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import os
import numpy as np
import json
from PIL import Image
DATA_PATH = 'datasets/crowdhuman/'
OUT_PATH = DATA_PATH + 'annotations/'
SPLITS = ['val', 'train']
DEBUG = False
def load_func(fpath):
print('fpath', fpath)
assert os.path.exists(fpath)
with open(fpath,'r') as fid:
lines = fid.readlines()
records =[json.loads(line.strip('\n')) for line in lines]
return records
if __name__ == '__main__':
if not os.path.exists(OUT_PATH):
os.mkdir(OUT_PATH)
for split in SPLITS:
data_path = DATA_PATH + split
out_path = OUT_PATH + '{}.json'.format(split)
out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}
ann_path = DATA_PATH + 'annotation_{}.odgt'.format(split)
anns_data = load_func(ann_path)
image_cnt = 0
ann_cnt = 0
video_cnt = 0
for ann_data in anns_data:
image_cnt += 1
file_path = DATA_PATH + 'CrowdHuman_{}/'.format(split) + '{}.jpg'.format(ann_data['ID'])
im = Image.open(file_path)
image_info = {'file_name': '{}.jpg'.format(ann_data['ID']),
'id': image_cnt,
'height': im.size[1],
'width': im.size[0]}
out['images'].append(image_info)
if split != 'test':
anns = ann_data['gtboxes']
for i in range(len(anns)):
ann_cnt += 1
fbox = anns[i]['fbox']
ann = {'id': ann_cnt,
'category_id': 1,
'image_id': image_cnt,
'track_id': -1,
'bbox_vis': anns[i]['vbox'],
'bbox': fbox,
'area': fbox[2] * fbox[3],
'iscrowd': 1 if 'extra' in anns[i] and \
'ignore' in anns[i]['extra'] and \
anns[i]['extra']['ignore'] == 1 else 0}
out['annotations'].append(ann)
print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))
json.dump(out, open(out_path, 'w')) |