#!/usr/bin/env python # -*- coding: utf-8 -*- ''' ########################################################################### ## ROBUST TRIANGULATION OF 2D COORDINATES ## ########################################################################### This module triangulates 2D json coordinates and builds a .trc file readable by OpenSim. The triangulation is weighted by the likelihood of each detected 2D keypoint (if they meet the likelihood threshold). If the reprojection error is above a threshold, right and left sides are swapped; if it is still above, a camera is removed for this point and this frame, until the threshold is met. If more cameras are removed than a predefined minimum, triangulation is skipped for the point and this frame. In the end, missing values are interpolated. In case of multiple subjects detection, make sure you first run the personAssociation module. It will then associate people across frames by measuring the frame-by-frame distance between them. INPUTS: - a calibration file (.toml extension) - json files for each camera with only one person of interest - a Config.toml file - a skeleton model OUTPUTS: - a .trc file with 3D coordinates in Y-up system coordinates ''' ## INIT import os import glob import fnmatch import re import numpy as np import json import itertools as it import pandas as pd import cv2 import toml from tqdm import tqdm from scipy import interpolate from collections import Counter from anytree import RenderTree from anytree.importer import DictImporter import logging from Pose2Sim.common import retrieve_calib_params, computeP, weighted_triangulation, \ reprojection, euclidean_distance, sort_stringlist_by_last_number, zup2yup, convert_to_c3d from Pose2Sim.skeletons import * ## AUTHORSHIP INFORMATION __author__ = "David Pagnon" __copyright__ = "Copyright 2021, Pose2Sim" __credits__ = ["David Pagnon"] __license__ = "BSD 3-Clause License" __version__ = "0.9.4" __maintainer__ = "David Pagnon" __email__ = "contact@david-pagnon.com" __status__ = "Development" ## FUNCTIONS def interpolate_zeros_nans(col, *args): ''' Interpolate missing points (of value zero), unless more than N contiguous values are missing. INPUTS: - col: pandas column of coordinates - args[0] = N: max number of contiguous bad values, above which they won't be interpolated - args[1] = kind: 'linear', 'slinear', 'quadratic', 'cubic'. Default: 'cubic' OUTPUT: - col_interp: interpolated pandas column ''' if len(args)==2: N, kind = args if len(args)==1: N = np.inf kind = args[0] if not args: N = np.inf # Interpolate nans mask = ~(np.isnan(col) | col.eq(0)) # true where nans or zeros idx_good = np.where(mask)[0] if 'kind' not in locals(): # 'linear', 'slinear', 'quadratic', 'cubic' f_interp = interpolate.interp1d(idx_good, col[idx_good], kind="linear", bounds_error=False) else: f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=kind, fill_value='extrapolate', bounds_error=False) col_interp = np.where(mask, col, f_interp(col.index)) #replace at false index with interpolated values # Reintroduce nans if length of sequence > N idx_notgood = np.where(~mask)[0] gaps = np.where(np.diff(idx_notgood) > 1)[0] + 1 # where the indices of true are not contiguous sequences = np.split(idx_notgood, gaps) if sequences[0].size>0: for seq in sequences: if len(seq) > N: # values to exclude from interpolation are set to false when they are too long col_interp[seq] = np.nan return col_interp def count_persons_in_json(file_path): with open(file_path, 'r') as file: data = json.load(file) return len(data.get('people', [])) def min_with_single_indices(L, T): ''' Let L be a list (size s) with T associated tuple indices (size s). Select the smallest values of L, considering that the next smallest value cannot have the same numbers in the associated tuple as any of the previous ones. Example: L = [ 20, 27, 51, 33, 43, 23, 37, 24, 4, 68, 84, 3 ] T = list(it.product(range(2),range(3))) = [(0,0),(0,1),(0,2),(0,3),(1,0),(1,1),(1,2),(1,3),(2,0),(2,1),(2,2),(2,3)] - 1st smallest value: 3 with tuple (2,3), index 11 - 2nd smallest value when excluding indices (2,.) and (.,3), i.e. [(0,0),(0,1),(0,2),X,(1,0),(1,1),(1,2),X,X,X,X,X]: 20 with tuple (0,0), index 0 - 3rd smallest value when excluding [X,X,X,X,X,(1,1),(1,2),X,X,X,X,X]: 23 with tuple (1,1), index 5 INPUTS: - L: list (size s) - T: T associated tuple indices (size s) OUTPUTS: - minL: list of smallest values of L, considering constraints on tuple indices - argminL: list of indices of smallest values of L - T_minL: list of tuples associated with smallest values of L ''' minL = [np.nanmin(L)] argminL = [np.nanargmin(L)] T_minL = [T[argminL[0]]] mask_tokeep = np.array([True for t in T]) i=0 while mask_tokeep.any()==True: mask_tokeep = mask_tokeep & np.array([t[0]!=T_minL[i][0] and t[1]!=T_minL[i][1] for t in T]) if mask_tokeep.any()==True: indicesL_tokeep = np.where(mask_tokeep)[0] minL += [np.nanmin(np.array(L)[indicesL_tokeep]) if not np.isnan(np.array(L)[indicesL_tokeep]).all() else np.nan] argminL += [indicesL_tokeep[np.nanargmin(np.array(L)[indicesL_tokeep])] if not np.isnan(minL[-1]) else indicesL_tokeep[0]] T_minL += (T[argminL[i+1]],) i+=1 return np.array(minL), np.array(argminL), np.array(T_minL) def sort_people(Q_kpt_old, Q_kpt): ''' Associate persons across frames Persons' indices are sometimes swapped when changing frame A person is associated to another in the next frame when they are at a small distance INPUTS: - Q_kpt_old: list of arrays of 3D coordinates [X, Y, Z, 1.] for the previous frame - Q_kpt: idem Q_kpt_old, for current frame OUTPUT: - Q_kpt_new: array with reordered persons - personsIDs_sorted: index of reordered persons ''' # Generate possible person correspondences across frames if len(Q_kpt_old) < len(Q_kpt): Q_kpt_old = np.concatenate((Q_kpt_old, [[0., 0., 0., 1.]]