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gen_posetrack_json.py
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gen_posetrack_json.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.
##############################################################
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import json
import os
import os.path as osp
import glob
from tqdm import tqdm
from PIL import Image
import shutil
import numpy as np
from convert.loader import load_mat
from convert.box import compute_boxes_from_pose
from utils.general import mkdir_p
from convert.data import get_posetrack_kpt_ordering
# Directory with annotation mat files downloaded from PoseTrack website
mat_dir = '/path/to/posetrack_data/annotations/{}'
out_path = '/path/to/output/jsons/posetrack_{}.json'
splits = ['test', 'train', 'val']
# Set this to true if need to re-create the video frames. Note that the
# original frames are in non-standard file format so will need to be fixed.
if 1: # `convert` the frames to standard format
recreate_videos = True
vid_indir = '/path/to/posetrack_data/images'
vid_outdir = '/path/to/output/images_renamed'
else: # `convert`-ed frames already exist, do not redo
recreate_videos = False
vid_indir = ''
vid_outdir = '/path/to/output/images_renamed'
def _get_video_info(vpath):
frame_ids = sorted([int(osp.basename(
el).split('.')[0]) for el in os.listdir(vpath)])
nframes = len(frame_ids)
assert(frame_ids[0] == 1)
assert(frame_ids[-1] == nframes)
frame1 = osp.join(vpath, '00000001.jpg')
wd, ht = Image.open(frame1).size
return {'nframes': nframes, 'width': wd, 'height': ht}
def _convert_video_frame_ids(inpath, outpath):
"""
PoseTrack videos follow no consistent naming for frames. Make it consistent
"""
mkdir_p(outpath)
frame_names = [osp.basename(el) for el in glob.glob(osp.join(
inpath, '*.jpg'))]
# Some videos have 00XX_crop.jpg style filenames
frame_ids = [int(el.split('.')[0].split('_')[0]) for el in frame_names]
id_to_name = dict(zip(frame_ids, frame_names))
for i, fid in enumerate(sorted(frame_ids)):
shutil.copy('{}/{}'.format(inpath, id_to_name[fid]),
'{}/{:08d}.jpg'.format(outpath, i + 1))
def _load_mat_files(annot_dir):
mat_data = {}
print('Loading data from MAT files...')
for fpath in tqdm(glob.glob(osp.join(annot_dir, '*.mat'))):
stuff = load_mat(fpath)
if len(stuff) > 0:
key = osp.dirname(stuff[0].im_name)
key = key[len('images/'):]
mat_data[key] = stuff
return mat_data
def _get_person_category_data():
category = {
"supercategory": "person",
"id": 1, # to be same as COCO, not using 0
"name": "person",
"skeleton": [[16, 14],
[14, 12],
[17, 15],
[15, 13],
[12, 13],
[6, 12],
[7, 13],
[6, 7],
[6, 8],
[7, 9],
[8, 10],
[9, 11],
[2, 3],
[1, 2],
[1, 3],
[2, 4],
[3, 5],
[4, 6],
[5, 7]],
"keypoints": ["nose",
"head_bottom", # "left_eye",
"head_top", # "right_eye",
"left_ear",
"right_ear", "left_shoulder", "right_shoulder",
"left_elbow", "right_elbow", "left_wrist",
"right_wrist", "left_hip", "right_hip", "left_knee",
"right_knee", "left_ankle", "right_ankle"]}
return category
def _get_categories_data():
return [_get_person_category_data()]
def _gen_image_structure(vname, frame_id, frame_data, vid_info, imid):
image = {}
# ordering in data based on tools/video/extract_metadata.py
image['nframes'] = int(vid_info['nframes'])
image['frame_id'] = int(frame_id)
image['width'] = int(vid_info['width'])
image['height'] = int(vid_info['height'])
# frames-in-dir kinda videos. The {:08d}.jpg is how I rename it
image['file_name'] = osp.join(vname, '{:08d}.jpg'.format(frame_id))
image['original_file_name'] = frame_data.im_name
image['is_labeled'] = frame_data.is_labeled
image['id'] = imid
return image
def _get_posetrack_to_coco_permut():
print('Computing permutation from posetrack to COCO.')
target_ordering = _get_person_category_data()['keypoints']
given_ordering, _ = get_posetrack_kpt_ordering()
permut_ordering = []
for given_kpt_id, given_kpt in enumerate(given_ordering):
new_id = target_ordering.index(given_kpt)
# Make sure all points get assigned somewhere.
# COCO has 17 kpts, so the other kpts in posetrack can replace the ones
# we don't have labels for in posetrack (eye/ear)
assert(new_id > -1)
print('{} -> {}'.format(given_kpt_id, new_id))
permut_ordering.append(new_id)
return permut_ordering
def _convert_posetrack_kps_to_coco(posetrack_pose, permut_ordering):
res = np.zeros(
(len(_get_person_category_data()['keypoints']), 3),
dtype=posetrack_pose.dtype)
res[np.array(permut_ordering), :] = posetrack_pose
return res
def _gen_annot_structure(box_data, kpt_permut_ordering, imid, annid):
ann = {}
ann['id'] = annid
ann['image_id'] = imid
ann['iscrowd'] = 0
ann['segmentation'] = []
ann['num_keypoints'] = 17 # COCO
ann['category_id'] = 1 # person
ann['track_id'] = box_data.track_id
ann['head_box'] = [float(el) for el in box_data.head]
ann['keypoints'] = _convert_posetrack_kps_to_coco(
box_data.pose, kpt_permut_ordering).reshape((-1)).tolist()
ann['bbox'] = compute_boxes_from_pose([[ann['keypoints']]])[0][0]
ann['area'] = ann['bbox'][-1] * ann['bbox'][-2]
return ann
def _convert_mat_to_COCO_json(
annot_dir, out_path, vid_indir, vid_outdir, recreate_videos,
permut_ordering):
# Generate the output structure
res = {}
res['images'] = []
res['annotations'] = []
res['categories'] = _get_categories_data()
# load all the mat files
all_annots = _load_mat_files(annot_dir)
print('Processing MAT files into JSON structures...')
for vid_name in tqdm(all_annots.keys()):
# Convert the posetrack video into a sane format
if recreate_videos:
assert(len(vid_indir) > 0)
_convert_video_frame_ids(
osp.join(vid_indir, vid_name),
osp.join(vid_outdir, vid_name))
vid_info = _get_video_info(osp.join(vid_outdir, vid_name))
vid_data = all_annots[vid_name]
nframes = len(vid_data)
for frame_id in range(1, nframes + 1):
frame_data = vid_data[frame_id - 1]
image_struct = _gen_image_structure(
vid_name, frame_id, frame_data, vid_info,
len(res['images']) + 1)
res['images'].append(image_struct)
if frame_data.is_labeled:
for box_data in frame_data.boxes:
annot_struct = _gen_annot_structure(
box_data,
permut_ordering,
imid=len(res['images']),
annid=len(res['annotations']) + 1)
res['annotations'].append(annot_struct)
with open(out_path, 'w') as fout:
json.dump(res, fout)
def main():
permut_ordering = _get_posetrack_to_coco_permut()
for split in splits:
print('Processing {} split'.format(split))
_convert_mat_to_COCO_json(
mat_dir.format(split), out_path.format(split),
vid_indir, vid_outdir, recreate_videos, permut_ordering)
if __name__ == '__main__':
main()