#!/usr/bin/env python # -*- coding: utf-8 -*- ''' ################################################## ## Reproject 3D points on camera planes ## ################################################## Reproject 3D points from a trc file to the camera planes determined by a toml calibration file. The output 2D points can be chosen to follow the DeepLabCut (default) or the OpenPose format. If OpenPose is chosen, the BODY_25B model is used, with ear and eye at coordinates (0,0) since they are not used by Pose2Sim. You can change the MODEL tree to a different one if you need to reproject in OpenPose format with a different model than BODY_25B. Usage: from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'', r'', '', r'') python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o -u python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -d -o output_file_root ''' ## INIT import os import pandas as pd import numpy as np import toml import cv2 import json from anytree import Node, RenderTree from copy import deepcopy import argparse ## AUTHORSHIP INFORMATION __author__ = "David Pagnon" __copyright__ = "Copyright 2021, Pose2Sim" __credits__ = ["David Pagnon"] __license__ = "BSD 3-Clause License" __version__ = "0.5" __maintainer__ = "David Pagnon" __email__ = "contact@david-pagnon.com" __status__ = "Development" ## SKELETON '''BODY_25B (full-body without hands, experimental, from OpenPose) https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/blob/master/experimental_models/README.md Adjust it if you want to reproject in OpenPose format with a different model''' nb_joints = 25 MODEL = Node("CHip", id=None, children=[ Node("RHip", id=12, children=[ Node("RKnee", id=14, children=[ Node("RAnkle", id=16, children=[ Node("RBigToe", id=22, children=[ Node("RSmallToe", id=23), ]), Node("RHeel", id=24), ]), ]), ]), Node("LHip", id=11, children=[ Node("LKnee", id=13, children=[ Node("LAnkle", id=15, children=[ Node("LBigToe", id=19, children=[ Node("LSmallToe", id=20), ]), Node("LHeel", id=21), ]), ]), ]), Node("Neck", id=17, children=[ Node("Head", id=18, children=[ Node("Nose", id=0), ]), Node("RShoulder", id=6, children=[ Node("RElbow", id=8, children=[ Node("RWrist", id=10), ]), ]), Node("LShoulder", id=5, children=[ Node("LElbow", id=7, children=[ Node("LWrist", id=9), ]), ]), ]), ]) # nb_joints = 17 # MODEL = Node("None", id=None, children=[ # Node("Origin", id=0), # Node("Board1", id=1), # Node("Board2", id=2), # Node("Board3", id=3), # Node("Board4", id=4), # Node("Furniture5", id=5), # Node("Furniture6", id=6), # Node("Furniture7", id=7), # Node("Screen8", id=8), # Node("Screen9", id=9), # Node("Furniture10", id=10), # Node("Furniture11", id=11), # Node("Furniture12", id=12), # Node("Furniture13", id=13), # Node("Furniture14", id=14), # Node("Furniture15", id=15), # Node("Table16", id=16)]) ## FUNCTIONS def computeP(calib_file, undistort=False): ''' Compute projection matrices from toml calibration file. INPUT: - calib_file: calibration .toml file. - undistort: boolean OUTPUT: - P: projection matrix as list of arrays ''' K, R, T, Kh, H = [], [], [], [], [] P = [] calib = toml.load(calib_file) for cam in list(calib.keys()): if cam != 'metadata': S = np.array(calib[cam]['size']) K = np.array(calib[cam]['matrix']) if undistort: dist = np.array(calib[cam]['distortions']) optim_K = cv2.getOptimalNewCameraMatrix(K, dist, [int(s) for s in S], 1, [int(s) for s in S])[0] Kh = np.block([optim_K, np.zeros(3).reshape(3,1)]) else: Kh = np.block([K, np.zeros(3).reshape(3,1)]) R, _ = cv2.Rodrigues(np.array(calib[cam]['rotation'])) T = np.array(calib[cam]['translation']) H = np.block([[R,T.reshape(3,1)], [np.zeros(3), 1 ]]) P.append(Kh @ H) return P def retrieve_calib_params(calib_file): ''' Compute projection matrices from toml calibration file. INPUT: - calib_file: calibration .toml file. OUTPUT: - S: (h,w) vectors as list of 2x1 arrays - K: intrinsic matrices as list of 3x3 arrays - dist: distortion vectors as list of 4x1 arrays - optim_K: intrinsic matrices for undistorting points as list of 3x3 arrays - R: rotation rodrigue vectors as list of 3x1 arrays - T: translation vectors as list of 3x1 arrays ''' calib = toml.load(calib_file) S, K, dist, optim_K, R, T = [], [], [], [], [], [] for c, cam in enumerate(calib.keys()): if cam != 'metadata': S.append(np.array(calib[cam]['size'])) K.append(np.array(calib[cam]['matrix'])) dist.append(np.array(calib[cam]['distortions'])) optim_K.append(cv2.getOptimalNewCameraMatrix(K[c], dist[c], [int(s) for