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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 re
import sys
from glob import glob
import numpy as np
from data_utils import suggest_metadata, suggest_pose_importer
sys.path.append('../')
from itertools import groupby
subjects = ['Train/S1', 'Train/S2', 'Train/S3', 'Validate/S1', 'Validate/S2', 'Validate/S3']
cam_map = {
'C1': 0,
'C2': 1,
'C3': 2,
}
# Frame numbers for train/test split
# format: [start_frame, end_frame[ (inclusive, exclusive)
index = {
'Train/S1': {
'Walking 1': (590, 1203),
'Jog 1': (367, 740),
'ThrowCatch 1': (473, 945),
'Gestures 1': (395, 801),
'Box 1': (385, 789),
},
'Train/S2': {
'Walking 1': (438, 876),
'Jog 1': (398, 795),
'ThrowCatch 1': (550, 1128),
'Gestures 1': (500, 901),
'Box 1': (382, 734),
},
'Train/S3': {
'Walking 1': (448, 939),
'Jog 1': (401, 842),
'ThrowCatch 1': (493, 1027),
'Gestures 1': (533, 1102),
'Box 1': (512, 1021),
},
'Validate/S1': {
'Walking 1': (5, 590),
'Jog 1': (5, 367),
'ThrowCatch 1': (5, 473),
'Gestures 1': (5, 395),
'Box 1': (5, 385),
},
'Validate/S2': {
'Walking 1': (5, 438),
'Jog 1': (5, 398),
'ThrowCatch 1': (5, 550),
'Gestures 1': (5, 500),
'Box 1': (5, 382),
},
'Validate/S3': {
'Walking 1': (5, 448),
'Jog 1': (5, 401),
'ThrowCatch 1': (5, 493),
'Gestures 1': (5, 533),
'Box 1': (5, 512),
},
}
# Frames to skip for each video (synchronization)
sync_data = {
'S1': {
'Walking 1': (82, 81, 82),
'Jog 1': (51, 51, 50),
'ThrowCatch 1': (61, 61, 60),
'Gestures 1': (45, 45, 44),
'Box 1': (57, 57, 56),
},
'S2': {
'Walking 1': (115, 115, 114),
'Jog 1': (100, 100, 99),
'ThrowCatch 1': (127, 127, 127),
'Gestures 1': (122, 122, 121),
'Box 1': (119, 119, 117),
},
'S3': {
'Walking 1': (80, 80, 80),
'Jog 1': (65, 65, 65),
'ThrowCatch 1': (79, 79, 79),
'Gestures 1': (83, 83, 82),
'Box 1': (1, 1, 1),
},
'S4': {}
}
if __name__ == '__main__':
if os.path.basename(os.getcwd()) != 'data':
print('This script must be launched from the "data" directory')
exit(0)
parser = argparse.ArgumentParser(description='HumanEva dataset converter')
parser.add_argument('-p', '--path', default='', type=str, metavar='PATH', help='path to the processed HumanEva dataset')
parser.add_argument('--convert-3d', action='store_true', help='convert 3D mocap data')
parser.add_argument('--convert-2d', default='', type=str, metavar='PATH', help='convert user-supplied 2D detections')
parser.add_argument('-o', '--output', default='', type=str, metavar='PATH', help='output suffix for 2D detections (e.g. detectron_pt_coco)')
args = parser.parse_args()
if not args.convert_2d and not args.convert_3d:
print('Please specify one conversion mode')
exit(0)
if args.path:
print('Parsing HumanEva dataset from', args.path)
output = {}
output_2d = {}
frame_mapping = {}
from scipy.io import loadmat
num_joints = None
for subject in subjects:
output[subject] = {}
output_2d[subject] = {}
split, subject_name = subject.split('/')
if subject_name not in frame_mapping:
frame_mapping[subject_name] = {}
file_list = glob(args.path + '/' + subject + '/*.mat')
for f in file_list:
action = os.path.splitext(os.path.basename(f))[0]
# Use consistent naming convention
canonical_name = action.replace('_', ' ')
hf = loadmat(f)
positions = hf['poses_3d']
positions_2d = hf['poses_2d'].transpose(1, 0, 2, 3) # Ground-truth 2D poses
assert positions.shape[0] == positions_2d.shape[0] and positions.shape[1] == positions_2d.shape[2]
assert num_joints is None or num_joints == positions.shape[1], "Joint number inconsistency among files"
num_joints = positions.shape[1]
# Sanity check for the sequence length
assert positions.shape[0] == index[subject][canonical_name][1] - index[subject][canonical_name][0]
