191 lines
7.7 KiB
Python
191 lines
7.7 KiB
Python
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from . import utilsDataman
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import numpy as np
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def TRC2numpy(pathFile, markers,rotation=None):
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# rotation is a dict, eg. {'y':90} with axis, angle for rotation
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trc_file = utilsDataman.TRCFile(pathFile)
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time = trc_file.time
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num_frames = time.shape[0]
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data = np.zeros((num_frames, len(markers)*3))
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if rotation != None:
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for axis,angle in rotation.items():
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trc_file.rotate(axis,angle)
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for count, marker in enumerate(markers):
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data[:,3*count:3*count+3] = trc_file.marker(marker)
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this_dat = np.empty((num_frames, 1))
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this_dat[:, 0] = time
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data_out = np.concatenate((this_dat, data), axis=1)
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return data_out
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def numpy2TRC(f, data, headers, fc=50.0, t_start=0.0, units="m"):
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header_mapping = {}
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for count, header in enumerate(headers):
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header_mapping[count+1] = header
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# Line 1.
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f.write('PathFileType 4\t(X/Y/Z) %s\n' % os.getcwd())
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# Line 2.
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f.write('DataRate\tCameraRate\tNumFrames\tNumMarkers\t'
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'Units\tOrigDataRate\tOrigDataStartFrame\tOrigNumFrames\n')
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num_frames=data.shape[0]
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num_markers=len(header_mapping.keys())
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# Line 3.
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f.write('%.1f\t%.1f\t%i\t%i\t%s\t%.1f\t%i\t%i\n' % (
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fc, fc, num_frames,
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num_markers, units, fc,
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1, num_frames))
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# Line 4.
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f.write("Frame#\tTime\t")
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for key in sorted(header_mapping.keys()):
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f.write("%s\t\t\t" % format(header_mapping[key]))
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# Line 5.
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f.write("\n\t\t")
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for imark in np.arange(num_markers) + 1:
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f.write('X%i\tY%s\tZ%s\t' % (imark, imark, imark))
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f.write('\n')
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# Line 6.
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f.write('\n')
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for frame in range(data.shape[0]):
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f.write("{}\t{:.8f}\t".format(frame+1,(frame)/fc+t_start)) # opensim frame labeling is 1 indexed
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for key in sorted(header_mapping.keys()):
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f.write("{:.5f}\t{:.5f}\t{:.5f}\t".format(data[frame,0+(key-1)*3], data[frame,1+(key-1)*3], data[frame,2+(key-1)*3]))
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f.write("\n")
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def getOpenPoseMarkerNames():
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markerNames = ["Nose", "Neck", "RShoulder", "RElbow", "RWrist",
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"LShoulder", "LElbow", "LWrist", "midHip", "RHip",
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"RKnee", "RAnkle", "LHip", "LKnee", "LAnkle", "REye",
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"LEye", "REar", "LEar", "LBigToe", "LSmallToe",
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"LHeel", "RBigToe", "RSmallToe", "RHeel"]
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return markerNames
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def getOpenPoseFaceMarkers():
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faceMarkerNames = ['Nose', 'REye', 'LEye', 'REar', 'LEar']
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markerNames = getOpenPoseMarkerNames()
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idxFaceMarkers = [markerNames.index(i) for i in faceMarkerNames]
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return faceMarkerNames, idxFaceMarkers
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def getOpenPoseMarkers_fullBody():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RKnee", "LKnee",
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"RAnkle", "LAnkle", "RHeel", "LHeel", "RSmallToe", "LSmallToe",
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"RBigToe", "LBigToe", "RElbow", "LElbow", "RWrist", "LWrist"]
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response_markers = ["C7_study", "r_shoulder_study", "L_shoulder_study",
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"r.ASIS_study", "L.ASIS_study", "r.PSIS_study",
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"L.PSIS_study", "r_knee_study", "L_knee_study",
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"r_mknee_study", "L_mknee_study", "r_ankle_study",
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"L_ankle_study", "r_mankle_study", "L_mankle_study",
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"r_calc_study", "L_calc_study", "r_toe_study",
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"L_toe_study", "r_5meta_study", "L_5meta_study",