s in S[c]], 1, [int(s) for s in S[c]])[0]) R.append(np.array(calib[cam]['rotation'])) T.append(np.array(calib[cam]['translation'])) calib_params = {'S': S, 'K': K, 'dist': dist, 'optim_K': optim_K, 'R': R, 'T': T} return calib_params def reprojection(P_all, Q): ''' Reprojects 3D point on all cameras. INPUTS: - P_all: list of arrays. Projection matrix for all cameras - Q: array of triangulated point (x,y,z,1.) OUTPUTS: - x_calc, y_calc: list of coordinates of point reprojected on all cameras ''' x_calc, y_calc = [], [] for c in range(len(P_all)): P_cam = P_all[c] x_calc.append(P_cam[0] @ Q / (P_cam[2] @ Q)) y_calc.append(P_cam[1] @ Q / (P_cam[2] @ Q)) return x_calc, y_calc def df_from_trc(trc_path): ''' Retrieve header and data from trc path. ''' # DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames df_header = pd.read_csv(trc_path, sep="\t", skiprows=1, header=None, nrows=2, encoding="ISO-8859-1") header = dict(zip(df_header.iloc[0].tolist(), df_header.iloc[1].tolist())) # Label1_X Label1_Y Label1_Z Label2_X Label2_Y df_lab = pd.read_csv(trc_path, sep="\t", skiprows=3, nrows=1) labels = df_lab.columns.tolist()[2:-1:3] labels_XYZ = np.array([[labels[i]+'_X', labels[i]+'_Y', labels[i]+'_Z'] for i in range(len(labels))], dtype='object').flatten() labels_FTXYZ = np.concatenate((['Frame#','Time'], labels_XYZ)) data = pd.read_csv(trc_path, sep="\t", skiprows=5, index_col=False, header=None, names=labels_FTXYZ) return header, data def yup2zup(Q): ''' Turns Y-up system coordinates into Z-up coordinates INPUT: - Q: pandas dataframe N 3D points as columns, ie 3*N columns in Z-up system coordinates and frame number as rows OUTPUT: - Q: pandas dataframe with N 3D points in Y-up system coordinates ''' # X->Y, Y->Z, Z->X cols = list(Q.columns) cols = np.array([[cols[i*3+2],cols[i*3],cols[i*3+1]] for i in range(int(len(cols)/3))]).flatten() Q = Q[cols] return Q def reproj_from_trc_calib_func(**args): ''' Reproject 3D points from a trc file to the camera planes determined by a toml calibration file. The output 2D points can be chosen to follow the DeepLabCut (default) or the OpenPose format. If OpenPose is chosen, the BODY_25B model is used, with ear and eye at coordinates (0,0) since they are not used by Pose2Sim. You can change the MODEL tree to a different one if you need to reproject in OpenPose format with a different model than BODY_25B. Usage: from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(input_trc_file = r'', input_calib_file = r'', openpose_output=True, deeplabcut_output=True, undistort_points=True, output_file_root = r'') python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose_output --deeplabcut_output --undistort_points --output_file_root output_file_root python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o -O output_file_root ''' input_trc_file = os.path.realpath(args.get('input_trc_file')) # invoked with argparse input_calib_file = os.path.realpath(args.get('input_calib_file')) openpose_output = args.get('openpose_output') deeplabcut_output = args.get('deeplabcut_output') undistort_points = args.get('undistort_points') output_file_root = args.get('output_file_root') if output_file_root == None: output_file_root = input_trc_file.replace('.trc', '_reproj') if not openpose_output and not deeplabcut_output: raise ValueError('Output_format must be specified either "openpose_output" (-o) or "deeplabcut_output (-d)"') # Extract data from trc file header_trc, data_trc = df_from_trc(input_trc_file) data_trc_zup = pd.concat([data_trc.iloc[:,:2], yup2zup(data_trc.iloc[:,2:])], axis=1) # yup to zup system coordinates bodyparts = [d[:-2] for d in data_trc_zup.columns[2::3]] num_bodyparts = int(header_trc['NumMarkers']) filename = os.path.splitext(os.path.basename(input_trc_file))[0] # Extract data from calibration file P_all = computeP(input_calib_file, undistort=undistort_points) if undistort_points: calib_params = retrieve_calib_params(input_calib_file) calib_params_R_filt = [calib_params['R'][i] for i in range(len(P_all))] calib_params_T_filt = [calib_params['T'][i] for i in range(len(P_all))] calib_params_K_filt = [calib_params['K'][i] for i in range(len(P_all))] calib_params_dist_filt = [calib_params['dist'][i] for i in range(len(P_all))] # Create camera folders reproj_dir = os.path.realpath(output_file_root) cam_dirs = [os.path.join(reproj_dir, f'cam{cam+1:02d}_json') for cam in range(len(P_all))] if not os.path.exists(reproj_dir): os.mkdir(reproj_dir) try: [os.mkdir(cam_dir) for cam_dir in cam_dirs] except: pass # header preparation columns_iterables = [['DavidPagnon'], ['person0'], bodyparts, ['x','y']] columns_h5 = pd.MultiIndex.from_product(columns_iterables, names=['scorer', 'individuals', 'bodyparts', 'coords']) rows_iterables = [['labeled_data'], [filename], [f'img_{i:03d}.png' for i in range(len(data_trc))]] rows_h5 = pd.MultiIndex.from_product(rows_iterables) data_h5 = pd.DataFrame(np.nan, index=rows_h5, columns=columns_h5) # Reproject 3D points on all cameras data_proj = [deepcopy(data_h5) for cam in range(len(P_all))] # copy data_h5 as many times as there are cameras Q = data_trc_zup.iloc[:,2:] for frame in range(len(Q)): coords = [[] for cam in range(len(P_all))] for keypoint in range(num_bodyparts): q = np.append(Q.iloc[frame,3*keypoint:3*keypoint+3], 1) if undistort_points: coords_2D_all = [cv2.projectPoints(np.array(q[:-1]), calib_params_R_filt[i], calib_params_T_filt[i], calib_params_K_filt[i], calib_params_dist_filt[i])[0] for i in range(len(P_all))] x_all = [coords_2D_all[i][0,0,0] for i in range(len(P_all))] y_all = [coords_2D_all[i][0,0,1] for i in range(len(P_all))] else: x_all, y_all = reprojection(P_all, q) [coords[cam].extend([x_all[cam], y_all[cam]]) for cam in range(len(P_all))] for cam in range(len(P_all)): data_proj[cam].iloc[frame,:] = coords[cam] # Save as h5 and csv if DeepLabCut format if deeplabcut_output: # to h5 h5_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}.h5') for i,cam_dir in enumerate(cam_dirs)] [data_proj[i].to_hdf(h5_files[i], index=True, key='reprojected_points') for i in range(len(P_all))] # to csv csv_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}.csv') for i,cam_dir in enumerate(cam_dirs)] [data_proj[i].to_csv(csv_files[i], sep=',', index=True, lineterminator='\n') for i in range(len(P_all))] # Save as json if OpenPose format if openpose_output: # read model tree model = MODEL print('Keypoint hierarchy:') for pre, _, node in RenderTree(model): print(f'{pre}{node.name} id={node.id}') bodyparts_ids = [[node.id for _, _, node in RenderTree(model) if node.name==b][0] for b in bodyparts] #prepare json files json_dict = {'version':1.3, 'people':[]} json_dict['people'] = [{'person_id':[-1], 'pose_keypoints_2d': np.zeros(nb_joints*3), 'face_keypoints_2d': [], 'hand_left_keypoints_2d':[], 'hand_right_keypoints_2d':[], 'pose_keypoints_3d':[], 'face_keypoints_3d':[], 'hand_left_keypoints_3d':[], 'hand_right_keypoints_3d':[]}] # write one json file per camera and per frame for cam, cam_dir in enumerate(cam_dirs): for frame in range(len(Q)): json_dict_copy = deepcopy(json_dict) data_proj_frame = data_proj[cam].iloc[frame]['DavidPagnon']['person0'] # store 2D keypoints and respect model keypoint order for (i,b) in zip(bodyparts_ids, bodyparts): # print(repr(data_proj_frame[b].values)) json_dict_copy['people'][0]['pose_keypoints_2d'][[i*3,i*3+1,i*3+2]] = np.append(data_proj_frame[b].values, 1) json_dict_copy['people'][0]['pose_keypoints_2d'] = json_dict_copy['people'][0]['pose_keypoints_2d'].tolist() # write json file json_file = os.path.join(cam_dir, f'{filename}_cam_{cam+1:02d}.{frame:05d}.json') with open(json_file, 'w') as js_f: js_f.write(json.dumps(json_dict_copy)) print('Camera #', cam, 'done.') # Wrong format else: raise ValueError('output_format must be either "openpose" or "deeplabcut"') print(f'Reprojected points saved at {output_file_root}.') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-t', '--input_trc_file', required = True, help='trc 3D coordinates input file path') parser.add_argument('-c', '--input_calib_file', required = True, help='toml calibration input file path') parser.add_argument('-o', '--openpose_output', required=False, action='store_true', help='output format in the openpose json format') parser.add_argument('-d', '--deeplabcut_output', required=False, action='store_true', help='output format in the deeplabcut csv and json formats') parser.add_argument('-u', '--undistort_points', required=False, action='store_true', help='takes distortion into account if True') parser.add_argument('-O', '--output_file_root', required=False, help='output file root path, without extension') args = vars(parser.parse_args()) reproj_from_trc_calib_func(**args)