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