415 lines
18 KiB
Python
415 lines
18 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.
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The output 2D points can be chosen to follow the DeepLabCut (default) or
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the OpenPose format. If OpenPose is chosen, the HALPE_26 model is used,
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with ear and eye at coordinates (0,0) since they are not used by Pose2Sim.
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You can change the MODEL tree to a different one if you need to reproject
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in OpenPose format with a different model than HALPE_26.
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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 -o
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o -u
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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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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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## SKELETON
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'''HALPE_26 (full-body without hands, from AlphaPose, MMPose, etc.)
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https://github.com/MVIG-SJTU/AlphaPose/blob/master/docs/MODEL_ZOO.md
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https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose'''
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MODEL = Node("Hip", id=19, children=[
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Node("RHip", id=12, children=[
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Node("RKnee", id=14, children=[
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Node("RAnkle", id=16, children=[
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Node("RBigToe", id=21, children=[
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Node("RSmallToe", id=23),
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]),
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Node("RHeel", id=25),
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]),
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]),
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]),
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Node("LHip", id=11, children=[
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Node("LKnee", id=13, children=[
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Node("LAnkle", id=15, children=[
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Node("LBigToe", id=20, children=[
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Node("LSmallToe", id=22),
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]),
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Node("LHeel", id=24),
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]),
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]),
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]),
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Node("Neck", id=18, children=[
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Node("Head", id=17, children=[
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Node("Nose", id=0),
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]),
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Node("RShoulder", id=6, children=[
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Node("RElbow", id=8, children=[
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Node("RWrist", id=10),
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]),
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]),
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Node("LShoulder", id=5, children=[
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Node("LElbow", id=7, children=[
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Node("LWrist", id=9),
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]),
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]),
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]),
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])
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## FUNCTIONS
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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 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.
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The output 2D points can be chosen to follow the DeepLabCut (default) or
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the OpenPose format. If OpenPose is chosen, the HALPE_26 model is used,
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with ear and eye at coordinates (0,0) since they are not used by Pose2Sim.
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You can change the MODEL tree to a different one if you need to reproject
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in OpenPose format with a different model than HALPLE_26.
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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(input_trc_file = r'<input_trc_file>', input_calib_file = r'<input_calib_file>', openpose_output=True, deeplabcut_output=True, undistort_points=True, output_file_root = r'<output_file_root>')
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o
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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
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python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o -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_output')
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deeplabcut_output = args.get('deeplabcut_output')
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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:
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raise ValueError('Output_format must be specified either "openpose_output" (-o) or "deeplabcut_output (-d)"')
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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}.png') 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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[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[:, 1::2] = data_proj[cam].iloc[:, ::2].where(x_valid, np.nan)
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data_proj[cam].iloc[:, ::2] = data_proj[cam].iloc[:, 1::2].where(y_valid, np.nan)
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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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# 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}.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}.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 OpenPose format
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elif openpose_output:
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# read model tree
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model = MODEL
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print('Keypoint hierarchy:')
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for pre, _, node in RenderTree(model):
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print(f'{pre}{node.name} id={node.id}')
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bodyparts_ids = [[node.id for _, _, node in RenderTree(model) if node.name==b][0] for b in bodyparts]
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nb_joints = len(bodyparts_ids)
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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(nb_joints*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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for cam, cam_dir in enumerate(cam_dirs):
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for frame in range(len(Q)):
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json_dict_copy = deepcopy(json_dict)
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data_proj_frame = data_proj[cam].iloc[frame]['DavidPagnon']['person0']
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# store 2D keypoints and respect model keypoint order
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for (i,b) in zip(bodyparts_ids, bodyparts):
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# visibility: 2 visible, 1 occluded, 0 out of frame
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coords = data_proj_frame[b].values
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json_dict_copy['people'][0]['pose_keypoints_2d'][[i*3,i*3+1,i*3+2]] = np.array([0.0, 0.0, 0]) if np.isnan(coords).any() else np.append(coords, 2)
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json_dict_copy['people'][0]['pose_keypoints_2d'] = json_dict_copy['people'][0]['pose_keypoints_2d'].tolist()
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# write json file
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json_file = os.path.join(cam_dir, f'{filename}_cam_{cam+1:02d}.{frame:05d}.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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|
print('Camera #', cam, 'done.')
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|
|
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# Wrong format
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else:
|
|
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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|
|
|
|
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if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
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_output', required=False, action='store_true', help='output format in the openpose json format')
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|
parser.add_argument('-d', '--deeplabcut_output', required=False, action='store_true', help='output format in the deeplabcut csv and json formats')
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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')
|
|
args = vars(parser.parse_args())
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|
|
|
reproj_from_trc_calib_func(**args)
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