entry is initial default.
- Note: the settings file stores the index of the user's selected reader
- approach, so if new approaches are added the should go at the
- end of the list.
- """
- return ["GDCM with DCMTK fallback", "DCMTK", "GDCM", "Archetype"]
-
- @staticmethod
- def settingsPanelEntry(panel, parent):
- """Create a settings panel entry for this plugin class.
- It is added to the DICOM panel of the application settings
- by the DICOM module.
- """
- formLayout = qt.QFormLayout(parent)
-
- readersComboBox = qt.QComboBox()
- for approach in DICOMScalarVolumePluginClass.readerApproaches():
- readersComboBox.addItem(approach)
- readersComboBox.toolTip = ("Preferred back end. Archetype was used by default in Slicer before June of 2017."
- "Change this setting if data that previously loaded stops working (and report an issue).")
- formLayout.addRow("DICOM reader approach:", readersComboBox)
- panel.registerProperty(
- "DICOM/ScalarVolume/ReaderApproach", readersComboBox,
- "currentIndex", str(qt.SIGNAL("currentIndexChanged(int)")))
-
- importFormatsComboBox = ctk.ctkComboBox()
- importFormatsComboBox.toolTip = ("Enable adding non-linear transform to regularize images acquired irregular geometry:"
- " non-rectilinear grid (such as tilted gantry CT acquisitions) and non-uniform slice spacing."
- " If no regularization is applied then image may appear distorted if it was acquired with irregular geometry.")
- importFormatsComboBox.addItem("default (none)", "default")
- importFormatsComboBox.addItem("none", "none")
- importFormatsComboBox.addItem("apply regularization transform", "transform")
- # in the future additional option, such as "resample" may be added
- importFormatsComboBox.currentIndex = 0
- formLayout.addRow("Acquisition geometry regularization:", importFormatsComboBox)
- panel.registerProperty(
- "DICOM/ScalarVolume/AcquisitionGeometryRegularization", importFormatsComboBox,
- "currentUserDataAsString", str(qt.SIGNAL("currentIndexChanged(int)")),
- "DICOM examination settings", ctk.ctkSettingsPanel.OptionRequireRestart)
- # DICOM examination settings are cached so we need to restart to make sure changes take effect
-
- allowLoadingByTimeCheckBox = qt.QCheckBox()
- allowLoadingByTimeCheckBox.toolTip = ("Offer loading of individual slices or group of slices"
- " that were acquired at a specific time (content or trigger time)."
- " If this option is enabled then a large number of loadable items may be displayed in the Advanced section of DICOM browser.")
- formLayout.addRow("Allow loading subseries by time:", allowLoadingByTimeCheckBox)
- allowLoadingByTimeMapper = ctk.ctkBooleanMapper(allowLoadingByTimeCheckBox, "checked", str(qt.SIGNAL("toggled(bool)")))
- panel.registerProperty(
- "DICOM/ScalarVolume/AllowLoadingByTime", allowLoadingByTimeMapper,
- "valueAsInt", str(qt.SIGNAL("valueAsIntChanged(int)")),
- "DICOM examination settings", ctk.ctkSettingsPanel.OptionRequireRestart)
- # DICOM examination settings are cached so we need to restart to make sure changes take effect
-
- @staticmethod
- def compareVolumeNodes(volumeNode1,volumeNode2):
- """
- Given two mrml volume nodes, return true of the numpy arrays have identical data
- and other metadata matches. Returns empty string on match, otherwise
- a string with a list of differences separated by newlines.
- """
- volumesLogic = slicer.modules.volumes.logic()
- comparison = ""
- comparison += volumesLogic.CompareVolumeGeometry(volumeNode1, volumeNode2)
- image1 = volumeNode1.GetImageData()
- image2 = volumeNode2.GetImageData()
- if image1.GetScalarType() != image2.GetScalarType():
- comparison += f"First volume is {image1.GetScalarTypeAsString()}, but second is {image2.GetScalarTypeAsString()}"
- array1 = slicer.util.array(volumeNode1.GetID())
- array2 = slicer.util.array(volumeNode2.GetID())
- if not numpy.all(array1 == array2):
- comparison += "Pixel data mismatch\n"
- return comparison
-
- def acquisitionGeometryRegularizationEnabled(self):
- settings = qt.QSettings()
- return (settings.value("DICOM/ScalarVolume/AcquisitionGeometryRegularization", "default") == "transform")
-
- def allowLoadingByTime(self):
- settings = qt.QSettings()
- return (int(settings.value("DICOM/ScalarVolume/AllowLoadingByTime", "0")) != 0)
-
- def examineForImport(self,fileLists):
- """ Returns a sorted list of DICOMLoadable instances
- corresponding to ways of interpreting the
- fileLists parameter (list of file lists).
- """
- loadables = []
- for files in fileLists:
- cachedLoadables = self.getCachedLoadables(files)
- if cachedLoadables:
- loadables += cachedLoadables
- else:
- loadablesForFiles = self.examineFiles(files)
- loadables += loadablesForFiles
- self.cacheLoadables(files,loadablesForFiles)
-
- # sort the loadables by series number if possible
- loadables.sort(key=cmp_to_key(lambda x,y: self.seriesSorter(x,y)))
-
- return loadables
-
- def cleanNodeName(self, value):
- cleanValue = value
- cleanValue = cleanValue.replace("|", "-")
- cleanValue = cleanValue.replace("/", "-")
- cleanValue = cleanValue.replace("\\", "-")
- cleanValue = cleanValue.replace("*", "(star)")
- cleanValue = cleanValue.replace("\\", "-")
- return cleanValue
-
- def examineFiles(self,files):
- """ Returns a list of DICOMLoadable instances
- corresponding to ways of interpreting the
- files parameter.
- """
-
- seriesUID = slicer.dicomDatabase.fileValue(files[0],self.tags['seriesUID'])
- seriesName = self.defaultSeriesNodeName(seriesUID)
-
- # default loadable includes all files for series
- allFilesLoadable = DICOMLoadable()
- allFilesLoadable.files = files
- allFilesLoadable.name = self.cleanNodeName(seriesName)
- allFilesLoadable.tooltip = "%d files, first file: %s" % (len(allFilesLoadable.files), allFilesLoadable.files[0])
- allFilesLoadable.selected = True
- # add it to the list of loadables later, if pixel data is available in at least one file
-
- # make subseries volumes based on tag differences
- subseriesTags = [
- "seriesInstanceUID",
- "acquisitionNumber",
- # GE volume viewer and Siemens Axiom CBCT systems put an overview (localizer) slice and all the reconstructed slices
- # in one series, using two different image types. Splitting based on image type allows loading of these volumes
- # (loading the series without localizer).
- "imageType",
- "imageOrientationPatient",
- "diffusionGradientOrientation",
- ]
-
- if self.allowLoadingByTime():
- subseriesTags.append("contentTime")
- subseriesTags.append("triggerTime")
-
- # Values for these tags will only be enumerated (value itself will not be part of the loadable name)
- # because the vale itself is usually too long and complicated to be displayed to users
- subseriesTagsToEnumerateValues = [
- "seriesInstanceUID",
- "imageOrientationPatient",
- "diffusionGradientOrientation",
- ]
-
- #
- # first, look for subseries within this series
- # - build a list of files for each unique value
- # of each tag
- #
- subseriesFiles = {}
- subseriesValues = {}
- for file in allFilesLoadable.files:
- # check for subseries values
- for tag in subseriesTags:
- value = slicer.dicomDatabase.fileValue(file,self.tags[tag])
- value = value.replace(",","_") # remove commas so it can be used as an index
- if tag not in subseriesValues:
- subseriesValues[tag] = []
- if not subseriesValues[tag].__contains__(value):
- subseriesValues[tag].append(value)
- if (tag,value) not in subseriesFiles:
- subseriesFiles[tag,value] = []
- subseriesFiles[tag,value].append(file)
-
- loadables = []
-
- # Pixel data is available, so add the default loadable to the output
- loadables.append(allFilesLoadable)
-
- #
- # second, for any tags that have more than one value, create a new
- # virtual series
- #
- subseriesCount = 0
- # List of loadables that look like subseries that contain the full series except a single frame
- probableLocalizerFreeLoadables = []
- for tag in subseriesTags:
- if len(subseriesValues[tag]) > 1:
- subseriesCount += 1
- for valueIndex, value in enumerate(subseriesValues[tag]):
- # default loadable includes all files for series
- loadable = DICOMLoadable()
- loadable.files = subseriesFiles[tag,value]
- # value can be a long string (and it will be used for generating node name)
- # therefore use just an index instead
- if tag in subseriesTagsToEnumerateValues:
- loadable.name = seriesName + " - %s %d" % (tag, valueIndex+1)
- else:
- loadable.name = seriesName + f" - {tag} {value}"
- loadable.name = self.cleanNodeName(loadable.name)
- loadable.tooltip = "%d files, grouped by %s = %s. First file: %s. %s = %s" % (len(loadable.files), tag, value, loadable.files[0], tag, value)
- loadable.selected = False
- loadables.append(loadable)
- if len(subseriesValues[tag]) == 2:
- otherValue = subseriesValues[tag][1-valueIndex]
- if len(subseriesFiles[tag,value]) > 1 and len(subseriesFiles[tag, otherValue]) == 1:
- # this looks like a subseries without a localizer image
- probableLocalizerFreeLoadables.append(loadable)
-
- # remove any files from loadables that don't have pixel data (no point sending them to ITK for reading)
- # also remove DICOM SEG, since it is not handled by ITK readers
- newLoadables = []
- for loadable in loadables:
- newFiles = []
- excludedLoadable = False
- for file in loadable.files:
- if slicer.dicomDatabase.fileValueExists(file,self.tags['pixelData']):
- newFiles.append(file)
- if slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])=='1.2.840.10008.5.1.4.1.1.66.4':
- excludedLoadable = True
- if 'DICOMSegmentationPlugin' not in slicer.modules.dicomPlugins:
- logging.warning('Please install Quantitative Reporting extension to enable loading of DICOM Segmentation objects')
- elif slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])=='1.2.840.10008.5.1.4.1.1.481.3':
- excludedLoadable = True
- if 'DicomRtImportExportPlugin' not in slicer.modules.dicomPlugins:
- logging.warning('Please install SlicerRT extension to enable loading of DICOM RT Structure Set objects')
- if len(newFiles) > 0 and not excludedLoadable:
- loadable.files = newFiles
- loadable.grayscale = ('MONOCHROME' in slicer.dicomDatabase.fileValue(newFiles[0],self.tags['photometricInterpretation']))
- newLoadables.append(loadable)
- elif excludedLoadable:
- continue
- else:
- # here all files in have no pixel data, so they might be
- # secondary capture images which will read, so let's pass
- # them through with a warning and low confidence
- loadable.warning += "There is no pixel data attribute for the DICOM objects, but they might be readable as secondary capture images. "
- loadable.confidence = 0.2
- loadable.grayscale = ('MONOCHROME' in slicer.dicomDatabase.fileValue(loadable.files[0],self.tags['photometricInterpretation']))
- newLoadables.append(loadable)
- loadables = newLoadables
-
- #
- # now for each series and subseries, sort the images
- # by position and check for consistency
- # then adjust confidence values based on warnings
- #
- for loadable in loadables:
- loadable.files, distances, loadable.warning = DICOMUtils.getSortedImageFiles(loadable.files, self.epsilon)
-
- loadablesBetterThanAllFiles = []
- if allFilesLoadable.warning != "":
- for probableLocalizerFreeLoadable in probableLocalizerFreeLoadables:
- if probableLocalizerFreeLoadable.warning == "":
- # localizer-free loadables are better then all files, if they don't have warning
- loadablesBetterThanAllFiles.append(probableLocalizerFreeLoadable)
- if not loadablesBetterThanAllFiles and subseriesCount == 1:
- # there was a sorting warning and
- # only one kind of subseries, so it's probably correct
- # to have lower confidence in the default all-files version.
- for loadable in loadables:
- if loadable != allFilesLoadable and loadable.warning == "":
- loadablesBetterThanAllFiles.append(loadable)
-
- # if there are loadables that are clearly better then all files, then use those (otherwise use all files loadable)
- preferredLoadables = loadablesBetterThanAllFiles if loadablesBetterThanAllFiles else [allFilesLoadable]
- # reduce confidence and deselect all non-preferred loadables
- for loadable in loadables:
- if loadable in preferredLoadables:
- loadable.selected = True
- else:
- loadable.selected = False
- if loadable.confidence > .45:
- loadable.confidence = .45
-
- return loadables
-
- def seriesSorter(self,x,y):
- """ returns -1, 0, 1 for sorting of strings like: "400: series description"
- Works for DICOMLoadable or other objects with name attribute
- """
- if not (hasattr(x,'name') and hasattr(y,'name')):
- return 0
- xName = x.name
- yName = y.name
- try:
- xNumber = int(xName[:xName.index(':')])
- yNumber = int(yName[:yName.index(':')])
- except ValueError:
- return 0
- cmp = xNumber - yNumber
- return cmp
-
- #
- # different ways to load a set of dicom files:
- # - Logic: relies on the same loading mechanism used
- # by the File->Add Data dialog in the Slicer GUI.
- # This uses vtkITK under the hood with GDCM as
- # the default loader.
- # - DCMTK: explicitly uses the DCMTKImageIO
- # - GDCM: explicitly uses the GDCMImageIO
- #
-
- def loadFilesWithArchetype(self,files,name):
- """Load files in the traditional Slicer manner
- using the volume logic helper class
- and the vtkITK archetype helper code
- """
- fileList = vtk.vtkStringArray()
- for f in files:
- fileList.InsertNextValue(f)
- volumesLogic = slicer.modules.volumes.logic()
- return(volumesLogic.AddArchetypeScalarVolume(files[0],name,0,fileList))
-
- def loadFilesWithSeriesReader(self,imageIOName,files,name,grayscale=True):
- """ Explicitly use the named imageIO to perform the loading
- """
-
- if grayscale:
- reader = vtkITK.vtkITKArchetypeImageSeriesScalarReader()
- else:
- reader = vtkITK.vtkITKArchetypeImageSeriesVectorReaderFile()
- reader.SetArchetype(files[0])
- for f in files:
- reader.AddFileName(f)
- reader.SetSingleFile(0)
- reader.SetOutputScalarTypeToNative()
- reader.SetDesiredCoordinateOrientationToNative()
- reader.SetUseNativeOriginOn()
- if imageIOName == "GDCM":
- reader.SetDICOMImageIOApproachToGDCM()
- elif imageIOName == "DCMTK":
- reader.SetDICOMImageIOApproachToDCMTK()
- else:
- raise Exception("Invalid imageIOName of %s" % imageIOName)
- logging.info("Loading with imageIOName: %s" % imageIOName)
- reader.Update()
-
- slicer.modules.reader = reader
- if reader.GetErrorCode() != vtk.vtkErrorCode.NoError:
- errorStrings = (imageIOName, vtk.vtkErrorCode.GetStringFromErrorCode(reader.GetErrorCode()))
- logging.error("Could not read scalar volume using %s approach. Error is: %s" % errorStrings)
- return
-
- imageChangeInformation = vtk.vtkImageChangeInformation()
- imageChangeInformation.SetInputConnection(reader.GetOutputPort())
- imageChangeInformation.SetOutputSpacing( 1, 1, 1 )
- imageChangeInformation.SetOutputOrigin( 0, 0, 0 )
- imageChangeInformation.Update()
-
- name = slicer.mrmlScene.GenerateUniqueName(name)
- if grayscale:
- volumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLScalarVolumeNode", name)
- else:
- volumeNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLVectorVolumeNode", name)
- volumeNode.SetAndObserveImageData(imageChangeInformation.GetOutputDataObject(0))
- slicer.vtkMRMLVolumeArchetypeStorageNode.SetMetaDataDictionaryFromReader(volumeNode, reader)
- volumeNode.SetRASToIJKMatrix(reader.GetRasToIjkMatrix())
- volumeNode.CreateDefaultDisplayNodes()
-
- slicer.modules.DICOMInstance.reader = reader
- slicer.modules.DICOMInstance.imageChangeInformation = imageChangeInformation
-
- return(volumeNode)
-
- def setVolumeNodeProperties(self,volumeNode,loadable):
- """After the scalar volume has been loaded, populate the node
- attributes and display node with values extracted from the dicom instances
- """
- if volumeNode:
- #
- # create subject hierarchy items for the loaded series
- #
- self.addSeriesInSubjectHierarchy(loadable,volumeNode)
-
- #
- # add list of DICOM instance UIDs to the volume node
- # corresponding to the loaded files
- #
- instanceUIDs = ""
- for file in loadable.files:
- uid = slicer.dicomDatabase.fileValue(file,self.tags['instanceUID'])
- if uid == "":
- uid = "Unknown"
- instanceUIDs += uid + " "
- instanceUIDs = instanceUIDs[:-1] # strip last space
- volumeNode.SetAttribute("DICOM.instanceUIDs", instanceUIDs)
-
- # Choose a file in the middle of the series as representative frame,
- # because that is more likely to contain the object of interest than the first or last frame.
- # This is important for example for getting a relevant window/center value for the series.
- file = loadable.files[int(len(loadable.files)/2)]
-
- #
- # automatically select the volume to display
- #
- appLogic = slicer.app.applicationLogic()
- selNode = appLogic.GetSelectionNode()
- selNode.SetActiveVolumeID(volumeNode.GetID())
- appLogic.PropagateVolumeSelection()
-
- #
- # apply window/level from DICOM if available (the first pair that is found)
- # Note: There can be multiple presets (multiplicity 1-n) in the standard [1]. We have
- # a way to put these into the display node [2], so they can be selected in the Volumes
- # module.
- # [1] http://medical.nema.org/medical/dicom/current/output/html/part06.html
- # [2] https://github.com/Slicer/Slicer/blob/3bfa2fc2b310d41c09b7a9e8f8f6c4f43d3bd1e2/Libs/MRML/Core/vtkMRMLScalarVolumeDisplayNode.h#L172
- #
- try:
- windowCenter = float( slicer.dicomDatabase.fileValue(file,self.tags['windowCenter']) )
- windowWidth = float( slicer.dicomDatabase.fileValue(file,self.tags['windowWidth']) )
- displayNode = volumeNode.GetDisplayNode()
- if displayNode:
- logging.info('Window/level found in DICOM tags (center=' + str(windowCenter) + ', width=' + str(windowWidth) + ') has been applied to volume ' + volumeNode.GetName())
- displayNode.AddWindowLevelPreset(windowWidth, windowCenter)
- displayNode.SetWindowLevelFromPreset(0)
- else:
- logging.info('No display node: cannot use window/level found in DICOM tags')
- except ValueError:
- pass # DICOM tags cannot be parsed to floating point numbers
-
- sopClassUID = slicer.dicomDatabase.fileValue(file,self.tags['sopClassUID'])
-
- # initialize color lookup table
- modality = self.mapSOPClassUIDToModality(sopClassUID)
- if modality == "PT":
- displayNode = volumeNode.GetDisplayNode()
- if displayNode:
- displayNode.SetAndObserveColorNodeID(slicer.modules.colors.logic().GetPETColorNodeID(slicer.vtkMRMLPETProceduralColorNode.PETheat))
-
- # initialize quantity and units codes
- (quantity,units) = self.mapSOPClassUIDToDICOMQuantityAndUnits(sopClassUID)
- if quantity is not None:
- volumeNode.SetVoxelValueQuantity(quantity)
- if units is not None:
- volumeNode.SetVoxelValueUnits(units)
-
- def loadWithMultipleLoaders(self,loadable):
- """Load using multiple paths (for testing)
- """
- volumeNode = self.loadFilesWithArchetype(loadable.files, loadable.name+"-archetype")
- self.setVolumeNodeProperties(volumeNode, loadable)
- volumeNode = self.loadFilesWithSeriesReader("GDCM", loadable.files, loadable.name+"-gdcm", loadable.grayscale)
- self.setVolumeNodeProperties(volumeNode, loadable)
- volumeNode = self.loadFilesWithSeriesReader("DCMTK", loadable.files, loadable.name+"-dcmtk", loadable.grayscale)
- self.setVolumeNodeProperties(volumeNode, loadable)
-
- return volumeNode
-
- def load(self,loadable,readerApproach=None):
- """Load the select as a scalar volume using desired approach
- """
- # first, determine which reader approach the user prefers
- if not readerApproach:
- readerIndex = slicer.util.settingsValue('DICOM/ScalarVolume/ReaderApproach', 0, converter=int)
- readerApproach = DICOMScalarVolumePluginClass.readerApproaches()[readerIndex]
- # second, try to load with the selected approach
- if readerApproach == "Archetype":
- volumeNode = self.loadFilesWithArchetype(loadable.files, loadable.name)
- elif readerApproach == "GDCM with DCMTK fallback":
- volumeNode = self.loadFilesWithSeriesReader("GDCM", loadable.files, loadable.name, loadable.grayscale)
- if not volumeNode:
- volumeNode = self.loadFilesWithSeriesReader("DCMTK", loadable.files, loadable.name, loadable.grayscale)
- else:
- volumeNode = self.loadFilesWithSeriesReader(readerApproach, loadable.files, loadable.name, loadable.grayscale)
- # third, transfer data from the dicom instances into the appropriate Slicer data containers
- self.setVolumeNodeProperties(volumeNode, loadable)
-
- # examine the loaded volume and if needed create a new transform
- # that makes the loaded volume match the DICOM coordinates of
- # the individual frames. Save the class instance so external
- # code such as the DICOMReaders test can introspect to validate.
-
- if volumeNode:
- self.acquisitionModeling = self.AcquisitionModeling()
- self.acquisitionModeling.createAcquisitionTransform(volumeNode,
- addAcquisitionTransformIfNeeded=self.acquisitionGeometryRegularizationEnabled())
-
- return volumeNode
-
- def examineForExport(self,subjectHierarchyItemID):
- """Return a list of DICOMExportable instances that describe the
- available techniques that this plugin offers to convert MRML
- data into DICOM data
- """
- # cannot export if there is no data node or the data node is not a volume
- shn = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- dataNode = shn.GetItemDataNode(subjectHierarchyItemID)
- if dataNode is None or not dataNode.IsA('vtkMRMLScalarVolumeNode'):
- return []
-
- # Define basic properties of the exportable
- exportable = slicer.qSlicerDICOMExportable()
- exportable.name = self.loadType
- exportable.tooltip = "Creates a series of DICOM files from scalar volumes"
- exportable.subjectHierarchyItemID = subjectHierarchyItemID
- exportable.pluginClass = self.__module__
- exportable.confidence = 0.5 # There could be more specialized volume types
-
- # Define required tags and default values
- exportable.setTag('SeriesDescription', 'No series description')
- exportable.setTag('Modality', 'CT')
- exportable.setTag('Manufacturer', 'Unknown manufacturer')
- exportable.setTag('Model', 'Unknown model')
- exportable.setTag('StudyDate', '')
- exportable.setTag('StudyTime', '')
- exportable.setTag('StudyInstanceUID', '')
- exportable.setTag('SeriesDate', '')
- exportable.setTag('SeriesTime', '')
- exportable.setTag('ContentDate', '')
- exportable.setTag('ContentTime', '')
- exportable.setTag('SeriesNumber', '1')
- exportable.setTag('SeriesInstanceUID', '')
- exportable.setTag('FrameOfReferenceInstanceUID', '')
-
- return [exportable]
-
- def export(self,exportables):
- for exportable in exportables:
- # Get volume node to export
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- if shNode is None:
- error = "Invalid subject hierarchy"
- logging.error(error)
- return error
- volumeNode = shNode.GetItemDataNode(exportable.subjectHierarchyItemID)
- if volumeNode is None or not volumeNode.IsA('vtkMRMLScalarVolumeNode'):
- error = "Series '" + shNode.GetItemName(exportable.subjectHierarchyItemID) + "' cannot be exported"
- logging.error(error)
- return error
-
- # Get output directory and create a subdirectory. This is necessary
- # to avoid overwriting the files in case of multiple exportables, as
- # naming of the DICOM files is static
- directoryName = 'ScalarVolume_' + str(exportable.subjectHierarchyItemID)
- directoryDir = qt.QDir(exportable.directory)
- directoryDir.mkpath(directoryName)
- directoryDir.cd(directoryName)
- directory = directoryDir.absolutePath()
- logging.info("Export scalar volume '" + volumeNode.GetName() + "' to directory " + directory)
-
- # Get study and patient items
- studyItemID = shNode.GetItemParent(exportable.subjectHierarchyItemID)
- if not studyItemID:
- error = "Unable to get study for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
- patientItemID = shNode.GetItemParent(studyItemID)
- if not patientItemID:
- error = "Unable to get patient for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
-
- # Assemble tags dictionary for volume export
- tags = {}
- tags['Patient Name'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientNameTagName())
- tags['Patient ID'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientIDTagName())
- tags['Patient Birth Date'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientBirthDateTagName())
- tags['Patient Sex'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientSexTagName())
- tags['Patient Comments'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMPatientCommentsTagName())
- tags['Study ID'] = self.defaultStudyID
- tags['Study Date'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyDateTagName())
- tags['Study Time'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyTimeTagName())
- tags['Study Description'] = exportable.tag(slicer.vtkMRMLSubjectHierarchyConstants.GetDICOMStudyDescriptionTagName())
- tags['Modality'] = exportable.tag('Modality')
- tags['Manufacturer'] = exportable.tag('Manufacturer')
- tags['Model'] = exportable.tag('Model')
- tags['Series Description'] = exportable.tag('SeriesDescription')
- tags['Series Number'] = exportable.tag('SeriesNumber')
- tags['Series Date'] = exportable.tag('SeriesDate')
- tags['Series Time'] = exportable.tag('SeriesTime')
- tags['Content Date'] = exportable.tag('ContentDate')
- tags['Content Time'] = exportable.tag('ContentTime')
-
- tags['Study Instance UID'] = exportable.tag('StudyInstanceUID')
- tags['Series Instance UID'] = exportable.tag('SeriesInstanceUID')
- tags['Frame of Reference Instance UID'] = exportable.tag('FrameOfReferenceInstanceUID')
-
- # Validate tags
- if tags['Modality'] == "":
- error = "Empty modality for series '" + volumeNode.GetName() + "'"
- logging.error(error)
- return error
- #TODO: more tag checks
-
- # Perform export
- exporter = DICOMExportScalarVolume(tags['Study ID'], volumeNode, tags, directory)
- if not exporter.export():
- return "Creating DICOM files from scalar volume failed"
-
- # Success
- return ""
-
- class AcquisitionModeling:
- """Code for representing and analyzing acquisition properties in slicer
- This is an internal class of the DICOMScalarVolumePluginClass so that
- it can be used here and from within the DICOMReaders test.
- TODO: This code work on legacy single frame DICOM images that have position and orientation
- flags in each instance (not on multiframe with per-frame positions).
- """
-
- def __init__(self,cornerEpsilon=1e-3,zeroEpsilon=1e-6):
- """cornerEpsilon sets the threshold for the amount of difference between the
- vtkITK generated volume geometry vs the DICOM geometry. Any spatial dimension with
- a difference larger than cornerEpsilon will trigger the addition of a grid transform.
- Any difference less than zeroEpsilon is assumed to be numerical error.
- """
- self.cornerEpsilon = cornerEpsilon
- self.zeroEpsilon = zeroEpsilon
-
- def gridTransformFromCorners(self,volumeNode,sourceCorners,targetCorners):
- """Create a grid transform that maps between the current and the desired corners.
