reproj to mmpose/coco format
This commit is contained in:
davidpagnon
parent
5484812929
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02c6512b15
@ -8,20 +8,19 @@
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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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toml calibration file, to the DeepLabCut (default), MMpose, or
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OpenPose format.
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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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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 -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 -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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@ -33,6 +32,7 @@ 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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@ -49,50 +49,20 @@ __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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# 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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@ -252,38 +222,171 @@ def yup2zup(Q):
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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 = ''.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': str(h),
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'width': str(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 += [0.0, 0.0, 0] if np.isnan(coords_mk).any() else coords_mk.tolist()+[2]
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# bbox
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min_x = np.nanmin(coords.loc[coords.index.get_level_values(3)=='x'])
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min_y = np.nanmin(coords.loc[coords.index.get_level_values(3)=='y'])
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max_x = np.nanmax(coords.loc[coords.index.get_level_values(3)=='x'])
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max_y = np.nanmax(coords.loc[coords.index.get_level_values(3)=='y'])
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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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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.
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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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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(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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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_output')
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deeplabcut_output = args.get('deeplabcut_output')
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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:
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raise ValueError('Output_format must be specified either "openpose_output" (-o) or "deeplabcut_output (-d)"')
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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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@ -342,60 +445,44 @@ def reproj_from_trc_calib_func(**args):
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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[:, ::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:
|
||||
marker_list = list(dict.fromkeys(data_proj[cam].columns.get_level_values(2)[1:]))
|
||||
|
||||
# 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)]
|
||||
h5_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.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)]
|
||||
csv_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.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
|
||||
elif 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]
|
||||
nb_joints = len(bodyparts_ids)
|
||||
#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
|
||||
# Save as json if Coco/MMpose format
|
||||
if mmpose_output:
|
||||
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):
|
||||
# visibility: 2 visible, 1 occluded, 0 out of frame
|
||||
coords = data_proj_frame[b].values
|
||||
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)
|
||||
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.')
|
||||
mmpose_json_file = os.path.join(cam_dir, f'{filename}_cam_{cam+1:02d}_mmpose.json')
|
||||
dataset_to_mmpose2d(data_proj[cam], mmpose_json_file, calib_params_size[cam], markerset=markerset, marker_list=marker_list)
|
||||
|
||||
# Save as json if OpenPose format
|
||||
if openpose_output:
|
||||
for cam, cam_dir in enumerate(cam_dirs):
|
||||
openpose_path_root = os.path.join(cam_dir, f'{filename}_cam{cam+1:02d}_openpose.json')
|
||||
dataset_to_openpose(data_proj[cam], openpose_path_root, marker_list=marker_list)
|
||||
|
||||
# Wrong format
|
||||
else:
|
||||
if not openpose_output and not deeplabcut_output and not mmpose_output:
|
||||
raise ValueError('output_format must be either "openpose" or "deeplabcut"')
|
||||
|
||||
print(f'Reprojected points saved at {output_file_root}.')
|
||||
@ -405,8 +492,10 @@ 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('-o', '--openpose', required=False, action='store_true', help='output format in the openpose json format')
|
||||
parser.add_argument('-d', '--deeplabcut', required=False, action='store_true', help='output format in the deeplabcut csv and h5 formats')
|
||||
parser.add_argument('-m', '--mmpose', required=False, action='store_true', help='output format in the Coco/MMpose json format')
|
||||
parser.add_argument('-s', '--markerset', required=False, help='markerset name, e.g. halpe26, halpeplus, biocvplus')
|
||||
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())
|
||||
|
||||
Loading…
Reference in New Issue
Block a user