509 lines
25 KiB
Python
509 lines
25 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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##################################################
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## Reproject 3D points on camera planes ##
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##################################################
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Reproject 3D points from a trc file to the camera planes determined by a
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toml calibration file, to the DeepLabCut (default), MMpose, or
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OpenPose format.
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The order or the markers depends on the markerset chosen markerset--it is the same as in the trc file if unspecified.
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You can change the marker order in CONSTANTS if you need to.
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New: Moving cameras and zooming cameras are now supported.
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Usage:
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from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm --markerset halpe26
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose --deeplabcut --mmpose --undistort
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -d -o output_file_root
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'''
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## INIT
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import os
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import pandas as pd
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import numpy as np
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import toml
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import cv2
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import json
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import re
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from anytree import Node, RenderTree
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from copy import deepcopy
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import argparse
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## AUTHORSHIP INFORMATION
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__author__ = "David Pagnon"
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__copyright__ = "Copyright 2021, Pose2Sim"
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__credits__ = ["David Pagnon"]
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__license__ = "BSD 3-Clause License"
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__version__ = "0.9.4"
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__maintainer__ = "David Pagnon"
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__email__ = "contact@david-pagnon.com"
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__status__ = "Development"
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# CONSTANTS
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halpe26_markers = ['NOSB', 'LEYE', 'REYE', 'LEAR', 'REAR', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R']
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halpeplus_markers = ['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']
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biocvplus_markers = ['ACROM_R', 'ACROM_L', 'C7', 'T10', 'CLAV', 'XIP_PROC', 'UA_R', 'ELB_LAT_R', 'ELB_MED_R', 'LA_R', 'WRI_LAT_R', 'WRI_MED_R', 'HAND_R', 'UA_L', 'ELB_LAT_L', 'ELB_MED_L', 'LA_L', 'WRI_LAT_L', 'WRI_MED_L', 'HAND_L', 'ASIS_R', 'ASIS_L', 'PSIS_R', 'PSIS_L', 'ILCREST_R', 'ILCREST_L', 'UL_R', 'KNEE_LAT_R', 'KNEE_MED_R', 'LL_R', 'MAL_LAT_R', 'MAL_MED_R', 'HEEL_R', 'MTP1_R', 'MTP5_R', 'TOE_R', 'UL_L', 'KNEE_LAT_L', 'KNEE_MED_L', 'LL_L', 'MAL_LAT_L', 'MAL_MED_L', 'HEEL_L', 'MTP1_L', 'MTP5_L', 'TOE_L', 'THD', 'NOSB']
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## FUNCTIONS
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def str_to_id(string):
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'''
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Convert a string to an integer id
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'''
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return ''.join([str(abs(ord(char) - 96)) for char in string])
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def computeP(calib_file, undistort=False):
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'''
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Compute projection matrices from toml calibration file.
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Zooming or moving cameras are handled.
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INPUT:
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- calib_file: calibration .toml file.