- """
- # sanity check
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- cornerShape = (slices, 2, 2, 3)
- if not (sourceCorners.shape == cornerShape and targetCorners.shape == cornerShape):
- raise Exception("Corner shapes do not match volume dimensions %s, %s, %s" %
- (sourceCorners.shape, targetCorners.shape, cornerShape))
-
- # create the grid transform node
- gridTransform = slicer.vtkMRMLGridTransformNode()
- gridTransform.SetName(slicer.mrmlScene.GenerateUniqueName(volumeNode.GetName()+' acquisition transform'))
- slicer.mrmlScene.AddNode(gridTransform)
-
- # place grid transform in the same subject hierarchy folder as the volume node
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- volumeParentItemId = shNode.GetItemParent(shNode.GetItemByDataNode(volumeNode))
- shNode.SetItemParent(shNode.GetItemByDataNode(gridTransform), volumeParentItemId)
-
- # create a grid transform with one vector at the corner of each slice
- # the transform is in the same space and orientation as the volume node
- gridImage = vtk.vtkImageData()
- gridImage.SetOrigin(*volumeNode.GetOrigin())
- gridImage.SetDimensions(2, 2, slices)
- sourceSpacing = volumeNode.GetSpacing()
- gridImage.SetSpacing(sourceSpacing[0] * columns, sourceSpacing[1] * rows, sourceSpacing[2])
- gridImage.AllocateScalars(vtk.VTK_DOUBLE, 3)
- transform = slicer.vtkOrientedGridTransform()
- directionMatrix = vtk.vtkMatrix4x4()
- volumeNode.GetIJKToRASDirectionMatrix(directionMatrix)
- transform.SetGridDirectionMatrix(directionMatrix)
- transform.SetDisplacementGridData(gridImage)
- gridTransform.SetAndObserveTransformToParent(transform)
- volumeNode.SetAndObserveTransformNodeID(gridTransform.GetID())
-
- # populate the grid so that each corner of each slice
- # is mapped from the source corner to the target corner
- displacements = slicer.util.arrayFromGridTransform(gridTransform)
- for sliceIndex in range(slices):
- for row in range(2):
- for column in range(2):
- displacements[sliceIndex][row][column] = targetCorners[sliceIndex][row][column] - sourceCorners[sliceIndex][row][column]
-
- def sliceCornersFromDICOM(self,volumeNode):
- """Calculate the RAS position of each of the four corners of each
- slice of a volume node based on the dicom headers
- Note: PixelSpacing is row spacing followed by column spacing [1] (i.e. vertical then horizontal)
- while ImageOrientationPatient is row cosines then column cosines [2] (i.e. horizontal then vertical).
- [1] http://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_10.7.1.1
- [2] http://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_C.7.6.2
- """
- spacingTag = "0028,0030"
- positionTag = "0020,0032"
- orientationTag = "0020,0037"
-
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- corners = numpy.zeros(shape=[slices,2,2,3])
- uids = volumeNode.GetAttribute('DICOM.instanceUIDs').split()
- if len(uids) != slices:
- # There is no uid for each slice, so most likely all frames are in a single file
- # or maybe there is a problem with the sequence
- logging.warning("Cannot get DICOM slice positions for volume "+volumeNode.GetName())
- return None
- for sliceIndex in range(slices):
- uid = uids[sliceIndex]
- # get slice geometry from instance
- positionString = slicer.dicomDatabase.instanceValue(uid, positionTag)
- orientationString = slicer.dicomDatabase.instanceValue(uid, orientationTag)
- spacingString = slicer.dicomDatabase.instanceValue(uid, spacingTag)
- if positionString == "" or orientationString == "" or spacingString == "":
- logging.warning('No geometry information available for DICOM data, skipping corner calculations')
- return None
-
- position = numpy.array(list(map(float, positionString.split('\\'))))
- orientation = list(map(float, orientationString.split('\\')))
- rowOrientation = numpy.array(orientation[:3])
- columnOrientation = numpy.array(orientation[3:])
- spacing = numpy.array(list(map(float, spacingString.split('\\'))))
- # map from LPS to RAS
- lpsToRAS = numpy.array([-1,-1,1])
- position *= lpsToRAS
- rowOrientation *= lpsToRAS
- columnOrientation *= lpsToRAS
- rowVector = columns * spacing[1] * rowOrientation # dicom PixelSpacing is between rows first, then columns
- columnVector = rows * spacing[0] * columnOrientation
- # apply the transform to the four corners
- for column in range(2):
- for row in range(2):
- corners[sliceIndex][row][column] = position
- corners[sliceIndex][row][column] += column * rowVector
- corners[sliceIndex][row][column] += row * columnVector
- return corners
-
- def sliceCornersFromIJKToRAS(self,volumeNode):
- """Calculate the RAS position of each of the four corners of each
- slice of a volume node based on the ijkToRAS matrix of the volume node
- """
- ijkToRAS = vtk.vtkMatrix4x4()
- volumeNode.GetIJKToRASMatrix(ijkToRAS)
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- corners = numpy.zeros(shape=[slices,2,2,3])
- for sliceIndex in range(slices):
- for column in range(2):
- for row in range(2):
- corners[sliceIndex][row][column] = numpy.array(ijkToRAS.MultiplyPoint([column * columns, row * rows, sliceIndex, 1])[:3])
- return corners
-
- def cornersToWorld(self,volumeNode,corners):
- """Map corners through the volumeNodes transform to world
- This can be used to confirm that an acquisition transform has correctly
- mapped the slice corners to match the dicom acquisition.
- """
- columns, rows, slices = volumeNode.GetImageData().GetDimensions()
- worldCorners = numpy.zeros(shape=[slices,2,2,3])
- for slice in range(slices):
- for row in range(2):
- for column in range(2):
- volumeNode.TransformPointToWorld(corners[slice,row,column], worldCorners[slice,row,column])
- return worldCorners
-
- def createAcquisitionTransform(self, volumeNode, addAcquisitionTransformIfNeeded = True):
- """Creates the actual transform if needed.
- Slice corners are cached for inpection by tests
- """
- self.originalCorners = self.sliceCornersFromIJKToRAS(volumeNode)
- self.targetCorners = self.sliceCornersFromDICOM(volumeNode)
- if self.originalCorners is None or self.targetCorners is None:
- # can't create transform without corner information
- return
- maxError = (abs(self.originalCorners - self.targetCorners)).max()
-
- if maxError > self.cornerEpsilon:
- warningText = f"Irregular volume geometry detected (maximum error of {maxError:g} mm is above tolerance threshold of {self.cornerEpsilon:g} mm)."
- if addAcquisitionTransformIfNeeded:
- logging.warning(warningText + " Adding acquisition transform to regularize geometry.")
- self.gridTransformFromCorners(volumeNode, self.originalCorners, self.targetCorners)
- self.fixedCorners = self.cornersToWorld(volumeNode, self.originalCorners)
- if not numpy.allclose(self.fixedCorners, self.targetCorners):
- raise Exception("Acquisition transform didn't fix slice corners!")
- else:
- logging.warning(warningText + " Regularization transform is not added, as the option is disabled.")
- elif maxError > 0 and maxError > self.zeroEpsilon:
- logging.debug("Irregular volume geometry detected, but maximum error is within tolerance"+
- f" (maximum error of {maxError:g} mm, tolerance threshold is {self.cornerEpsilon:g} mm).")
-
-
-#
-# DICOMScalarVolumePlugin
-#
-
-class DICOMScalarVolumePlugin:
- """
- This class is the 'hook' for slicer to detect and recognize the plugin
- as a loadable scripted module
- """
- def __init__(self, parent):
- parent.title = "DICOM Scalar Volume Plugin"
- parent.categories = ["Developer Tools.DICOM Plugins"]
- parent.contributors = ["Steve Pieper (Isomics Inc.), Csaba Pinter (Queen's)"]
- parent.helpText = """
- Plugin to the DICOM Module to parse and load scalar volumes
- from DICOM files.
- No module interface here, only in the DICOM module
- """
- parent.acknowledgementText = """
- This DICOM Plugin was developed by
- Steve Pieper, Isomics, Inc.
- and was partially funded by NIH grant 3P41RR013218.
- """
-
- # don't show this module - it only appears in the DICOM module
- parent.hidden = True
-
- # Add this extension to the DICOM module's list for discovery when the module
- # is created. Since this module may be discovered before DICOM itself,
- # create the list if it doesn't already exist.
- try:
- slicer.modules.dicomPlugins
- except AttributeError:
- slicer.modules.dicomPlugins = {}
- slicer.modules.dicomPlugins['DICOMScalarVolumePlugin'] = DICOMScalarVolumePluginClass
\ No newline at end of file
diff --git a/LigamentStudy/Elevation PlotLateral.py b/LigamentStudy/Elevation PlotLateral.py
deleted file mode 100644
index 81b9d30..0000000
--- a/LigamentStudy/Elevation PlotLateral.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import numpy as np
-import pyvista as pv
-import json
-import os
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-segment = 'femur'
-
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- file_resample = os.path.join(path, 'Segmentation_femur_transform.stl')
- file_wires = os.path.join(path, 'Segmentation_femur_wires_transform.stl')
- epicondyle_mw = os.path.join(path, 'Femur', 'FemurLateralCondyle', 'MarkupsFiducial.mrk.json')
- epicondyle_tv = os.path.join(path, 'Femur', 'FemurLateralCondyle', 'MarkupsFiducial' + str(subject) + 'TV.mrk.json')
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
- mesh = pv.read(file_resample)
- if side == 'R':
- clipped = mesh.clip(normal=[-1, 0, 0], origin=[30, 0, 0])
- else:
- clipped = mesh.clip(normal=[1, 0, 0], origin=[-30, 0, 0])
- clipped = clipped.clip(normal=[0, 0, 1], origin=[0, 0, 40])
- z = clipped.points[:,0]
- mi, ma = round(min(z)), round(max(z))
- step = 1
- cntrs = np.arange(mi, ma + step, step)
- contours = clipped.contour(cntrs, scalars=clipped.points[:,0])
-
- wires = pv.read(file_wires)
-
- f = open(epicondyle_mw, "r")
- data = json.loads(f.read())
- position_mw = np.asarray(data['markups'][0]['controlPoints'][0]['position'])
- f.close()
- pos_mw = pv.wrap(position_mw).transform(rot_mat)
- pos_mw = pos_mw.glyph(scale=1000, geom=pv.Sphere())
-
- f = open(epicondyle_tv, "r")
- data = json.loads(f.read())
- position_tv = np.asarray(data['markups'][0]['controlPoints'][0]['position'])
- f.close()
- pos_tv = pv.wrap(position_tv).transform(rot_mat)
- pos_tv = pos_tv.glyph(scale=1000, geom=pv.Sphere())
-
- pv.set_plot_theme("document")
- pv.global_theme.auto_close = True
- p = pv.Plotter()
- p.add_mesh(contours, line_width=5, color="black")
- if side == 'R':
- p.add_mesh(clipped, colormap='terrain_r')
- p.camera_position = 'yz'
- p.camera.roll += 0
- else:
- p.add_mesh(clipped, colormap='terrain')
- p.camera_position = 'zy'
- p.camera.roll += 90
- p.show(screenshot=path+r'\Femur\elevation_map.png')
-
- p2 = pv.Plotter()
- p2.add_mesh(contours, line_width=5, color="black")
- if side == 'R':
- p2.add_mesh(clipped, colormap='terrain_r')
- p2.camera_position = 'yz'
- p2.camera.roll += 0
- else:
- p2.add_mesh(clipped, colormap='terrain')
- p2.camera_position = 'zy'
- p2.camera.roll += 90
- p2.add_mesh(pos_mw,color='tomato')
- p2.add_mesh(pos_tv,color='springgreen')
- p2.add_mesh(wires, opacity=0.50,color='cyan')
- p2.show(screenshot=path+r'\Femur\elevation_map_all.png', auto_close=True)
\ No newline at end of file
diff --git a/LigamentStudy/HausdorffDistance.py b/LigamentStudy/HausdorffDistance.py
deleted file mode 100644
index b229802..0000000
--- a/LigamentStudy/HausdorffDistance.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import pymeshlab
-# from plyfile import PlyData, PlyElement
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-# from vtk import *
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-import trimesh
-
-def writeply(surface,filename):
- """Write mesh as ply file."""
- writer = vtkPLYWriter()
- writer.SetInputData(surface)
- writer.SetFileTypeToASCII()
- writer.SetFileName(filename)
- writer.Write()
-
-def readVTK(file):
- reader = vtkDataSetReader()
- reader.SetFileName(file)
- reader.ReadAllVectorsOn()
- reader.ReadAllScalarsOn()
- reader.Update()
-
- data = reader.GetOutput()
- return data
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #
-segments = ['femur'] #'femur','tibia' ','tibia','fibula'
-
-for segment in segments:
- RMS = []
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
- if segment == 'fibula':
- remesh = '_remesh'
- else:
- remesh = ''
-
- # xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(
- # subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # points1 = trimesh.load_mesh(xyz_file)
- # # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- # points2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\reconstructed\shape9.xyz')
- # kwargs = {"scale": True}
- # icp = trimesh.registration.icp(points2.vertices, points1.vertices, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- # points2.apply_transform(icp[0])
- # # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\reconstructed\9_reconstruct_transform_icp_test.xyz', points2.vertices, delimiter=" ")
-
- # files from SSM workflow shapeworks
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + remesh + reflect + '.isores.pad.com.nrrd'
- # particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\4096\Segmentation_' + segment + '_' + side + '_short_' + str(
- # subject) + remesh + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- reconstructed_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\reconstructed\mesh' + str(subject) + 'dt.stl'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input'
- if segment == 'fibula':
- org_mesh = path_bones + '/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_remesh2.stl'
- else:
- org_mesh = path_bones + '/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.stl'
- # get change in position from nrrd header files
- header = nrrd.read_header(pad_file)
- pad_position = header['space origin']
- header = nrrd.read_header(com_file)
- com_position = header['space origin']
-
- # get translation from align from rotation matrix
- rot_ssm = np.loadtxt(file_align)
-
- # translate reconstructed SSM instance to align with original mesh
- translate = pad_position - com_position + rot_ssm[3, :]
- mesh3 = reconstructed_file # =local.particle file
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(mesh3)
-
- max_val = 200
- iters = 1
- while translate[2] + max_val * iters < -max_val:
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-max_val)
- iters = iters + 1
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-222)
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0,
- axisz=translate[2] + max_val * iters)
- ms6.apply_filter('invert_faces_orientation')
- ms6.apply_filter('simplification_quadric_edge_collapse_decimation', targetfacenum=7500)
- # ms6.save_current_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=-15,
- axisy=-90, axisz=-180)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=translate[0],
- # axisy=translate[1], axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-450)
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0,
- # axisz=translate[2] + 1350)
- ms6.save_current_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- # run ICP to get final position SSM point cloud on original mesh
- ms1 = pymeshlab.MeshSet()
- ms1.load_new_mesh(org_mesh)
- ms1.apply_filter('simplification_quadric_edge_collapse_decimation', targetfacenum=10000)
- ms1.save_current_mesh(path + '\8192\Segmentation_' + segment + '_resample.stl')
-
- mesh = trimesh.load_mesh(path + '\8192\Segmentation_' + segment + '_resample.stl')
- # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform.stl')
-
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- points.apply_transform(M)
- kwargs = {"scale": False}
- icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- points.apply_transform(icp[0])
- icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20, **kwargs)
- points.apply_transform(icp[0])
- points.export(path + '\8192\SSM_' + segment + '_reconstruct_transform_icp.stl')
-
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(path + '\8192\SSM_' + segment + '_reconstruct_transform_icp.stl')
- ms5.load_new_mesh(org_mesh)
- out2 = ms5.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True)
- out1 = ms5.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=True)
-
- RMS.append(max(out1['RMS'], out2['RMS']))
-
- print('max: ' + str(max(out1['max'], out2['max'])))
- print('min: ' + str(max(out1['min'], out2['min'])))
- print('mean: ' + str(max(out1['mean'], out2['mean'])))
- print('RMS: ' + str(max(out1['RMS'], out2['RMS'])))
-
- # dist_to_use = np.argmax([out1['max'], out2['max']])
- #
- # vq1 = ms5.mesh(2+dist_to_use*2).vertex_quality_array()
- #
- # samples = [sum(vq1 < 0.5), sum((vq1 > 0.5) & (vq1 < 1)), sum((vq1 > 1) & (vq1 < 1.5)),
- # sum((vq1 > 1.5) & (vq1 < 2)), sum(vq1 > 2)]
- #
- # x = np.arange(5) # the label locations
- # width = 0.35 # the width of the bars
- # fig, ax = plt.subplots()
- # rects1 = ax.bar(x, samples, width, label='femoral cartilage')
-
- ms5.save_current_mesh(path + r'/8192/Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_HD.ply', binary=False,
- save_vertex_quality=True)
- np.save(path + r'/8192/' + segment + '_HD.np',[out1,out2])
-
- print('RMS ' + segment + str(np.average(RMS)))
diff --git a/LigamentStudy/OAIdownload.py b/LigamentStudy/OAIdownload.py
deleted file mode 100644
index 4957e53..0000000
--- a/LigamentStudy/OAIdownload.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import base64
-import requests
-import json
-import urllib.request
-import shutil
-from pathlib import Path
-
-# Encode our credentials then convert it to a string.
-credentials = base64.b64encode(b'mariskawesseling:p1SM3csN5xXsGFfo').decode('utf-8')
-
-# Create the headers we will be using for all requests.
-headers = {
- 'Authorization': 'Basic ' + credentials,
- 'User-Agent': 'Example Client',
- 'Accept': 'application/json'
-}
-
-# Send Http request
-response = requests.get('https://nda.nih.gov/api/package/auth', headers=headers)
-
-# Business Logic.
-
-# If the response status code does not equal 200
-# throw an exception up.
-if response.status_code != requests.codes.ok:
- print('failed to authenticate')
- response.raise_for_status()
-
-# The auth endpoint does no return any data to parse
-# only a Http response code is returned.
-
-# Assume code in authentication section is present.
-
-packageId = 1190875
-
-# Construct the request to get the files of package 1234
-# URL structure is: https://nda.nih.gov/api/package/{packageId}/files
-response = requests.get('https://nda.nih.gov/api/package/' + str(packageId) + '/files', headers=headers)
-
-# Get the results array from the json response.
-results = response.json()['results']
-
-# Business Logic.
-
-files = {}
-
-# Add important file data to the files dictionary.
-for f in results:
- files[f['package_file_id']] = {'name': f['download_alias']}
-
-# Assume code in authentication section is present.
-# Assume that one of the retrieving files implementations is present too
-
-# Create a post request to the batch generate presigned urls endpoint.
-# Use keys from files dictionary to form a list, which is converted to
-# a json array which is posted.
-response = requests.post('https://nda.nih.gov/api/package/' + str(packageId) + '/files/batchGeneratePresignedUrls',
- json=list(files.keys()), headers=headers)
-
-# Get the presigned urls from the response.
-results = response.json()['presignedUrls']
-
-# Business Logic.
-
-# Add a download key to the file's data.
-for url in results:
- files[url['package_file_id']]['download'] = url['downloadURL']
-
-# Iterate on file id and it's data to perform the downloads.
-# for id, data in files:
-# name = data['name']
-# downloadUrl = data['download']
-# # Create a downloads directory
-# file = 'downloads/' + name
-# # Strip out the file's name for creating non-existent directories
-# directory = file[:file.rfind('/')]
-#
-# # Create non-existent directories, package files have their
-# # own directory structure, and this will ensure that it is
-# # kept in tact when downloading.
-# Path(directory).mkdir(parents=True, exist_ok=True)
-#
-# # Initiate the download.
-# with urllib.request.urlopen(downloadUrl) as dl, open(file, 'wb') as out_file:
-# shutil.copyfileobj(dl, out_file)
-
-import csv
-
-# Assume code in authentication section is present.
-
-# packageId = 1234
-
-s3Files = []
-
-# Load in and process the manifest file.
-# Not all manifest files are structured like this, all you require is
-# an S3 url and a package that has the files associated with it.
-# with open('datastructure_manifest.txt', 'r') as manifest:
-# for rows in csv.reader(manifest, dialect='excel-tab'):
-# for row in rows:
-# if row.startsWith('s3://'):
-# s3Files.append(row)
-
-# The manifest files have their column declarations listed twice, trim those out
-# s3Files = s3Files[2:]
-s3Files = ['s3://NDAR_Central_1/submission_13364/00m/0.E.1/9005075/20050926/10593811.tar.gz']
-
-# Construct the request to get the files of package 1234
-# URL structure is: https://nda.nih.gov/api/package/{packageId}/files
-response = requests.post('https://nda.nih.gov/api/package/' + str(packageId) + '/files', json=s3Files, headers=headers)
-
-# Business Logic.
-
-files = {}
-
-# Add important file data to the files dictionary.
-# We can skip having to transform the json because a json array is returned.
-for f in response.json():
- files[f['package_file_id']] = {'name': f['download_alias']}
\ No newline at end of file
diff --git a/LigamentStudy/ParaviewLoad.py b/LigamentStudy/ParaviewLoad.py
deleted file mode 100644
index ee87a40..0000000
--- a/LigamentStudy/ParaviewLoad.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import glob
-
-# subject = 9,13,19,23,26,29,32,35,37,41
-subject = 41
-segments = ['femur'] #['femur'] #
-renderView1 = GetActiveViewOrCreate('RenderView')
-
-for segment in segments:
- path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/" + str(subject) + '/'
- Counter = len(glob.glob1(path, 'Segmentation_' + segment + '_area*.stl'))
-
- for count in range(1,Counter+1):
-
- segmentation_femur_area1stl = STLReader(registrationName='Segmentation_' + segment + '_area' + str(count) + '.stl', #_transform
- FileNames=[os.path.join(path,'Segmentation_' + segment + '_area' + str(count) + '.stl')]) #_transform
- # show data in view
- segmentation_femur_area1stlDisplay = Show(segmentation_femur_area1stl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femur_area1stlDisplay.Representation = 'Surface'
- segmentation_femur_area1stlDisplay.ColorArrayName = ['CELLS', 'STLSolidLabeling']
- segmentation_femur_area1stlDisplay.SelectTCoordArray = 'None'
- segmentation_femur_area1stlDisplay.SelectNormalArray = 'None'
- segmentation_femur_area1stlDisplay.SelectTangentArray = 'None'
- segmentation_femur_area1stlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.SelectOrientationVectors = 'None'
- segmentation_femur_area1stlDisplay.ScaleFactor = 3.404553985595703
- segmentation_femur_area1stlDisplay.SelectScaleArray = 'None'
- segmentation_femur_area1stlDisplay.GlyphType = 'Arrow'
- segmentation_femur_area1stlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femur_area1stlDisplay.GaussianRadius = 0.17022769927978515
- segmentation_femur_area1stlDisplay.SetScaleArray = [None, '']
- segmentation_femur_area1stlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.OpacityArray = [None, '']
- segmentation_femur_area1stlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femur_area1stlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femur_area1stlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
-
- segmentation_femurstl = STLReader(registrationName='Segmentation_' + segment + '.stl', FileNames=[os.path.join(path,'Segmentation_' + segment + '.stl')]) #_transform
- segmentation_femur_wiresstl = STLReader(registrationName='Segmentation_' + segment + '_wires.stl', FileNames=[os.path.join(path,'Segmentation_' + segment + '_wires.stl')]) #_transform
-
- # show data in view
- segmentation_femurstlDisplay = Show(segmentation_femurstl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femurstlDisplay.Representation = 'Surface'
- segmentation_femurstlDisplay.ColorArrayName = [None, '']
- segmentation_femurstlDisplay.SelectTCoordArray = 'None'
- segmentation_femurstlDisplay.SelectNormalArray = 'None'
- segmentation_femurstlDisplay.SelectTangentArray = 'None'
- segmentation_femurstlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.SelectOrientationVectors = 'None'
- segmentation_femurstlDisplay.ScaleFactor = 10.438916015625
- segmentation_femurstlDisplay.SelectScaleArray = 'None'
- segmentation_femurstlDisplay.GlyphType = 'Arrow'
- segmentation_femurstlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femurstlDisplay.GaussianRadius = 0.52194580078125
- segmentation_femurstlDisplay.SetScaleArray = [None, '']
- segmentation_femurstlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.OpacityArray = [None, '']
- segmentation_femurstlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femurstlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femurstlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
- # show data in view
- segmentation_femur_wiresstlDisplay = Show(segmentation_femur_wiresstl, renderView1, 'GeometryRepresentation')
-
- # trace defaults for the display properties.
- segmentation_femur_wiresstlDisplay.Representation = 'Surface'
- segmentation_femur_wiresstlDisplay.ColorArrayName = [None, '']
- segmentation_femur_wiresstlDisplay.SelectTCoordArray = 'None'
- segmentation_femur_wiresstlDisplay.SelectNormalArray = 'None'
- segmentation_femur_wiresstlDisplay.SelectTangentArray = 'None'
- segmentation_femur_wiresstlDisplay.OSPRayScaleFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.SelectOrientationVectors = 'None'
- segmentation_femur_wiresstlDisplay.ScaleFactor = 9.296994972229005
- segmentation_femur_wiresstlDisplay.SelectScaleArray = 'None'
- segmentation_femur_wiresstlDisplay.GlyphType = 'Arrow'
- segmentation_femur_wiresstlDisplay.GlyphTableIndexArray = 'None'
- segmentation_femur_wiresstlDisplay.GaussianRadius = 0.46484974861145023
- segmentation_femur_wiresstlDisplay.SetScaleArray = [None, '']
- segmentation_femur_wiresstlDisplay.ScaleTransferFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.OpacityArray = [None, '']
- segmentation_femur_wiresstlDisplay.OpacityTransferFunction = 'PiecewiseFunction'
- segmentation_femur_wiresstlDisplay.DataAxesGrid = 'GridAxesRepresentation'
- segmentation_femur_wiresstlDisplay.PolarAxes = 'PolarAxesRepresentation'
-
- # update the view to ensure updated data information
- renderView1.Update()
-
- # change solid color
- segmentation_femur_wiresstlDisplay.AmbientColor = [1.0, 1.0, 0.0]
- segmentation_femur_wiresstlDisplay.DiffuseColor = [1.0, 1.0, 0.0]
-
- # ================================================================
- # addendum: following script captures some of the application
- # state to faithfully reproduce the visualization during playback
- # ================================================================
-
- # get layout
- layout1 = GetLayout()
-
- # --------------------------------
- # saving layout sizes for layouts
-
- # layout/tab size in pixels
- layout1.SetSize(866, 780)
-
- # -----------------------------------
- # saving camera placements for views
-
- # current camera placement for renderView1
- renderView1.CameraPosition = [230.80556325282282, -162.27554399127564, -1805.7694344571757]
- renderView1.CameraFocalPoint = [203.418271000369, -148.44984503432718, -1793.8450018543017]
- renderView1.CameraViewUp = [0.12379209123259044, -0.4960295986051203, 0.8594359519219018]
- renderView1.CameraParallelScale = 8.519062667601332
-
-ResetCamera()
-# --------------------------------------------
-# uncomment the following to render all views
-# RenderAllViews()
-# alternatively, if you want to write images, you can use SaveScreenshot(...).
