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'))