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- undistort: boolean
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OUTPUT:
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- P: projection matrix as list of arrays
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'''
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K, R, T, Kh, H = [], [], [], [], []
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P = []
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calib = toml.load(calib_file)
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for cam in list(calib.keys()):
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if cam != 'metadata':
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S = np.array(calib[cam]['size'])
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K = np.array(calib[cam]['matrix'])
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if len(K.shape) == 2: # static camera
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if undistort:
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dist = np.array(calib[cam]['distortions'])
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optim_K = cv2.getOptimalNewCameraMatrix(K, dist, [int(s) for s in S], 1, [int(s) for s in S])[0]
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Kh = np.block([optim_K, np.zeros(3).reshape(3,1)])
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else:
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Kh = np.block([K, np.zeros(3).reshape(3,1)])
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elif len(K.shape) == 3: # zooming camera
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if undistort:
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dist = np.array(calib[cam]['distortions'])
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optim_K = [cv2.getOptimalNewCameraMatrix(K[f], dist, [int(s) for s in S], 1, [int(s) for s in S])[0] for f in range(len(K))]
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Kh = [np.block([optim_K[f], np.zeros(3).reshape(3,1)]) for f in range(len(K))]
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else:
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Kh = [np.block([K[f], np.zeros(3).reshape(3,1)]) for f in range(len(K))]
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R = np.array(calib[cam]['rotation'])
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T = np.array(calib[cam]['translation'])
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if len(R.shape) == 1: # static camera
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R_mat, _ = cv2.Rodrigues(np.array(calib[cam]['rotation']))
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H = np.block([[R_mat,T.reshape(3,1)], [np.zeros(3), 1 ]])
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elif len(R.shape) == 2: # moving camera
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R_mat = [cv2.Rodrigues(R[f])[0] for f in range(len(R))]
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H = [np.block([[R_mat[f],T[f].reshape(3,1)], [np.zeros(3), 1 ]]) for f in range(len(R))]
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if len(K.shape) == 2 and len(R.shape)==1: # static camera
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P.append([Kh @ H])
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elif len(K.shape) == 3 and len(R.shape)==1: # zooming camera
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P.append([Kh[f] @ H for f in range(len(K))])
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elif len(K.shape) == 2 and len(R.shape)==2: # moving camera
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P.append([Kh @ H[f] for f in range(len(R))])
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elif len(K.shape) == 3 and len(R.shape)==2: # zooming and moving camera
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P.append([Kh[f] @ H[f] for f in range(len(K))])
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return np.array(P)
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def retrieve_calib_params(calib_file):
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'''
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Compute projection matrices from toml calibration file.
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Zooming or moving cameras are handled.
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INPUT:
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- calib_file: calibration .toml file.
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OUTPUT:
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- S: (h,w) vectors as list of 2x1 arrays
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- K: intrinsic matrices as list of 3x3 arrays
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- dist: distortion vectors as list of 4x1 arrays
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- optim_K: intrinsic matrices for undistorting points as list of 3x3 arrays
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- R: rotation rodrigue vectors as list of 3x1 arrays
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- T: translation vectors as list of 3x1 arrays
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'''
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calib = toml.load(calib_file)
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S, K, dist, optim_K, R, T = [], [], [], [], [], []
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for c, cam in enumerate(calib.keys()):
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if cam != 'metadata':
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S.append(np.array(calib[cam]['size']))
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K.append(np.array(calib[cam]['matrix']))
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dist.append(np.array(calib[cam]['distortions']))
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if len(K[c].shape) == 2: # static camera
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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])
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elif len(K[c].shape) == 3: # zooming camera
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optim_K.append([cv2.getOptimalNewCameraMatrix(K[c][f], dist[c], [int(s) for s in S[c]], 1, [int(s) for s in S[c]])[0] for f in range(len(K[c]))])
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R.append(np.array(calib[cam]['rotation']))
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T.append(np.array(calib[cam]['translation']))
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calib_params = {'S': S, 'K': K, 'dist': dist, 'optim_K': optim_K, 'R': R, 'T': T}
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return calib_params
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def reprojection(P_all, Q):
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'''
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Reprojects 3D point on all cameras.
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INPUTS:
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- P_all: list of arrays. Projection matrix for all cameras
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- Q: array of triangulated point (x,y,z,1.)
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OUTPUTS:
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- x_calc, y_calc: list of coordinates of point reprojected on all cameras
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'''
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x_calc, y_calc = [], []
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for c in range(len(P_all)):
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P_cam = P_all[c]
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x_calc.append(P_cam[0] @ Q / (P_cam[2] @ Q))
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y_calc.append(P_cam[1] @ Q / (P_cam[2] @ Q))
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return x_calc, y_calc
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def df_from_trc(trc_path):
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'''
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Retrieve header and data from trc path.