-
-# paraview.simple.Box(Center=(-0.3667695,10.1671895,95.5673735),XLength = 91.910263, YLength = 71.482658, ZLength = 71.482658)
\ No newline at end of file
diff --git a/LigamentStudy/ProjectCentroids.py b/LigamentStudy/ProjectCentroids.py
deleted file mode 100644
index 79d7079..0000000
--- a/LigamentStudy/ProjectCentroids.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import pandas as pd
-import os
-import trimesh
-import numpy as np
-import matplotlib.path as plt
-import copy
-import time
-
-def heron(a,b,c):
- s = (a + b + c) / 2
- area = (s*(s-a) * (s-b)*(s-c)) ** 0.5
- return area
-
-def distance3d(x1,y1,z1,x2,y2,z2):
- a=(x1-x2)**2+(y1-y2)**2 + (z1-z2)**2
- d= a ** 0.5
- return d
-
-def area(x1,y1,z1,x2,y2,z2,x3,y3,z3):
- a=distance3d(x1,y1,z1,x2,y2,z2)
- b=distance3d(x2,y2,z2,x3,y3,z3)
- c=distance3d(x3,y3,z3,x1,y1,z1)
- A = heron(a,b,c)
- return A
- # print("area of triangle is %r " %A)
-
-# A utility function to calculate area
-# of triangle formed by (x1, y1),
-# (x2, y2) and (x3, y3)
-
-# def area(x1, y1, x2, y2, x3, y3):
-# return abs((x1 * (y2 - y3) + x2 * (y3 - y1)
-# + x3 * (y1 - y2)) / 2.0)
-
-
-# A function to check whether point P(x, y)
-# lies inside the triangle formed by
-# A(x1, y1), B(x2, y2) and C(x3, y3)
-def isInside(p1, p2, p3, p):
- x1 = p1[0]
- y1 = p1[1]
- z1 = p1[2]
- x2 = p2[0]
- y2 = p2[1]
- z2 = p2[2]
- x3 = p3[0]
- y3 = p3[1]
- z3 = p3[2]
- x = p[0]
- y = p[1]
- z = p[2]
-
- # Calculate area of triangle ABC
- A = area(x1, y1,z1, x2, y2,z2, x3, y3,z3)
-
- # Calculate area of triangle PBC
- A1 = area(x, y,z, x2, y2,z2, x3, y3,z3)
-
- # Calculate area of triangle PAC
- A2 = area(x1, y1,z1, x, y, z,x3, y3,z3)
-
- # Calculate area of triangle PAB
- A3 = area(x1, y1,z1, x2, y2,z2, x, y,z)
-
- # Check if sum of A1, A2 and A3
- # is same as A
- if abs(A - (A1 + A2 + A3)) < 1e-6:
- return True
- else:
- return False
-
-def intersection(planeNormal,planePoint,rayDirection,rayPoint):
- epsilon=1e-6
-
- #Define plane
- # planeNormal = np.array([0, 0, 1])
- # planePoint = np.array([0, 0, 5]) #Any point on the plane
-
- #Define ray
- # rayDirection = np.array([0, -1, -1])
- # rayPoint = np.array([0, 0, 10]) #Any point along the ray
-
- ndotu = planeNormal.dot(rayDirection)
-
- if abs(ndotu) < epsilon:
- intersect = 0
- else:
- w = rayPoint - planePoint[0,:]
- si = -planeNormal.dot(w) / ndotu
- Psi = w + si * rayDirection + planePoint[0,:]
- if isInside(planePoint[0], planePoint[1], planePoint[2], Psi) == False:
- intersect = 0
- else:
- intersect = Psi[0]
-
- return intersect
-
-
-subjects = [9] #[9,13,19,] #23,26,29,32,35,37,
-segment = 'femur'
-
-df = pd.read_excel(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","surfaces2.xlsx"),
- sheet_name='perc_of_len femur')
-lig_names = ['PCL'] #, 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)'
-
-for subject in subjects:
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- else:
- side = 'L'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- mesh = path + '/Segmentation_' + segment + '_transform.stl'
- bone = trimesh.load_mesh(mesh)
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
- AP_size = np.max(bone.vertices[:,1])-np.min(bone.vertices[:,1]) # AP length
-
- Rx = trimesh.transformations.rotation_matrix(1.57, [0, 1, 0])
- for name in lig_names:
- if side == 'R':
- most_med_point = np.min(bone.vertices[:, 0]) # med
- most_lat_point = np.max(bone.vertices[:, 0]) # lat
- med_section_dir = + 15
- lat_section_dir = - 15
- else:
- most_med_point = np.max(bone.vertices[:, 0]) # med
- most_lat_point = np.min(bone.vertices[:, 0]) # lat
- med_section_dir = - 10
- lat_section_dir = + 10
- if name == 'PCL' or name == 'ACL':
- most_med_point = most_med_point*0.10
- most_lat_point = most_lat_point*0.10
- med_section = most_med_point #- med_section_dir
- lat_section = most_lat_point #- lat_section_dir
- else:
- most_med_point = most_med_point
- most_lat_point = most_lat_point
- med_section = most_med_point + med_section_dir
- lat_section = most_lat_point + lat_section_dir
-
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- LCL_points = [most_med_point * np.ones(10),
- np.min(bone.vertices[:, 1]) + np.asarray(AP_size * df[name + 'y'][0:10]),
- np.min(bone.vertices[:, 2]) + np.asarray(AP_size * df[name + 'z'][0:10])]
- LCL_points = np.transpose(np.asarray(LCL_points))
- else:
- LCL_points = [most_lat_point*np.ones(10), np.min(bone.vertices[:,1])+np.asarray(AP_size*df[name+'y'][0:10]), np.min(bone.vertices[:,2])+np.asarray(AP_size*df[name+'z'][0:10])]
- LCL_points = np.transpose(np.asarray(LCL_points))
-
- for pts in range(0,10):
- if not np.isnan(LCL_points[pts,:]).any():
- intersect = []
- bone_part = copy.deepcopy(bone)
- top = max(LCL_points[:,2])+2.5
- far_verts = bone_part.vertices[:, 2] < top
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- if side == 'R':
- far_verts = bone_part.vertices[:, 0] < med_section
- else:
- far_verts = bone_part.vertices[:, 0] > med_section
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- # trimesh.Scene(bone_part).show()
- else:
- if side == 'R':
- far_verts = bone_part.vertices[:, 0] > lat_section
- else:
- far_verts = bone_part.vertices[:, 0] < lat_section
- face_mask = far_verts[bone_part.faces].all(axis=1)
- bone_part.update_faces(face_mask)
- # trimesh.Scene(bone_part).show()
- # tic = time.perf_counter()
- for count, tr in enumerate(bone_part.face_normals):
- intersect.append(intersection(tr, bone_part.vertices[bone_part.faces[count,:]], np.array([1,0,0]), LCL_points[pts,:]))
- # toc = time.perf_counter()
- # print(f"Downloaded the tutorial in {toc - tic:0.4f} seconds")
-
- # T = trimesh.transformations.translation_matrix(LCL_points[pts])
- # point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=T)
- # trimesh.Scene([bone_part, point]).show()
-
- x_coord = [i for i in intersect if i != 0]
- if not len(x_coord) == 0:
- if name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- to_use = np.argmin(abs(x_coord - most_med_point))
- elif name == 'PCL':
- to_use = np.argmin(abs(x_coord - most_med_point))
- elif name == 'ACL':
- to_use = np.argmin(abs(x_coord - most_lat_point))
- else:
- to_use = np.argmax(abs(x_coord - most_lat_point))
- if not abs(x_coord[to_use]-LCL_points[pts, 0]) > 20:
- LCL_points[pts, 0] = x_coord[to_use]
-
-
- # points = trimesh.PointCloud(LCL_points, colors=None, metadata=None) # create point cloud
-
- points = []
- for ind in range(0,10):
- T = trimesh.transformations.translation_matrix(LCL_points[ind])
- R = np.linalg.inv(rot_mat)
- M = trimesh.transformations.concatenate_matrices(R, T, Rx)
- point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=M)
- # point = trimesh.creation.icosphere(subdivisions=3, radius=1.0, color=None, transform=T)
- if ind == 0:
- points = point
- else:
- points = trimesh.boolean.union([points,point])
-
- points.export(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),name+'centroids.stl'))
diff --git a/LigamentStudy/ReadMe.md b/LigamentStudy/ReadMe.md
deleted file mode 100644
index 86d06f7..0000000
--- a/LigamentStudy/ReadMe.md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-X. Fit SSM to new bone
-Make sure you have a SSM of the specific bone in Shapeworks and know which points represent the ligament attachments.
-
-1. Segment bone from images
-2. Groom pipeline for new bone
-Reflect bone if left (put _L in the name) as SSM is for right bones.
-Make sure the bone is correctly aligned and cropped as the input bones of the original SSM.
-For cropping use the median bone of the original SSM
-If needed adapt the padding
-3. Create the mean shape particle file of the original SSM (getMeanShape.py)
-4. Run the SSM with fixed domains, where the number of fixed domains is the number of original input bones used for the SSM.
-If needed increase the narrow band
-5. Position the particle file (SSM point cloud) at the original bone location (FitSSM_mri.py)
-Make sure the original bone is not too large, to avoid very slow ICP. Aim for a file size below 10MB (about 20,000 faces) (Meshlab - Simplificaion: Quadric Edge Collapse Decimation)
-Required variables:
-subjects => name of the subjects you want to process
-sides => for each subject the side that is being analyzed
-segments => segments you want to analyze
-short_ssm => for each segment, is the shorter SSM needed (0=false, 1=true)
-no_particles => for each segment, number of particles in the SSM
-6. Get the points associated with all ligament locations on the original bone mesh location
-7. For each ligament, determine the SSM points associated to the ligament attachments (adaptLigaments.py)
-Interpolate the points to obtain the number of points needed for the OpenSim model
-Write points to osim file
-8. Scale the ligament parameter based on the length of the ligament (still in Matlab)
-
diff --git a/LigamentStudy/Registration4DCT.py b/LigamentStudy/Registration4DCT.py
deleted file mode 100644
index e69de29..0000000
diff --git a/LigamentStudy/SlicerEnableUndo.py b/LigamentStudy/SlicerEnableUndo.py
deleted file mode 100644
index 826a0d6..0000000
--- a/LigamentStudy/SlicerEnableUndo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Enable undo for the scene
-# exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerEnableUndo.py').read())
-
-slicer.mrmlScene.SetUndoOn()
-
-# Enable undo for markups fiducial nodes
-
-defaultMarkupsNode = slicer.mrmlScene.GetDefaultNodeByClass("vtkMRMLMarkupsFiducialNode")
-if not defaultMarkupsNode:
- defaultMarkupsNode = slicer.vtkMRMLMarkupsFiducialNode()
- slicer.mrmlScene.AddDefaultNode(defaultMarkupsNode)
-
-defaultMarkupsNode.UndoEnabledOn()
-
-# Add standard keyboard shortcuts for scene undo/redo
-
-redoKeyBindings = qt.QKeySequence.keyBindings(qt.QKeySequence.Redo)
-for redoBinding in redoKeyBindings:
- redoShortcut = qt.QShortcut(slicer.util.mainWindow())
- redoShortcut.setKey(redoBinding)
- redoShortcut.connect("activated()", slicer.mrmlScene.Redo)
-
-undoKeyBindings = qt.QKeySequence.keyBindings(qt.QKeySequence.Undo)
-for undoBinding in undoKeyBindings:
- undoShortcut = qt.QShortcut(slicer.util.mainWindow())
- undoShortcut.setKey(undoBinding)
- undoShortcut.connect("activated()", slicer.mrmlScene.Undo)
\ No newline at end of file
diff --git a/LigamentStudy/SlicerExportXray.py b/LigamentStudy/SlicerExportXray.py
deleted file mode 100644
index f6604d9..0000000
--- a/LigamentStudy/SlicerExportXray.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import glob
-import shutil
-import os
-import DICOMScalarVolumePlugin
-import slicer
-import vtk
-#exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerExportXray.py').read())
-
-subjects = [13,19,23,26,29,32,35,37,41] # 9
-for subject in subjects:
- lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),'DRR')
- slicer.mrmlScene.Clear(0)
- slicer.util.loadScene(glob.glob(os.path.join(path,"*.mrml"))[0])
- no_med=-1
- no_lat=-1
- for name in lig_names:
- slicer.util.loadSegmentation(os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData',str(subject),name+'centroids.stl'))
-
- if name == 'PCL' or name == 'MCL-p' or name == 'MCL-d' or name == 'posterior oblique':
- segmentationNode = slicer.util.getNode('Segmentation_med')
- else:
- segmentationNode = slicer.util.getNode('Segmentation_lat')
- segmentationNode.GetSegmentation().CopySegmentFromSegmentation(slicer.util.getNode(name+'centroids').GetSegmentation(),name+'centroids')
-
- labelmapVolumeNode = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLabelMapVolumeNode')
- # slicer.modules.segmentations.logic().ExportVisibleSegmentsToLabelmapNode(segmentationNode, labelmapVolumeNode)
- segmentIds = vtk.vtkStringArray()
- segmentIds.InsertNextValue(name + 'centroids')
- slicer.vtkSlicerSegmentationsModuleLogic.ExportSegmentsToLabelmapNode(segmentationNode, segmentIds, labelmapVolumeNode)
-
- outputvolumenode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLScalarVolumeNode", 'Labelmap'+name)
- sef = slicer.modules.volumes.logic().CreateScalarVolumeFromVolume(slicer.mrmlScene, outputvolumenode, labelmapVolumeNode)
- volumeNode = slicer.util.getNode("Labelmap"+name)
- voxels = slicer.util.arrayFromVolume(volumeNode)
- voxels[voxels==1] = 8000
- voxels[voxels==2] = 8000
- voxels[voxels==3] = 8000
- voxels[voxels==4] = 8000
- voxels[voxels==0] = -8000
-
- rtImagePlan = slicer.util.getNode("RTPlan")
- if name=='PCL' or name=='MCL-p' or name == 'MCL-d' or name=='posterior oblique':
- beam_name = "NewBeam_med"
- no_med +=1
- no=no_med
- else:
- beam_name = "NewBeam_lat"
- no_lat +=1
- no=no_lat
- rtImageBeam = rtImagePlan.GetBeamByName(beam_name)
- Volume = slicer.util.getNode("Labelmap"+name)
- # Create DRR image computation node for user imager parameters
- drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
- # Set and observe RTImage beam by the DRR node
- drrParameters.SetAndObserveBeamNode(rtImageBeam)
- # Get DRR computation logic
- drrLogic = slicer.modules.drrimagecomputation.logic()
- # Update imager markups for the 3D view and slice views (optional)
- drrLogic.UpdateMarkupsNodes(drrParameters)
- # Update imager normal and view-up vectors (mandatory)
- drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
- # Compute DRR image
- drr_image = drrLogic.ComputePlastimatchDRR(drrParameters, Volume)
- # slicer.mrmlScene.Clear(0)
- if no == 0:
- volumeNode = slicer.util.getNode("DRR : " + beam_name)
- else:
- volumeNode = slicer.util.getNode("DRR : " + beam_name + "_" + str(no))
- outputFolder = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), "DRR")
- # Create patient and study and put the volume under the study
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- patientItemID = shNode.CreateSubjectItem(shNode.GetSceneItemID(), "test patient")
- studyItemID = shNode.CreateStudyItem(patientItemID, "test study")
- volumeShItemID = shNode.GetItemByDataNode(volumeNode)
- shNode.SetItemParent(volumeShItemID, studyItemID)
- exporter = DICOMScalarVolumePlugin.DICOMScalarVolumePluginClass()
- exportables = exporter.examineForExport(volumeShItemID)
- for exp in exportables:
- exp.directory = outputFolder
-
- exporter.export(exportables)
- folders = [x[0] for x in os.walk(outputFolder)]
- im_folder = [s for s in folders if str(volumeShItemID) in s]
- shutil.move(im_folder[0] + '\IMG0001.dcm', outputFolder + '/' + name + '0001.dcm')
- os.rmdir(im_folder[0])
-
- names = ['all','med','lat']
- for name in names:
- volumeNode = slicer.util.getNode("Segmentation_"+name+'-label')
- voxels = slicer.util.arrayFromVolume(volumeNode)
- voxels[voxels==1] = 8000
- voxels[voxels==2] = 8000
- voxels[voxels==3] = 8000
- voxels[voxels==4] = 8000
- voxels[voxels == 5] = 8000
- voxels[voxels == 6] = 8000
- voxels[voxels == 7] = 8000
- voxels[voxels == 8] = 8000
- voxels[voxels==0] = -8000
-
- names = ["med_fem", "lat_fem", "med_wires", "lat_wires", "med_all_wires", "lat_all_wires"]
- for name in names:
- rtImagePlan = slicer.util.getNode("RTPlan")
- if 'lat' in name:
- beam_name = "NewBeam_lat"
- no_lat += 1
- no = no_lat
- else:
- beam_name = "NewBeam_med"
- no_med += 1
- no = no_med
- rtImageBeam = rtImagePlan.GetBeamByName(beam_name)
- if 'fem' in name:
- Volume = slicer.util.getNode("resampled06")
- elif 'med_wires' in name:
- Volume = slicer.util.getNode("Segmentation_med-label")
- elif 'lat_wires' in name:
- Volume = slicer.util.getNode("Segmentation_lat-label")
- elif 'all' in name:
- Volume = slicer.util.getNode("Segmentation_all-label")
- # Create DRR image computation node for user imager parameters
- drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
- # Set and observe RTImage beam by the DRR node
- drrParameters.SetAndObserveBeamNode(rtImageBeam)
- # Get DRR computation logic
- drrLogic = slicer.modules.drrimagecomputation.logic()
- # Update imager markups for the 3D view and slice views (optional)
- drrLogic.UpdateMarkupsNodes(drrParameters)
- # Update imager normal and view-up vectors (mandatory)
- drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
- # Compute DRR image
- drr_image = drrLogic.ComputePlastimatchDRR(drrParameters, Volume)
- # slicer.mrmlScene.Clear(0)
-
- if no == 0:
- volumeNode = slicer.util.getNode("DRR : " + beam_name) #getNode("DRR : Beam_" + name)
- else:
- volumeNode = slicer.util.getNode("DRR : " + beam_name + '_' + str(no))
- outputFolder = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), "DRR")
- # Create patient and study and put the volume under the study
- shNode = slicer.vtkMRMLSubjectHierarchyNode.GetSubjectHierarchyNode(slicer.mrmlScene)
- patientItemID = shNode.CreateSubjectItem(shNode.GetSceneItemID(), "test patient")
- studyItemID = shNode.CreateStudyItem(patientItemID, "test study")
- volumeShItemID = shNode.GetItemByDataNode(volumeNode)
- shNode.SetItemParent(volumeShItemID, studyItemID)
- exporter = DICOMScalarVolumePlugin.DICOMScalarVolumePluginClass()
- exportables = exporter.examineForExport(volumeShItemID)
- for exp in exportables:
- exp.directory = outputFolder
-
- exporter.export(exportables)
- folders = [x[0] for x in os.walk(outputFolder)]
- im_folder = [s for s in folders if str(volumeShItemID) in s]
- shutil.move(im_folder[0] + '\IMG0001.dcm', outputFolder+'/' + name + '0001.dcm')
- os.rmdir(im_folder[0])
-
-# in slicer
-# import resampled data
-# import segmented seperate wires as segmentation
-# create 3 new segmentations (all, med, lat) with resampled image as master volume
-# in segmentations - copy wires segmentation to segmentation resampled volume
-# add correct wires to med/lat/all
-# export visible segments to label map
-# in volumes - convert to scalar volume
-
-# import resampled femur
-# external beam planning
-# Ref volume: resampled06
-# Gantry: 101/281
-# Structure set: segmentsation all
-# DRR image computation
-# export to DICOM - crate dicom series
diff --git a/LigamentStudy/SlicerPositionBeam.py b/LigamentStudy/SlicerPositionBeam.py
deleted file mode 100644
index 5a903be..0000000
--- a/LigamentStudy/SlicerPositionBeam.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\SlicerPositionBeam.py').read())
-import os,glob
-subject = 23
-
-path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject),'DRR')
-slicer.mrmlScene.Clear(0)
-slicer.util.loadScene(glob.glob(os.path.join(path,"*.mrml"))[0])
-# Create dummy RTPlan
-rtImagePlan = getNode("RTPlan")
-rtImageBeam = rtImagePlan.GetBeamByName("NewBeam_lat")
-# Set required beam parameters
-current_angle = rtImageBeam.GetGantryAngle()
-rtImageBeam.SetGantryAngle(current_angle-7)
-rtImageBeam.SetCouchAngle(355)
-
-rtImageBeam2 = rtImagePlan.GetBeamByName("NewBeam_med")
-rtImageBeam2.SetGantryAngle(current_angle-7+180)
-rtImageBeam2.SetCouchAngle(355)
-
-# # Get CT volume
-# ctVolume = getNode('resampled06')
-# # Create DRR image computation node for user imager parameters
-# drrParameters = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLDrrImageComputationNode', 'rtImageBeamParams')
-# # Set and observe RTImage beam by the DRR node
-# drrParameters.SetAndObserveBeamNode(rtImageBeam)
-# # Get DRR computation logic
-# drrLogic = slicer.modules.drrimagecomputation.logic()
-# # Update imager markups for the 3D view and slice views (optional)
-# drrLogic.UpdateMarkupsNodes(drrParameters)
-# # Update imager normal and view-up vectors (mandatory)
-# drrLogic.UpdateNormalAndVupVectors(drrParameters) # REQUIRED
-# # Compute DRR image
-# drrLogic.ComputePlastimatchDRR(drrParameters, ctVolume)
-
-# save scene
-slicer.util.saveScene(glob.glob(os.path.join(path,"*.mrml"))[0])
diff --git a/LigamentStudy/SlicerXrayMeanSSM.py b/LigamentStudy/SlicerXrayMeanSSM.py
deleted file mode 100644
index 27c0315..0000000
--- a/LigamentStudy/SlicerXrayMeanSSM.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import DICOMScalarVolumePlugin
-import slicer
-import vtk
-
-volumeNode = slicer.util.getNode("Volume")
-voxels = slicer.util.arrayFromVolume(volumeNode)
-voxels[voxels==0] = -1000
-voxels[voxels==1] = 1000
-voxels[voxels==2] = 2000
-
-volumeNode = slicer.util.getNode("LCLpoints7_1-LCLpoints7-label")
-voxels = slicer.util.arrayFromVolume(volumeNode)
-voxels[voxels==1] = 8000
-voxels[voxels==0] = -8000
-
-import seaborn as sns
-c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
diff --git a/LigamentStudy/TibiaGrid.py b/LigamentStudy/TibiaGrid.py
deleted file mode 100644
index ed1d2eb..0000000
--- a/LigamentStudy/TibiaGrid.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Find most anterior edge of the femoral notch roof - representation Blumensaat line for 3D shapes
-# https://journals.lww.com/jbjsjournal/Fulltext/2010/06000/The_Location_of_Femoral_and_Tibial_Tunnels_in.10.aspx?__hstc=215929672.82af9c9a98fa600b1bb630f9cde2cb5f.1528502400314.1528502400315.1528502400316.1&__hssc=215929672.1.1528502400317&__hsfp=1773666937&casa_token=BT765BcrC3sAAAAA:Vu9rn-q5ng4c8339KQuq2mGZDgrAgBStwvn4lvYEbvCgvKQZkbJL24hWbKFdnHTc8VBmAIXA3HVvuWg22-9Mvwv1sw
-# https://www.dropbox.com/sh/l7pd43t7c4hrjdl/AABkncBbleifnpLDKSDDc0dCa/D3%20-%20Dimitriou%202020%20-%20Anterior%20cruciate%20ligament%20bundle%20insertions%20vary.pdf?dl=0
-
-import trimesh
-import numpy as np
-import os
-import math
-import pandas as pd
-import pymeshlab
-import seaborn as sns
-
-
-def findIntersection(x1, y1, x2, y2, x3, y3, x4, y4):
- px = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
- py = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / (
- (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))
-
- ang = math.atan2(py - y3, px - x3) - math.atan2(y1 - y3, x1 - x3)
-
- l = math.cos(ang)*np.linalg.norm(np.asarray((x3,y3))-np.asarray((x4,y4)))
-
- return l, px, py
-
-def split(start, end, segments):
- x_delta = (end[0] - start[0]) / float(segments)
- y_delta = (end[1] - start[1]) / float(segments)
- z_delta = (end[2] - start[2]) / float(segments)
- points = []
- for i in range(1, segments):
- points.append([start[0] + i * x_delta, start[1] + i * y_delta, start[2] + i * z_delta])
- return [start] + points + [end]
-
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments = ligaments_tib
-
-# find most ant point in yz plane
-subjects = [100] # [9,13,19,23,26,29,32,35,37,41] #
-lig = 'ACL'
-segment = 'tibia'
-
-d = []
-h = []
-h_centriods = []
-d_centriods = []
-for ind, subject in enumerate(subjects):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_rot.stl')
- path_col = r'C:\\Users\\mariskawesseli\\Documents\\GitLab\\knee_ssm\\OAI\\Output/tibia_bone\\new_bone\\shape_models'
- side = 'R'
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject), 'Segmentation_tibia_transform.STL')
-
- mesh = trimesh.load_mesh(path)
- verts = mesh.vertices
- AP = mesh.bounding_box.bounds[1,1] - mesh.bounding_box.bounds[0,1]
- ML = mesh.bounding_box.bounds[1,0] - mesh.bounding_box.bounds[0,0]
- bbox = mesh.bounding_box.bounds
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, (0,-1,0), (0,20,0), cached_dots=None, return_both=False)
- # posterior_mesh.show()
-
- # find anterior line
- lines, to_3D, face_index = trimesh.intersections.mesh_multiplane(mesh, (0, 0, 0), (0, 0, 1),
- heights=np.linspace(-15, 5, 21))
- ant_point = []
- prox_point = []
- for i in range(0, len(face_index)):
- plane_verts = np.unique(mesh.faces[face_index[i]])
- plane_points = mesh.vertices[plane_verts]
-
- # goon = 1
- # tel = 2
- # while goon == 1:
- min_y = np.where(plane_points[:, 1] == plane_points[:, 1].max())
- ant_point.append(plane_points[min_y])
- # min_y2 = np.where(plane_points[:, 1] == np.partition(plane_points[:, 1], tel + 1)[tel + 1])
- # z_min2 = plane_points[min_y2][0][1]
- # if z_min - z_min2 > -15:
- # goon = 1
- # tel += 1
- # else:
- # goon = 0
- # dist_point.append(plane_points[min_y][0])
- # min_y = np.where(plane_points[:, 1] == plane_points[:, 1].min())
- # prox_point.append(plane_points[min_y][0])
-
- # most_ant_ind1 = np.asarray(dist_point)[:, 1].argmax()
- # most_ant_ind2 = np.asarray(prox_point)[:, 1].argmax()
- p1=ant_point[np.argmin(np.squeeze(np.array(ant_point))[:,1])][0]
-
- # min_y = mesh.vertices[np.argmin(mesh.vertices[:, 1])]
- # p1 = min_y
- p2 = np.array((0,p1[1],p1[2]))
-
- # find posterior line
- min_y = mesh.vertices[np.argmin(mesh.vertices[:, 1])]
- p3 = min_y
- p4 = np.array((0, min_y[1], min_y[2]))
-
- # find medial line
- min_x = mesh.vertices[np.argmin(mesh.vertices[:, 0])]
- p5 = min_x
- p6 = np.array((min_x[0], 0, min_x[2]))
-
- # find lateral line
- max_x = mesh.vertices[np.argmax(mesh.vertices[:, 0])]
- p7 = max_x
- p8 = np.array((max_x[0], 0, max_x[2]))
-
-
- # find height