*(len(Q_kpt)-len(Q_kpt_old)))) personsIDs_comb = sorted(list(it.product(range(len(Q_kpt_old)),range(len(Q_kpt))))) # Compute distance between persons from one frame to another frame_by_frame_dist = [] for comb in personsIDs_comb: frame_by_frame_dist += [euclidean_distance(Q_kpt_old[comb[0]],Q_kpt[comb[1]])] # sort correspondences by distance minL, _, associated_tuples = min_with_single_indices(frame_by_frame_dist, personsIDs_comb) # print('Distances :', minL) # associate 3D points to same index across frames, nan if no correspondence Q_kpt_new, personsIDs_sorted = [], [] for i in range(len(Q_kpt_old)): id_in_old = associated_tuples[:,1][associated_tuples[:,0] == i].tolist() # print('id_in_old ', i, id_in_old) if len(id_in_old) > 0: personsIDs_sorted += id_in_old Q_kpt_new += [Q_kpt[id_in_old[0]]] else: personsIDs_sorted += [-1] Q_kpt_new += [Q_kpt_old[i]] return Q_kpt_new, personsIDs_sorted, associated_tuples def make_trc(config_dict, Q, keypoints_names, f_range, id_person=-1): ''' Make Opensim compatible trc file from a dataframe with 3D coordinates INPUT: - config_dict: dictionary of configuration parameters - Q: pandas dataframe with 3D coordinates as columns, frame number as rows - keypoints_names: list of strings - f_range: list of two numbers. Range of frames OUTPUT: - trc file ''' # Read config_dict project_dir = config_dict.get('project').get('project_dir') multi_person = config_dict.get('project').get('multi_person') if multi_person: seq_name = f'{os.path.basename(os.path.realpath(project_dir))}_P{id_person+1}' else: seq_name = f'{os.path.basename(os.path.realpath(project_dir))}' pose3d_dir = os.path.join(project_dir, 'pose-3d') # Get frame_rate video_dir = os.path.join(project_dir, 'videos') vid_img_extension = config_dict['pose']['vid_img_extension'] video_files = glob.glob(os.path.join(video_dir, '*'+vid_img_extension)) frame_rate = config_dict.get('project').get('frame_rate') if frame_rate == 'auto': try: cap = cv2.VideoCapture(video_files[0]) cap.read() if cap.read()[0] == False: raise frame_rate = int(cap.get(cv2.CAP_PROP_FPS)) except: frame_rate = 60 trc_f = f'{seq_name}_{f_range[0]}-{f_range[1]}.trc' #Header DataRate = CameraRate = OrigDataRate = frame_rate NumFrames = len(Q) NumMarkers = len(keypoints_names) header_trc = ['PathFileType\t4\t(X/Y/Z)\t' + trc_f, 'DataRate\tCameraRate\tNumFrames\tNumMarkers\tUnits\tOrigDataRate\tOrigDataStartFrame\tOrigNumFrames', '\t'.join(map(str,[DataRate, CameraRate, NumFrames, NumMarkers, 'm', OrigDataRate, f_range[0], f_range[1]])), 'Frame#\tTime\t' + '\t\t\t'.join(keypoints_names) + '\t\t', '\t\t'+'\t'.join([f'X{i+1}\tY{i+1}\tZ{i+1}' for i in range(len(keypoints_names))])] # Zup to Yup coordinate system Q = zup2yup(Q) #Add Frame# and Time columns Q.index = np.array(range(f_range[0], f_range[1])) Q.insert(0, 't', Q.index/ frame_rate) # Q = Q.fillna(' ') #Write file if not os.path.exists(pose3d_dir): os.mkdir(pose3d_dir) trc_path = os.path.realpath(os.path.join(pose3d_dir, trc_f)) with open(trc_path, 'w') as trc_o: [trc_o.write(line+'\n') for line in header_trc] Q.to_csv(trc_o, sep='\t', index=True, header=None, lineterminator='\n') return trc_path def retrieve_right_trc_order(trc_paths): ''' Lets the user input which static file correspond to each generated trc file. INPUT: - trc_paths: list of strings OUTPUT: - trc_id: list of integers ''' logging.info('\n\nReordering trc file IDs:') logging.info(f'\nPlease visualize the generated trc files in Blender or OpenSim.\nTrc files are stored in {os.path.dirname(trc_paths[0])}.\n') retry = True while retry: retry = False logging.info('List of trc files:') [logging.info(f'#{t_list}: {os.path.basename(trc_list)}') for t_list, trc_list in enumerate(trc_paths)] trc_id = [] for t, trc_p in enumerate(trc_paths): logging.info(f'\nStatic trial #{t} corresponds to trc number:') trc_id += [input('Enter ID:')] # Check non int and duplicates try: trc_id = [int(t) for t in trc_id] duplicates_in_input = (len(trc_id) != len(set(trc_id))) if duplicates_in_input: retry = True print('\n\nWARNING: Same ID entered twice: please check IDs again.\n') except: print('\n\nWARNING: The ID must be an integer: please check IDs again.\n') retry = True return trc_id def recap_triangulate(config_dict, error, nb_cams_excluded, keypoints_names, cam_excluded_count, interp_frames, non_interp_frames, trc_path): ''' Print a message giving statistics on reprojection errors (in pixel and in m) as well as the number of cameras that had to be excluded to reach threshold conditions. Also stored in User/logs.txt. INPUT: - a Config.toml file - error: dataframe - nb_cams_excluded: dataframe - keypoints_names: list of strings OUTPUT: - Message in console ''' # Read config_dict project_dir = config_dict.get('project').get('project_dir') # if batch session_dir = os.path.realpath(os.path.join(project_dir, '..')) # if single trial session_dir = session_dir if 'Config.toml' in os.listdir(session_dir) else os.getcwd() calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if os.path.isdir(os.path.join(session_dir, c)) and 'calib' in c.lower()][0] calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file calib = toml.load(calib_file) cam_names = np.array([calib[c].get('name') for c in list(calib.keys())]) cam_names = cam_names[list(cam_excluded_count[0].keys())] error_threshold_triangulation = config_dict.get('triangulation').get('reproj_error_threshold_triangulation') likelihood_threshold = config_dict.get('triangulation').get('likelihood_threshold_triangulation') show_interp_indices = config_dict.get('triangulation').get('show_interp_indices') interpolation_kind = config_dict.get('triangulation').get('interpolation') interp_gap_smaller_than = config_dict.get('triangulation').get('interp_if_gap_smaller_than') fill_large_gaps_with = config_dict.get('triangulation').get('fill_large_gaps_with') make_c3d = config_dict.get('triangulation').get('make_c3d') handle_LR_swap = config_dict.get('triangulation').get('handle_LR_swap') undistort_points = config_dict.get('triangulation').get('undistort_points') # Recap calib_cam1 = calib[list(calib.keys())[0]] fm = calib_cam1['matrix'][0][0] Dm = euclidean_distance(calib_cam1['translation'], [0,0,0]) logging.info('') nb_persons_to_detect = len(error) for n in range(nb_persons_to_detect): if nb_persons_to_detect > 1: logging.info(f'\n\nPARTICIPANT {n+1}\n') for idx, name in enumerate(keypoints_names): mean_error_keypoint_px = np.around(error[n].iloc[:,idx].mean(), decimals=1) # RMS à la place? mean_error_keypoint_m = np.around(mean_error_keypoint_px * Dm / fm, decimals=3) mean_cam_excluded_keypoint = np.around(nb_cams_excluded[n].iloc[:,idx].mean(), decimals=2) logging.info(f'Mean reprojection error for {name} is {mean_error_keypoint_px} px (~ {mean_error_keypoint_m} m), reached with {mean_cam_excluded_keypoint} excluded cameras. ') if show_interp_indices: if interpolation_kind != 'none': if len(list(interp_frames[n][idx])) == 0 and len(list(non_interp_frames[n][idx])) == 0: logging.info(f' No frames needed to be interpolated.') if len(list(interp_frames[n][idx]))>0: interp_str = str(interp_frames[n][idx]).replace(":", " to ").replace("'", "").replace("]", "").replace("[", "") logging.info(f' Frames {interp_str} were interpolated.') if len(list(non_interp_frames[n][idx]))>0: noninterp_str = str(non_interp_frames[n][idx]).replace(":", " to ").replace("'", "").replace("]", "").replace("[", "") logging.info(f' Frames {noninterp_str} were not interpolated.') else: logging.info(f' No frames were interpolated because \'interpolation_kind\' was set to none. ') mean_error_px = np.around(error[n]['mean'].mean(), decimals=1) mean_error_mm = np.around(mean_error_px * Dm / fm *1000, decimals=1) mean_cam_excluded = np.around(nb_cams_excluded[n]['mean'].mean(), decimals=2) logging.info(f'\n--> Mean reprojection error for all points on all frames is {mean_error_px} px, which roughly corresponds to {mean_error_mm} mm. ') logging.info(f'Cameras were excluded if likelihood was below {likelihood_threshold} and if the reprojection error was above {error_threshold_triangulation} px.') if interpolation_kind != 'none': logging.info(f'Gaps were interpolated with {interpolation_kind} method if smaller than {interp_gap_smaller_than} frames. Larger gaps were filled with {["the last valid value" if fill_large_gaps_with == "last_value" else "zeros" if fill_large_gaps_with == "zeros" else "NaNs"][0]}.') logging.info(f'In average, {mean_cam_excluded} cameras had to be excluded to reach these thresholds.') cam_excluded_count[n] = {i: v for i, v in zip(cam_names, cam_excluded_count[n].values())} cam_excluded_count[n] = {k: v for k, v in sorted(cam_excluded_count[n].items(), key=lambda item: item[1])[::-1]} str_cam_excluded_count = '' for i, (k, v) in enumerate(cam_excluded_count[n].items()): if i ==0: str_cam_excluded_count += f'Camera {k} was excluded {int(np.round(v*100))}% of the time, ' elif i == len(cam_excluded_count[n])-1: str_cam_excluded_count += f'and Camera {k}: {int(np.round(v*100))}%.' else: str_cam_excluded_count += f'Camera {k}: {int(np.round(v*100))}%, ' logging.info(str_cam_excluded_count) logging.info(f'\n3D coordinates are stored at {trc_path[n]}.') logging.info('\n\n') if make_c3d: logging.info('All trc files have been converted to c3d.') logging.info(f'Limb swapping was {"handled" if handle_LR_swap else "not handled"}.') logging.info(f'Lens distortions were {"taken into account" if undistort_points else "not taken into account"}.') def triangulation_from_best_cameras(config_dict, coords_2D_kpt, coords_2D_kpt_swapped, projection_matrices, calib_params): ''' Triangulates 2D keypoint coordinates. If reprojection error is above threshold, tries swapping left and right sides. If still above, removes a camera until error is below threshold unless the number of remaining cameras is below a predefined number. 1. Creates subset with N cameras excluded 2. Tries all possible triangulations 3. Chooses the one with smallest reprojection error If error too big, take off one more camera. If then below threshold, retain result. If better but still too big, take off one more camera. INPUTS: - a Config.toml file - coords_2D_kpt: (x,y,likelihood) * ncams array - coords_2D_kpt_swapped: (x,y,likelihood) * ncams array with left/right swap - projection_matrices: list of arrays OUTPUTS: - Q: array of triangulated point (x,y,z,1.) - error_min: float - nb_cams_excluded: int ''' # Read config_dict error_threshold_triangulation = config_dict.get('triangulation').get('reproj_error_threshold_triangulation') min_cameras_for_triangulation = config_dict.get('triangulation').get('min_cameras_for_triangulation') handle_LR_swap = config_dict.get('triangulation').get('handle_LR_swap') undistort_points = config_dict.get('triangulation').get('undistort_points') if undistort_points: calib_params_K = calib_params['K'] calib_params_dist = calib_params['dist'] calib_params_R = calib_params['R'] calib_params_T = calib_params['T'] # Initialize x_files, y_files, likelihood_files = coords_2D_kpt x_files_swapped, y_files_swapped, likelihood_files_swapped = coords_2D_kpt_swapped n_cams = len(x_files) error_min = np.inf nb_cams_off = 0 # cameras will be taken-off until