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'''
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# DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames
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df_header = pd.read_csv(trc_path, sep="\t", skiprows=1, header=None, nrows=2, encoding="ISO-8859-1")
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header = dict(zip(df_header.iloc[0].tolist(), df_header.iloc[1].tolist()))
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# Label1_X Label1_Y Label1_Z Label2_X Label2_Y
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df_lab = pd.read_csv(trc_path, sep="\t", skiprows=3, nrows=1)
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labels = df_lab.columns.tolist()[2:-1:3]
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labels_XYZ = np.array([[labels[i]+'_X', labels[i]+'_Y', labels[i]+'_Z'] for i in range(len(labels))], dtype='object').flatten()
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labels_FTXYZ = np.concatenate((['Frame#','Time'], labels_XYZ))
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data = pd.read_csv(trc_path, sep="\t", skiprows=5, index_col=False, header=None, names=labels_FTXYZ)
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return header, data
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def yup2zup(Q):
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'''
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Turns Y-up system coordinates into Z-up coordinates
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INPUT:
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- Q: pandas dataframe
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N 3D points as columns, ie 3*N columns in Z-up system coordinates
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and frame number as rows
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OUTPUT:
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- Q: pandas dataframe with N 3D points in Y-up system coordinates
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'''
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# X->Y, Y->Z, Z->X
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cols = list(Q.columns)
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cols = np.array([[cols[i*3+2],cols[i*3],cols[i*3+1]] for i in range(int(len(cols)/3))]).flatten()
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Q = Q[cols]
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return Q
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def dataset_to_openpose(coords_df, openpose_path_root, marker_list=['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']):
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'''
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Write 2D labels to OpenPose format.
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INPUTS:
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- coords_df: pandas dataframe with 2D labels. E.g.: all_dfs = pd.read_csv(dlc_labels_path, header = [0,1,2,3], index_col=0)
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- openpose_path_root: path to save the json files (frame number will be appended)
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- marker_list: list of markers in the order provided by the dataset. E.g. for Halpeplus: ['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']
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OUTPUTS:
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- coordinates written in the openpose json format (one per frame)
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'''
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#prepare json files
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json_dict = {'version':1.3, 'people':[]}
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json_dict['people'] = [{'person_id':[-1],
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'pose_keypoints_2d': np.zeros(len(marker_list)*3),
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'face_keypoints_2d': [],
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'hand_left_keypoints_2d':[],
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'hand_right_keypoints_2d':[],
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'pose_keypoints_3d':[],
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'face_keypoints_3d':[],
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'hand_left_keypoints_3d':[],
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'hand_right_keypoints_3d':[]}]
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# write one json file per camera and per frame
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persons = list(set(['_'.join(item.split('_')[:5]) for item in coords_df.columns.levels[1]]))
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for frame in range(len(coords_df)):
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for person in persons:
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json_dict_copy = deepcopy(json_dict)
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coords = coords_df.iloc[frame, coords_df.columns.get_level_values(1)==person]
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# store 2D keypoints and respect model keypoint order
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coords_list = []
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for marker in marker_list:
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coords_mk = coords.loc[coords.index.get_level_values(2)==marker]
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coords_list += [0.0, 0.0, 0] if np.isnan(coords_mk).any() else coords_mk.tolist()+[1]
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json_dict_copy['people'][0]['pose_keypoints_2d'] = coords_list
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# write json file
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json_file = os.path.join(os.path.dirname(openpose_path_root), f'{os.path.splitext(os.path.basename(openpose_path_root))[0]}_{frame:04d}.json')
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with open(json_file, 'w') as js_f:
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js_f.write(json.dumps(json_dict_copy))
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def dataset_to_mmpose2d(coords_df, mmpose_json_file, img_size, markerset='custom', marker_list=['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ank_l', 'ankle_r', 'THD', 'CY', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']):
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'''
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Export 2D labels to MMPose format.