- # vec1 = (p1[0][0] - p2[0][0], p1[0][1] - p2[0][1], p1[0][2] - p2[0][2])
- # norm = np.sqrt(vec1[0] ** 2 + vec1[1] ** 2 + vec1[2] ** 2)
- # direction = [vec1[0] / norm, vec1[1] / norm, vec1[2] / norm]
-
-
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
-
- # trimesh.path.segments.parameters_to_segments(p1[-1], -1*direction, ((0,0,0),(0,1,0)))
- # trimesh.path.segments.segments_to_parameters(np.asarray(segments))
-
- # posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,0,10), cached_dots=None, return_both=False)
-
-
-
- # segments = np.asarray([p3[np.asarray(dist).argmax()], p4[np.asarray(dist).argmax()]])
- # p_dist = trimesh.load_path(segments)
- p1_2d = p1[0:2]
- p2_2d = p2[0:2]
- p3_2d = p3[0:2]
- # d.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-p3_2d))/np.linalg.norm(p2_2d-p1_2d))
- d.append(p3[1]-p1[1])
-
- # find depth
- p5_2d = p5[0:2]
- p6_2d = p6[0:2]
- p7_2d = p7[0:2]
- # h.append(np.linalg.norm(np.cross(p6_2d - p5_2d, p5_2d - p7_2d)) / np.linalg.norm(p6_2d - p7_2d))
- h.append(p7[0] - p5[0])
-
- # visualization
- # p1[0][0] = 0
- # p2[0][0] = 0
- # p3[np.asarray(dist1).argmax()][0] = 0
- # p4[jump_ind + 1][0] = 0
- # p5[0] = 0
- # p6[jump_ind + 1][0] = 0
-
- points = trimesh.points.PointCloud(np.asarray((p1,p2,p3,p4,p5,p6,p7,p8)), colors=None, metadata=None)
- # segments = np.asarray([p1[-1], p2[-1]])
- # p = trimesh.load_path(segments)
- # segments = np.asarray([p6[jump_ind+1], p5])
- # p_dist = trimesh.load_path(segments)
-
- mesh.visual.face_colors[:] = np.array([227, 218, 201, 100])
- mesh.visual.vertex_colors[:] = np.array([227, 218, 201, 100])
- direction = (0,1,0)
- direction_perp = (1,0,0)
- line = trimesh.path.segments.parameters_to_segments([p5,p7,p1,p3], [direction,direction,direction_perp,direction_perp],
- np.array(((27,d[-1]+30),(-26,-d[-1]-27),(-42,h[-1]-37),(48,-h[-1]+46))).astype(float))
-
- box_points = trimesh.load_path(np.squeeze(line)).vertices
- grid_points1 = split(box_points[0], box_points[5], 4)
- grid_points2 = split(box_points[0], box_points[2], 4)
- grid_line = trimesh.path.segments.parameters_to_segments([grid_points1[1], grid_points1[2], grid_points1[3]],
- [direction_perp], np.array(
- ((h[-1] + 4, -0), (h[-1] + 2.5, 0), (h[-1] + 0.5, -0))).astype(float))
- grid_line2 = trimesh.path.segments.parameters_to_segments([grid_points2[1], grid_points2[2], grid_points2[3]],
- [direction],
- np.array(((d[-1] - 1.5, 0), (d[-1] - 1.5, 0),
- (d[-1] - 1.5, 0))).astype(
- float))
- grid_line_path = trimesh.load_path(np.squeeze(grid_line),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
- grid_line2_path = trimesh.load_path(np.squeeze(grid_line2),
- colors=((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
-
- scene = trimesh.Scene([mesh, trimesh.load_path(np.squeeze(line)),grid_line_path,grid_line2_path]) #, points
- scene.show()
- # mesh.vertices[:, 0] = 0
- # trimesh.Scene([mesh, points, trimesh.load_path(np.squeeze(line))]).show()
-
-# posterior_mesh = trimesh.intersections.slice_mesh_plane(mesh, direction, (0,-30,0), cached_dots=None, return_both=False)
-# posterior_mesh.show()
- if subject == 100:
- points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_rot.xyz')
- if lig == 'ACL':
- center = np.arange(470 - 341) + 341 # ACL
- mean = np.array((48.2, -45.1, -9.4))/100 * np.array((ML,AP,AP)) + np.array((bbox[0,0],bbox[1,1],bbox[1,2]))
- else:
- center = np.arange(131) # PCL np.array((0,0,0)) #
- mean = np.array((50.8, -87.6, -23.9))/100 * np.array((ML,AP,AP)) + np.array((bbox[0,0],bbox[1,1],bbox[1,2]))
- points_lig = points_lig[center]
- # origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- # Rz = trimesh.transformations.rotation_matrix(180/np.pi, zaxis)
- # points_lig.apply_transform(Rz)
- color_file = np.loadtxt(path_col + '\meanshape_ligs_color.xyz')[:, 3]
- color_file = color_file[center]
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
-
- color = []
- for ind_col, point in enumerate(points_lig):
- center_2d = point[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d - p1_2d, p1_2d - center_2d)) / np.linalg.norm(p2_2d - p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0],
- p5_2d[1])
- d_centriods.append(l)
- vcolors=[c[int(color_file[ind_col] - 1)][0] * 255, c[int(color_file[ind_col] - 1)][1] * 255,
- c[int(color_file[ind_col] - 1)][2] * 255]
- color.append(vcolors)
- p_lig = trimesh.points.PointCloud(points_lig, colors=color)
- p_mean = trimesh.primitives.Sphere(radius=1, center=mean, subdivisions=3, color=[255, 0, 0]) # trimesh.points.PointCloud([mean,mean], colors=[[255,0,0],[255,0,0]])
- p_mean.visual.face_colors = np.array([255, 0, 0, 255])
- # scene2 = trimesh.Scene([mesh, points, p_lig, trimesh.load_path(np.squeeze(line))])
- # scene2.apply_transform(R)
- # scene2.camera_transform = camera_trans
- # scene2.show()
- scene.add_geometry([p_lig, p_mean]) #p_lig ,transform=R
- scene.show()
- else:
- if lig == 'ACL':
- lig_no = ligaments[8][ind]
- elif lig == 'PCL':
- lig_no = ligaments[0][ind]
- if not lig_no == 0:
- segment = 'femur'
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
-
- ms4.apply_filter('flatten_visible_layers', deletelayer=True)
- ms4.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- center = geometric_measures['shell_barycenter']
- center_2d = center[1:3]
- h_centriods.append(np.linalg.norm(np.cross(p2_2d-p1_2d, p1_2d-center_2d))/np.linalg.norm(p2_2d-p1_2d))
- l, px, py = findIntersection(p1_2d[0], p1_2d[1], p2_2d[0], p2_2d[1], center_2d[0], center_2d[1], p5_2d[0], p5_2d[1])
- d_centriods.append(l)
- else:
- h_centriods.append(0)
- d_centriods.append(0)
-
-[1-abs(i / j) for i, j in zip(d_centriods, d)]
-[i / j for i, j in zip(h_centriods, h)]
-
-d_centriods/np.asarray(d)
-h_centriods/np.asarray(h)
-
-np.mean(abs(np.asarray(d_centriods))/np.asarray(d))
-np.mean(h_centriods/np.asarray(h))
-
-
-
diff --git a/LigamentStudy/TransformWires.py b/LigamentStudy/TransformWires.py
deleted file mode 100644
index 35ab2ae..0000000
--- a/LigamentStudy/TransformWires.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-from openpyxl import load_workbook
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #9,13,19,23,26,29,32,35,41
-segments = ['tibia','femur'] #'femur',
-short = 1
-ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
-ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
-ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
-for segment in segments:
- if segment == 'femur':
- ligaments = ligaments_fem
- else:
- ligaments = ligaments_tib
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
- mesh2 = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/' + str(
- subject) + '\Segmentation_' + segment + '_wires.stl'
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(mesh2)
- ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- ms5.save_current_mesh(path + '\Segmentation_' + segment + '_wires_transform.stl', binary=False)
-
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- mesh2 = path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl'
- # transform femur to local coordinate system to get anatomical directions
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(mesh2)
- ms5.apply_filter('matrix_set_copy_transformation', transformmatrix=rot_mat)
- ms5.save_current_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '_transform.stl', binary=False)
diff --git a/LigamentStudy/VisualiseSSM.py b/LigamentStudy/VisualiseSSM.py
deleted file mode 100644
index df356a1..0000000
--- a/LigamentStudy/VisualiseSSM.py
+++ /dev/null
@@ -1,412 +0,0 @@
-import sys
-import os
-import vtk
-from numpy import random
-import trimesh
-import numpy as np
-import seaborn as sns
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-def create_pointcloud_polydata(points, colors=None, seg=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- # vpoints.SetMarkerStyle(vtk.vtkPlotPoints.CIRCLE)
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
- rgb_col = []
- if not colors is None:
- # if seg == 'femur':
- # max_val=8
- # color[112:len(color)] = (color[112:len(color)]/max_val)*10
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- rgb_col = []
- for i in range(points.shape[0]):
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- vcolors.SetTuple3(i, c[int(colors[i] *10)][0]*255, c[int(colors[i] *10)][1]*255, c[int(colors[i] *10)][2]*255)
- rgb_col.append([c[int(colors[i] *10)][0] * 255, c[int(colors[i] *10)][1] * 255, c[int(colors[i] *10)][2] * 255])
- # print(i, c[int(colors[i] - 1)][0], c[int(colors[i] - 1)][1], c[int(colors[i] - 1)][2])
- # c = rgb(1,10,colors[i])
- # vcolors.SetTuple3(i, c[0], c[1], c[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
-
- return vpoly, rgb_col
-
-
-def rgb(minimum, maximum, value):
- minimum, maximum = float(minimum), float(maximum)
- ratio = (value-minimum) / (maximum - minimum) #2 *
- g = int(max(0, 255*(1 - ratio)))
- r = int(max(0, 255*(ratio - 0)))
- b = 0 #255 - b - r
- return r, g, b
-
-
-def createSpline(points):
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
-
- spline = vtk.vtkParametricSpline()
- spline.SetPoints(vpoints)
-
- functionSource = vtk.vtkParametricFunctionSource()
- functionSource.SetParametricFunction(spline)
- functionSource.Update()
-
- # Create a mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputConnection(functionSource.GetOutputPort())
-
- # Create an actor
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-if __name__ == '__main__':
- center_only = 0
- lateral_only = 0
-
- if center_only == 1:
- center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
- elif lateral_only == 1:
- center_femur = np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # LCL+pop
- center_tibia = np.arange(242) # LCL
-
- subjects = [100] #[100] # ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-
- segments = ['tibia'] #'femur',
- ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
- ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
- ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
- for segment in segments:
- SSMpoints = [[] for i in range(11)]
- if center_only == 1 or lateral_only == 1:
- if segment == 'tibia':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
-
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject == 100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject == 100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs_color.xyz') # _8192
- color = np.loadtxt(path + r'\meanshape_ligs_color.xyz')[:, 3] # _8192
-
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)
- bone_actor = load_stl(path + '/mean_shape.stl') # _8192
- bone_actor.GetProperty().SetOpacity(1.0)
-
- mesh = trimesh.load_mesh(path + '/mean_shape.stl') # _8192
- # dist = trimesh.proximity.nearby_faces(mesh, np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- dist3 = trimesh.proximity.closest_point_naive(mesh, np.squeeze(
- np.asarray(points_lig[np.argwhere(color >= 7)])), tol=1.0)
-
- # faces = np.unique(np.asarray([item for sublist in dist for item in sublist]))
- faces = np.unique(np.asarray([item for sublist in dist3[3] for item in sublist]))
- mesh.update_faces(faces)
- mesh.export(path + '/mean_shape_80percsurf.stl') # _8192
- surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') # _8192
- else:
- # points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_areas.xyz') #_pred_points_color
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points_color.xyz') # _pred_points_color
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color.xyz')[:,3] #_areas _short_areas _pred_points
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- # color = color[center]
- point_cloud_lig = create_pointcloud_polydata(points_lig, seg=segment) #,color colors=color,
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- # bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_fibula_1_tissue_rot.stl')
- else:
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_resample.stl') # '/SSM_' + segment + '_reconstruct_transform_icp.stl'
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- # if center_only == 1:
- # wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires1.stl')
- # wire_actor2 = load_stl(path + '/Segmentation_' + segment_temp + '_wires3.stl')
- # wire_actor2.GetProperty().SetColor(1, 1, 0)
- # else:
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.75)
-
- points_bone = trimesh.load_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
- point_cloud_bone = create_pointcloud_polydata(points_bone)
-
- # orders = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_order.npy')
-
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(point_cloud_bone)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- actor.GetProperty().SetColor(0,0,0)
- actor.GetProperty().SetPointSize(2)
- # actor.GetProperty().SetOpacity(1.0)
-
- # spline_actor = createSpline(np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().RenderPointsAsSpheresOn()
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(7.5)
-
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(11)
- lut.SetTableRange(1, 11)
- for j in range(0,11):
- lut.SetTableValue(int(j*1), c[j*10][0], c[j*10][1], c[j*10][2])
- # print(int(j*1), c[j*10-1][0], c[j*10-1][1], c[j*10-1][2])
-
- j = 10-1
- surf_col = [c[j][0], c[j][1], c[j][2]]
- surf_col = [169/255, 169/255, 169/255]
- surf_actor.GetProperty().SetColor(surf_col)
- surf_actor.GetProperty().SetOpacity(1.0)
-
- legend = vtk.vtkScalarBarActor()
- legend.SetOrientationToHorizontal()
- labelFormat = vtk.vtkTextProperty()
- labelFormat.SetFontSize(16)
- titleFormat = vtk.vtkTextProperty()
- titleFormat.SetFontSize(8)
- legend.SetLabelTextProperty(labelFormat)
- # legend.SetTitleTextProperty(titleFormat)
-
- legend.SetNumberOfLabels(11)
- lut.SetTableRange(0, 100)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
-
- legend.SetTitle("% of specimens \n")
- legend.SetLabelFormat("%1.0f")
- legend.SetUnconstrainedFontSize(1)
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0,0,0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- # legend.SetMaximumWidthInPixels(75)
- # legend.SetMaximumHeightInPixels(300)
- legend.SetMaximumWidthInPixels(300)
- legend.SetMaximumHeightInPixels(75)
- legend.SetTitleTextProperty(text_prop_cb)
- # legend.SetPosition(0.85,0.5)
- legend.SetPosition(0.5, 0.85)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(spline_actor)
- renderer.AddActor(surf_actor)
- if not subject == 100 and not subject == 'S0':
- renderer.AddActor(wire_actor)
- # renderer.AddActor(wire_actor2)
- renderer.AddActor(legend)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
-
-
- polyData = vtk.vtkPolyData()
- polyData.DeepCopy(actor2.GetMapper().GetInput())
- transform = vtk.vtkTransform()
- transform.SetMatrix(actor2.GetMatrix())
- fil = vtk.vtkTransformPolyDataFilter()
- fil.SetTransform(transform)
- fil.SetInputDataObject(polyData)
- fil.Update()
- polyData.DeepCopy(fil.GetOutput())
-
- writer = vtk.vtkPLYWriter()
- writer.SetFileTypeToASCII()
- writer.SetColorModeToDefault()
- filename = r'C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col2.ply'
- writer.SetFileName(filename)
- writer.SetInputData(polyData)
- writer.Write()
-
- # import pandas as pd
- # pd.DataFrame(color).to_clipboard()
\ No newline at end of file
diff --git a/LigamentStudy/VisualizeCenter.py b/LigamentStudy/VisualizeCenter.py
deleted file mode 100644
index bb85692..0000000
--- a/LigamentStudy/VisualizeCenter.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import pymeshlab
-import os
-import vtk
-from VisualiseSSM import create_pointcloud_polydata
-import numpy as np
-import glob
-
-
-def load_stl(filename, rot_mat):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.SetMatrix([item for sublist in rot_mat for item in sublist])
- # transform.Translate(10, 0, 0)
-
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(transformFilter.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-class MyInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):
-
- def __init__(self,parent=None):
- self.parent = renderWindowInteractor
-
- self.AddObserver("KeyPressEvent",self.keyPressEvent)
-
- def keyPressEvent(self,obj,event):
- key = self.parent.GetKeySym()
- if key == 'b':
- vis = outlineActor.GetVisibility()
- if vis:
- outlineActor.SetVisibility(False)
- else:
- outlineActor.SetVisibility(True)
-
- return
-
-
-subject = 35 # [9,13,19,23,26,29,32,35,37,41]
-segment = 'femur'
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
-if segment == 'femur':
- ligaments = ligaments_fem
-else:
- ligaments = ligaments_tib
-
-ind = np.where(np.asarray([9,13,19,23,26,29,32,35,37,41]) == subject)
-# Renderer
-renderer = vtk.vtkRenderer()
-
-path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-rot_mat = np.linalg.inv(np.loadtxt(path + '\Segmentation_' + segment + '_resample._ACS.txt'))
-
-for lig in range(0, 8):
- lig_no = ligaments[lig][ind[0][0]]
- if not lig_no == 0:
- ms4 = pymeshlab.MeshSet()
- ms4.load_new_mesh(path + '\Segmentation_' + segment + '_area' + str(lig_no) + '.stl')
- geometric_measures = ms4.apply_filter('compute_geometric_measures')
- surface = geometric_measures['surface_area']
- center= geometric_measures['shell_barycenter']
-
- point_cloud_lig = create_pointcloud_polydata(np.asarray([center,center]))
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.SetMatrix([item for sublist in rot_mat for item in sublist])
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetTransform(transform)
- transformFilter.SetInputData(point_cloud_lig)
- transformFilter.Update()
-
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputConnection(transformFilter.GetOutputPort())
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(10)
-
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
-
- Counter = len(glob.glob1(path, 'Segmentation_' + segment + '_area*.stl'))
- for count in range(1, Counter + 1):
- bone_actor = load_stl(path + '\Segmentation_' + segment + '_area' + str(count) + '.stl', rot_mat)
- bone_actor.GetProperty().SetOpacity(0.75)
- bone_actor.GetProperty().SetColor(0, 0, 1)
- renderer.AddActor(bone_actor)
-
-wire_actor = load_stl(path + '/Segmentation_' + segment + '_wires.stl', rot_mat)
-wire_actor.GetProperty().SetOpacity(1.0)
-wire_actor.GetProperty().SetColor(1, 1, 0)
-renderer.AddActor(wire_actor)
-
-reader = vtk.vtkSTLReader()
-reader.SetFileName(path + '/Segmentation_' + segment + '_resample.stl')
-transform = vtk.vtkTransform()
-transform.Identity()
-transform.SetMatrix([item for sublist in rot_mat for item in sublist])
-transformFilter = vtk.vtkTransformPolyDataFilter()
-transformFilter.SetInputConnection(reader.GetOutputPort())
-transformFilter.SetTransform(transform)
-transformFilter.Update()
-mapper = vtk.vtkPolyDataMapper()
-mapper.SetInputConnection(transformFilter.GetOutputPort())
-bone_actor = vtk.vtkActor()
-bone_actor.SetMapper(mapper)
-bone_actor.GetProperty().SetOpacity(0.75)
-
-renderer.AddActor(bone_actor)
-
-outline = vtk.vtkOutlineFilter()
-outline.SetInputConnection(transformFilter.GetOutputPort())
-outlineMapper = vtk.vtkPolyDataMapper()
-outlineMapper.SetInputConnection(outline.GetOutputPort())
-outlineActor = vtk.vtkActor()
-outlineActor.SetMapper(outlineMapper)
-outlineActor.GetProperty().SetColor(0,0,0)
-outlineActor.SetVisibility(False)
-
-renderer.AddActor(outlineActor)
-
-renderer.SetBackground(1.0, 1.0, 1.0)
-renderer.ResetCamera()
-
-# Render Window
-renderWindow = vtk.vtkRenderWindow()
-renderWindow.AddRenderer(renderer)
-
-# Interactor
-renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-renderWindowInteractor.SetRenderWindow(renderWindow)
-renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-renderWindowInteractor.SetInteractorStyle(MyInteractorStyle())
-
-# Begin Interaction
-renderWindow.Render()
-renderWindow.SetWindowName("XYZ Data Viewer")
-renderWindowInteractor.Start()
\ No newline at end of file
diff --git a/LigamentStudy/VisualizeMeanSSM.ipynb b/LigamentStudy/VisualizeMeanSSM.ipynb
deleted file mode 100644
index 153a725..0000000
--- a/LigamentStudy/VisualizeMeanSSM.ipynb
+++ /dev/null
@@ -1,942 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "85e5e2e4-aac7-4e29-beb2-725fd4934c4c",
- "metadata": {},
- "source": [
- "# Interactive figure ligament attachment locations anterior and posterior cruciate ligaments\n",
- "3D figures showing the ligament attachment locations of the ACL and PCL ligaments on the mean SSM shape of the femur and tibia.\n",
- "Interactive figure for paper:\n",
- "Voskuijl, T., Wesseling, M., Pennings, M., Piscaer, T., Hanff, D., Meuffels, D.E. \"The adaption of anterior and posterior cruciate ligament attachment sites to the variance of three dimensional bony knee shapes\". Submitted to "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65b03f42-0f5e-4433-bbc1-9d9479691852",
- "metadata": {},
- "source": [
- "Install required packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0a85e4e1-b480-4f37-a5de-4d2098d01e66",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: vtk in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (9.2.6)\n",
- "Requirement already satisfied: matplotlib>=2.0.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from vtk) (3.7.1)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (1.0.5)\n",
- "Requirement already satisfied: cycler>=0.10 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (0.11.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (4.25.0)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mariskawesseli\\appdata\\local\\anaconda\\lib\\site-packages (from matplotlib>=2.0.0->vtk) (1.4.4)\n",
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- "Collecting trimesh\n",
- " Obtaining dependency information for trimesh from https://files.pythonhosted.org/packages/c9/10/c5925a556ae5eebca155524443cb94d84ba5715b56085fbbdd8438eb5509/trimesh-3.23.5-py3-none-any.whl.metadata\n",
- " Using cached trimesh-3.23.5-py3-none-any.whl.metadata (17 kB)\n",
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- "Using cached trimesh-3.23.5-py3-none-any.whl (685 kB)\n",
- "Installing collected packages: trimesh\n",
- "Successfully installed trimesh-3.23.5\n",
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- ]
- }
- ],
- "source": [
- "# ! pip install vtk\n",
- "# ! pip install trimesh\n",
- "# ! pip install seaborn\n",
- "# ! pip install pyvista\n",
- "## ! pip install pythreejs\n",
- "# ! pip install trame\n",
- "# ! pip install trame-vtk\n",
- "# ! pip install trame-vuetify"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "54ddf022-251d-427d-a33a-6305fe69aa57",
- "metadata": {},
- "source": [
- "Import required libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "9ad2e1fd-d4a4-48d2-8966-671b64ce093d",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import vtk\n",
- "import trimesh\n",
- "import numpy as np\n",
- "import seaborn as sns\n",
- "import pyvista as pv"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "176e33a2-d0a1-4124-9f75-a993904edf03",
- "metadata": {},
- "source": [
- "Function to create pointcloud that represents attachment regions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "4efddfba-c04e-4023-8f6c-fdca0a5f25de",
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_pointcloud_polydata(points, colors=None, seg=None):\n",
- "\n",
- " vpoints = vtk.vtkPoints()\n",
- " vpoints.SetNumberOfPoints(points.shape[0])\n",
- " for i in range(points.shape[0]):\n",
- " vpoints.SetPoint(i, points[i])\n",
- "\n",
- " vpoly = vtk.vtkPolyData()\n",
- " vpoly.SetPoints(vpoints)\n",
- " rgb_col = []\n",
- " if not colors is None:\n",
- " if seg == 'femur':\n",
- " max_val=8\n",
- " color[112:len(color)] = (color[112:len(color)]/max_val)*10\n",
- " vcolors = vtk.vtkUnsignedCharArray()\n",
- " vcolors.SetNumberOfComponents(3)\n",
- " vcolors.SetName(\"Colors\")\n",
- " vcolors.SetNumberOfTuples(points.shape[0])\n",
- " rgb_col = []\n",
- " for i in range(points.shape[0]):\n",
- " c = sns.color_palette(\"viridis_r\", n_colors=101, as_cmap=False)\n",
- " vcolors.SetTuple3(i, c[int(colors[i] *10)][0]*255, c[int(colors[i] *10)][1]*255, c[int(colors[i] *10)][2]*255)\n",
- " rgb_col.append([c[int(colors[i] *10)][0] * 255, c[int(colors[i] *10)][1] * 255, c[int(colors[i] *10)][2] * 255])\n",
- " vpoly.GetPointData().SetScalars(vcolors)\n",
- "\n",
- " vcells = vtk.vtkCellArray()\n",
- "\n",
- " for i in range(points.shape[0]):\n",
- " vcells.InsertNextCell(1)\n",
- " vcells.InsertCellPoint(i)\n",
- "\n",
- " vpoly.SetVerts(vcells)\n",
- "\n",