reprojection error is under threshold # print('\n') while error_min > error_threshold_triangulation and n_cams - nb_cams_off >= min_cameras_for_triangulation: # print("error min ", error_min, "thresh ", error_threshold_triangulation, 'nb_cams_off ', nb_cams_off) # Create subsets with "nb_cams_off" cameras excluded id_cams_off = np.array(list(it.combinations(range(n_cams), nb_cams_off))) if undistort_points: calib_params_K_filt = [calib_params_K]*len(id_cams_off) calib_params_dist_filt = [calib_params_dist]*len(id_cams_off) calib_params_R_filt = [calib_params_R]*len(id_cams_off) calib_params_T_filt = [calib_params_T]*len(id_cams_off) projection_matrices_filt = [projection_matrices]*len(id_cams_off) x_files_filt = np.vstack([x_files.copy()]*len(id_cams_off)) y_files_filt = np.vstack([y_files.copy()]*len(id_cams_off)) x_files_swapped_filt = np.vstack([x_files_swapped.copy()]*len(id_cams_off)) y_files_swapped_filt = np.vstack([y_files_swapped.copy()]*len(id_cams_off)) likelihood_files_filt = np.vstack([likelihood_files.copy()]*len(id_cams_off)) if nb_cams_off > 0: for i in range(len(id_cams_off)): x_files_filt[i][id_cams_off[i]] = np.nan y_files_filt[i][id_cams_off[i]] = np.nan x_files_swapped_filt[i][id_cams_off[i]] = np.nan y_files_swapped_filt[i][id_cams_off[i]] = np.nan likelihood_files_filt[i][id_cams_off[i]] = np.nan # Excluded cameras index and count id_cams_off_tot_new = [np.argwhere(np.isnan(x)).ravel() for x in likelihood_files_filt] nb_cams_excluded_filt = [np.count_nonzero(np.nan_to_num(x)==0) for x in likelihood_files_filt] # count nans and zeros nb_cams_off_tot = max(nb_cams_excluded_filt) # print('likelihood_files_filt ',likelihood_files_filt) # print('nb_cams_excluded_filt ', nb_cams_excluded_filt, 'nb_cams_off_tot ', nb_cams_off_tot) if nb_cams_off_tot > n_cams - min_cameras_for_triangulation: break id_cams_off_tot = id_cams_off_tot_new # print('still in loop') if undistort_points: calib_params_K_filt = [ [ c[i] for i in range(n_cams) if not np.isnan(likelihood_files_filt[j][i]) and not likelihood_files_filt[j][i]==0. ] for j, c in enumerate(calib_params_K_filt) ] calib_params_dist_filt = [ [ c[i] for i in range(n_cams) if not np.isnan(likelihood_files_filt[j][i]) and not likelihood_files_filt[j][i]==0. ] for j, c in enumerate(calib_params_dist_filt) ] calib_params_R_filt = [ [ c[i] for i in range(n_cams) if not np.isnan(likelihood_files_filt[j][i]) and not likelihood_files_filt[j][i]==0. ] for j, c in enumerate(calib_params_R_filt) ] calib_params_T_filt = [ [ c[i] for i in range(n_cams) if not np.isnan(likelihood_files_filt[j][i]) and not likelihood_files_filt[j][i]==0. ] for j, c in enumerate(calib_params_T_filt) ] projection_matrices_filt = [ [ p[i] for i in range(n_cams) if not np.isnan(likelihood_files_filt[j][i]) and not likelihood_files_filt[j][i]==0. ] for j, p in enumerate(projection_matrices_filt) ] # print('\nnb_cams_off', repr(nb_cams_off), 'nb_cams_excluded', repr(nb_cams_excluded_filt)) # print('likelihood_files ', repr(likelihood_files)) # print('y_files ', repr(y_files)) # print('x_files ', repr(x_files)) # print('x_files_swapped ', repr(x_files_swapped)) # print('likelihood_files_filt ', repr(likelihood_files_filt)) # print('x_files_filt ', repr(x_files_filt)) # print('id_cams_off_tot ', id_cams_off_tot) x_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(likelihood_files_filt[i][ii]) and not likelihood_files_filt[i][ii]==0. ]) for i,x in enumerate(x_files_filt) ] y_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(likelihood_files_filt[i][ii]) and not likelihood_files_filt[i][ii]==0. ]) for i,x in enumerate(y_files_filt) ] x_files_swapped_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(likelihood_files_filt[i][ii]) and not likelihood_files_filt[i][ii]==0. ]) for i,x in enumerate(x_files_swapped_filt) ] y_files_swapped_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(likelihood_files_filt[i][ii]) and not likelihood_files_filt[i][ii]==0. ]) for i,x in enumerate(y_files_swapped_filt) ] likelihood_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in likelihood_files_filt ] # print('y_files_filt ', repr(y_files_filt)) # print('x_files_filt ', repr(x_files_filt)) # Triangulate 2D points Q_filt = [weighted_triangulation(projection_matrices_filt[i], x_files_filt[i], y_files_filt[i], likelihood_files_filt[i]) for i in range(len(id_cams_off))] # Reprojection if undistort_points: coords_2D_kpt_calc_filt = [np.array([cv2.projectPoints(np.array(Q_filt[i][:-1]), calib_params_R_filt[i][j], calib_params_T_filt[i][j], calib_params_K_filt[i][j], calib_params_dist_filt[i][j])[0].ravel() for j in range(n_cams-nb_cams_excluded_filt[i])]) for i in range(len(id_cams_off))] coords_2D_kpt_calc_filt = [[coords_2D_kpt_calc_filt[i][:,0], coords_2D_kpt_calc_filt[i][:,1]] for i in range(len(id_cams_off))] else: coords_2D_kpt_calc_filt = [reprojection(projection_matrices_filt[i], Q_filt[i]) for i in range(len(id_cams_off))] coords_2D_kpt_calc_filt = np.array(coords_2D_kpt_calc_filt, dtype=object) x_calc_filt = coords_2D_kpt_calc_filt[:,0] # print('x_calc_filt ', x_calc_filt) y_calc_filt = coords_2D_kpt_calc_filt[:,1] # Reprojection error error = [] for config_off_id in range(len(x_calc_filt)): q_file = [(x_files_filt[config_off_id][i], y_files_filt[config_off_id][i]) for i in range(len(x_files_filt[config_off_id]))] q_calc = [(x_calc_filt[config_off_id][i], y_calc_filt[config_off_id][i]) for i in range(len(x_calc_filt[config_off_id]))] error.append( np.mean( [euclidean_distance(q_file[i], q_calc[i]) for i in range(len(q_file))] ) ) # print('error ', error) # Choosing best triangulation (with min reprojection