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INPUTS:
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- coords_df: pandas dataframe with 2D labels. E.g.: all_dfs = pd.read_csv(dlc_labels_path, header = [0,1,2,3]), index_col=0)
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- mmpose_json_file: path to save the json file
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- img_size: image size [width, height]
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- markerset: name of the markerset. E.g.: 'halpe26', 'halpeplus', 'biocvplus'
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- marker_list: list of markers from inverse kinematics and/or SMPL mesh. E.g.: ['ankle_l', 'NOSB',]
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OUTPUTS:
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- labels2d_json: saved json file
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'''
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# transform first name in integer, and append other numbers from persons
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persons = list(set(['_'.join(item.split('_')[:5]) for item in coords_df.columns.levels[1]]))
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person_ids = [str_to_id(p.split('_')[1]) + ''.join(p.split('_')[3:]) if len(p.split('_'))>=3
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else str_to_id(p.split('_')[0])
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for p in persons]
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labels2d_json_data = {}
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labels2d_json_data['info'] = {'description': f'Bedlam Pose {markerset}',
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'url': 'https://github.com/davidpagnon/bedlam_pose',
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'version': '0.1',
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'year': 2024,
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'contributor': 'David Pagnon',
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'date_created': '2024/08/14'}
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labels2d_json_data['licenses'] = [{'url': 'https://bedlam.is.tue.mpg.de/license.html', 'id': 1, 'name': 'Non-commercial scientific research purposes'},
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{'url': 'https://creativecommons.org/licenses/by/4.0/deed.en', 'id': 2, 'name': 'Attribution License'}]
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labels2d_json_data['images'] = []
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labels2d_json_data['annotations'] = []
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labels2d_json_data['categories'] = [{'id': 1, 'name': 'person'}]
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# for each image
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for i in range(len(coords_df)):
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file_name = coords_df.index[i]
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w, h = img_size
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# id from concatenation of numbers from path
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file_id = int(''.join(re.findall(r'\d+', str(file_name))))
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labels2d_json_data['images'] += [{'file_name': file_name,
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'height': h,
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'width': w,
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'id': file_id,
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'license': 1}]
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# for each person
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for p, person in enumerate(persons):
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# store 2D keypoints and respect model keypoint order
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coords = coords_df.iloc[i, coords_df.columns.get_level_values(1)==person]
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coords_list = []
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for marker in marker_list:
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# visibility: 2 visible, 1 occluded, 0 out of frame
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coords_mk = coords.loc[coords.index.get_level_values(2)==marker]
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coords_list += coords_mk.tolist()+[2] if not np.isnan(coords_mk).any() else [0.0, 0.0, 0]
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# bbox
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x_coords = coords.loc[coords.index.get_level_values(3)=='x']
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y_coords = coords.loc[coords.index.get_level_values(3)=='y']
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min_x, min_y, max_x, max_y = np.nanmin(x_coords), np.nanmin(y_coords), np.nanmax(x_coords), np.nanmax(y_coords)
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bbox = [min_x, min_y, max_x, max_y]
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# bbox_width = max_x - min_x
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# bbox_height = max_y - min_y
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# bbox = [min_x, min_y, bbox_width, bbox_height]
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# num_keypoints, id, category_id
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num_keypoints = len(marker_list)
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id = person_ids[p]
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category_id = 1
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# segmentation and area not filled, and each annotation represents one single person
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segmentation = []
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area = 0
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iscrowd = 0 # 1 if len(persons)>1 else 0
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if not np.isnan(bbox).any():
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labels2d_json_data['annotations'] += [{ 'keypoints': coords_list,
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'num_keypoints': num_keypoints,
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'bbox': bbox,
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'id': id,
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'image_id': file_id,
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'category_id': category_id,
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'segmentation': segmentation,
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'area': area,
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'iscrowd': iscrowd}]
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with open(mmpose_json_file, 'w') as f:
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json.dump(labels2d_json_data, f)
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def reproj_from_trc_calib_func(**args):
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'''
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Reproject 3D points from a trc file to the camera planes determined by a
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toml calibration file, to the DeepLabCut (default), MMpose, or
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OpenPose format.
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The order or the markers depends on the markerset chosen markerset--it is the same as in the trc file if unspecified.
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You can change the marker order in CONSTANTS if you need to.
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New: Moving cameras and zooming cameras are now supported.