- " return vpoly, rgb_col"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1b90cf6-d3c5-40d5-9299-c60dc1aa510b",
- "metadata": {},
- "source": [
- "Function to load STL file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "8f40bba1-dcd8-4d37-9682-d7d19e21bea0",
- "metadata": {},
- "outputs": [],
- "source": [
- "def load_stl(filename):\n",
- " reader = vtk.vtkSTLReader()\n",
- " reader.SetFileName(filename)\n",
- "\n",
- " mapper = vtk.vtkPolyDataMapper()\n",
- " if vtk.VTK_MAJOR_VERSION <= 5:\n",
- " mapper.SetInput(reader.GetOutput())\n",
- " else:\n",
- " mapper.SetInputConnection(reader.GetOutputPort())\n",
- "\n",
- " actor = vtk.vtkActor()\n",
- " actor.SetMapper(mapper)\n",
- "\n",
- " return actor"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c5029e33-bd8b-4653-a785-f06f980fb543",
- "metadata": {},
- "source": [
- "### Femur attachments"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "56ae25b4-d99b-4ddf-81dd-26285834f9fd",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "77dfff23-beb1-413e-b73b-c20a9e00245f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# segment = 'femur'\n",
- "# center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL\n",
- "# center = center_femur"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "371b2f84",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'femur'\n",
- "# center_femur = np.concatenate((np.arange(706-641)+641,np.arange(776-706)+706)) # np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # LCL+pop\n",
- "center_femur = np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) # ACL+PCL\n",
- "\n",
- "center = center_femur"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "14e03598-c3ac-4706-a503-b18a06d7dada",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "e418af21-2c51-441b-8e2c-94d800a1fd73",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment + '8192')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "70f93a0e-6d37-4c52-ae78-cc346d2e082c",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "154780b1-e011-4a8c-8935-03900ef064d2",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)\n",
- "\n",
- "surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ea25079c-f397-4661-a92e-e59128ab29c4",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "d587c057-3e90-4632-9c92-f54a34f9aa7f",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)\n",
- "\n",
- "surf_col = [169/255, 169/255, 169/255]\n",
- "surf_actor.GetProperty().SetColor(surf_col)\n",
- "surf_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "018a9286-c797-47fb-a793-407ec1c7c7c9",
- "metadata": {},
- "source": [
- "Set colors for ligament attachment points depending on the number of specimens in which each point was identified as attachment region"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "4b4318f7-1dea-4975-a368-f4cfe9840485",
- "metadata": {},
- "outputs": [],
- "source": [
- "c = sns.color_palette(\"viridis_r\", n_colors=101, as_cmap=False)\n",
- "lut = vtk.vtkLookupTable()\n",
- "lut.SetNumberOfColors(11)\n",
- "lut.SetTableRange(1, 11)\n",
- "for j in range(0,11):\n",
- " lut.SetTableValue(int(j*1), c[j*10][0], c[j*10][1], c[j*10][2])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8c62a61-76b1-4ae0-b8f9-3684a41d2d68",
- "metadata": {},
- "source": [
- "Create legend"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "8b2c983b-43b9-4e52-b489-821eb41d9e1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "legend = vtk.vtkScalarBarActor()\n",
- "labelFormat = vtk.vtkTextProperty()\n",
- "labelFormat.SetFontSize(16)\n",
- "titleFormat = vtk.vtkTextProperty()\n",
- "titleFormat.SetFontSize(8)\n",
- "legend.SetLabelTextProperty(labelFormat)\n",
- "\n",
- "legend.SetNumberOfLabels(11)\n",
- "lut.SetTableRange(0, 100)\n",
- "legend.SetLookupTable(lut)\n",
- "\n",
- "legend.SetTitle(\"% of specimens \\n\")\n",
- "legend.SetLabelFormat(\"%1.0f\")\n",
- "legend.SetUnconstrainedFontSize(1)\n",
- "\n",
- "text_prop_cb = legend.GetLabelTextProperty()\n",
- "text_prop_cb.SetFontFamilyAsString('Arial')\n",
- "text_prop_cb.SetFontFamilyToArial()\n",
- "text_prop_cb.SetColor(0,0,0)\n",
- "text_prop_cb.ShadowOff()\n",
- "legend.SetLabelTextProperty(text_prop_cb)\n",
- "legend.SetMaximumWidthInPixels(75)\n",
- "legend.SetMaximumHeightInPixels(300)\n",
- "legend.SetTitleTextProperty(text_prop_cb)\n",
- "legend.SetPosition(0.85,0.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21820712-33a4-4fe4-b97d-0e6381d975b6",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "b9032586",
- "metadata": {},
- "outputs": [],
- "source": [
- "# ! pip install --upgrade trame-vuetify"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "92cf7566",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9d4cec15d4b9477eb10107895221037b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe4dbcc910_3&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "p = pv.Plotter()\n",
- "p.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "# p.add_mesh(edges, color=\"red\", line_width=5)\n",
- "# p.camera_position = [(-0.2, -0.13, 0.12), (-0.015, 0.10, -0.0), (0.28, 0.26, 0.9)]\n",
- "p.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "c98f226f-a28a-4e44-a342-5771f4984d63",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cdb8734269af433ab11dd6fe581eeebf",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe4dc46c90_4&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "\n",
- "# bla=pv.PolyData(point_cloud_lig)\n",
- "# bla.plot()\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "#plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "cd376163",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "147eb2c24de74f62afdf323fe5aa3f13",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Widget(value=\"<iframe src='http://localhost:51547/index.html?ui=P_0x1fe40f4f4d0_2&reconnect=auto' style='width…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show() # show the two spheres from two PolyData\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5324c624",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "74e52268",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur_lateral.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "990d654f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\femur_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfemur.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9ae0ddf1-b716-4e73-ab28-06015b2a8bc7",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1ce09a3c-0311-4047-884e-9033530412c0",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dedd30e3-4598-4b88-9d0d-e8b4b6fe5f26",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'tibia'\n",
- "center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL\n",
- "center = center_tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "075b81cd-8176-4b48-87f8-9c151951a7a8",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eeb6abea-1e7c-42a3-be1c-6a0d1730ff58",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "11ca3571-06e8-49eb-a610-bef66f8057a3",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2445f518-0319-4075-800b-d35bdabe434a",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "59c3faa7-6783-4203-bb08-141891633172",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "606f3104-8996-4529-b101-796ac53e00c2",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c336cfc1-921c-49fc-afee-9d437eba70dd",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd8b342b-39bd-4bcb-b0f6-f25d6d3edc2e",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "944d007a",
- "metadata": {},
- "outputs": [],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show() # show the two spheres from two PolyData\n",
- "\n",
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0b196960-88d0-471c-9bec-a7231a969c85",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\tibia_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "17302db6",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Fibula"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4a222656",
- "metadata": {},
- "source": [
- "Define variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "818cb614",
- "metadata": {},
- "outputs": [],
- "source": [
- "segment = 'fibula'\n",
- "center_tibia = np.arange(242) # LCL\n",
- "center = center_tibia"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21a123ba",
- "metadata": {},
- "source": [
- "Path to bone files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5ab4903",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = os.path.join(r'./data/' + segment)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7e125bc0",
- "metadata": {},
- "source": [
- "Load mean SSM and ligament attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "24ea09d5",
- "metadata": {},
- "outputs": [],
- "source": [
- "points_lig = trimesh.load_mesh(path + '\\meanshape_ligs_color.xyz')\n",
- "color = np.loadtxt(path + r'\\meanshape_ligs_color.xyz')[:, 3]\n",
- "\n",
- "points_lig = points_lig[center]\n",
- "color = color[center]\n",
- "\n",
- "point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)\n",
- "bone_actor = load_stl(path + '/mean_shape.stl')\n",
- "bone_actor.GetProperty().SetOpacity(1.0)\n",
- "\n",
- "surf_actor = load_stl(path + '/mean_shape_80percsurf.stl') "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7efad518",
- "metadata": {},
- "source": [
- "Create actors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ab0dab8b",
- "metadata": {},
- "outputs": [],
- "source": [
- "bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)\n",
- "mapper2 = vtk.vtkPolyDataMapper()\n",
- "mapper2.SetInputData(point_cloud_lig)\n",
- "actor2 = vtk.vtkActor()\n",
- "actor2.SetMapper(mapper2)\n",
- "actor2.GetProperty().SetColor(1, 0, 0)\n",
- "actor2.GetProperty().SetPointSize(7.5)\n",
- "\n",
- "surf_col = [169/255, 169/255, 169/255]\n",
- "surf_actor.GetProperty().SetColor(surf_col)\n",
- "surf_actor.GetProperty().SetOpacity(1.0)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a9c84df6",
- "metadata": {},
- "source": [
- "Visualize bone and attachment locations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c69f7d93",
- "metadata": {},
- "outputs": [],
- "source": [
- "plotter = pv.Plotter(window_size=(600, 600),notebook=True)\n",
- "\n",
- "plotter.background_color = 'w'\n",
- "plotter.enable_anti_aliasing()\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_mesh(point_cloud_lig, show_scalar_bar=False)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "\n",
- "pv.set_plot_theme(\"document\")\n",
- "\n",
- "plotter.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aec5eda7",
- "metadata": {},
- "outputs": [],
- "source": [
- "spheres=[]\n",
- "plotter = pv.Plotter()\n",
- "for i in range(0,len(points_lig)):\n",
- " spheres.append(pv.Sphere(center=points_lig[i], radius=0.25))\n",
- " cols = np.tile(rgb_col[i], (spheres[i].number_of_points,1))\n",
- " spheres[i][\"colors\"] = cols\n",
- " plotter.add_mesh(spheres[i])\n",
- "\n",
- "plotter.add_actor(bone_actor)\n",
- "plotter.add_actor(legend)\n",
- "plotter.add_actor(surf_actor)\n",
- "pv.set_plot_theme(\"document\")\n",
- "plotter.show()\n",
- "\n",
- "plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMfibula_lateralxx.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c4b0e4f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotter = pv.Plotter(window_size=(900, 900),notebook=True)\n",
- "# mesh= pv.read(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\tibia_lig_ply_col.ply\")\n",
- "# scalars = mesh['RGBA']\n",
- "# plotter.add_actor(bone_actor)\n",
- "# plotter.add_mesh(mesh, show_scalar_bar=False, scalars=scalars[:,0:3])\n",
- "# plotter.add_actor(legend)\n",
- "# pv.set_plot_theme(\"document\")\n",
- "# plotter.show()\n",
- "\n",
- "# plotter.export_html(r\"C:\\Users\\mariskawesseli\\Documents\\GitLab\\2022_JCWMSK_tutorials\\SSMtibia.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c364012",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e3cb15e",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.4"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autoclose": false,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 1,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
- "toc_cell": false,
- "toc_position": {},
- "toc_section_display": true,
- "toc_window_display": false
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/LigamentStudy/VisualizeProjectedCentroids.py b/LigamentStudy/VisualizeProjectedCentroids.py
deleted file mode 100644
index 28aa072..0000000
--- a/LigamentStudy/VisualizeProjectedCentroids.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import vtk
-import sys
-import os
-import vtk
-from numpy import random, genfromtxt, size
-import trimesh
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def create_pointcloud_polydata(points, colors=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
-
- if not colors is None:
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- for i in range(points.shape[0]):
- vcolors.SetTuple3(i, colors[0], colors[1], colors[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly
-
-
-lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
-color = ((0.75,1,0.5),
- (0,0.5,0),
- (1,0,1),
- (0.5,1,1),
- (1,1,0),
- (1,0.5,0.75),
- (1,0.5,0))
-
-if __name__ == '__main__':
- subjects = [35] #9,13,19,23,26,29,32,35,37,41
-
- segments = ['femur'] #'femur',
- ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
- ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
- for segment in segments:
- SSMpoints = [[] for i in range(8)]
- for ind in range(0,8):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject==100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject==100:
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(0.75)
- else:
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_pred_points.xyz') # _areas
- point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_resample.stl')
- bone_actor.GetProperty().SetOpacity(0.75)
- wire_actor = load_stl(path + '/Segmentation_' + segment + '_wires.stl')
- wire_actor.GetProperty().SetColor(0, 0, 1)
- lig_actor = []
- for count, lig in enumerate(lig_names):
- lig_actor.append(load_stl(os.path.join(path,lig+'centroids.stl')))
- lig_actor[count].GetProperty().SetColor(color[count])
-
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(5)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- renderer.AddActor(bone_actor)
- if not subject==100:
- renderer.AddActor(wire_actor)
- for count, lig in enumerate(lig_names):
- renderer.AddActor(lig_actor[count])
- # renderer.AddActor(actor2)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer")
- renderWindowInteractor.Start()
-
diff --git a/LigamentStudy/Visualize_modes.py b/LigamentStudy/Visualize_modes.py
deleted file mode 100644
index 4ce19dc..0000000
--- a/LigamentStudy/Visualize_modes.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import vtk
-import sys
-import os
-import vtk
-from numpy import random, genfromtxt, size
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-def load_stl(filename,signed_distance):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- # mapper = vtk.vtkPolyDataMapper()
- # if vtk.VTK_MAJOR_VERSION <= 5:
- # mapper.SetInput(reader.GetOutput())
- # else:
- # mapper.SetInputConnection(reader.GetOutputPort())
-
- # vcolors = vtk.vtkUnsignedCharArray()
- # vcolors.SetNumberOfComponents(3)
- # vcolors.SetName("Colors")
- # vcolors.SetNumberOfTuples(signed_distance.shape[0])
-
- c = sns.color_palette("viridis_r", n_colors=round(max(signed_distance) - min(signed_distance)), as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfTableValues(int(round(max(signed_distance) - min(signed_distance))))
- lut.SetTableRange(min(signed_distance),max(signed_distance))
- lut.Build()
- # Fill in a few known colors, the rest will be generated if needed
- for j in range(0,round(max(signed_distance) - min(signed_distance))):
- lut.SetTableValue(j, c[j][0]*255,c[j][1]*255,c[j][2]*255)
-
- heights = vtk.vtkDoubleArray()
- for i in range(obj.GetNumberOfPoints()):
- z = signed_distance[i]
- heights.InsertNextValue(z)
- obj.GetPointData().SetScalars(heights)
- # for i in range(signed_distance.shape[0]):
- # ind = round(signed_distance[i]-min(signed_distance))
- # # print(ind)
- # vcolors.SetTuple3(i, c[ind-1][0] * 255,
- # c[ind-1][1] * 255,
- # c[ind-1][2] * 255)
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper.SetScalarRange(min(signed_distance),max(signed_distance))
- mapper.SetLookupTable(lut)
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def load_vtk(filename,tx=0):
- reader = vtk.vtkPolyDataReader()
- reader.SetFileName(filename)
-
- transform = vtk.vtkTransform()
- transform.Identity()
- transform.Translate(tx, 0, 0)
-
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(transformFilter.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-def create_pointcloud_polydata(points, colors=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- vpoly = vtk.vtkPolyData()
- vpoly.SetPoints(vpoints)
-
- if not colors is None:
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- for i in range(points.shape[0]):
- vcolors.SetTuple3(i, colors[0], colors[1], colors[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly
-
-segments = ['femur'] #'femur',
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1],
- [6,5,6,6,6,6,4,4,5,5],
- [3,2,5,3,3,2,2,0,3,3],
- [7,8,7,7,7,5,7,6,7,0],
- [4,6,3,5,4,0,0,3,4,4],
- [5,7,4,4,5,7,6,5,6,6],
- [2,4,2,2,2,3,3,2,2,2],
- [0,3,8,0,0,0,0,0,0,0]]
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4],
- [3,3,7,3,5,3,5,4,3,3],
- [1,1,1,1,1,1,1,1,1,1],
- [4,5,3,4,4,5,3,2,4,3],
- [6,8,9,6,6,6,6,6,6,5],
- [2,2,2,2,2,2,2,3,2,2],
- [0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0]]
-
-for segment in segments:
-
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode1_+2SD.stl'
- mode_min = 'mode1_-2SD.stl'
- plus2sd = trimesh.load_mesh(path + mode_plus)
- min2sd = trimesh.load_mesh(path + mode_min)
- signed_distance = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices)
-
- colors = np.array(((241,163,64),
- (247,247,247),
- (153,142,195)))/255
-
- mode_plus = 'mode1_+2SD.vtk'
- mode_min = 'mode1_-2SD.vtk'
-
- bone_actor = load_stl(path + mean_shape,signed_distance)
- bone_actor.GetProperty().SetOpacity(1)
-
- # bone_actor.GetProperty().SetColor(colors[1])
-
- # plus_actor = load_vtk(path + mode_plus)
- # plus_actor.GetProperty().SetOpacity(1)
- # plus_actor.GetProperty().SetColor(colors[2])
- #
- # min_actor = load_vtk(path + mode_min)
- # min_actor.GetProperty().SetOpacity(0.8)
- # min_actor.GetProperty().SetColor(colors[0])
-
- # mapper = vtk.vtkPolyDataMapper()
- # mapper.SetInputData(point_cloud)
- # actor = vtk.vtkActor()
- # actor.SetMapper(mapper)
- # actor.GetProperty().SetColor(0,0,0)
- # actor.GetProperty().SetOpacity(1.0)
-
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(min_actor)
- # renderer.AddActor(plus_actor)
-
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer")
- renderWindowInteractor.Start()
-
diff --git a/LigamentStudy/Visualize_modes_ligaments.py b/LigamentStudy/Visualize_modes_ligaments.py
deleted file mode 100644
index ae75a1d..0000000
--- a/LigamentStudy/Visualize_modes_ligaments.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-from VisualiseSSM import create_pointcloud_polydata, load_stl
-import math as m
-
-
-def Rx(theta):
- return np.matrix([[1, 0, 0],
- [0, m.cos(theta), -m.sin(theta)],
- [0, m.sin(theta), m.cos(theta)]])
-
-
-def Ry(theta):
- return np.matrix([[m.cos(theta), 0, m.sin(theta)],
- [0, 1, 0],
- [-m.sin(theta), 0, m.cos(theta)]])
-
-
-def Rz(theta):
- return np.matrix([[m.cos(theta), -m.sin(theta), 0],
- [m.sin(theta), m.cos(theta), 0],
- [0, 0, 1]])
-
-
-segment = 'femur'
-
-rw = vtk.vtkRenderWindow()
-# xmins = [0, .5, 0, .5, 0, .5]
-# xmaxs = [0.5, 1, 0.5, 1, .5, 1]
-# ymins = [.66, .66, .33, .33, 0, 0, ]
-# ymaxs = [1, 1, .66, .66, 0.33, 0.33]
-
-xmins = [0, 0, .33, .33, .66, .66]
-xmaxs = [.33, .33, .66, .66, 1, 1]
-ymins = [0, .5, 0, .5, 0, .5]
-ymaxs = [0.5, 1, 0.5, 1, .5, 1]
-iren = vtk.vtkRenderWindowInteractor()
-iren.SetRenderWindow(rw)
-
-renderer = vtk.vtkRenderer()
-
-center_only = 0
-lateral_only = 1
-if center_only == 1:
- center_tibia = np.concatenate((np.arange(131),np.arange(470-341)+341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112),np.arange(341-263)+263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
-elif lateral_only == 1:
- center_femur = np.concatenate((np.arange(706-641)+641,np.arange(776-706)+706)) # np.concatenate((np.arange(370 - 341) + 341,np.arange(401-370)+370)) = 4096 # LCL+pop
- center_tibia = np.arange(242) # LCL
-
-tel=0
-
-if segment == 'tibia':
- center = center_tibia
-elif segment == 'femur':
- center = center_femur
-
-for modes in range(1,4):
-
- if segment == 'fibula':
- d = -40
- else:
- d = -100
-
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode' + str(modes) + '_+2SD_8192.stl'
- mode_min = 'mode' + str(modes) + '_-2SD_8192.stl'
-
- # ligament points
- points_lig = trimesh.load_mesh(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_+2sd_8192.xyz')
- color = np.loadtxt(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_+2sd_8192.xyz')[:, 3]
- if center_only == 1 or lateral_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- R = Rx(90 * np.pi / 180)*Ry(180 * np.pi / 180) * Rz(0 * np.pi / 180)
- points_lig2 = []
- for point in points_lig:
- points_lig2.append(np.asarray(R * point[np.newaxis].T))
- points_lig2 = np.squeeze(np.asarray(points_lig2))
- # points_lig2 = points_lig2 + np.array((0, modes * d, 0))
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig2, color)
-
- points_lig_neg = trimesh.load_mesh(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_-2sd_8192.xyz')
- color_neg = np.loadtxt(path + '\SSM_' + segment + r'_pred_points_color_mode' + str(modes) + '_-2sd_8192.xyz')[:, 3]
- if center_only == 1 or lateral_only == 1:
- points_lig_neg = points_lig_neg[center]
- color_neg = color_neg[center]
- R = Rx(90 * np.pi / 180) * Ry(180 * np.pi / 180) * Rz(0 * np.pi / 180)
- points_lig_neg2 = []
- for point in points_lig_neg:
- points_lig_neg2.append(np.asarray(R * point[np.newaxis].T))
- points_lig_neg2 = np.squeeze(np.asarray(points_lig_neg2))
- # points_lig_neg2 = points_lig_neg2 + np.array((d*-1, modes*d, 0))
- point_cloud_lig_neg, rgb_col_neg = create_pointcloud_polydata(points_lig_neg2, color_neg)
-
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(1.0)
-
- # load mesh via trimesh to get the correct order for distance transform
- reader = vtk.vtkSTLReader()
- reader.SetFileName(path + mode_plus)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- reader2 = vtk.vtkSTLReader()
- reader2.SetFileName(path + mode_min)
- reader2.Update()
- obj2 = reader2.GetOutputDataObject(0)
-
- # mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputDataObject(obj2)
-
- # translation
- transform = vtk.vtkTransform()
- transform.Identity()
- # transform.Translate(0,modes * d, 0)
- transform.RotateX(90)
- transform.RotateY(180)
- transform.RotateZ(0)
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- transform2 = vtk.vtkTransform()
- transform2.Identity()
- # transform2.Translate(d*-1, modes*d, 0)
- transform2.RotateX(90)
- transform2.RotateY(180)
- transform2.RotateZ(0)
- transformFilter2 = vtk.vtkTransformPolyDataFilter()
- transformFilter2.SetInputConnection(reader2.GetOutputPort())
- transformFilter2.SetTransform(transform2)
- transformFilter2.Update()
-
- # actors
- bone_actor = vtk.vtkActor()
- bone_actor.SetMapper(mapper)
- mapper.SetInputConnection(transformFilter.GetOutputPort())
- bone_actor.SetMapper(mapper)
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- bone_actor2 = vtk.vtkActor()
- mapper2.SetInputConnection(transformFilter2.GetOutputPort())
- bone_actor2.SetMapper(mapper2)
- bone_actor2.GetProperty().SetColor(0.89, 0.85, 0.79)
-
- mapper2lig = vtk.vtkPolyDataMapper()
- mapper2lig.SetInputData(point_cloud_lig)
- actor2lig = vtk.vtkActor()
- actor2lig.SetMapper(mapper2lig)
- actor2lig.GetProperty().SetColor(1, 0, 0)
- actor2lig.GetProperty().SetPointSize(7.5)
-
- mapper3 = vtk.vtkPolyDataMapper()
- mapper3.SetInputData(point_cloud_lig_neg)
- actor3 = vtk.vtkActor()
- actor3.SetMapper(mapper3)
- actor3.GetProperty().SetColor(1, 0, 0)
- actor3.GetProperty().SetPointSize(7.5)
-
- for ind in range(2):
- ren = vtk.vtkRenderer()
- rw.AddRenderer(ren)
- ren.SetViewport(xmins[tel], ymins[tel], xmaxs[tel], ymaxs[tel])
-
- # Share the camera between viewports.