# Split corrupted motion capture streams into contiguous chunks
# e.g. 012XX567X9 is split into "012", "567", and "9".
all_chunks = [list(v) for k, v in groupby(positions, lambda x: np.isfinite(x).all())]
all_chunks_2d = [list(v) for k, v in groupby(positions_2d, lambda x: np.isfinite(x).all())]
assert len(all_chunks) == len(all_chunks_2d)
current_index = index[subject][canonical_name][0]
chunk_indices = []
for i, chunk in enumerate(all_chunks):
next_index = current_index + len(chunk)
name = canonical_name + ' chunk' + str(i)
if np.isfinite(chunk).all():
output[subject][name] = np.array(chunk, dtype='float32') / 1000
output_2d[subject][name] = list(np.array(all_chunks_2d[i], dtype='float32').transpose(1, 0, 2, 3))
chunk_indices.append((current_index, next_index, np.isfinite(chunk).all(), split, name))
current_index = next_index
assert current_index == index[subject][canonical_name][1]
if canonical_name not in frame_mapping[subject_name]:
frame_mapping[subject_name][canonical_name] = []
frame_mapping[subject_name][canonical_name] += chunk_indices
metadata = suggest_metadata('humaneva' + str(num_joints))
output_filename = 'data_3d_' + metadata['layout_name']
output_prefix_2d = 'data_2d_' + metadata['layout_name'] + '_'
if args.convert_3d:
print('Saving...')
np.savez_compressed(output_filename, positions_3d=output)
np.savez_compressed(output_prefix_2d + 'gt', positions_2d=output_2d, metadata=metadata)
print('Done.')
else:
print('Please specify the dataset source')
exit(0)
if args.convert_2d:
if not args.output:
print('Please specify an output suffix (e.g. detectron_pt_coco)')
exit(0)
import_func = suggest_pose_importer(args.output)
metadata = suggest_metadata(args.output)
print('Parsing 2D detections from', args.convert_2d)
output = {}
file_list = glob(args.convert_2d + '/S*/*.avi.npz')
for f in file_list:
path, fname = os.path.split(f)
subject = os.path.basename(path)
assert subject.startswith('S'), subject + ' does not look like a subject directory'
m = re.search('(.*) \\((.*)\\)', fname.replace('_', ' '))
action = m.group(1)
camera = m.group(2)
camera_idx = cam_map[camera]
keypoints = import_func(f)
assert keypoints.shape[1] == metadata['num_joints']
if action in sync_data[subject]:
sync_offset = sync_data[subject][action][camera_idx] - 1
else:
sync_offset = 0
if subject in frame_mapping and action in frame_mapping[subject]:
chunks = frame_mapping[subject][action]
for (start_idx, end_idx, labeled, split, name) in chunks:
canonical_subject = split + '/' + subject
if not labeled:
canonical_subject = 'Unlabeled/' + canonical_subject
if canonical_subject not in output:
output[canonical_subject] = {}
kps = keypoints[start_idx + sync_offset:end_idx + sync_offset]
assert len(kps) == end_idx - start_idx, "Got len {}, expected {}".format(len(kps), end_idx - start_idx)
if name not in output[canonical_subject]:
output[canonical_subject][name] = [None, None, None]
output[canonical_subject][name][camera_idx] = kps.astype('float32')
else:
canonical_subject = 'Unlabeled/' + subject
if canonical_subject not in output:
output[canonical_subject] = {}
if action not in output[canonical_subject]:
output[canonical_subject][action] = [None, None, None]
output[canonical_subject][action][camera_idx] = keypoints.astype('float32')
print('Saving...')
np.savez_compressed(output_prefix_2d + args.output, positions_2d=output, metadata=metadata)
print('Done.')
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