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Usage:
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from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose --deeplabcut --mmpose --undistort
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -d -o output_file_root
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'''
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input_trc_file = os.path.realpath(args.get('input_trc_file')) # invoked with argparse
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input_calib_file = os.path.realpath(args.get('input_calib_file'))
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openpose_output = args.get('openpose')
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deeplabcut_output = args.get('deeplabcut')
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mmpose_output = args.get('mmpose')
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markerset = args.get('markerset')
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undistort_points = args.get('undistort_points')
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output_file_root = args.get('output_file_root')
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if output_file_root == None:
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output_file_root = input_trc_file.replace('.trc', '_reproj')
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if os.path.exists(output_file_root):
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os.makedirs(output_file_root, exist_ok=True)
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if not openpose_output and not deeplabcut_output and not mmpose_output:
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raise ValueError('Output_format must be specified either "openpose" (-o), "deeplabcut" (-d), or "mmpose" (-m)')
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# Extract data from trc file
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header_trc, data_trc = df_from_trc(input_trc_file)
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data_trc_zup = pd.concat([data_trc.iloc[:,:2], yup2zup(data_trc.iloc[:,2:])], axis=1) # yup to zup system coordinates
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bodyparts = [d[:-2] for d in data_trc_zup.columns[2::3]]
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num_bodyparts = int(header_trc['NumMarkers'])
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filename = os.path.splitext(os.path.basename(input_trc_file))[0]
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# Extract data from calibration file
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P_all = computeP(input_calib_file, undistort=undistort_points)
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calib_params = retrieve_calib_params(input_calib_file)
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calib_params_size = [calib_params['S'][i] for i in range(len(P_all))]
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if undistort_points:
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calib_params_R_filt = [calib_params['R'][i] for i in range(len(P_all))]
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calib_params_T_filt = [calib_params['T'][i] for i in range(len(P_all))]
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calib_params_K_filt = [calib_params['K'][i] for i in range(len(P_all))]
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calib_params_dist_filt = [calib_params['dist'][i] for i in range(len(P_all))]
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# Create camera folders
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reproj_dir = os.path.realpath(output_file_root)
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cam_dirs = [os.path.join(reproj_dir, f'cam{cam+1:02d}_json') for cam in range(len(P_all))]
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if not os.path.exists(reproj_dir): os.mkdir(reproj_dir)
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try:
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[os.mkdir(cam_dir) for cam_dir in cam_dirs]
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except:
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pass
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# header preparation
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num_frames = [len(data_trc) if P_all.shape[1]==1 else min(P_all.shape[1], len(data_trc))][0]
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columns_iterables = [['DavidPagnon'], ['person0'], bodyparts, ['x','y']]
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columns_h5 = pd.MultiIndex.from_product(columns_iterables, names=['scorer', 'individuals', 'bodyparts', 'coords'])
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rows_iterables = [[os.path.join(os.path.splitext(input_trc_file)[0],f'img_{i:03d}.jpg') for i in range(num_frames)]]
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rows_h5 = pd.MultiIndex.from_product(rows_iterables)
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data_h5 = pd.DataFrame(np.nan, index=rows_h5, columns=columns_h5)
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# Reproject 3D points on all cameras
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data_proj = [deepcopy(data_h5) for cam in range(len(P_all))] # copy data_h5 as many times as there are cameras
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Q = data_trc_zup.iloc[:,2:]
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for frame in range(num_frames):
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coords = [[] for cam in range(len(P_all))]
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P_all_frame = [P_all[cam][0] if P_all.shape[1]==1 else P_all[cam][frame] for cam in range(len(P_all))]
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for keypoint in range(num_bodyparts):
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q = np.append(Q.iloc[frame,3*keypoint:3*keypoint+3], 1)
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if undistort_points:
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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))]
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x_all = [coords_2D_all[i][0,0,0] for i in range(len(P_all_frame))]
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y_all = [coords_2D_all[i][0,0,1] for i in range(len(P_all_frame))]
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else:
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x_all, y_all = reprojection(P_all_frame, q)
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# Store with one single decimal
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x_all = np.round(np.array(x_all), decimals=1)
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y_all = np.round(np.array(y_all), decimals=1)
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[coords[cam].extend([x_all[cam], y_all[cam]]) for cam in range(len(P_all_frame))]
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for cam in range(len(P_all_frame)):
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data_proj[cam].iloc[frame,:] = coords[cam]
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# Replace by nan when reprojection out of image
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for cam in range(len(P_all_frame)):
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x_valid = data_proj[cam].iloc[:,::2] < calib_params_size[cam][0]
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y_valid = data_proj[cam].iloc[:,1::2] < calib_params_size[cam][1]
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data_proj[cam].iloc[:, ::2] = data_proj[cam].iloc[:, ::2].where(x_valid, np.nan)
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data_proj[cam].iloc[:, ::2] = np.where(y_valid==False, np.nan, data_proj[cam].iloc[:, ::2])
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data_proj[cam].iloc[:, 1::2] = data_proj[cam].iloc[:, 1::2].where(y_valid, np.nan)
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data_proj[cam].iloc[:, 1::2] = np.where(x_valid==False, np.nan, data_proj[cam].iloc[:, 1::2])
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# Marker list in the right order
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if markerset == 'halpe26':
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marker_list = halpe26_markers
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elif markerset == 'halpeplus':
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marker_list = halpeplus_markers
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elif markerset == 'biocvplus':
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marker_list = biocvplus_markers
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else:
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marker_list = list(dict.fromkeys(data_proj[cam].columns.get_level_values(2)[1:]))
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# Save as h5 and csv if DeepLabCut format
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if deeplabcut_output:
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# to h5
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h5_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.h5') for i,cam_dir in enumerate(cam_dirs)]
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[data_proj[i].to_hdf(h5_files[i], index=True, key='reprojected_points') for i in range(len(P_all))]
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# to csv
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csv_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.csv') for i,cam_dir in enumerate(cam_dirs)]
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[data_proj[i].to_csv(csv_files[i], sep=',', index=True, lineterminator='\n') for i in range(len(P_all))]
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# Save as json if Coco/MMpose format
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if mmpose_output:
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for cam, cam_dir in enumerate(cam_dirs):
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mmpose_json_file = os.path.join(cam_dir, f'{filename}_cam_{cam+1:02d}_mmpose.json')
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dataset_to_mmpose2d(data_proj[cam], mmpose_json_file, calib_params_size[cam], markerset=markerset, marker_list=marker_list)
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# Save as json if OpenPose format
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if openpose_output:
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for cam, cam_dir in enumerate(cam_dirs):
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openpose_path_root = os.path.join(cam_dir, f'{filename}_cam{cam+1:02d}_openpose.json')
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dataset_to_openpose(data_proj[cam], openpose_path_root, marker_list=marker_list)
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# Wrong format
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if not openpose_output and not deeplabcut_output and not mmpose_output:
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raise ValueError('output_format must be either "openpose" or "deeplabcut"')
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print(f'Reprojected points saved at {output_file_root}.')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-t', '--input_trc_file', required = True, help='trc 3D coordinates input file path')
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parser.add_argument('-c', '--input_calib_file', required = True, help='toml calibration input file path')
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parser.add_argument('-o', '--openpose', required=False, action='store_true', help='output format in the openpose json format')
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parser.add_argument('-d', '--deeplabcut', required=False, action='store_true', help='output format in the deeplabcut csv and h5 formats')
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parser.add_argument('-m', '--mmpose', required=False, action='store_true', help='output format in the Coco/MMpose json format')
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parser.add_argument('-s', '--markerset', required=False, help='markerset name, e.g. halpe26, halpeplus, biocvplus')
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parser.add_argument('-u', '--undistort_points', required=False, action='store_true', help='takes distortion into account if True')
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parser.add_argument('-O', '--output_file_root', required=False, help='output file root path, without extension')
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args = vars(parser.parse_args())
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reproj_from_trc_calib_func(**args)
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