- if tel == 0:
- camera = ren.GetActiveCamera()
- else:
- ren.SetActiveCamera(camera)
-
- # Create a mapper and actor
- if tel == 0 or tel == 2 or tel == 4:
- ren.AddActor(bone_actor)
- ren.AddActor(actor2lig)
- else:
- ren.AddActor(bone_actor2)
- ren.AddActor(actor3)
-
- ren.SetBackground(1.0, 1.0, 1.0)
-
- ren.ResetCamera()
-
- tel+=1
-
- # Renderer
- renderer.AddActor(bone_actor)
- renderer.AddActor(bone_actor2)
- renderer.AddActor(actor2lig)
- renderer.AddActor(actor3)
- # renderer.AddActor(legend)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
-
-# Render Window
-renderWindow = vtk.vtkRenderWindow()
-renderWindow.AddRenderer(renderer)
-renderWindow.SetSize(750, 750)
-
-# Interactor
-renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-renderWindowInteractor.SetRenderWindow(renderWindow)
-renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
-# Begin Interaction
-renderWindow.Render()
-renderWindow.SetWindowName("SSM distances")
-renderWindowInteractor.Start()
-
-rw.Render()
-rw.SetWindowName('MultipleViewPorts')
-rw.SetSize(1500, 650)
-iren.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-iren.Start()
\ No newline at end of file
diff --git a/LigamentStudy/Xray.py b/LigamentStudy/Xray.py
deleted file mode 100644
index 182c281..0000000
--- a/LigamentStudy/Xray.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import cv2
-import numpy as np
-import SimpleITK as sitk
-import pydicom as dicom
-import os
-import glob
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-for subject in subjects:
- path_drr = r'C:/Users/mariskawesseli/Documents/LigamentStudy/ImageData/'+str(subject)+'/DRR/'
- images = ['med_fem0001.dcm','med_wires0001.dcm','lat_fem0001.dcm','lat_wires0001.dcm',
- 'med_fem0001.dcm','med_all_wires0001.dcm','lat_fem0001.dcm','lat_all_wires0001.dcm']
- lig_names = ['PCL', 'MCL-p','MCL-d','posterior oblique','ACL','LCL (prox)','popliteus (dist)']
-
- for file in glob.glob(os.path.join(path_drr,"*.dcm")):
- image = file
- ds = dicom.dcmread(image)
- pixel_array_numpy = ds.pixel_array
- image = image.replace('.dcm', '.jpg')
- cv2.imwrite(os.path.join(path_drr, image), pixel_array_numpy)
-
-
- for ind in range(0,4):
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- else:
- side = 'L'
-
- mask2 = []
- src = cv2.imread(os.path.join(path_drr,images[0+2*ind].replace('.dcm', '.jpg')))
- mask = cv2.imread(os.path.join(path_drr, images[1+2*ind].replace('.dcm', '.jpg')))
- if 'med' in images[0+2*ind]:
- for ind2 in range(0,4):
- mask2.append(cv2.imread(os.path.join(path_drr, lig_names[ind2] + '0001.dcm'.replace('.dcm', '.jpg'))))
- else:
- for ind2 in range(4, 7):
- mask2.append(cv2.imread(os.path.join(path_drr, lig_names[ind2] + '0001.dcm'.replace('.dcm', '.jpg'))))
-
- # convert mask to gray and then threshold it to convert it to binary
- gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
- gray2 = []
- for new_mask in mask2:
- gray2.append(cv2.cvtColor(new_mask, cv2.COLOR_BGR2GRAY))
- # ret, binary = cv2.threshold(gray, 40, 255, cv2.THRESH_BINARY)
- blur = cv2.GaussianBlur(gray, (3,3), 0)
- blur2 = []
- for new_gray in gray2:
- blur2.append(cv2.GaussianBlur(new_gray, (3, 3), 0))
- binary = cv2.threshold(blur, 250, 255, cv2.THRESH_BINARY_INV)[1] # + cv2.THRESH_OTSU
- binary2 = []
- for new_blur in blur2:
- binary2.append(cv2.threshold(new_blur, 250, 255, cv2.THRESH_BINARY_INV)[1]) # + cv2.THRESH_OTSU
-
- # find contours of two major blobs present in the mask
- contours,hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
- contours2 = []
- for new_binary in binary2:
- contoursX, hierarchyX = cv2.findContours(new_binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
- contours2.append(contoursX)
-
- # draw the found contours on to source image
- for contour in contours:
- cv2.drawContours(src, contour, -1, (255,0,0), thickness = 1)
- colors = [(0,0,255),(0,255,0),(255,0,255),(0,255,255)]
- count = -1
- for new_contours in contours2:
- count += 1
- c = colors[count]
- for contour in new_contours:
- cv2.drawContours(src, contour, -1, c, thickness = 1)
-
- # split source to B,G,R channels
- b,g,r = cv2.split(src)
- b2, g2, r2 = cv2.split(src)
-
- # add a constant to R channel to highlight the selected area in red
- r = cv2.add(b, 30, dst = b, mask = binary, dtype = cv2.CV_8U)
- #r2 = []
- # for new_binary in binary2:
- # r2 = cv2.add(b, 30, dst=b, mask=new_binary, dtype=cv2.CV_8U)
-
- # merge the channels back together
- img_overlay = cv2.merge((b,g,r), src)
- # for new_r in r2:
- img_overlay = cv2.merge((b2, g2, r2), img_overlay)
- # cv2.imshow('overlay', img_overlay)
- if side == 'R':
- if (ind == 1) or (ind == 3):
- img_rot = cv2.rotate(img_overlay, cv2.ROTATE_180)
- img_rot = cv2.flip(img_rot, 1)
- else:
- img_rot = img_overlay
- else:
- if (ind == 0) or (ind == 2):
- img_rot = cv2.rotate(img_overlay, cv2.ROTATE_180)
- img_rot = cv2.flip(img_rot, 1)
- else:
- img_rot = img_overlay
- cv2.imwrite(os.path.join(path_drr, images[1+2*ind].replace('.dcm', '_combine.jpg')),img_rot)
-
-
diff --git a/LigamentStudy/average_points_to_stls.py b/LigamentStudy/average_points_to_stls.py
deleted file mode 100644
index 9727565..0000000
--- a/LigamentStudy/average_points_to_stls.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import trimesh
-import numpy as np
-import os
-
-points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz')
-color = np.loadtxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz')[:, 3]
-
-segment = 'femur'
-subjects = ['100'] #, S0 [100]
-lig = 'pop'
-center_only = 1
-
-if lig == 'LCL':
- center_femur = np.arange(706-641)+641 # np.arange(415-379)+379 # np.arange(370-341)+341 = 4096
- center_tibia = np.arange(242)
-if lig == 'pop':
- center_femur = np.arange(776-706)+706 #np.arange(454-415)+415 # np.arange(401-370)+370 = 4096
- center_tibia = 0
-
-if segment == 'tibia' or segment == 'fibula':
- center = center_tibia
-elif segment == 'femur':
- center = center_femur
-
-points10 = []
-points9 = []
-points8 = []
-points7 = []
-points6 = []
-points5 = []
-points4 = []
-points3 = []
-points2 = []
-points1 = []
-
-if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
-print(color)
-for ind in range(0,len(color)):
- T = trimesh.transformations.translation_matrix(points_lig[ind])
- point = trimesh.creation.cylinder(0.5, height=0.5, sections=None, segment=None, transform=T)
- # point = trimesh.creation.icosphere(subdivisions=3, radius=1.0, color=None, transform=T)
-
- if color[ind] == 10:
- if bool(points10):
- points10 = trimesh.boolean.union([points10, point])
- else:
- points10 = point
- elif color[ind] == 9:
- if bool(points9):
- points9 = trimesh.boolean.union([points9, point])
- else:
- points9 = point
- elif color[ind] == 8:
- if bool(points8):
- points8 = trimesh.boolean.union([points8, point])
- else:
- points8 = point
- elif color[ind] == 7:
- if bool(points7):
- points7 = trimesh.boolean.union([points7, point])
- else:
- points7 = point
- elif color[ind] == 6:
- if bool(points6):
- points6 = trimesh.boolean.union([points6, point])
- else:
- points6 = point
- elif color[ind] == 5:
- if bool(points5):
- points5 = trimesh.boolean.union([points5, point])
- else:
- points5 = point
- elif color[ind] == 4:
- if bool(points4):
- points4 = trimesh.boolean.union([points4, point])
- else:
- points4 = point
- elif color[ind] == 3:
- if bool(points3):
- points3 = trimesh.boolean.union([points3, point])
- else:
- points3 = point
- elif color[ind] == 2:
- if bool(points2):
- points2 = trimesh.boolean.union([points2, point])
- else:
- points2 = point
- elif color[ind] == 1:
- if bool(points1):
- points1 = trimesh.boolean.union([points1, point])
- else:
- points1 = point
-
-points10.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points10.stl'))
-points9.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points9.stl'))
-points8.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points8.stl'))
-points7.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points7.stl'))
-points6.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points6.stl'))
-points5.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points5.stl'))
-points4.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points4.stl'))
-points3.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points3.stl'))
-points2.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points2.stl'))
-points1.export(os.path.join(r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models", lig+'points1.stl'))
\ No newline at end of file
diff --git a/LigamentStudy/close_mesh.py b/LigamentStudy/close_mesh.py
deleted file mode 100644
index 5f9cebc..0000000
--- a/LigamentStudy/close_mesh.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-
-def cylinder_between(p1, p2, r):
- dx = p2[0] - p1[0]
- dy = p2[1] - p1[1]
- dz = p2[2] - p1[2]
- dist = np.sqrt(dx**2 + dy**2 + dz**2)+0.5
-
- phi = np.arctan2(dy, dx)
- theta = np.arccos(dz/dist)
-
- T = trimesh.transformations.translation_matrix([dx/2 + p1[0], dy/2 + p1[1], dz/2 + p1[2]])
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(phi, zaxis)
- Ry = trimesh.transformations.rotation_matrix(theta, yaxis)
- R = trimesh.transformations.concatenate_matrices(T,Rz, Ry)
-
- cylinder = trimesh.creation.cylinder(r, height=dist, sections=None, segment=None, transform=R)
- cylinder.export(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19\cylinder.stl")
-
-path = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19"
-i=2
-
-ms3= pymeshlab.MeshSet()
-ms3.load_new_mesh(path + '\Segmentation_femur_wires' + str(i) + '.stl')
-dist_matrix = []
-dist_matrix_ind = []
-start_ind = []
-verts = ms3.mesh(0).vertex_matrix()
-for ind in range(0,len(verts)):
- ms3.apply_filter('colorize_by_geodesic_distance_from_a_given_point', startpoint=verts[ind],maxdistance=100)
- dist_matrix.append(np.max(ms3.mesh(0).vertex_quality_array()))
- dist_matrix_ind.append(np.argmax(ms3.mesh(0).vertex_quality_array()))
- start_ind.append(ind)
-
-max1 = np.argmax(dist_matrix)
-end_point = verts[dist_matrix_ind[max1]]
-start_point = verts[start_ind[max1]]
-r = 0.5
-cylinder_between(start_point, end_point, r)
-
-path_cylinder = r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\19\cylinder.stl"
-ms3.load_new_mesh(path_cylinder)
-ms3.apply_filter('mesh_boolean_union', first_mesh=0, second_mesh=1)
-ms3.save_current_mesh(path + '\Segmentation_femur_wires' + str(i) + 'close.stl', binary=False)
-
-# ms4 = pymeshlab.MeshSet()
-# ms4.load_new_mesh(path + '\Segmentation_femur_wire' + str(i) + 'union.stl')
-# ms4.load_new_mesh(path + '\Segmentation_femur_area' + str(i) + '.stl')
-#
-# # compute signed distance
-# out3 = ms4.apply_filter('distance_from_reference_mesh', measuremesh=1, refmesh=0, signeddist=True)
-#
-# # select and delete vertices with negative distance
-# ms4.conditional_vertex_selection(condselect="q<0")
-# ms4.delete_selected_vertices()
-# # split mesh
-# out4 = ms4.apply_filter('split_in_connected_components')
-#
-# no_meshes = ms4.number_meshes()
-# meshes_to_remove = no_meshes-4
-# for ind in range(0,meshes_to_remove):
-# no_vertices = ms4.mesh(ind+4).vertex_matrix().shape[0]
-# ms4.set_current_mesh(ind+4)
-# ms4.delete_current_mesh()
-#
-# no_vertices = ms4.mesh(3).vertex_matrix().shape[0]
-# if no_vertices < 10:
-# ms4.set_current_mesh(3)
-# ms4.delete_current_mesh()
-#
-# ms4.set_current_mesh(2)
-# ms4.apply_filter('select_border')
-# ms4.mesh(2).selected_face_number()
-# ms4.apply_filter('dilate_selection')
-# ms4.mesh(2).selected_face_number()
-# ms4.apply_filter('dilate_selection')
-#
-# geometric_measures = ms4.apply_filter('compute_geometric_measures')
-# surface = geometric_measures['surface_area']
-# print('Surface area femur ligament' + str(i) + ': ' + str(surface) + ' mm2')
-# # ms4.save_project(path + '\Segmentation_femur_area' + str(i) + 'test.mlp')
\ No newline at end of file
diff --git a/LigamentStudy/extractSegmentations.py b/LigamentStudy/extractSegmentations.py
deleted file mode 100644
index 79301ae..0000000
--- a/LigamentStudy/extractSegmentations.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# exec(open(r'C:\Users\mariskawesseli\Documents\GitLab\Other\LigamentStudy\extractSegmentations.py').read())
-
-import os,glob
-import shutil
-
-dir = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\Fibula"
-
-for name in os.listdir(dir):
- print(name)
- slicer.mrmlScene.Clear(0)
- path = os.path.join(dir, name)
-
- slicer.util.loadSegmentation(glob.glob(os.path.join(path, "Segmentation.seg.nrrd"))[0])
- slicer.util.loadVolume(glob.glob(os.path.join(path, "*RIGHT.nrrd"))[0])
- volumeNode = slicer.mrmlScene.GetFirstNodeByClass("vtkMRMLScalarVolumeNode")
- segmentationNode = getNode("Segmentation")
-
- # use islands to get noise out
- segmentEditorNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLSegmentEditorNode")
-
- segmentEditorWidget = slicer.qMRMLSegmentEditorWidget()
- segmentEditorWidget.setMRMLScene(slicer.mrmlScene)
- segmentEditorWidget.setMRMLSegmentEditorNode(segmentEditorNode)
- segmentEditorWidget.setSegmentationNode(segmentationNode)
- segmentEditorWidget.setMasterVolumeNode(volumeNode)
-
- segmentEditorWidget.setActiveEffectByName("Islands")
- effect = segmentEditorWidget.activeEffect()
- effect.setParameter("Operation", 'KEEP_LARGEST_ISLAND')
- effect.self().onApply()
-
- # export segmentation to volume
- shNode = slicer.mrmlScene.GetSubjectHierarchyNode()
-
- exportFolderItemId = shNode.CreateFolderItem(shNode.GetSceneItemID(), "Segments")
- slicer.modules.segmentations.logic().ExportAllSegmentsToModels(segmentationNode, exportFolderItemId)
-
- outputFolder = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\fibula_bone\mesh"
- segmentationNode.SetName(str(name)+'_R')
- slicer.vtkSlicerSegmentationsModuleLogic.ExportSegmentsClosedSurfaceRepresentationToFiles(outputFolder,
- segmentationNode,
- None, "STL",
- True, 1.0, False)
-
- outputFolder_seg = r"C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\fibula_bone\segmentation"
- slicer.util.saveNode(segmentationNode, outputFolder_seg + "/" + str(name) + "_R.nrrd")
- # shutil.copy(glob.glob(os.path.join(path, "Segmentation.seg.nrrd"))[0], outputFolder_seg + "/" + str(name) + "_R.nrrd")
diff --git a/LigamentStudy/fitErrorMRI.py b/LigamentStudy/fitErrorMRI.py
deleted file mode 100644
index 3d90545..0000000
--- a/LigamentStudy/fitErrorMRI.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import numpy as np
-import os
-import trimesh
-import pymeshlab
-import glob
-
-subjects = ['1'] #['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['R']
-segments = ['femur','tibia', 'fibula'] #
-short_ssm = [0, 1, 0] #
-data_folder = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/'
-run_fit = 1
-
-for subj_ind, subject in enumerate(subjects):
-
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- path = data_folder
- ssm_path = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- input_path = path + segment + '_bone' + short + r'\new_bone_mri\input/'
- ssm_files = glob.glob(ssm_path + "*.stl")
- input_files = glob.glob(input_path + "*.stl")
-
- # get ligament locations
- if segment == 'femur':
- no_pathpoint = 0
- else:
- no_pathpoint = 1
- if run_fit == 1:
- # run ICP to get final position SSM point cloud on original mesh
- # mesh OpenSim model - make sure this is high quality
- mesh1 = trimesh.load_mesh(ssm_files[subj_ind]) # SSM mesh
- # mesh segmented from MRI
- mesh2 = trimesh.load_mesh(input_files[subj_ind]) # mesh in position MRI
-
- # Mirror if needed (only for left as SSM/model is right? - check how to deal with left model)
- if side == 'L':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- else:
- M = trimesh.transformations.scale_and_translate((1, 1, 1))
- # mesh2.apply_transform(M)
- # Rotate segmented bone (check why needed?)
- origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rx = trimesh.transformations.rotation_matrix(-90 / (180 / np.pi), xaxis)
- Ry = trimesh.transformations.rotation_matrix(90 / (180 / np.pi), yaxis)
- # Rz = trimesh.transformations.rotation_matrix(180 / (180 / np.pi), zaxis)
- R = trimesh.transformations.concatenate_matrices(Ry, Rx)
- mesh1.apply_transform(M)
- # Translate segmented mesh to OpenSim bone location
- T = trimesh.transformations.translation_matrix(mesh2.center_mass - mesh1.center_mass)
- mesh1.apply_transform(T)
-
- new_path = ssm_path + '/fit/'
- if not os.path.exists(new_path):
- # If the path does not exist, create it
- os.makedirs(new_path)
- mesh1.export(new_path + os.path.split(ssm_files[subj_ind])[1])
-
- # ICP to fit segmented bone to OpenSim mesh
- kwargs = {"scale": False}
- # icp = trimesh.registration.icp(mesh1.vertices, mesh2, initial=np.identity(4), threshold=1e-5,
- # max_iterations=20, **kwargs)
-
- icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=np.identity(4), threshold=1e-5, max_iterations=20,
- **kwargs)
- mesh1.apply_transform(icp[0])
- mesh1.export(new_path + 'icp_' + os.path.split(ssm_files[subj_ind])[1])
-
- # hausdorff distance
- ms5 = pymeshlab.MeshSet()
- ms5.load_new_mesh(new_path + 'icp_' + os.path.split(ssm_files[subj_ind])[1])
- ms5.load_new_mesh(input_files[subj_ind])
- out1 = ms5.apply_filter('hausdorff_distance', targetmesh=1, sampledmesh=0, savesample=True)
- out2 = ms5.apply_filter('hausdorff_distance', targetmesh=0, sampledmesh=1, savesample=True)
-
- print(segment + ' max: ' + str(max(out1['max'], out2['max'])))
- print(segment + ' min: ' + str(max(out1['min'], out2['min'])))
- print(segment + ' mean: ' + str(max(out1['mean'], out2['mean'])))
- print(segment + ' RMS: ' + str(max(out1['RMS'], out2['RMS'])))
-
- print(segment + ' max: ' + str(out1['max']))
- print(segment + ' min: ' + str(out1['min']))
- print(segment + ' mean: ' + str(out1['mean']))
- print(segment + ' RMS: ' + str(out1['RMS']))
\ No newline at end of file
diff --git a/LigamentStudy/fitSSM.py b/LigamentStudy/fitSSM.py
deleted file mode 100644
index e63ce15..0000000
--- a/LigamentStudy/fitSSM.py
+++ /dev/null
@@ -1,348 +0,0 @@
-import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-
-subjects = [9,13,19,23,26,29,32,35,37,41] #[9,13,19,23,26,29,32,35,37,41]
-segments = ['femur'] #'femur',
-short = 0
-run = 1
-
-occurances=[]
-all_occ = []
-orders=[]
-
-ligaments_fem = [[1,1,1,1,1,1,1,1,1,1], # PCL
- [6,5,6,6,6,6,4,4,5,5], # MCLp
- [3,2,5,3,3,2,2,0,3,3], # MCLd
- [0,8,0,0,0,0,0,0,0,0], # MCLd2
- [7,3,7,7,7,5,7,6,7,0], # POL
- [0,0,8,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [4,6,3,5,4,0,0,3,4,4], # ACL
- [5,7,4,4,5,7,6,5,6,6], # LCL
- [2,4,2,2,2,3,3,2,2,2]] # POP
-
-ligaments_tib = [[5,7,6,5,3,4,4,5,5,4], # PCL
- [1,1,1,1,1,1,1,1,1,1], # MCLp
- [3,3,8,3,5,3,5,0,3,3], # MCLd
- [0,4,0,0,0,0,0,0,0,0], # MCLd2
- [4,5,3,4,4,5,3,2,4,0], # POL
- [0,6,4,0,0,0,0,0,0,0], # POL2
- [0,0,5,0,0,0,0,0,0,0], # POL3
- [0,0,7,0,0,0,0,0,0,0], # POL4
- [6,8,9,6,6,6,6,6,6,5], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-ligaments_fib = [[0,0,0,0,0,0,0,0,0,0], # PCL
- [0,0,0,0,0,0,0,0,0,0], # MCLp
- [0,0,0,0,0,0,0,0,0,0], # MCLd
- [0,0,0,0,0,0,0,0,0,0], # MCLd2
- [0,0,0,0,0,0,0,0,0,0], # POL
- [0,0,0,0,0,0,0,0,0,0], # POL2
- [0,0,0,0,0,0,0,0,0,0], # POL3
- [0,0,0,0,0,0,0,0,0,0], # POL4
- [0,0,0,0,0,0,0,0,0,0], # ACL
- [2,2,2,2,2,2,2,3,2,2], # LCL
- [0,0,0,0,0,0,0,0,0,0]] # POP
-
-for segment in segments:
- if segment == 'femur':
- ligaments = ligaments_fem
- elif segment == 'fibula':
- ligaments = ligaments_fib
- else:
- ligaments = ligaments_tib
-
- SSMpoints = [[] for i in range(11)]
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if run == 1:
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
- if segment == 'fibula':
- points = str(2048)
- elif segment == 'femur':
- points = str(8192) # 4096
- else:
- points = str(4096)
- """SSM part"""
- # files from SSM workflow shapeworks
- if short == 1:
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.nrrd'
- particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\shape_models/' + points + '\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone_short\new_bone\input'
- else:
- file_resample = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\resampled\segmentations\Segmentation_' + segment + '_' + side + '_short_' +str(
- subject) + reflect + '.isores.nrrd'
- file_crop = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\cropped\segmentations\Segmentation_' + segment + '_' + side + '_short_' +str(
- subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.nrrd'
- file_com = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.txt'
- file_align = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.txt'
- pad_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\padded\segementations\Segmentation_' + segment + '_short_' + side + '_' + str(subject) + reflect + '.isores.pad.nrrd'
- com_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\groomed\com_aligned\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.nrrd'
- particle_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r"_bone\new_bone\shape_models/" + points + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.particles'
- xyz_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.clipped.cropped.tpSmoothDT_local.xyz'
- # align_file = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\groomed\aligned\Segmentation_femur_' + side + '_short_' + str(subject) + reflect + '.isores.pad.com.center.aligned.nrrd'
- path_bones = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input'
-
- # get change in position from nrrd header files
- # header = nrrd.read_header(pad_file)
- # pad_position = header['space origin']
- header = nrrd.read_header(com_file)
- com_position = header['space origin']
- header = nrrd.read_header(file_resample)
- resample_position = header['space origin']
- header = nrrd.read_header(file_crop)
- crop_position = header['space origin']
-
- # with open(file_com) as fobj:
- # for line in fobj:
- # line = line.replace('[',']')
- # line_data = re.split("]|,", line)
- #
- # NA = np.asarray(line_data[1:4])
- # trans_mat = NA.astype(float)
-
- # get translation from align from rotation matrix
- rot_ssm = np.loadtxt(file_align)
-
- # rot_mat_ssm = np.transpose(rot_ssm)
- # rot_mat_ssm = np.vstack((rot_mat_ssm, [0,0,0,1]))
-
- # translate points cloud SSM instance to align with original mesh
- # com_position[0] = com_position[0]*-1
-
- diff = resample_position -rot_ssm[3,:] -crop_position
- translate = diff
- translate[0] = diff[1]
- translate[1] = diff[0]
- translate[2] = diff[2]
- if subject == 32:
- translate[0] = translate[0]+40.5
- translate[1] = translate[1]-8
- # translate[1] = translate[1] - 2*rot_ssm[3, 1]
- # if reflect == '.reflect':
- # translate[0] = 0# -46 #-resample_position[0] -rot_ssm[3,0] +com_position[0]
-
- # r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\Segmentation_femur_R_short_ssm_reconstruct3.stl' #
- pre, ext = os.path.splitext(xyz_file)
- copyfile(particle_file, pre + '.paticles')
- if not os.path.isfile(pre + '.xyz'):
- os.rename(pre + '.paticles', pre + '.xyz')
- mesh3 = xyz_file # =local.particle file
- ms6 = pymeshlab.MeshSet()
- ms6.load_new_mesh(mesh3)
- max_val = 200
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=translate[0], axisy=translate[1], axisz=-max_val)
-
- iters = 1
- while translate[2]+max_val*iters < -max_val:
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod =0, axisx=0, axisy=0, axisz=-max_val)
- iters=iters+1
- # ms6.apply_filter('transform_translate_center_set_origin', traslmethod=0, axisx=0, axisy=0, axisz=-222)
- ms6.apply_filter('transform_translate_center_set_origin', traslmethod =0, axisx=0, axisy=0, axisz=translate[2]+max_val*iters)
- ms6.save_current_mesh(path + '\SSM_' + segment + '_transform.xyz')
-
- # run ICP to get final position SSM point cloud on original mesh
- mesh = trimesh.load_mesh(path + '\Segmentation_' + segment + '_resample.stl')
- # mesh = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\input\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '_remesh.stl')
- # mesh = trimesh.load_mesh(path_bones + '\Segmentation_' + segment + '_' + side + '_short_' + str(subject) + '.STL')
- points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1,1,1))
- points.apply_transform(M)
- # np.savetxt(path + '\SSM_' + segment + '_short_transform_mirror.xyz', points.vertices, delimiter=" ")
- kwargs = {"scale": False}
- top_points = np.asarray(points.vertices)
- exclude_bottom = top_points[top_points[:, 2] > min(top_points[:, 2])+1]
- # icp = trimesh.registration.mesh_other(mesh, exclude_bottom, samples=2000, scale=False, icp_first=10, icp_final=50)
- # points.apply_transform(np.linalg.inv(icp[0]))
- icp = trimesh.registration.icp(exclude_bottom,mesh,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
- points.apply_transform(icp[0])
-
- # icp = trimesh.registration.icp(points.vertices, mesh, initial=np.identity(4), threshold=1e-5, max_iterations=20,**kwargs)
- # points.apply_transform(icp[0])
-
- np.savetxt(path + '\SSM_' + segment + '_transform_icp.xyz', points.vertices, delimiter=" ")
-
- # ms5 = pymeshlab.MeshSet()
- # ms5.load_new_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
-
- if short == 1:
- short_name = '_short'
- else:
- short_name = ''
- if run == 2:
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + short_name + '_transform_icp.xyz') #_short
- if segment == 'fibula':
- segment_temp = 'tibia'
- Counter = len(glob.glob1(path, 'Segmentation_' + segment_temp + '_area' + str(ligaments_fib[9][ind]) + '*.stl'))
- else:
- segment_temp = segment
- Counter = len(glob.glob1(path, 'Segmentation_' + segment_temp + '_area*.stl'))
- close_verts = []
- close_verts_verts = np.empty([0,3])
- for count in range(1, int(np.ceil(Counter/2)) + 1):
- if segment == 'fibula':
- count_n = count + ligaments_fib[9][ind] - 1
- else:
- count_n = count
- mesh = trimesh.load_mesh(os.path.join(path,'Segmentation_' + segment_temp + '_area' + str(count_n) + '.stl'))
- # [closest, distance, id] = trimesh.proximity.closest_point(mesh, points.vertices)
- distance = trimesh.proximity.signed_distance(mesh, points.vertices)
- if segment == 'fibula':
- max_dist = 1.5
- elif segment == 'tibia':
- max_dist = 1
- else:
- max_dist = 1
- close_verts.append(np.where(abs(distance)<max_dist))
- # close_verts = np.vstack([close_verts, np.where(abs(distance)<2)])
- # close_verts_verts.append(points.vertices[np.where(abs(distance)<2)])
- close_verts_verts = np.vstack([close_verts_verts, points.vertices[np.where(abs(distance)<max_dist)]])
- # np.savetxt(path + '\SSM_' + segment + '_areas_test.xyz', np.asarray(close_verts_verts), delimiter=" ")
-
- if segment == 'fibula':
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- SSMpoints[lig][ind] = close_verts[0][0]
- else:
- for lig in range(0, 11):
- lig_no = ligaments[lig][ind]
- if not lig_no == 0:
- SSMpoints[lig][ind] = close_verts[lig_no-1][0]
-
- # dupes = [x for n, x in enumerate(np.concatenate(SSMpoints[0])) if x in np.concatenate(SSMpoints[0])[:n]]
- from collections import Counter
- if run == 2:
- occurances = []
- all_occ = []
- orders = []
- for ind in range(0,11):
- count=0
- occur = []
- if ind == 2:
- occur = Counter(np.concatenate(SSMpoints[ind]+SSMpoints[ind+1], axis=0))
- elif ind == 4:
- occur = Counter(np.concatenate(SSMpoints[ind]+ SSMpoints[ind + 1]+ SSMpoints[ind + 2]+ SSMpoints[ind + 3], axis=0))
- elif ind == 3 or ind == 5 or ind == 6 or ind == 7:
- continue
- else:
- occur = Counter(np.concatenate(SSMpoints[ind], axis=0))
- order = occur.most_common()
- for j in range(0,10):
- if len(SSMpoints[ind][j]):
- count=count+1
- if ind == 4:
- print(count)
- try:
- index = [x for x, y in enumerate(order) if y[1] == int(count/2)]
- most_occ = order[0:index[-1]]
- except:
- most_occ = order[0:1]
- all_occ.append(np.asarray([i[0] for i in order]))
- occurances.append(np.asarray([i[0] for i in most_occ]))
- orders.append(np.asarray([i[1] for i in order]))
- elif run == 0:
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment + '.npz')
- occurances.append(bla['PCL']); occurances.append(bla['MCLp']); occurances.append(bla['MCLd']); occurances.append(bla['post_obl']); occurances.append(bla['ACL']);\
- occurances.append(bla['LCL']); occurances.append(bla['pop'])
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_8192' + segment + '.npz')
- all_occ.append(bla['PCL']); all_occ.append(bla['MCLp']); all_occ.append(bla['MCLd']); all_occ.append(bla['post_obl']); all_occ.append(bla['ACL']);
- all_occ.append(bla['LCL']); all_occ.append(bla['pop'])
- bla = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_8192' + segment + '.npz')
- orders.append(bla['PCL']); orders.append(bla['MCLp']); orders.append(bla['MCLd']); orders.append(bla['post_obl']); orders.append(bla['ACL']);
- orders.append(bla['LCL']); orders.append(bla['pop'])
-
- points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\mean_shape_8192.xyz')
- np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_8192.xyz', points.vertices[np.hstack(occurances).astype(int)], delimiter=" ")
-
- pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- np.savetxt(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_ligs_color_8192.xyz',
- pred_lig_points_color, delimiter=" ")
-
- mask = np.ones(len(points.vertices), dtype=bool)
- mask[np.hstack(occurances).astype(int)] = False
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz',
- # points.vertices[np.where(mask)], delimiter=" ")
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment + '_order.npy', PCL=orders[0],MCLp=orders[1],
- MCLd=orders[2],post_obl=orders[3],ACL=orders[4],LCL=orders[5],pop=orders[6])
-
-
- # print('Surface area femur ligament' + str(lig_no) + ': ' + str(surface) + ' mm2')
- # ms5.load_new_mesh(os.path.join(path,'Segmentation_femur_area' + str(count) + '.stl'))
- # no_meshes = ms5.number_meshes()
- # ms5.apply_filter('distance_from_reference_mesh', measuremesh=0, refmesh=no_meshes-1, signeddist=False)
- # ms5.set_current_mesh(0)
- # ms5.conditional_vertex_selection(condselect="q<1")
- # m = ms5.current_mesh()
- subjects = [9,13,19,23,26,29,32,35,37,41]
- for ind, subject in enumerate(subjects):
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if subject in [9,13,26,29,32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- points = trimesh.load_mesh(path + '\8192\SSM_' + segment + short_name +'_transform_icp.xyz') #_short
- pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- np.savetxt(path + '\8192\SSM_' + segment + '_pred_points_8192.xyz', np.asarray(pred_lig_points), delimiter=" ")
- pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)],np.hstack(orders).astype(int)]
- np.savetxt(path + '\8192\SSM_' + segment + '_pred_points_color_8192.xyz', np.asarray(pred_lig_points_color), delimiter=" ")
-
- # for modes in range(1,4):
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\mode' + str(modes) + '_+2sd.xyz')
- # pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_mode' + str(modes) + '_+2sd.xyz', np.asarray(pred_lig_points), delimiter=" ")
- # pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_color_mode' + str(modes) + '_+2sd.xyz',
- # np.asarray(pred_lig_points_color), delimiter=" ")
- #
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\mode' + str(modes) + '_-2sd2.xyz')
- # pred_lig_points = points.vertices[np.hstack(occurances).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_mode' + str(modes) + '_-2sd.xyz',
- # np.asarray(pred_lig_points), delimiter=" ")
- # pred_lig_points_color = np.c_[points.vertices[np.hstack(all_occ).astype(int)], np.hstack(orders).astype(int)]
- # np.savetxt(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + '_bone\SSM_' + segment + '_pred_points_color_mode' + str(modes) + '_-2sd.xyz',
- # np.asarray(pred_lig_points_color), delimiter=" ")
-
-
-
-
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_8192' + segment ,PCL=occurances[0],MCLp=occurances[1],
- MCLd=occurances[2],post_obl=occurances[3],ACL=occurances[4],LCL=occurances[5],pop=occurances[6])
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_8192' + segment, PCL=all_occ[0],MCLp=all_occ[1],
- MCLd=all_occ[2],post_obl=all_occ[3],ACL=all_occ[4],LCL=all_occ[5],pop=all_occ[6])
- np.savez(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_8192' + segment, PCL=orders[0],
- MCLp=orders[1], MCLd=orders[2], post_obl=orders[3], ACL=orders[4], LCL=orders[5], pop=orders[6])
diff --git a/LigamentStudy/fitSSM_mri.py b/LigamentStudy/fitSSM_mri.py
deleted file mode 100644
index 1026396..0000000
--- a/LigamentStudy/fitSSM_mri.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# import pymeshlab
-import numpy as np
-import trimesh
-import nrrd
-import re
-import os
-import pandas as pd
-from tabulate import tabulate
-from shutil import copyfile
-import glob
-
-
-def csv2xyz(csv_path):
- import csv
-
- # Define the paths to the input and output files
- xyz_path = os.path.splitext(csv_path)[0] + ".xyz"
-
- # Load the CSV file and extract the relevant data
- data = []
- with open(csv_path, "r") as csvfile:
- reader = np.genfromtxt(csvfile) # csv.reader(csvfile)
- data = reader
- # for i, row in enumerate(reader):
- # if i == 0:
- # continue # Skip the first row
- # x, y, *z = row[1:-1] # Extract x, y, and any additional columns as z
- # data.append([x, y, *z])
-
- # Write the data to the output file in XYZ format
- with open(xyz_path, "w") as xyzfile:
- for row in data:
- x, y, *z = row
- xyzfile.write(f"{x} {y} {z[0]}\n")
-
- return xyz_path
-
-
-subjects = ['1L','2L','3L','4L','5L','6L','8L','9L','1R','2R','3R','4R','5R','6R','8R','9R'] # ['1'] # ['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['L','L','L','L','L','L','L','L', 'R','R','R','R','R','R','R','R']
-segments = ['femur','tibia', 'fibula'] #['tibia'] #
-short_ssm = [0,1,0] #[1] #
-no_particles = [4096,4096,2048] #[4096] #
-data_folder = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/"
-
-for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- # Load which SSM points are related to which ligaments
- occ = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_' + segment + short + '.npz')
- occurances = [occ['PCL'], occ['MCLp'], occ['MCLd'], occ['post_obl'], occ['ACL'], occ['LCL'], occ['pop']]
-
- all_occ = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_' + segment + '.npz')
- all_occ = [all_occ['PCL'], all_occ['MCLp'], all_occ['MCLd'], all_occ['post_obl'], all_occ['ACL'], all_occ['LCL'], all_occ['pop']]
-
- order = np.load(
- r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\all_occurances_orders_' + segment + '.npz')
- orders = [order['PCL'], order['MCLp'], order['MCLd'], order['post_obl'], order['ACL'], order['LCL'],
- order['pop']]
-
- for subj_ind, subject in enumerate(subjects):
- path = data_folder
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- # files from SSM workflow shapeworks
- ssm_path = path + segment + '_bone' + short + r'\new_bone_4DCT/'
- ssm_files = glob.glob(ssm_path + "*.particles")
- # ssm_files = glob.glob(ssm_path + "*.csv")
- particle_file_name = ssm_files[subj_ind]
- shape_model_folder = ssm_path
-
- new_path = ssm_path + '/fit/'
- if not os.path.exists(new_path):
- # If the path does not exist, create it
- os.makedirs(new_path)
-
- # path_bones = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- input_files = glob.glob(ssm_path + "*.stl")
- mesh_inp = input_files[subj_ind]
-
- # Create xyz file from particles file
- xyz_file = csv2xyz(particle_file_name)
-
- # pre, ext = os.path.splitext(particle_file_name)
- # particle_file = os.path.join(shape_model_folder, str(no_particles[seg_ind]), particle_file_name)
- # xyz_file = os.path.join(shape_model_folder, pre + '.xyz')
- # copyfile(particle_file, xyz_file)
-
- # Reflect (mirror) points if needed and translate to the position of the original mesh
- mesh_inp = trimesh.load_mesh(mesh_inp)
- points_xyz = trimesh.load_mesh(xyz_file)
- if reflect == '.reflect':
- M = trimesh.transformations.scale_and_translate((-1, 1, 1))
- points_xyz.apply_transform(M)
- mesh_inp.apply_transform(M)
- translate = mesh_inp.center_mass-points_xyz.centroid
- points_xyz.apply_transform(trimesh.transformations.translation_matrix(translate))
- np.savetxt(new_path + '\SSM_' + segment + str(subject) + '_transform.xyz', points_xyz.vertices, delimiter=" ") # save intermediate translation to check
-
- # run ICP to get final position SSM point cloud on original mesh
- kwargs = {"scale": False}
- icp = trimesh.registration.icp(points_xyz.vertices,mesh_inp,initial=np.identity(4),threshold=1e-5,max_iterations=40,**kwargs)
- # icp = trimesh.registration.icp(points_xyz.vertices, mesh_inp, initial=icp[0], threshold=1e-5, max_iterations=20,**kwargs) # run icp twice to improve fit
- points_xyz.apply_transform(icp[0])
- np.savetxt(new_path + r'\SSM_' + segment + str(subject) + '_transform_icp.xyz', points_xyz.vertices, delimiter=" ") # save position SSM points on original mesh
-
- mesh_inp.apply_transform(trimesh.transformations.translation_matrix(translate))
- mesh_inp.apply_transform(icp[0])
- mesh_inp.export(new_path + r'\SSM_' + segment + short + str(subject) + '.stl')
-
- # link which SSM points are related to which ligaments to new point cloud
- pred_lig_points = points_xyz.vertices[np.hstack(occurances).astype(int)]
- np.savetxt(new_path + r'\SSM_' + segment + short + str(subject) + '_pred_points.xyz', np.asarray(pred_lig_points), delimiter=" ")
- pred_lig_points_color = np.c_[points_xyz.vertices[np.hstack(all_occ).astype(int)],np.hstack(orders).astype(int)]
- np.savetxt(new_path + '\SSM_' + segment + str(subject) + '_pred_points_color.xyz', np.asarray(pred_lig_points_color), delimiter=" ")
-
- print('processing ' + segment + ' done for ' + str(subject))
diff --git a/LigamentStudy/plotHausdorffDistance.py b/LigamentStudy/plotHausdorffDistance.py
deleted file mode 100644
index 514713b..0000000
--- a/LigamentStudy/plotHausdorffDistance.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-import trimesh
-import os
-from openpyxl import load_workbook
-import pandas as pd
-
-
-subjects = [9,13,19,23,26,29,32,35,37,41]
-segments = ['femur','tibia','fibula']
-
-data = []
-for segment in segments:
- RMS = []
- for subject in subjects:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
- if segment=='femur':
- HD = np.load(path + r'/8192/' + segment + '_HD.np.npy', allow_pickle=True)
- else:
- HD = np.load(path + r'/' + segment + '_HD.np.npy',allow_pickle=True)
- RMS.append(HD[0]['RMS'])
- # RMS.append(max(HD[0]['RMS'], HD[1]['RMS']))
-
- data.append(np.mean(RMS).round(decimals=2).astype(str) + ' ±' + np.std(RMS).round(decimals=2).astype(str))
-
-df = pd.DataFrame(data, index=['femur','tibia','fibula'], columns=['Hausdorff distance RMS (mm)'])
-
-book = load_workbook(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","HausdorffDistance.xlsx"))
-writer = pd.ExcelWriter(os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData","HausdorffDistance.xlsx"), engine='openpyxl')
-writer.book = book
-df.to_excel(writer, sheet_name='HD')
-writer.save()
-writer.close()
diff --git a/LigamentStudy/remesh.py b/LigamentStudy/remesh.py
deleted file mode 100644
index 4ba854c..0000000
--- a/LigamentStudy/remesh.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import trimesh
-import pymeshlab
-import os
-import numpy as np
-
-# mesh = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\femur_cartilage\mesh\9005075.segmentation_masks_femoral_cartilage_R.ply')
-# trimesh.remesh.subdivide_to_size(mesh.vertices, mesh.faces, 5, max_iter=10, return_index=False)
-# trimesh.exchange.export.export_mesh(mesh,r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation\segmentation_meshes\femur_cartilage\mesh_resample\9005075.segmentation_masks_femoral_cartilage_R.ply', 'ply')
-
-inputDir = r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\OAI-ZIB\segmentation'
-datasetName = "tibia_cartilage"
-mesh_dir = inputDir + r'/segmentation_meshes/' + datasetName + '/mesh/med/'
-mesh_dir_out = inputDir + r'/segmentation_meshes/' + datasetName + '/mesh_resample/med/'
-
-files_mesh = []
-for file in sorted(os.listdir(mesh_dir)):
- files_mesh.append(mesh_dir + file)
-
-pt_to_use = r'C:\Users\mariskawesseli\Documents\Data\OAI\segmentation\2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB\healthyKL_pts.txt'
-with open(pt_to_use) as f:
- pts = f.readlines()
-pts = [i.split('\n')[0] for i in pts]
-pts_use = pts
-
-matches_mesh = []
-for pt in pts_use:
- if any(pt in s for s in files_mesh):
- matches_mesh.append([match for match in files_mesh if pt in match])
-
-files_mesh = [item for sublist in matches_mesh for item in sublist]
-
-#
-# for file in files_mesh:
-# ms6 = pymeshlab.MeshSet()
-# ms6.load_new_mesh(file)
-# ms6.apply_filter('uniform_mesh_resampling', cellsize=1.05, offset=0.5, mergeclosevert=False)
-# ms6.save_current_mesh(file_name=mesh_dir_out + file.split('/')[-1], save_textures=False)
-
- # for femur
- # m = ms6.current_mesh()
- # fm = m.face_matrix()
- #
- # # ms6 = pymeshlab.MeshSet()
- # # ms6.load_new_mesh(mesh_dir_out + file.split('/')[-1])
- # ms6.apply_filter('simplification_quadric_edge_collapse_decimation',targetfacenum=np.max(fm),targetperc=0,qualitythr=0.3,preserveboundary=True,preservenormal=True,preservetopology=True)
- # ms6.save_current_mesh(file_name=mesh_dir_out + file.split('/')[-1], save_textures=False)
-
-datasetName = "tibia_cartilage"
-side = 'med' # 'lat' #
-outputDirectory = r'C:/Users/mariskawesseli/Documents/GitLab/knee_ssm/OAI/Output/tibia_cartilage_' + side + '/groomed/'
-
-origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-Rx = trimesh.transformations.rotation_matrix(np.pi/4, xaxis)
-
-mesh_reg = trimesh.load_mesh(outputDirectory + 'reference.ply')
-for file in files_mesh:
- mesh1 = trimesh.load_mesh(mesh_dir_out + file.split('/')[-1])
-
- T, cost = trimesh.registration.mesh_other(mesh1, mesh_reg, samples=500, scale=False, icp_first=10, icp_final=50)
- mesh1.apply_transform(T)
- mesh1.apply_transform(Rx)
-
- mesh1.export(outputDirectory + 'meshes/' + file.split('/')[-1])
-
-
diff --git a/LigamentStudy/rotateMesh.py b/LigamentStudy/rotateMesh.py
deleted file mode 100644
index c4607b5..0000000
--- a/LigamentStudy/rotateMesh.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import trimesh
-import os
-import numpy as np
-import glob
-
-
-subjects = ['1'] #['S0'] # [9,13,19,23,26,29,32,35,37,41]
-sides = ['R']
-segments = ['femur','tibia', 'fibula']
-short_ssm = [0, 1, 0]
-no_particles = [4096, 4096, 2048]
-opensim_meshes = ['smith2019-L-femur-bone_remesh.stl','smith2019-L-tibia-bone_remesh.stl',
- 'smith2019-L-fibula-bone_remesh.stl']
-run_fit = 1
-run_find_points = 1
-create_cmd = 0
-add_contact = 1
-for_linux = 0
-
-opensim_geometry_folder = r'C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry'
-input_file_folder = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output'
-gen_model = r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\4DCT\1\lenhart2015_nocontact.osim' # generic scaled model without contact
-
-data_folder = r"C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\4DCT/"
-path = r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/'
-cadaver_folder = r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData/'
-
-for sample in range(0,1): #100
-
- for subj_ind, subject in enumerate(subjects):
-
- if sides[subj_ind] == 'R':
- side = '_R'
- reflect = ''
- else:
- side = '_L'
- reflect = '.reflect'
-
- for seg_ind, segment in enumerate(segments):
- if short_ssm[seg_ind]:
- short = '_short'
- else:
- short = ''
-
- ssm_path = path + segment + '_bone' + short + r'\new_bone_mri\shape_models/'
- ssm_files = glob.glob(ssm_path + "*.stl")
- mesh_inp = ssm_files[subj_ind]
- new_path = ssm_path + '/fit/'
-
- out_file = os.path.join(data_folder, str(subject), 'input_mesh_' + segment + short + '_translated.stl')
- out_file2 = os.path.join(data_folder, str(subject), 'input_mesh_' + segment + short + '_icp.stl')
-
- origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
- Rz = trimesh.transformations.rotation_matrix(0 / (180 / np.pi), xaxis)
- Ry = trimesh.transformations.rotation_matrix(-90 / (180 / np.pi), zaxis)
- R = trimesh.transformations.concatenate_matrices(Rz, Ry)
-
- mesh = trimesh.load_mesh(mesh_inp)
- mesh.apply_transform(R)
- mesh.export(os.path.join(args.inputDir,mesh_path))
diff --git a/LigamentStudy/scaleOsim.py b/LigamentStudy/scaleOsim.py
deleted file mode 100644
index 689e65c..0000000
--- a/LigamentStudy/scaleOsim.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import trimesh
-import numpy as np
-from scipy import interpolate
-import matplotlib.pyplot as plt
-
-
-def interpolate_lig_points(lig_points, no_points, plot=1):
- x = lig_points[:, 0]
- y = lig_points[:, 1]
- z = lig_points[:, 2]
- goon = 1
- n=0
- # Create a uniformly spaced grid
- while goon == 1:
- steps = no_points/2+n # number of rows and columns for the grid
- grid_steps = complex(str(steps) + 'j')
-
- interp = interpolate.Rbf(x, y, z, function='thin_plate')
- yi, xi = np.mgrid[min(lig_points[:, 1]):max(lig_points[:, 1]):grid_steps,
- min(lig_points[:, 0]):max(lig_points[:, 0]):grid_steps]
- zi = interp(xi, yi)
- inds_remove = []
- inds_nan = []
- diff_val = []
- xi_nan = xi
- yi_nan = yi
- zi_nan = zi
- for i in range(0, len(xi)):
- for j in range(0, len(xi)):
- diff = np.linalg.norm(lig_points[:, :] - np.asarray([xi[i, j], yi[i, j], zi[i, j]]), axis=1)
- diff_val.append(np.abs(np.amin(diff)))
- if np.amin(diff) > 1.5:
- inds_remove.append([i * steps + j])
- inds_nan.append([i, j])
- xi_nan[i, j] = np.nan
- yi_nan[i, j] = np.nan
- zi_nan[i, j] = np.nan
- n=n+1
- if np.count_nonzero(~np.isnan(xi_nan)) >= no_points or n==10:
- # print(str(np.count_nonzero(~np.isnan(xi_nan))) + ' ' + str(no_points))
- goon = 0
- diff_val = np.zeros([len(xi_nan),len(xi_nan)])
- if np.count_nonzero(~np.isnan(xi_nan)) > no_points:
- to_remove = np.count_nonzero(~np.isnan(xi_nan))-no_points
- for i in range(0, len(xi_nan)):
- for j in range(0, len(xi_nan)):
- diff = np.linalg.norm(lig_points[:, :] - np.asarray([xi_nan[i, j], yi_nan[i, j], zi_nan[i, j]]), axis=1)
- diff_val[i,j] = np.abs(np.amin(diff))
- for k in range(0,to_remove):
- i,j = np.unravel_index(np.nanargmax(diff_val),diff_val.shape)
- diff_val[i,j] = np.nan
- xi_nan[i,j] = np.nan
- yi_nan[i,j] = np.nan
- zi_nan[i,j] = np.nan
- # print('-1 ' + str(np.count_nonzero(~np.isnan(xi_nan))) + ' ' + str(no_points))
-
- if len(lig_points) == 2:
- tck, u = interpolate.splprep([lig_points[:, 0], lig_points[:, 1], lig_points[:, 2]],s=10,k=1)
- x_knots, y_knots, z_knots = interpolate.splev(tck[0], tck)
- u_fine = np.linspace(0, 1, no_points)
- x_fine, y_fine, z_fine = interpolate.splev(u_fine, tck, der=0)
- # lig_points_osim = np.transpose(np.asarray([x_fine, y_fine, z_fine]))
- xi_nan, yi_nan, zi_nan = x_fine, y_fine, z_fine
-
- lig_points_osim = xi_nan[np.logical_not(np.isnan(xi_nan))]/1000, yi_nan[np.logical_not(np.isnan(yi_nan))]/1000, zi_nan[np.logical_not(np.isnan(zi_nan))]/1000
-
- if plot == 1:
- fig2 = plt.figure()
- ax3d = fig2.add_subplot(111, projection='3d')
- ax3d.plot(lig_points[:, 0], lig_points[:, 1], lig_points[:, 2], 'r*')
- # ax3d.plot(x_knots, y_knots, z_knots, 'bo')
- # ax3d.plot(x_fine, y_fine, z_fine, 'go')
- ax3d.scatter(xi_nan[:],yi_nan[:],zi_nan[:],c='g')
- fig2.show()
- plt.show()
-
- return lig_points_osim
-
-osim_model = r'C:\Users\mariskawesseli\Documents\MOBI\data\S0_2_meniscus_lig.osim'
-"""femur"""
-# # run ICP to get final position SSM point cloud on original mesh
-# mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-femur-bone_remesh.stl')
-# mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_remesh2.STL')
-# # points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
-#
-# M = trimesh.transformations.scale_and_translate((1,1,-1))
-# mesh1.apply_transform(M)
-# T = trimesh.transformations.translation_matrix([64.724205, -26.297621, -95.929390])
-# origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-# Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-# Ry = trimesh.transformations.rotation_matrix(90/(180/np.pi), yaxis)
-# R = trimesh.transformations.concatenate_matrices(T, Ry, Rx)
-# mesh2.apply_transform(R)
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_test.STL')
-#
-# s, fi = trimesh.sample.sample_surface_even(mesh2, 32015, radius=None)
-# mesh3 = trimesh.icp[0]imesh(vertices=s, faces=mesh2.faces[fi])
-# mesh3.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_test.STL')
-# sort_index = np.argsort(fi) # sort and group the face indices
-# points = s[sort_index, :]
-# faceIndices = fi[sort_index]
-# uniqueFaceIndices = np.unique(faceIndices)
-# allMeshPatches = trimesh.icp[0]imesh()
-# pointGroups = [points[faceIndices == i] for i in uniqueFaceIndices]
-# for faceIndex, pointsOnFace in zip(uniqueFaceIndices, pointGroups):
-# meshpatch = trimesh.icp[0]imesh(mesh2.vertices[mesh2.faces[faceIndex, :]].reshape(3, 3),
-# np.array([0, 1, 2]).reshape(1, 3))
-# allMeshPatches += meshpatch
-# allMeshPatches.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_remesh_test.STL')
-#
-# kwargs = {"scale": False}
-# icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-#
-# kwargs = {"scale": True}
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_icp.STL')
-# icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-# mesh2.apply_transform(icp[0])
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_femur2_2_bone_rot_icp.STL')
-# scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-# # mesh1.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\test.STL')
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points.xyz')
-# ligs.apply_transform(R)
-# ligs.apply_transform(icp[0])
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim.xyz')
-# PCL = ligs.vertices[0:61]
-# al = [0,1,2,3,4,5,7,10,11,13,29,30,31,34,35,39,40,42,43,47,49,50,51,55,56,57,58,59,24,27,28,48]
-# pm = [item for item in list(range(61)) if item not in al]
-# PCLal_osim = interpolate_lig_points(PCL[al,:],5)
-# PCLpm_osim = interpolate_lig_points(PCL[pm,:],5)
-# MCLs = ligs.vertices[61:71]
-# MCLs_osim = interpolate_lig_points(MCLs,6)
-# MCLd = ligs.vertices[71:73]
-# MCLd_osim = interpolate_lig_points(MCLd,5)
-# post_obl = ligs.vertices[73:81]
-# post_obl_osim = interpolate_lig_points(post_obl,5)
-# ACL = ligs.vertices[81:100]
-# al = [0,1,2,4,7,9,12,15,18]
-# pm = [item for item in list(range(19)) if item not in al]
-# ACLal_osim = interpolate_lig_points(ACL[al,:],6)
-# ACLpm_osim = interpolate_lig_points(ACL[pm,:],6)
-# LCL = ligs.vertices[100:105]
-# LCL_osim = interpolate_lig_points(LCL,4)
-#
-# osim_points_fem = np.concatenate([np.asarray(PCLal_osim),np.asarray(PCLpm_osim), np.asarray(MCLs_osim),
-# np.asarray(MCLd_osim), np.asarray(post_obl_osim), np.asarray(ACLal_osim),
-# np.asarray(ACLpm_osim), np.asarray(LCL_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\femur\SSM_femur_pred_points_osim_interp.xyz', osim_points_fem, delimiter=" ")
-#
-# # fig2 = plt.figure()
-# # ax3d = fig2.add_subplot(111, projection='3d')
-# # ax3d.plot(ACL[al, 0], ACL[al, 1], ACL[al, 2], 'r*')
-# # ax3d.plot(ACL[pm, 0], ACL[pm, 1], ACL[pm, 2], 'bo')
-# # fig2.show()
-# # plt.show()
-
-"""tibia"""
-# # run ICP to get final position SSM point cloud on original mesh
-# mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-tibia-bone_remesh.stl')
-# mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_remesh.STL')
-# # points = trimesh.load_mesh(path + '\SSM_' + segment + '_transform.xyz')
-#
-# M = trimesh.transformations.scale_and_translate((1,1,-1))
-# mesh1.apply_transform(M)
-# T = trimesh.transformations.translation_matrix([101.562462, -72.768566, -17.893391])
-# origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-# Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-# Ry = trimesh.transformations.rotation_matrix(90/(180/np.pi), yaxis)
-# R = trimesh.transformations.concatenate_matrices(Ry, Rx)
-# mesh2.apply_transform(T)
-# mesh2.apply_transform(R)
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_remesh_test.STL')
-#
-# kwargs = {"scale": False}
-# icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-#
-# kwargs = {"scale": True}
-# # mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_icp.STL')
-# icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-# mesh2.apply_transform(icp[0])
-# mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_tibia_2_bone_rot_icp.STL')
-# scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-# mesh1.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\test_tib.STL')
-#
-# ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\SSM_tibia_short_pred_points.xyz')
-# ligs.apply_transform(T)
-# ligs.apply_transform(R)
-# ligs.apply_transform(icp[0])
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_short_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_short_pred_points_osim.xyz')
-PCL = ligs.vertices[0:71]
-al = [0,1,2,4,6,7,10,12,13,19,21,22,23,24,25,26,27,31,32,34,35,40,45,46,47,50,52,56,59,60,61,62,63,65,70]
-pm = [item for item in list(range(71)) if item not in al]
-PCLal_osim = interpolate_lig_points(PCL[al,:],5)
-PCLpm_osim = interpolate_lig_points(PCL[pm,:],5)
-MCLd = ligs.vertices[71:82]
-MCLd_osim = interpolate_lig_points(MCLd,5)
-post_obl = ligs.vertices[82:86]
-post_obl_osim = interpolate_lig_points(post_obl,5)
-ACL = ligs.vertices[86:150]
-al = [0,1,2,4,6,7,10,12,13,19,21,22,23,24,25,26,27,31,32,34,35,40,45,46,47,50,52,56,59,60,61,62,63]
-pm = [item for item in list(range(64)) if item not in al]
-ACLal_osim = interpolate_lig_points(ACL[al,:],6)
-ACLpm_osim = interpolate_lig_points(ACL[pm,:],6)
-
-osim_points_tib = np.concatenate([np.asarray(PCLal_osim),np.asarray(PCLpm_osim),
- np.asarray(MCLd_osim), np.asarray(post_obl_osim), np.asarray(ACLal_osim),
- np.asarray(ACLpm_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\tibia\SSM_tibia_pred_points_osim_interp.xyz', osim_points_tib, delimiter=" ")
-
-
-"""fibula"""
-# run ICP to get final position SSM point cloud on original mesh
-# mesh OpenSim model - make sure this is high quality
-mesh1 = trimesh.load_mesh('C:\opensim-jam\jam-resources\jam-resources-main\models\knee_healthy\smith2019\Geometry\smith2019-R-fibula-bone_remesh.stl')
-# mesh segmented from MRI
-mesh2 = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh.STL')
-
-# Mirror if needed (only for left as SSM/model is right? - check how to deal with left model)
-M = trimesh.transformations.scale_and_translate((1,-1,1))
-mesh2.apply_transform(M)
-# Rotate segmented bone (check why needed?)
