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prepare_data_h36m.py
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prepare_data_h36m.py
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# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import os
import zipfile
import numpy as np
import h5py
from glob import glob
from shutil import rmtree
import sys
sys.path.append('../')
from common.h36m_dataset import Human36mDataset
from common.camera import world_to_camera, project_to_2d, image_coordinates
from common.utils import wrap
output_filename = 'data_3d_h36m'
output_filename_2d = 'data_2d_h36m_gt'
subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11']
if __name__ == '__main__':
# preparation
if os.path.basename(os.getcwd()) != 'data':
print('This script must be launched from the "data" directory')
exit(0)
parser = argparse.ArgumentParser(description='Human3.6M dataset downloader/converter')
# Convert dataset preprocessed by Martinez et al. in https://github.com/una-dinosauria/3d-pose-baseline
parser.add_argument('--from-archive', default='', type=str, metavar='PATH', help='convert preprocessed dataset')
# Convert dataset from original source, using files converted to .mat (the Human3.6M dataset path must be specified manually)
# This option requires MATLAB to convert files using the provided script
parser.add_argument('--from-source', default='', type=str, metavar='PATH', help='convert original dataset')
# Convert dataset from original source, using original .cdf files (the Human3.6M dataset path must be specified manually)
# This option does not require MATLAB, but the Python library cdflib must be installed
parser.add_argument('--from-source-cdf', default='', type=str, metavar='PATH', help='convert original dataset')
args = parser.parse_args()
if args.from_archive and args.from_source:
print('Please specify only one argument')
exit(0)
# if os.path.exists(output_filename + '.npz'):
# print('The dataset already exists at', output_filename + '.npz')
# exit(0)
# main code
if args.from_archive:
print('Extracting Human3.6M dataset from', args.from_archive)
with zipfile.ZipFile(args.from_archive, 'r') as archive:
archive.extractall()
print('Converting...')
output = {}
for subject in subjects:
output[subject] = {}
file_list = glob('h36m/' + subject + '/MyPoses/3D_positions/*.h5')
assert len(file_list) == 30, "Expected 30 files for subject " + subject + ", got " + str(len(file_list))
for f in file_list:
action = os.path.splitext(os.path.basename(f))[0]
if subject == 'S11' and action == 'Directions':
continue # Discard corrupted video
with h5py.File(f) as hf:
positions = hf['3D_positions'].value.reshape(32, 3, -1).transpose(2, 0, 1)
positions /= 1000 # Meters instead of millimeters
output[subject][action] = positions.astype('float32')
print('Saving...')
np.savez_compressed(output_filename, positions_3d=output)
print('Cleaning up...')
rmtree('h36m')
print('Done.')
elif args.from_source:
print('Converting original Human3.6M dataset from', args.from_source)
output = {}
from scipy.io import loadmat
for subject in subjects:
output[subject] = {}
file_list = glob(args.from_source + '/' + subject + '/MyPoseFeatures/D3_Positions/*.cdf.mat')
assert len(file_list) == 30, "Expected 30 files for subject " + subject + ", got " + str(len(file_list))
for f in file_list:
action = os.path.splitext(os.path.splitext(os.path.basename(f))[0])[0]
if subject == 'S11' and action == 'Directions':
continue # Discard corrupted video
# Use consistent naming convention
canonical_name = action.replace('TakingPhoto', 'Photo') \
.replace('WalkingDog', 'WalkDog')
hf = loadmat(f)
positions = hf['data'][0, 0].reshape(-1, 32, 3)
positions /= 1000 # Meters instead of millimeters
output[subject][canonical_name] = positions.astype('float32')
print('Saving...')
np.savez_compressed(output_filename, positions_3d=output)
print('Done.')
elif args.from_source_cdf:
print('Converting original Human3.6M dataset from', args.from_source_cdf, '(CDF files)')
output = {}
import cdflib
for subject in subjects:
output[subject] = {}
file_list = glob(args.from_source_cdf + '/' + subject + '/MyPoseFeatures/D3_Positions/*.cdf')
assert len(file_list) == 30, "Expected 30 files for subject " + subject + ", got " + str(len(file_list))
for f in file_list:
action = os.path.splitext(os.path.basename(f))[0]
if subject == 'S11' and action == 'Directions':
continue # Discard corrupted video
# Use consistent naming convention
canonical_name = action.replace('TakingPhoto', 'Photo') \
.replace('WalkingDog', 'WalkDog')
hf = cdflib.CDF(f)
positions = hf['Pose'].reshape(-1, 32, 3)
positions /= 1000 # Meters instead of millimeters
output[subject][canonical_name] = positions.astype('float32')
print('Saving...')
np.savez_compressed(output_filename, positions_3d=output)
print('Done.')
else:
print('Please specify the dataset source')
exit(0)
# Create 2D pose file
print('')
print('Computing ground-truth 2D poses...')
dataset = Human36mDataset(output_filename + '.npz')
output_2d_poses = {}
for subject in dataset.subjects():
output_2d_poses[subject] = {}
for action in dataset[subject].keys():
anim = dataset[subject][action]
positions_2d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
pos_2d = wrap(project_to_2d, pos_3d, cam['intrinsic'], unsqueeze=True)
pos_2d_pixel_space = image_coordinates(pos_2d, w=cam['res_w'], h=cam['res_h'])
positions_2d.append(pos_2d_pixel_space.astype('float32'))
output_2d_poses[subject][action] = positions_2d
print('Saving...')
metadata = {
'num_joints': dataset.skeleton().num_joints(),
'keypoints_symmetry': [dataset.skeleton().joints_left(), dataset.skeleton().joints_right()]
}
np.savez_compressed(output_filename_2d, positions_2d=output_2d_poses, metadata=metadata)
print('Done.')