error) # print('\n', error) # print('len(error) ', len(error)) # print('len(x_calc_filt) ', len(x_calc_filt)) # print('len(likelihood_files_filt) ', len(likelihood_files_filt)) # print('len(id_cams_off_tot) ', len(id_cams_off_tot)) # print('min error ', np.nanmin(error)) # print('argmin error ', np.nanargmin(error)) error_min = np.nanmin(error) # print(error_min) best_cams = np.nanargmin(error) nb_cams_excluded = nb_cams_excluded_filt[best_cams] Q = Q_filt[best_cams][:-1] # Swap left and right sides if reprojection error still too high if handle_LR_swap and error_min > error_threshold_triangulation: # print('handle') n_cams_swapped = 1 error_off_swap_min = error_min while error_off_swap_min > error_threshold_triangulation and n_cams_swapped < (n_cams - nb_cams_off_tot) / 2: # more than half of the cameras switched: may triangulate twice the same side # print('SWAP: nb_cams_off ', nb_cams_off, 'n_cams_swapped ', n_cams_swapped, 'nb_cams_off_tot ', nb_cams_off_tot) # Create subsets id_cams_swapped = np.array(list(it.combinations(range(n_cams-nb_cams_off_tot), n_cams_swapped))) # print('id_cams_swapped ', id_cams_swapped) x_files_filt_off_swap = [[x] * len(id_cams_swapped) for x in x_files_filt] y_files_filt_off_swap = [[y] * len(id_cams_swapped) for y in y_files_filt] # print('x_files_filt_off_swap ', x_files_filt_off_swap) # print('y_files_filt_off_swap ', y_files_filt_off_swap) for id_off in range(len(id_cams_off)): # for each configuration with nb_cams_off_tot removed for id_swapped, config_swapped in enumerate(id_cams_swapped): # for each of these configurations, test all subconfigurations with with n_cams_swapped swapped # print('id_off ', id_off, 'id_swapped ', id_swapped, 'config_swapped ', config_swapped) x_files_filt_off_swap[id_off][id_swapped][config_swapped] = x_files_swapped_filt[id_off][config_swapped] y_files_filt_off_swap[id_off][id_swapped][config_swapped] = y_files_swapped_filt[id_off][config_swapped] # Triangulate 2D points Q_filt_off_swap = np.array([[weighted_triangulation(projection_matrices_filt[id_off], x_files_filt_off_swap[id_off][id_swapped], y_files_filt_off_swap[id_off][id_swapped], likelihood_files_filt[id_off]) for id_swapped in range(len(id_cams_swapped))] for id_off in range(len(id_cams_off))] ) # Reprojection if undistort_points: coords_2D_kpt_calc_off_swap = [np.array([[cv2.projectPoints(np.array(Q_filt_off_swap[id_off][id_swapped][:-1]), calib_params_R_filt[id_off][j], calib_params_T_filt[id_off][j], calib_params_K_filt[id_off][j], calib_params_dist_filt[id_off][j])[0].ravel() for j in range(n_cams-nb_cams_off_tot)] for id_swapped in range(len(id_cams_swapped))]) for id_off in range(len(id_cams_off))] coords_2D_kpt_calc_off_swap = np.array([[[coords_2D_kpt_calc_off_swap[id_off][id_swapped,:,0], coords_2D_kpt_calc_off_swap[id_off][id_swapped,:,1]] for id_swapped in range(len(id_cams_swapped))] for id_off in range(len(id_cams_off))]) else: coords_2D_kpt_calc_off_swap = [np.array([reprojection(projection_matrices_filt[id_off], Q_filt_off_swap[id_off][id_swapped]) for id_swapped in range(len(id_cams_swapped))]) for id_off in range(len(id_cams_off))] # print(repr(coords_2D_kpt_calc_off_swap)) x_calc_off_swap = [c[:,0] for c in coords_2D_kpt_calc_off_swap] y_calc_off_swap = [c[:,1] for c in coords_2D_kpt_calc_off_swap] # Reprojection error # print('x_files_filt_off_swap ', x_files_filt_off_swap) # print('x_calc_off_swap ', x_calc_off_swap) error_off_swap = [] for id_off in range(len(id_cams_off)): error_percam = [] for id_swapped, config_swapped in enumerate(id_cams_swapped): # print(id_off,id_swapped,n_cams,nb_cams_off) # print(repr(x_files_filt_off_swap)) q_file_off_swap = [(x_files_filt_off_swap[id_off][id_swapped][i], y_files_filt_off_swap[id_off][id_swapped][i]) for i in range(n_cams - nb_cams_off_tot)] q_calc_off_swap = [(x_calc_off_swap[id_off][id_swapped][i], y_calc_off_swap[id_off][id_swapped][i]) for i in range(n_cams - nb_cams_off_tot)] error_percam.append( np.mean( [euclidean_distance(q_file_off_swap[i], q_calc_off_swap[i]) for i in range(len(q_file_off_swap))] ) ) error_off_swap.append(error_percam) error_off_swap = np.array(error_off_swap) # print('error_off_swap ', error_off_swap) # Choosing best triangulation (with min reprojection error) error_off_swap_min = np.min(error_off_swap) best_off_swap_config = np.unravel_index(error_off_swap.argmin(), error_off_swap.shape) id_off_cams = best_off_swap_config[0] id_swapped_cams = id_cams_swapped[best_off_swap_config[1]] Q_best = Q_filt_off_swap[best_off_swap_config][:-1] n_cams_swapped += 1 if error_off_swap_min < error_min: error_min = error_off_swap_min best_cams = id_off_cams Q = Q_best # print(error_min) nb_cams_off += 1 # Index of excluded cams for this keypoint # print('Loop ended') if 'best_cams' in locals(): # print(id_cams_off_tot) # print('len(id_cams_off_tot) ', len(id_cams_off_tot)) # print('id_cams_off_tot ', id_cams_off_tot) id_excluded_cams = id_cams_off_tot[best_cams] # print('id_excluded_cams ', id_excluded_cams) else: id_excluded_cams = list(range(n_cams)) nb_cams_excluded = n_cams # print('id_excluded_cams ', id_excluded_cams) # If triangulation not successful, error = nan, and 3D coordinates as missing values if error_min > error_threshold_triangulation: error_min = np.nan Q = np.array([np.nan, np.nan, np.nan]) return Q, error_min, nb_cams_excluded, id_excluded_cams def extract_files_frame_f(json_tracked_files_f, keypoints_ids, nb_persons_to_detect): ''' Extract data from json files for frame f, in the order of the body model hierarchy. INPUTS: - json_tracked_files_f: list of str. Paths of json_files for frame f. - keypoints_ids: list of int. Keypoints IDs in the order of the hierarchy. - nb_persons_to_detect: int OUTPUTS: - x_files, y_files, likelihood_files: [[[list of coordinates] * n_cams ] * nb_persons_to_detect] ''' n_cams = len(json_tracked_files_f) x_files = [[] for n in range(nb_persons_to_detect)] y_files = [[] for n in range(nb_persons_to_detect)] likelihood_files = [[] for n in range(nb_persons_to_detect)] for n in range(nb_persons_to_detect): for cam_nb in range(n_cams): x_files_cam, y_files_cam, likelihood_files_cam = [], [], [] try: with open(json_tracked_files_f[cam_nb], 'r') as json_f: js = json.load(json_f) for keypoint_id in keypoints_ids: try: x_files_cam.append( js['people'][n]['pose_keypoints_2d'][keypoint_id*3] ) y_files_cam.append( js['people'][n]['pose_keypoints_2d'][keypoint_id*3+1] ) likelihood_files_cam.append( js['people'][n]['pose_keypoints_2d'][keypoint_id*3+2] ) except: x_files_cam.append( np.nan ) y_files_cam.append( np.nan ) likelihood_files_cam.append( np.nan ) except: x_files_cam = [np.nan] * len(keypoints_ids) y_files_cam = [np.nan] * len(keypoints_ids) likelihood_files_cam = [np.nan] * len(keypoints_ids) x_files[n].append(x_files_cam) y_files[n].append(y_files_cam) likelihood_files[n].append(likelihood_files_cam) x_files = np.array(x_files) y_files = np.array(y_files) likelihood_files = np.array(likelihood_files) return x_files, y_files, likelihood_files def triangulate_all(config_dict): ''' For each frame For each keypoint - Triangulate keypoint - Reproject it on all cameras - Take off cameras until requirements are met Interpolate missing values Create trc file Print recap message INPUTS: - a calibration file (.toml extension) - json files for each camera with indices matching the detected persons - a Config.toml file - a skeleton model OUTPUTS: - a .trc file with 3D coordinates in Y-up system coordinates ''' # Read config_dict project_dir = config_dict.get('project').get('project_dir') # if batch session_dir = os.path.realpath(os.path.join(project_dir, '..')) # if single trial session_dir = session_dir if 'Config.toml' in os.listdir(session_dir) else os.getcwd() multi_person = config_dict.get('project').get('multi_person') pose_model = config_dict.get('pose').get('pose_model') frame_range = config_dict.get('project').get('frame_range') reorder_trc = config_dict.get('triangulation').get('reorder_trc') likelihood_threshold = config_dict.get('triangulation').get('likelihood_threshold_triangulation') interpolation_kind = config_dict.get('triangulation').get('interpolation') interp_gap_smaller_than = config_dict.get('triangulation').get('interp_if_gap_smaller_than') fill_large_gaps_with = config_dict.get('triangulation').get('fill_large_gaps_with') show_interp_indices = config_dict.get('triangulation').get('show_interp_indices') undistort_points = config_dict.get('triangulation').get('undistort_points') make_c3d = config_dict.get('triangulation').get('make_c3d') try: calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if os.path.isdir(os.path.join(session_dir, c)) and 'calib' in c.lower()][0] except: raise Exception(f'No .toml calibration direcctory found.') try: calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file except: raise Exception(f'No .toml calibration file found in the {calib_dir}.') pose_dir = os.path.join(project_dir, 'pose') poseSync_dir = os.path.join(project_dir, 'pose-sync') poseTracked_dir = os.path.join(project_dir, 'pose-associated') # Projection matrix from toml calibration file P = computeP(calib_file, undistort=undistort_points) calib_params = retrieve_calib_params(calib_file) # Retrieve keypoints from model try: # from skeletons.py model = eval(pose_model) except: try: # from Config.toml model = DictImporter().import_(config_dict.get('pose').get(pose_model)) if model.id == 'None': model.id = None except: raise NameError('Model not found in skeletons.py nor in Config.toml') keypoints_ids = [node.id for _, _, node in RenderTree(model) if node.id!=None] keypoints_names = [node.name for _, _, node in RenderTree(model) if node.id!=None] keypoints_idx = list(range(len(keypoints_ids))) keypoints_nb = len(keypoints_ids) # for pre, _, node in RenderTree(model): # print(f'{pre}{node.name} id={node.id}') # left/right swapped keypoints keypoints_names_swapped = [keypoint_name.replace('R', 'L') if keypoint_name.startswith('R') else keypoint_name.replace('L', 'R') if keypoint_name.startswith('L') else keypoint_name for keypoint_name in keypoints_names] keypoints_names_swapped = [keypoint_name_swapped.replace('right', 'left') if keypoint_name_swapped.startswith('right') else keypoint_name_swapped.replace('left', 'right') if keypoint_name_swapped.startswith('left') else keypoint_name_swapped for keypoint_name_swapped in keypoints_names_swapped] keypoints_idx_swapped = [keypoints_names.index(keypoint_name_swapped) for keypoint_name_swapped in keypoints_names_swapped] # find index of new keypoint_name # 2d-pose files selection pose_listdirs_names = next(os.walk(pose_dir))[1] pose_listdirs_names = sort_stringlist_by_last_number(pose_listdirs_names) json_dirs_names = [k for k in pose_listdirs_names if 'json' in k] n_cams = len(json_dirs_names) try: json_files_names = [fnmatch.filter(os.listdir(os.path.join(poseTracked_dir, js_dir)), '*.json') for js_dir in json_dirs_names] pose_dir = poseTracked_dir except: try: json_files_names = [fnmatch.filter(os.listdir(os.path.join(poseSync_dir, js_dir)), '*.json') for js_dir in json_dirs_names] pose_dir = poseSync_dir except: json_files_names = [fnmatch.filter(os.listdir(os.path.join(pose_dir, js_dir)), '*.json') for js_dir in json_dirs_names] json_files_names = [sort_stringlist_by_last_number(js) for js in json_files_names] # frame range selection f_range = [[0,max([len(j) for