#!/usr/bin/env python # -*- coding: utf-8 -*- ''' ########################################################################### ## TRACKING OF PERSON OF INTEREST ## ########################################################################### Openpose detects all people in the field of view. Which is the one of interest? This module tries all possible triangulations of a chosen anatomical point, and chooses the person for whom the reprojection error is smallest. INPUTS: - a calibration file (.toml extension) - json files from each camera folders with several detected persons - a Config.toml file - a skeleton model OUTPUTS: - json files for each camera with only one person of interest ''' ## INIT import os import glob import fnmatch import numpy as np import json import itertools as it import toml from tqdm import tqdm from anytree import RenderTree from anytree.importer import DictImporter import logging from Pose2Sim.common import computeP, weighted_triangulation, reprojection, \ euclidean_distance, natural_sort from Pose2Sim.skeletons import * ## AUTHORSHIP INFORMATION __author__ = "David Pagnon" __copyright__ = "Copyright 2021, Pose2Sim" __credits__ = ["David Pagnon"] __license__ = "BSD 3-Clause License" __version__ = '0.4' __maintainer__ = "David Pagnon" __email__ = "contact@david-pagnon.com" __status__ = "Development" ## FUNCTIONS def persons_combinations(json_files_framef): ''' Find all possible combinations of detected persons' ids. Person's id when no person detected is set to -1. INPUT: - json_files_framef: list of strings OUTPUT: - personsIDs_comb: array, list of lists of int ''' n_cams = len(json_files_framef) # amount of persons detected for each cam nb_persons_per_cam = [] for c in range(n_cams): with open(json_files_framef[c], 'r') as js: nb_persons_per_cam += [len(json.load(js)['people'])] # persons_combinations id_no_detect = [i for i, x in enumerate(nb_persons_per_cam) if x == 0] # ids of cameras that have not detected any person nb_persons_per_cam = [x if x != 0 else 1 for x in nb_persons_per_cam] # temporarily replace persons count by 1 when no detection range_persons_per_cam = [range(nb_persons_per_cam[c]) for c in range(n_cams)] personsIDs_comb = np.array(list(it.product(*range_persons_per_cam)), float) # all possible combinations of persons' ids personsIDs_comb[:,id_no_detect] = np.nan # -1 = persons' ids when no person detected return personsIDs_comb def best_persons_and_cameras_combination(config, json_files_framef, personsIDs_combinations, projection_matrices, tracked_keypoint_id): ''' At the same time, chooses the right person among the multiple ones found by OpenPose & excludes cameras with wrong 2d-pose estimation. 1. triangulate the tracked keypoint for all possible combinations of people, 2. compute difference between reprojection & original openpose detection, 3. take combination with smallest difference. If error is too big, take off one or several of the cameras until err is lower than "max_err_px". INPUTS: - a Config.toml file - json_files_framef: list of strings - personsIDs_combinations: array, list of lists of int - projection_matrices: list of arrays - tracked_keypoint_id: int OUTPUTS: - error_min: float - persons_and_cameras_combination: array of ints ''' error_threshold_tracking = config.get('personAssociation').get('reproj_error_threshold_association') min_cameras_for_triangulation = config.get('triangulation').get('min_cameras_for_triangulation') likelihood_threshold = config.get('triangulation').get('likelihood_threshold') n_cams = len(json_files_framef) error_min = np.inf nb_cams_off = 0 # cameras will be taken-off until the reprojection error is under threshold while error_min > error_threshold_tracking and n_cams - nb_cams_off >= min_cameras_for_triangulation: # Try all persons combinations for combination in personsIDs_combinations: # Get x,y,likelihood values from files x_files, y_files,likelihood_files = [], [], [] for index_cam, person_nb in enumerate(combination): with open(json_files_framef[index_cam], 'r') as json_f: js = json.load(json_f) try: x_files.append( js['people'][int(person_nb)]['pose_keypoints_2d'][tracked_keypoint_id*3] ) y_files.append( js['people'][int(person_nb)]['pose_keypoints_2d'][tracked_keypoint_id*3+1] ) likelihood_files.append( js['people'][int(person_nb)]['pose_keypoints_2d'][tracked_keypoint_id*3+2] ) except: x_files.append(np.nan) y_files.append(np.nan) likelihood_files.append(np.nan) # Replace likelihood by 0. if under likelihood_threshold likelihood_files = [0. if lik < likelihood_threshold else lik for lik in likelihood_files] # For each persons combination, create subsets with "nb_cams_off" cameras excluded id_cams_off = list(it.combinations(range(len(combination)), nb_cams_off)) combinations_with_cams_off = np.array([combination.copy()]*len(id_cams_off)) for i, id in enumerate(id_cams_off): combinations_with_cams_off[i,id] = np.nan # Try all subsets error_comb = [] for comb in combinations_with_cams_off: # Filter x, y, likelihood, projection_matrices, with subset x_files_filt = [x_files[i] for i in range(len(comb)) if not np.isnan(comb[i])] y_files_filt = [y_files[i] for i in range(len(comb)) if not np.isnan(comb[i])] likelihood_files_filt = [likelihood_files[i] for i in range(len(comb)) if not np.isnan(comb[i])] projection_matrices_filt = [projection_matrices[i] for i in range(len(comb)) if not np.isnan(comb[i])] # Triangulate 2D points Q_comb = weighted_triangulation(projection_matrices_filt, x_files_filt, y_files_filt, likelihood_files_filt) # Reprojection x_calc, y_calc = reprojection(projection_matrices_filt, Q_comb) # Reprojection error error_comb_per_cam = [] for cam in range(len(x_calc)): q_file = (x_files_filt[cam], y_files_filt[cam]) q_calc = (x_calc[cam], y_calc[cam]) error_comb_per_cam.append( euclidean_distance(q_file, q_calc) ) error_comb.append( np.mean(error_comb_per_cam) ) error_min = min(error_comb) persons_and_cameras_combination = combinations_with_cams_off[np.argmin(error_comb)] if error_min < error_threshold_tracking: break nb_cams_off += 1 return error_min, persons_and_cameras_combination def recap_tracking(config, error, nb_cams_excluded): ''' 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 OUTPUT: - Message in console ''' # Read config project_dir = config.get('project').get('project_dir') if project_dir == '': project_dir = os.getcwd() tracked_keypoint = config.get('personAssociation').get('tracked_keypoint') error_threshold_tracking = config.get('personAssociation').get('error_threshold_tracking') poseTracked_dir = os.path.join(project_dir, 'pose-associated') calib_dir = os.path.join(project_dir, 'Calibration') calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # Error mean_error_px = np.around(np.mean(error), decimals=1) calib = toml.load(calib_file) calib_cam1 = calib[list(calib.keys())[0]] fm = calib_cam1['matrix'][0][0] Dm = euclidean_distance(calib_cam1['translation'], [0,0,0]) mean_error_mm = np.around(mean_error_px * Dm / fm * 1000, decimals=1) # Excluded cameras mean_cam_off_count = np.around(np.mean(nb_cams_excluded), decimals=2) # Recap logging.info(f'\n--> Mean reprojection error for {tracked_keypoint} point on all frames is {mean_error_px} px, which roughly corresponds to {mean_error_mm} mm. ') logging.info(f'--> In average, {mean_cam_off_count} cameras had to be excluded to reach the demanded {error_threshold_tracking} px error threshold.') logging.info(f'\nTracked json files are stored in {poseTracked_dir}.') def track_2d_all(config): ''' For each frame, - Find all possible combinations of detected persons - Triangulate 'tracked_keypoint' for all combinations - Reproject the point on all cameras - Take combination with smallest reprojection error - Write json file with only one detected person Print recap message INPUTS: - a calibration file (.toml extension) - json files from each camera folders with several detected persons - a Config.toml file - a skeleton model OUTPUTS: - json files for each camera with only one person of interest ''' # Read config project_dir = config.get('project').get('project_dir') if project_dir == '': project_dir = os.getcwd() pose_model = config.get('pose').get('pose_model') tracked_keypoint = config.get('personAssociation').get('tracked_keypoint') frame_range = config.get('project').get('frame_range') calib_dir = os.path.join(project_dir, 'Calibration') calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] pose_dir = os.path.join(project_dir, 'pose') poseTracked_dir = os.path.join(project_dir, 'pose-associated') # projection matrix from toml calibration file P = computeP(calib_file) # selection of tracked keypoint id try: # from skeletons.py model = eval(pose_model) except: try: # from Config.toml model = DictImporter().import_(config.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') tracked_keypoint_id = [node.id for _, _, node in RenderTree(model) if node.name==tracked_keypoint][0] # 2d-pose files selection pose_listdirs_names = next(os.walk(pose_dir))[1] pose_listdirs_names = natural_sort(pose_listdirs_names) json_dirs_names = [k for k in pose_listdirs_names if 'json' in k] 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 = [natural_sort(j) for j in json_files_names] json_files = [[os.path.join(pose_dir, j_dir, j_file) for j_file in json_files_names[j]] for j, j_dir in enumerate(json_dirs_names)] # 2d-pose-associated files creation if not os.path.exists(poseTracked_dir): os.mkdir(poseTracked_dir) try: [os.mkdir(os.path.join(poseTracked_dir,k)) for k in json_dirs_names] except: pass json_tracked_files = [[os.path.join(poseTracked_dir, j_dir, j_file) for j_file in json_files_names[j]] for j, j_dir in enumerate(json_dirs_names)] # person's tracking f_range = [[min([len(j) for j in json_files])] if frame_range==[] else frame_range][0] n_cams = len(json_dirs_names) error_min_tot, cameras_off_tot = [], [] for f in tqdm(range(*f_range)): json_files_f = [json_files[c][f] for c in range(n_cams)] json_tracked_files_f = [json_tracked_files[c][f] for c in range(n_cams)] # all possible combinations of persons personsIDs_comb = persons_combinations(json_files_f) # choose person of interest and exclude cameras with bad pose estimation error_min, persons_and_cameras_combination = best_persons_and_cameras_combination(config, json_files_f, personsIDs_comb, P, tracked_keypoint_id) error_min_tot.append(error_min) cameras_off_count = np.count_nonzero(np.isnan(persons_and_cameras_combination)) cameras_off_tot.append(cameras_off_count) # rewrite json files with only one person of interest for cam_nb, person_id in enumerate(persons_and_cameras_combination): with open(json_tracked_files_f[cam_nb], 'w') as json_tracked_f: with open(json_files_f[cam_nb], 'r') as json_f: js = json.load(json_f) if not np.isnan(person_id): js['people'] = [js['people'][int(person_id)]] else: js['people'] = [] json_tracked_f.write(json.dumps(js)) # recap message recap_tracking(config, error_min_tot, cameras_off_tot)