-origin, xaxis, yaxis, zaxis = [0,0,0], [1, 0, 0], [0, 1, 0], [0, 0, 1]
-Rx = trimesh.transformations.rotation_matrix(-90/(180/np.pi), xaxis)
-Ry = trimesh.transformations.rotation_matrix(-90/(180/np.pi), yaxis)
-Rz = trimesh.transformations.rotation_matrix(180/(180/np.pi), zaxis)
-R = trimesh.transformations.concatenate_matrices(Ry, Rx, Rz)
-mesh2.apply_transform(R)
-# Translate segmented mesh to OpenSim bone location
-T = trimesh.transformations.translation_matrix(mesh1.center_mass-mesh2.center_mass)
-mesh2.apply_transform(T)
-mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh_test.STL')
-
-# ICP to fit segmented bone to OpenSim mesh
-kwargs = {"scale": False}
-icp1 = trimesh.registration.icp(mesh2.vertices,mesh1,initial=np.identity(4),threshold=1e-5,max_iterations=20,**kwargs)
-kwargs = {"scale": True}
-icp = trimesh.registration.icp(mesh2.vertices, mesh1, initial=icp1[0], threshold=1e-5, max_iterations=20,**kwargs)
-mesh2.apply_transform(icp[0])
-mesh2.export(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\bone_fibula_1_tissue_rot_remesh_icp.STL')
-scale, shear, angles, trans, persp = trimesh.transformations.decompose_matrix(icp[0])
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\SSM_fibula_short_pred_points.xyz')
-ligs.apply_transform(M)
-ligs.apply_transform(R)
-ligs.apply_transform(T)
-ligs.apply_transform(icp[0])
-np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim.xyz', ligs.vertices, delimiter=" ")
-
-ligs = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim.xyz')
-
-LCL = ligs.vertices[0:79]
-LCL_osim = interpolate_lig_points(LCL,4)
-
-osim_points_fib = np.concatenate([np.asarray(LCL_osim)],1)
-# np.savetxt(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim\fibula\SSM_fibula_pred_points_osim_interp.xyz', osim_points_tib, delimiter=" ")
diff --git a/LigamentStudy/showAxes.py b/LigamentStudy/showAxes.py
deleted file mode 100644
index 526f6f9..0000000
--- a/LigamentStudy/showAxes.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-import seaborn as sns
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-subjects = ['9'] #['9','13','19','23','26','29','32','35','37','41'] #, S0 [100] #
-
-segments = ['tibia'] #'femur',
-ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
-ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
-ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
-for segment in segments:
- SSMpoints = [[] for i in range(11)]
- for ind in range(0,11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject==100:
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject==100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- bone_actor = load_stl(path + '/mean_shape.stl')
- # bone_actor.GetProperty().SetOpacity(0.75)
- else:
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- else:
- bone_actor = load_stl(path + '/Segmentation_' + segment + '_transform.stl')
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.85)
-
- # actor.GetProperty().SetOpacity(1.0)
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
-
- c = sns.color_palette("viridis_r", n_colors=10, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(10)
- lut.SetTableRange(1, 10)
- for j in range(0,10):
- lut.SetTableValue(int(j), c[j][0], c[j][1], c[j][2])
-
- legend = vtk.vtkScalarBarActor()
- legend.SetNumberOfLabels(10)
- lut.SetTableRange(1, 10)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
- legend.SetTitle("Specimens \n")
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0,0,0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- legend.SetMaximumWidthInPixels(75)
- legend.SetMaximumHeightInPixels(300)
- legend.SetTitleTextProperty(text_prop_cb)
- legend.SetPosition(0.85,0.6)
-
- axes = vtk.vtkAxesActor()
- axes.SetTotalLength(75,75,100)
- axes.SetXAxisLabelText('M-L')
- axes.SetYAxisLabelText('A-P')
- axes.SetZAxisLabelText('S-I')
- axes.GetXAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetXAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
- axes.GetYAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetYAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
- axes.GetZAxisCaptionActor2D().GetTextActor().SetTextScaleMode(vtk.vtkTextActor.TEXT_SCALE_MODE_NONE)
- axes.GetZAxisCaptionActor2D().GetCaptionTextProperty().SetFontSize(25)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(bone_actor)
- # if not subject == 100 and not subject == 'S0':
- # renderer.AddActor(wire_actor)
- # renderer.AddActor(legend)
- renderer.AddActor(axes)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
diff --git a/LigamentStudy/stl2vtk.py b/LigamentStudy/stl2vtk.py
deleted file mode 100644
index 9a8b81f..0000000
--- a/LigamentStudy/stl2vtk.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import vtk
-
-# Define the input and output directories
-input_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\tibia_bone_short\new_bone\shape_models"
-output_dir = input_dir
-
-# Create a VTK STL reader
-stl_reader = vtk.vtkSTLReader()
-
-# Loop through all STL files in the input directory
-for filename in os.listdir(input_dir):
- if filename.endswith("mean_shape.stl"):
- # Load the STL file
- stl_path = os.path.join(input_dir, filename)
- stl_reader.SetFileName(stl_path)
- stl_reader.Update()
-
- # Convert the STL data to polydata
- polydata_filter = vtk.vtkDataSetSurfaceFilter()
- polydata_filter.SetInputConnection(stl_reader.GetOutputPort())
- polydata_filter.Update()
- polydata = polydata_filter.GetOutput()
-
- # Modify the VTK file header to version 4.2
- header = "# vtk DataFile Version 4.2\n"
-
- # Save the polydata as a VTK file in format 4.2
- vtk_path = os.path.join(output_dir, filename.replace(".stl", ".vtk"))
- vtk_writer = vtk.vtkPolyDataWriter()
- vtk_writer.SetFileName(vtk_path)
- vtk_writer.SetInputData(polydata)
- vtk_writer.SetFileTypeToBinary()
- vtk_writer.SetHeader(header)
- vtk_writer.Update()
diff --git a/LigamentStudy/testVisualizeSSM.py b/LigamentStudy/testVisualizeSSM.py
deleted file mode 100644
index d3273f9..0000000
--- a/LigamentStudy/testVisualizeSSM.py
+++ /dev/null
@@ -1,441 +0,0 @@
-# import pyvista as pv
-# mesh= pv.read(r"C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col.ply")
-# mesh.plot()
-
-# import pyvista as pv
-# import numpy as np
-# # Re cast PolyData because file was not properly saved
-# bad = pv.read(r"C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col.ply")
-# bad.plot()
-# mesh = pv.PolyData(bad.points)
-# # Plot it
-# scalars = bad['RGBA']
-# # mesh.plot(scalars=scalars)
-# mesh.plot(scalars=scalars[:,0:3])
-# mesh.plot(scalars=scalars)
-# mesh.plot(scalars=scalars, rgba=True)
-
-import sys
-import os
-import vtk
-from numpy import random
-import trimesh
-import numpy as np
-import seaborn as sns
-
-
-class VtkPointCloud:
- def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
- self.maxNumPoints = maxNumPoints
- self.vtkPolyData = vtk.vtkPolyData()
- self.clearPoints()
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(self.vtkPolyData)
- mapper.SetColorModeToDefault()
- mapper.SetScalarRange(zMin, zMax)
- mapper.SetScalarVisibility(1)
- self.vtkActor = vtk.vtkActor()
- self.vtkActor.SetMapper(mapper)
-
- def addPoint(self, point):
- if (self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints):
- pointId = self.vtkPoints.InsertNextPoint(point[:])
- self.vtkDepth.InsertNextValue(point[2])
- self.vtkCells.InsertNextCell(1)
- self.vtkCells.InsertCellPoint(pointId)
- else:
- r = random.randint(0, self.maxNumPoints)
- self.vtkPoints.SetPoint(r, point[:])
- self.vtkCells.Modified()
- self.vtkPoints.Modified()
- self.vtkDepth.Modified()
-
- def clearPoints(self):
- self.vtkPoints = vtk.vtkPoints()
- self.vtkCells = vtk.vtkCellArray()
- self.vtkDepth = vtk.vtkDoubleArray()
- self.vtkDepth.SetName('DepthArray')
- self.vtkPolyData.SetPoints(self.vtkPoints)
- self.vtkPolyData.SetVerts(self.vtkCells)
- self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
- self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
-
-
-def load_data(data, pointCloud):
- # data = genfromtxt(filename, dtype=float, usecols=[0, 1, 2])
- for k in range(size(data, 0)):
- point = data[k] # 20*(random.rand(3)-0.5)
- pointCloud.addPoint(point)
-
- return pointCloud
-
-
-def load_stl(filename):
- reader = vtk.vtkSTLReader()
- reader.SetFileName(filename)
-
- mapper = vtk.vtkPolyDataMapper()
- if vtk.VTK_MAJOR_VERSION <= 5:
- mapper.SetInput(reader.GetOutput())
- else:
- mapper.SetInputConnection(reader.GetOutputPort())
-
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-def create_pointcloud_polydata(points, colors=None, seg=None):
- """https://github.com/lmb-freiburg/demon
- Creates a vtkPolyData object with the point cloud from numpy arrays
-
- points: numpy.ndarray
- pointcloud with shape (n,3)
-
- colors: numpy.ndarray
- uint8 array with colors for each point. shape is (n,3)
-
- Returns vtkPolyData object
- """
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
- # vpoints.SetMarkerStyle(vtk.vtkPlotPoints.CIRCLE)
-
- vpoly = vtk.vtkPolyData()
-
- appendFilter = vtk.vtkAppendPolyData()
- for i in range(points.shape[0]):
- sphereSource = vtk.vtkSphereSource()
- # spheres.SetThetaResolution(1)
- # spheres.SetPhiResolution(1)
- sphereSource.SetRadius(1)
- sphereSource.SetCenter(vpoints.GetPoint(i))
- sphereSource.Update()
-
- appendFilter.AddInputData(sphereSource.GetOutput())
-
- # vpoly.SetPoints(vpoints)
- rgb_col = []
- if not colors is None:
- if seg == 'femur':
- max_val = 8
- color[112:len(color)] = (color[112:len(color)] / max_val) * 10
- vcolors = vtk.vtkUnsignedCharArray()
- vcolors.SetNumberOfComponents(3)
- vcolors.SetName("Colors")
- vcolors.SetNumberOfTuples(points.shape[0])
- rgb_col = []
- for i in range(points.shape[0]):
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- vcolors.SetTuple3(i, c[int(colors[i] * 10)][0] * 255, c[int(colors[i] * 10)][1] * 255,
- c[int(colors[i] * 10)][2] * 255)
- rgb_col.append(
- [c[int(colors[i] * 10)][0] * 255, c[int(colors[i] * 10)][1] * 255, c[int(colors[i] * 10)][2] * 255])
- # print(i, c[int(colors[i] - 1)][0], c[int(colors[i] - 1)][1], c[int(colors[i] - 1)][2])
- # c = rgb(1,10,colors[i])
- # vcolors.SetTuple3(i, c[0], c[1], c[2])
- vpoly.GetPointData().SetScalars(vcolors)
-
- actor.GetProperty().SetColor(color)
-
- vcells = vtk.vtkCellArray()
-
- for i in range(points.shape[0]):
- vcells.InsertNextCell(1)
- vcells.InsertCellPoint(i)
-
- vpoly.SetVerts(vcells)
-
- return vpoly, rgb_col
-
-
-def rgb(minimum, maximum, value):
- minimum, maximum = float(minimum), float(maximum)
- ratio = (value - minimum) / (maximum - minimum) # 2 *
- g = int(max(0, 255 * (1 - ratio)))
- r = int(max(0, 255 * (ratio - 0)))
- b = 0 # 255 - b - r
- return r, g, b
-
-
-def createSpline(points):
- vpoints = vtk.vtkPoints()
- vpoints.SetNumberOfPoints(points.shape[0])
- for i in range(points.shape[0]):
- vpoints.SetPoint(i, points[i])
-
- spline = vtk.vtkParametricSpline()
- spline.SetPoints(vpoints)
-
- functionSource = vtk.vtkParametricFunctionSource()
- functionSource.SetParametricFunction(spline)
- functionSource.Update()
-
- # Create a mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputConnection(functionSource.GetOutputPort())
-
- # Create an actor
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
-
- return actor
-
-
-if __name__ == '__main__':
- center_tibia = np.concatenate((np.arange(131), np.arange(470 - 341) + 341)) # PCL + ACL
- center_femur = np.concatenate((np.arange(112), np.arange(341 - 263) + 263)) # PCL + ACL
- # center_femur = np.concatenate((np.arange(64), np.arange(101 - 68) + 68)) # PCL + ACL
- center_only = 1
- subjects = [100] # [100] # ['9','13','19','23','26','29','32','35','37','41'] #, S0 [100]
-
- segments = ['femur'] # 'femur',
- ligaments_fem = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [6, 5, 6, 6, 6, 6, 4, 4, 5, 5],
- [3, 2, 5, 3, 3, 2, 2, 0, 3, 3],
- [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [7, 3, 7, 7, 7, 5, 7, 6, 7, 0],
- [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [4, 6, 3, 5, 4, 0, 0, 3, 4, 4],
- [5, 7, 4, 4, 5, 7, 6, 5, 6, 6],
- [2, 4, 2, 2, 2, 3, 3, 2, 2, 2]]
-
- ligaments_tib = [[5, 7, 6, 5, 3, 4, 4, 5, 5, 4],
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
- [3, 3, 8, 3, 5, 3, 5, 0, 3, 3],
- [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [4, 5, 3, 4, 4, 5, 3, 2, 4, 0],
- [0, 6, 4, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], # POL4
- [6, 8, 9, 6, 6, 6, 6, 6, 6, 5],
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
-
- ligaments_fib = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # PCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLp
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # MCLd2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL2
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL3
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # POL4
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ACL
- [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], # LCL
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # POP
-
- for segment in segments:
- SSMpoints = [[] for i in range(11)]
- if segment == 'tibia':
- center = center_tibia
- elif segment == 'femur':
- center = center_femur
-
- for ind in range(0, 11):
- SSMpoints[ind] = [[] for i in range(10)]
-
- for ind, subject in enumerate(subjects):
- if subject == 100:
- path = os.path.join(
- r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone\new_bone\shape_models')
- elif subject == 'S0':
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\LigamentStudy\MRI\S0_prelim')
- else:
- path = os.path.join(r"C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData", str(subject))
-
- if subject in [9, 13, 26, 29, 32]:
- side = 'R'
- reflect = ''
- else:
- side = 'L'
- reflect = '.reflect'
-
- # points = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_bone_no_lig.xyz')
- # point_cloud = create_pointcloud_polydata(points)
- # pointCloud = VtkPointCloud()
- # pointCloud = load_data(point_cloud, pointCloud)
- # points_lig = trimesh.load_mesh(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output\femur_bone\new_bone\shape_models\meanshape_ligs.xyz')
- if subject == 100:
- # points_lig = trimesh.load_mesh(path + '\meanshape_ligs.xyz')
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(path + '\meanshape_ligs_color.xyz')
- color = np.loadtxt(path + r'\meanshape_ligs_color.xyz')[:, 3]
-
- if center_only == 1:
- points_lig = points_lig[center]
- color = color[center]
- point_cloud_lig, rgb_col = create_pointcloud_polydata(points_lig, colors=color, seg=segment)
- bone_actor = load_stl(path + '/mean_shape.stl')
- bone_actor.GetProperty().SetOpacity(1.0)
-
- mesh = trimesh.load_mesh(path + '/mean_shape.stl')
- # dist = trimesh.proximity.nearby_faces(mesh, np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- dist3 = trimesh.proximity.closest_point_naive(mesh, np.squeeze(
- np.asarray(points_lig[np.argwhere(color >= 7)])), tol=1.0)
-
- # faces = np.unique(np.asarray([item for sublist in dist for item in sublist]))
- faces = np.unique(np.asarray([item for sublist in dist3[3] for item in sublist]))
- mesh.update_faces(faces)
- mesh.export(path + '/mean_shape_80percsurf.stl')
- surf_actor = load_stl(path + '/mean_shape_80percsurf.stl')
- else:
- # points_lig = trimesh.load_mesh(path + '\SSM_' + segment + '_areas.xyz') #_pred_points_color
- # point_cloud_lig = create_pointcloud_polydata(points_lig)
- points_lig = trimesh.load_mesh(
- path + '\SSM_' + segment + '_pred_points_color.xyz') # _pred_points_color
- color = np.loadtxt(path + '\SSM_' + segment + '_pred_points_color.xyz')[:,
- 3] # _areas _short_areas _pred_points
- if center_only == 1:
- points_lig = points_lig[center]
- # color = color[center]
- point_cloud_lig = create_pointcloud_polydata(points_lig, seg=segment) # ,color colors=color,
- if subject == 'S0':
- # bone_actor = load_stl(path + '/bone_femur2_2_bone_rot.stl')
- # bone_actor = load_stl(path + '/bone_tibia_2_bone_rot.stl')
- bone_actor = load_stl(path + '/bone_fibula_1_tissue_rot.stl')
- else:
- bone_actor = load_stl(
- path + '/Segmentation_' + segment + '_resample.stl') # '/SSM_' + segment + '_reconstruct_transform_icp.stl'
- if segment == 'fibula':
- segment_temp = 'tibia'
- else:
- segment_temp = segment
- # if center_only == 1:
- # wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires1.stl')
- # wire_actor2 = load_stl(path + '/Segmentation_' + segment_temp + '_wires3.stl')
- # wire_actor2.GetProperty().SetColor(1, 1, 0)
- # else:
- wire_actor = load_stl(path + '/Segmentation_' + segment_temp + '_wires.stl')
- wire_actor.GetProperty().SetColor(1, 1, 0)
- bone_actor.GetProperty().SetOpacity(0.75)
-
- points_bone = trimesh.load_mesh(path + '\SSM_' + segment + '_transform_icp.xyz')
- point_cloud_bone = create_pointcloud_polydata(points_bone)
-
- # orders = np.load(r'C:\Users\mariskawesseli\Documents\LigamentStudy\ImageData\occurances_order.npy')
-
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(point_cloud_bone)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- actor.GetProperty().SetColor(0, 0, 0)
- actor.GetProperty().SetPointSize(2)
- # actor.GetProperty().SetOpacity(1.0)
-
- # spline_actor = createSpline(np.squeeze(np.asarray(points_lig[np.argwhere(color >= 8)])))
- bone_actor.GetProperty().SetColor(0.89, 0.85, 0.79)
- # bone_actor.GetProperty().LightingOff()
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputData(point_cloud_lig)
- actor2 = vtk.vtkActor()
- actor2.SetMapper(mapper2)
- actor2.GetProperty().RenderPointsAsSpheresOn()
- actor2.GetProperty().SetColor(1, 0, 0)
- actor2.GetProperty().SetPointSize(7.5)
-
- c = sns.color_palette("viridis_r", n_colors=101, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(11)
- lut.SetTableRange(1, 11)
- for j in range(0, 11):
- lut.SetTableValue(int(j * 1), c[j * 10][0], c[j * 10][1], c[j * 10][2])
- # print(int(j*1), c[j*10-1][0], c[j*10-1][1], c[j*10-1][2])
-
- j = 10 - 1
- surf_col = [c[j][0], c[j][1], c[j][2]]
- surf_col = [169 / 255, 169 / 255, 169 / 255]
- surf_actor.GetProperty().SetColor(surf_col)
- surf_actor.GetProperty().SetOpacity(1.0)
-
- legend = vtk.vtkScalarBarActor()
- legend.SetOrientationToHorizontal()
- labelFormat = vtk.vtkTextProperty()
- labelFormat.SetFontSize(16)
- titleFormat = vtk.vtkTextProperty()
- titleFormat.SetFontSize(8)
- legend.SetLabelTextProperty(labelFormat)
- # legend.SetTitleTextProperty(titleFormat)
-
- legend.SetNumberOfLabels(11)
- lut.SetTableRange(0, 100)
- legend.SetLookupTable(lut)
- # pos = legend.GetPositionCoordinate()
- # pos.SetCoordinateSystemToNormalizedViewport()
-
- legend.SetTitle("% of specimens \n")
- legend.SetLabelFormat("%1.0f")
- legend.SetUnconstrainedFontSize(1)
-
- text_prop_cb = legend.GetLabelTextProperty()
- text_prop_cb.SetFontFamilyAsString('Arial')
- text_prop_cb.SetFontFamilyToArial()
- text_prop_cb.SetColor(0, 0, 0)
- # text_prop_cb.SetFontSize(500)
- text_prop_cb.ShadowOff()
- legend.SetLabelTextProperty(text_prop_cb)
- # legend.SetMaximumWidthInPixels(75)
- # legend.SetMaximumHeightInPixels(300)
- legend.SetMaximumWidthInPixels(300)
- legend.SetMaximumHeightInPixels(75)
- legend.SetTitleTextProperty(text_prop_cb)
- # legend.SetPosition(0.85,0.5)
- legend.SetPosition(0.5, 0.85)
-
- # Renderer
- renderer = vtk.vtkRenderer()
- # renderer.AddActor(actor)
- renderer.AddActor(actor2)
- renderer.AddActor(bone_actor)
- # renderer.AddActor(spline_actor)
- # renderer.AddActor(surf_actor)
- if not subject == 100 and not subject == 'S0':
- renderer.AddActor(wire_actor)
- # renderer.AddActor(wire_actor2)
- renderer.AddActor(legend)
- # renderer.SetBackground(.2, .3, .4)
- renderer.SetBackground(1.0, 1.0, 1.0)
- renderer.ResetCamera()
- # light = vtk.vtkLight()
- # light.SetIntensity(1)
- # renderer.AddLight(light)
-
- # Render Window
- renderWindow = vtk.vtkRenderWindow()
- renderWindow.AddRenderer(renderer)
- renderWindow.SetSize(750, 750)
-
- # Interactor
- renderWindowInteractor = vtk.vtkRenderWindowInteractor()
- renderWindowInteractor.SetRenderWindow(renderWindow)
- renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-
- # Begin Interaction
- renderWindow.Render()
- renderWindow.SetWindowName("XYZ Data Viewer " + str(subject))
- renderWindowInteractor.Start()
-
-polyData = vtk.vtkPolyData()
-polyData.DeepCopy(actor2.GetMapper().GetInput())
-transform = vtk.vtkTransform()
-transform.SetMatrix(actor2.GetMatrix())
-fil = vtk.vtkTransformPolyDataFilter()
-fil.SetTransform(transform)
-fil.SetInputDataObject(polyData)
-fil.Update()
-polyData.DeepCopy(fil.GetOutput())
-
-writer = vtk.vtkPLYWriter()
-writer.SetFileTypeToASCII()
-writer.SetColorModeToDefault()
-filename = r'C:\Users\mariskawesseli\Documents\GitLab\femur_lig_ply_col2.ply'
-writer.SetFileName(filename)
-writer.SetInputData(polyData)
-writer.Write()
-
-# import pandas as pd
-# pd.DataFrame(color).to_clipboard()
\ No newline at end of file
diff --git a/LigamentStudy/vislualize_distances.py b/LigamentStudy/vislualize_distances.py
deleted file mode 100644
index 6b167ab..0000000
--- a/LigamentStudy/vislualize_distances.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import os
-import vtk
-import trimesh
-import numpy as np
-from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
-import seaborn as sns
-
-segment = 'femur'
-renderer = vtk.vtkRenderer()
-
-rw = vtk.vtkRenderWindow()
-# xmins = [0, .5, 0, .5, 0, .5]
-# xmaxs = [0.5, 1, 0.5, 1, .5, 1]
-# ymins = [.66, .66, .33, .33, 0, 0, ]
-# ymaxs = [1, 1, .66, .66, 0.33, 0.33]
-
-xmins = [0, 0, .33, .33, .66, .66]
-xmaxs = [.33, .33, .66, .66, 1, 1]
-ymins = [0, .5, 0, .5, 0, .5]
-ymaxs = [0.5, 1, 0.5, 1, .5, 1]
-iren = vtk.vtkRenderWindowInteractor()
-iren.SetRenderWindow(rw)
-
-tel=0
-
-for modes in range(1,4):
- path = os.path.join(r'C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\Output/' + segment + r'_bone/')
- mean_shape = 'mean_shape.stl'
- mode_plus = 'mode' + str(modes) + '_+2SD_8192.stl'
- mode_min = 'mode' + str(modes) + '_-2SD_8192.stl'
-
- # determine signed distance
- plus2sd = trimesh.load_mesh(path + mode_plus)
- min2sd = trimesh.load_mesh(path + mode_min)
- mean = trimesh.load_mesh(path + mean_shape)
- # signed_distance = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices) # improve this?
- # signed_distance2 = trimesh.proximity.signed_distance(plus2sd, mean.vertices)
- # signed_distance3 = trimesh.proximity.signed_distance(min2sd, mean.vertices)
- # signed_distance = signed_distance2 + -signed_distance3
- signed_distance4 = trimesh.proximity.signed_distance(plus2sd, min2sd.vertices)
- signed_distance = signed_distance4
-
- # load mesh via trimesh to get the correct order for distance transform
- reader = vtk.vtkSTLReader()
- reader.SetFileName(path + mode_min)
- reader.Update()
- obj = reader.GetOutputDataObject(0)
-
- # create lookup table
- c = sns.diverging_palette(25, 262, s=60, n=100, as_cmap=False)
- lut = vtk.vtkLookupTable()
- lut.SetNumberOfColors(100)
- lut.SetTableRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- for j in range(0,100):
- lut.SetTableValue(int(j), c[j][0], c[j][1], c[j][2])
- lut.Build()
-
- # fix order signed distance transform as these are from trimesh
- vtk_nodes = vtk_to_numpy(obj.GetPoints().GetData())
- trimesh_nodes = min2sd.vertices
- dist = np.zeros((obj.GetNumberOfPoints(),1))
- for i in range(obj.GetNumberOfPoints()):
- # np.linalg.norm(vtk_nodes - trimesh_nodes)
- # idx = (np.hypot(*(vtk_nodes - trimesh_nodes[i]).T)).argmin()
- result = np.where((vtk_nodes[:,0] == trimesh_nodes[i][0]) & (vtk_nodes[:,1] == trimesh_nodes[i][1]) & (vtk_nodes[:,2] == trimesh_nodes[i][2]))
- # result = idx
- dist[result[0][0]] = signed_distance[i]
-
- vtk_dist = vtk.vtkDoubleArray()
- # z = np.zeros((obj.GetNumberOfPoints(),1))
- for i in range(obj.GetNumberOfPoints()):
- vtk_dist.InsertNextValue(dist[i])
- obj.GetPointData().SetScalars(vtk_dist)
-
- # mapper
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputDataObject(obj)
- mapper.SetScalarRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- mapper.SetLookupTable(lut)
- mapper2 = vtk.vtkPolyDataMapper()
- mapper2.SetInputDataObject(obj)
- mapper2.SetScalarRange(max(abs(signed_distance))*-1, max(abs(signed_distance)))
- mapper2.SetLookupTable(lut)
-
- if segment == 'fibula':
- d = -40
- else:
- d = -100
- # translation
- transform = vtk.vtkTransform()
- transform.Identity()
- # transform.Translate(0,modes * d, 0)
- transform.RotateX(90)
- transform.RotateY(180)
- transform.RotateZ(0)
- transformFilter = vtk.vtkTransformPolyDataFilter()
- transformFilter.SetInputConnection(reader.GetOutputPort())
- transformFilter.SetTransform(transform)
- transformFilter.Update()
-
- transform2 = vtk.vtkTransform()
- transform2.Identity()
- # transform2.Translate(d*-1, modes*d, 0)
- transform2.RotateX(90)
- transform2.RotateY(180)
- transform2.RotateZ(-90)
- transformFilter2 = vtk.vtkTransformPolyDataFilter()
- transformFilter2.SetInputConnection(reader.GetOutputPort())
- transformFilter2.SetTransform(transform2)
- transformFilter2.Update()
-
- # actors
- bone_actor = vtk.vtkActor()
- bone_actor.SetMapper(mapper)
- mapper.SetInputConnection(transformFilter.GetOutputPort())
- bone_actor.SetMapper(mapper)
- legend = vtk.vtkScalarBarActor()
- legend.SetLookupTable(lut)
- bone_actor2 = vtk.vtkActor()
- mapper2.SetInputConnection(transformFilter2.GetOutputPort())
- bone_actor2.SetMapper(mapper2)
-
- for ind in range(2):
- ren = vtk.vtkRenderer()
- rw.AddRenderer(ren)
- ren.SetViewport(xmins[tel], ymins[tel], xmaxs[tel], ymaxs[tel])
-
- # Share the camera between viewports.
- if tel == 0:
- camera = ren.GetActiveCamera()
- else:
- ren.SetActiveCamera(camera)
-
- # Create a mapper and actor
- if tel == 0 or tel == 2 or tel == 4:
- ren.AddActor(bone_actor)
- # ren.AddActor(actor2lig)
- else:
- ren.AddActor(bone_actor2)
- # ren.AddActor(actor3)
-
- ren.SetBackground(1.0, 1.0, 1.0)
-
- ren.ResetCamera()
-
- tel+=1
-
- # # Renderer
- # renderer.AddActor(bone_actor)
- # renderer.AddActor(bone_actor2)
- # # renderer.AddActor(legend)
- # renderer.SetBackground(1.0, 1.0, 1.0)
- # renderer.ResetCamera()
-
-# # Render Window
-# renderWindow = vtk.vtkRenderWindow()
-# renderWindow.AddRenderer(renderer)
-# renderWindow.SetSize(750, 750)
-#
-# # Interactor
-# renderWindowInteractor = vtk.vtkRenderWindowInteractor()
-# renderWindowInteractor.SetRenderWindow(renderWindow)
-# renderWindowInteractor.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-#
-# # Begin Interaction
-# renderWindow.Render()
-# renderWindow.SetWindowName("SSM distances")
-# renderWindowInteractor.Start()
-
-rw.Render()
-rw.SetWindowName('MultipleViewPorts')
-rw.SetSize(850, 400)
-iren.GetInteractorStyle().SetCurrentStyleToTrackballCamera()
-iren.Start()
\ No newline at end of file
diff --git a/LigamentStudy/vtk2stl.py b/LigamentStudy/vtk2stl.py
deleted file mode 100644
index 8323819..0000000
--- a/LigamentStudy/vtk2stl.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import os
-import vtk
-
-# Define the input and output directories
-input_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\tibia_bone_short"
-output_dir = r"C:\Users\mariskawesseli\Documents\GitLab\knee_ssm\OAI\tibia_bone_short"
-
-# Create a renderer and a render window
-renderer = vtk.vtkRenderer()
-render_window = vtk.vtkRenderWindow()
-render_window.AddRenderer(renderer)
-
-# Loop through all VTK files in the input directory
-for filename in os.listdir(input_dir):
- if filename.endswith(".vtk"):
- # Load the VTK file
- vtk_path = os.path.join(input_dir, filename)
- reader = vtk.vtkDataSetReader()
- reader.SetFileName(vtk_path)
- reader.Update()
- polydata = reader.GetOutput()
-
- # Convert the polydata to a stl file
- stl_path = os.path.join(output_dir, filename.replace(".vtk", ".stl"))
- writer = vtk.vtkSTLWriter()
- writer.SetFileName(stl_path)
- writer.SetInputData(polydata)
- writer.Write()
-
- # Add the polydata to the renderer for visualization
- mapper = vtk.vtkPolyDataMapper()
- mapper.SetInputData(polydata)
- actor = vtk.vtkActor()
- actor.SetMapper(mapper)
- renderer.AddActor(actor)
-
-# Set up the interactor and start the rendering loop
-interactor = vtk.vtkRenderWindowInteractor()
-interactor.SetRenderWindow(render_window)
-render_window.Render()
-interactor.Start()
--
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