j in json_files_names])] if frame_range==[] else frame_range][0] frame_nb = f_range[1] - f_range[0] # Check that camera number is consistent between calibration file and pose folders if n_cams != len(P): raise Exception(f'Error: The number of cameras is not consistent:\ Found {len(P)} cameras in the calibration file,\ and {n_cams} cameras based on the number of pose folders.') # Triangulation if multi_person: try: nb_persons_to_detect = max(max(count_persons_in_json(os.path.join(poseTracked_dir, json_dirs_names[c], json_fname)) for json_fname in json_files_names[c]) for c in range(n_cams)) except: try: nb_persons_to_detect = max(max(count_persons_in_json(os.path.join(poseSync_dir, json_dirs_names[c], json_fname)) for json_fname in json_files_names[c]) for c in range(n_cams)) except: nb_persons_to_detect = max(max(count_persons_in_json(os.path.join(pose_dir, json_dirs_names[c], json_fname)) for json_fname in json_files_names[c]) for c in range(n_cams)) else: nb_persons_to_detect = 1 Q = [[[np.nan]*3]*keypoints_nb for n in range(nb_persons_to_detect)] Q_old = [[[np.nan]*3]*keypoints_nb for n in range(nb_persons_to_detect)] error = [[] for n in range(nb_persons_to_detect)] nb_cams_excluded = [[] for n in range(nb_persons_to_detect)] id_excluded_cams = [[] for n in range(nb_persons_to_detect)] Q_tot, error_tot, nb_cams_excluded_tot,id_excluded_cams_tot = [], [], [], [] for f in tqdm(range(*f_range)): # print(f'\nFrame {f}:') # Get x,y,likelihood values from files json_files_names_f = [[j for j in json_files_names[c] if int(re.split(r'(\d+)',j)[-2])==f] for c in range(n_cams)] json_files_names_f = [j for j_list in json_files_names_f for j in (j_list or ['none'])] json_files_f = [os.path.join(pose_dir, json_dirs_names[c], json_files_names_f[c]) for c in range(n_cams)] x_files, y_files, likelihood_files = extract_files_frame_f(json_files_f, keypoints_ids, nb_persons_to_detect) # [[[list of coordinates] * n_cams ] * nb_persons_to_detect] # vs. [[list of coordinates] * n_cams ] # undistort points if undistort_points: for n in range(nb_persons_to_detect): points = [np.array(tuple(zip(x_files[n][i],y_files[n][i]))).reshape(-1, 1, 2).astype('float32') for i in range(n_cams)] undistorted_points = [cv2.undistortPoints(points[i], calib_params['K'][i], calib_params['dist'][i], None, calib_params['optim_K'][i]) for i in range(n_cams)] x_files[n] = np.array([[u[i][0][0] for i in range(len(u))] for u in undistorted_points]) y_files[n] = np.array([[u[i][0][1] for i in range(len(u))] for u in undistorted_points]) # This is good for slight distortion. For fisheye camera, the model does not work anymore. See there for an example https://github.com/lambdaloop/aniposelib/blob/d03b485c4e178d7cff076e9fe1ac36837db49158/aniposelib/cameras.py#L301 # Replace likelihood by 0 if under likelihood_threshold with np.errstate(invalid='ignore'): for n in range(nb_persons_to_detect): x_files[n][likelihood_files[n] < likelihood_threshold] = np.nan y_files[n][likelihood_files[n] < likelihood_threshold] = np.nan likelihood_files[n][likelihood_files[n] < likelihood_threshold] = np.nan # Q_old = Q except when it has nan, otherwise it takes the Q_old value nan_mask = np.isnan(Q) Q_old = np.where(nan_mask, Q_old, Q) Q = [[] for n in range(nb_persons_to_detect)] error = [[] for n in range(nb_persons_to_detect)] nb_cams_excluded = [[] for n in range(nb_persons_to_detect)] id_excluded_cams = [[] for n in range(nb_persons_to_detect)] for n in range(nb_persons_to_detect): for keypoint_idx in keypoints_idx: # keypoints_nb = 2 # for keypoint_idx in range(2): # Triangulate cameras with min reprojection error # print('\n', keypoints_names[keypoint_idx]) coords_2D_kpt = np.array( (x_files[n][:, keypoint_idx], y_files[n][:, keypoint_idx], likelihood_files[n][:, keypoint_idx]) ) coords_2D_kpt_swapped = np.array(( x_files[n][:, keypoints_idx_swapped[keypoint_idx]], y_files[n][:, keypoints_idx_swapped[keypoint_idx]], likelihood_files[n][:, keypoints_idx_swapped[keypoint_idx]] )) Q_kpt, error_kpt, nb_cams_excluded_kpt, id_excluded_cams_kpt = triangulation_from_best_cameras(config_dict, coords_2D_kpt, coords_2D_kpt_swapped, P, calib_params) # P has been modified if undistort_points=True Q[n].append(Q_kpt) error[n].append(error_kpt) nb_cams_excluded[n].append(nb_cams_excluded_kpt) id_excluded_cams[n].append(id_excluded_cams_kpt) if multi_person: # reID persons across frames by checking the distance from one frame to another # print('Q before ordering ', np.array(Q)[:,:2]) if f !=0: Q, personsIDs_sorted, associated_tuples = sort_people(Q_old, Q) # print('Q after ordering ', personsIDs_sorted, associated_tuples, np.array(Q)[:,:2]) error_sorted, nb_cams_excluded_sorted, id_excluded_cams_sorted = [], [], [] for i in range(len(Q)): id_in_old = associated_tuples[:,1][associated_tuples[:,0] == i].tolist() if len(id_in_old) > 0: personsIDs_sorted += id_in_old error_sorted += [error[id_in_old[0]]] nb_cams_excluded_sorted += [nb_cams_excluded[id_in_old[0]]] id_excluded_cams_sorted += [id_excluded_cams[id_in_old[0]]] else: personsIDs_sorted += [-1] error_sorted += [error[i]] nb_cams_excluded_sorted += [nb_cams_excluded[i]] id_excluded_cams_sorted += [id_excluded_cams[i]] error, nb_cams_excluded, id_excluded_cams = error_sorted, nb_cams_excluded_sorted, id_excluded_cams_sorted # TODO: if distance > threshold, new person # Add triangulated points, errors and excluded cameras to pandas dataframes Q_tot.append([np.concatenate(Q[n]) for n in range(nb_persons_to_detect)]) error_tot.append([error[n] for n in range(nb_persons_to_detect)]) nb_cams_excluded_tot.append([nb_cams_excluded[n] for n in range(nb_persons_to_detect)]) id_excluded_cams = [[id_excluded_cams[n][k] for k in range(keypoints_nb)] for n in range(nb_persons_to_detect)] id_excluded_cams_tot.append(id_excluded_cams) # fill values for if a person that was not initially detected has entered the frame Q_tot = [list(tpl) for tpl in zip(*it.zip_longest(*Q_tot, fillvalue=[np.nan]*keypoints_nb*3))] error_tot = [list(tpl) for tpl in zip(*it.zip_longest(*error_tot, fillvalue=[np.nan]*keypoints_nb*3))] nb_cams_excluded_tot = [list(tpl) for tpl in zip(*it.zip_longest(*nb_cams_excluded_tot, fillvalue=[np.nan]*keypoints_nb*3))] id_excluded_cams_tot = [list(tpl) for tpl in zip(*it.zip_longest(*id_excluded_cams_tot, fillvalue=[np.nan]*keypoints_nb*3))] # dataframes for each person Q_tot = [pd.DataFrame([Q_tot_f[n] for Q_tot_f in Q_tot]) for n in range(nb_persons_to_detect)] error_tot = [pd.DataFrame([error_tot_f[n] for error_tot_f in error_tot]) for n in range(nb_persons_to_detect)] nb_cams_excluded_tot = [pd.DataFrame([nb_cams_excluded_tot_f[n] for nb_cams_excluded_tot_f in nb_cams_excluded_tot]) for n in range(nb_persons_to_detect)] id_excluded_cams_tot = [pd.DataFrame([id_excluded_cams_tot_f[n] for id_excluded_cams_tot_f in id_excluded_cams_tot]) for n in range(nb_persons_to_detect)] for n in range(nb_persons_to_detect): error_tot[n]['mean'] = error_tot[n].mean(axis = 1) nb_cams_excluded_tot[n]['mean'] = nb_cams_excluded_tot[n].mean(axis = 1) # Delete participants with less than 4 valid triangulated frames # for each person, for each keypoint, frames to interpolate zero_nan_frames = [np.where( Q_tot[n].iloc[:,::3].T.eq(0) | ~np.isfinite(Q_tot[n].iloc[:,::3].T) ) for n in range(nb_persons_to_detect)] zero_nan_frames_per_kpt = [[zero_nan_frames[n][1][np.where(zero_nan_frames[n][0]==k)[0]] for k in range(keypoints_nb)] for n in range(nb_persons_to_detect)] non_nan_nb_first_kpt = [frame_nb - len(zero_nan_frames_per_kpt[n][0]) for n in range(nb_persons_to_detect)] deleted_person_id = [n for n in range(len(non_nan_nb_first_kpt)) if non_nan_nb_first_kpt[n]<4] Q_tot = [Q_tot[n] for n in range(len(Q_tot)) if n not in deleted_person_id] error_tot = [error_tot[n] for n in range(len(error_tot)) if n not in deleted_person_id] nb_cams_excluded_tot = [nb_cams_excluded_tot[n] for n in range(len(nb_cams_excluded_tot)) if n not in deleted_person_id] id_excluded_cams_tot = [id_excluded_cams_tot[n] for n in range(len(id_excluded_cams_tot)) if n not in deleted_person_id] nb_persons_to_detect = len(Q_tot) if nb_persons_to_detect ==0: raise Exception('No persons have been triangulated. Please check your calibration and your synchronization, or the triangulation parameters in Config.toml.') # IDs of excluded cameras # id_excluded_cams_tot = [np.concatenate([id_excluded_cams_tot[f][k] for f in range(frames_nb)]) for k in range(keypoints_nb)] id_excluded_cams_tot = [np.hstack(np.hstack(np.array(id_excluded_cams_tot[n]))) for n in range(nb_persons_to_detect)] cam_excluded_count = [dict(Counter(k)) for k in id_excluded_cams_tot] [cam_excluded_count[n].update((x, y/frame_nb/keypoints_nb) for x, y in cam_excluded_count[n].items()) for n in range(nb_persons_to_detect)] # Optionally, for each person, for each keypoint, show indices of frames that should be interpolated if show_interp_indices: gaps = [[np.where(np.diff(zero_nan_frames_per_kpt[n][k]) > 1)[0] + 1 for k in range(keypoints_nb)] for n in range(nb_persons_to_detect)] sequences = [[np.split(zero_nan_frames_per_kpt[n][k], gaps[n][k]) for k in range(keypoints_nb)] for n in range(nb_persons_to_detect)] interp_frames = [[[f'{seq[0]}:{seq[-1]}' for seq in seq_kpt if len(seq)<=interp_gap_smaller_than and len(seq)>0] for seq_kpt in sequences[n]] for n in range(nb_persons_to_detect)] non_interp_frames = [[[f'{seq[0]}:{seq[-1]}' for seq in seq_kpt if len(seq)>interp_gap_smaller_than] for seq_kpt in sequences[n]] for n in range(nb_persons_to_detect)] else: interp_frames = None non_interp_frames = [] # Interpolate missing values if interpolation_kind != 'none': for n in range(nb_persons_to_detect): try: Q_tot[n] = Q_tot[n].apply(interpolate_zeros_nans, axis=0, args = [interp_gap_smaller_than, interpolation_kind]) except: logging.info(f'Interpolation was not possible for person {n}. This means that not enough points are available, which is often due to a bad calibration.') # Fill non-interpolated values with last valid one if fill_large_gaps_with == 'last_value': for n in range(nb_persons_to_detect): Q_tot[n] = Q_tot[n].ffill(axis=0).bfill(axis=0) elif fill_large_gaps_with == 'zeros': for n in range(nb_persons_to_detect): Q_tot[n].replace(np.nan, 0, inplace=True) # Create TRC file trc_paths = [make_trc(config_dict, Q_tot[n], keypoints_names, f_range, id_person=n) for n in range(len(Q_tot))] if make_c3d: c3d_paths = [convert_to_c3d(t) for t in trc_paths] # Reorder TRC files if multi_person and reorder_trc and len(trc_paths)>1: trc_id = retrieve_right_trc_order(trc_paths) [os.rename(t, t+'.old') for t in trc_paths] [os.rename(t+'.old', trc_paths[i]) for i, t in zip(trc_id,trc_paths)] if make_c3d: [os.rename(c, c+'.old') for c in c3d_paths] [os.rename(c+'.old', c3d_paths[i]) for i, c in zip(trc_id,c3d_paths)] error_tot = [error_tot[i] for i in trc_id] nb_cams_excluded_tot = [nb_cams_excluded_tot[i] for i in trc_id] cam_excluded_count = [cam_excluded_count[i] for i in trc_id] interp_frames = [interp_frames[i] for i in trc_id] non_interp_frames = [non_interp_frames[i] for i in trc_id] logging.info('\nThe trc and c3d files have been renamed to match the order of the static sequences.') # Recap message recap_triangulate(config_dict, error_tot, nb_cams_excluded_tot, keypoints_names, cam_excluded_count, interp_frames, non_interp_frames, trc_paths)