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"r_lelbow_study", "L_lelbow_study", "r_melbow_study",
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"L_melbow_study", "r_lwrist_study", "L_lwrist_study",
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"r_mwrist_study", "L_mwrist_study",
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"r_thigh1_study", "r_thigh2_study", "r_thigh3_study",
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"L_thigh1_study", "L_thigh2_study", "L_thigh3_study",
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"r_sh1_study", "r_sh2_study", "r_sh3_study",
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"L_sh1_study", "L_sh2_study", "L_sh3_study",
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"RHJC_study", "LHJC_study"]
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return feature_markers, response_markers
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def getOpenPoseMarkers_lowerExtremity():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RKnee", "LKnee",
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"RAnkle", "LAnkle", "RHeel", "LHeel", "RSmallToe", "LSmallToe",
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"RBigToe", "LBigToe"]
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response_markers = ["C7_study", "r_shoulder_study", "L_shoulder_study",
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"r.ASIS_study", "L.ASIS_study", "r.PSIS_study",
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"L.PSIS_study", "r_knee_study", "L_knee_study",
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"r_mknee_study", "L_mknee_study", "r_ankle_study",
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"L_ankle_study", "r_mankle_study", "L_mankle_study",
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"r_calc_study", "L_calc_study", "r_toe_study",
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"L_toe_study", "r_5meta_study", "L_5meta_study",
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"r_thigh1_study", "r_thigh2_study", "r_thigh3_study",
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"L_thigh1_study", "L_thigh2_study", "L_thigh3_study",
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"r_sh1_study", "r_sh2_study", "r_sh3_study",
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"L_sh1_study", "L_sh2_study", "L_sh3_study",
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"RHJC_study", "LHJC_study"]
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return feature_markers, response_markers
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# Different order of markers compared to getOpenPoseMarkers_lowerExtremity
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def getOpenPoseMarkers_lowerExtremity2():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RKnee", "LKnee",
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"RAnkle", "LAnkle", "RHeel", "LHeel", "RSmallToe", "LSmallToe",
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"RBigToe", "LBigToe"]
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response_markers = [
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'r.ASIS_study', 'L.ASIS_study', 'r.PSIS_study',
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'L.PSIS_study', 'r_knee_study', 'r_mknee_study',
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'r_ankle_study', 'r_mankle_study', 'r_toe_study',
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'r_5meta_study', 'r_calc_study', 'L_knee_study',
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'L_mknee_study', 'L_ankle_study', 'L_mankle_study',
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'L_toe_study', 'L_calc_study', 'L_5meta_study',
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'r_shoulder_study', 'L_shoulder_study', 'C7_study',
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'r_thigh1_study', 'r_thigh2_study', 'r_thigh3_study',
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'L_thigh1_study', 'L_thigh2_study', 'L_thigh3_study',
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'r_sh1_study', 'r_sh2_study', 'r_sh3_study', 'L_sh1_study',
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'L_sh2_study', 'L_sh3_study', 'RHJC_study', 'LHJC_study']
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return feature_markers, response_markers
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def getMarkers_upperExtremity_pelvis():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RElbow", "LElbow",
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"RWrist", "LWrist"]
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response_markers = ["r_lelbow_study", "L_lelbow_study", "r_melbow_study",
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"L_melbow_study", "r_lwrist_study", "L_lwrist_study",
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"r_mwrist_study", "L_mwrist_study"]
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return feature_markers, response_markers
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def getMarkers_upperExtremity_noPelvis():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RElbow", "LElbow", "RWrist",
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"LWrist"]
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response_markers = ["r_lelbow_study", "L_lelbow_study", "r_melbow_study",
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"L_melbow_study", "r_lwrist_study", "L_lwrist_study",
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"r_mwrist_study", "L_mwrist_study"]
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return feature_markers, response_markers
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# Different order of markers compared to getMarkers_upperExtremity_noPelvis.
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def getMarkers_upperExtremity_noPelvis2():
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feature_markers = [
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"Neck", "RShoulder", "LShoulder", "RElbow", "LElbow", "RWrist",
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"LWrist"]
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response_markers = ["r_lelbow_study", "r_melbow_study", "r_lwrist_study",
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"r_mwrist_study", "L_lelbow_study", "L_melbow_study",
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"L_lwrist_study", "L_mwrist_study"]
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return feature_markers, response_markers
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