import os import numpy as np import json from PIL import Image DATA_PATH = 'datasets/ETHZ/' DATA_FILE_PATH = 'datasets/data_path/eth.train' OUT_PATH = DATA_PATH + 'annotations/' def load_paths(data_path): with open(data_path, 'r') as file: img_files = file.readlines() img_files = [x.replace('\n', '') for x in img_files] img_files = list(filter(lambda x: len(x) > 0, img_files)) label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt') for x in img_files] return img_files, label_files if __name__ == '__main__': if not os.path.exists(OUT_PATH): os.mkdir(OUT_PATH) out_path = OUT_PATH + 'train.json' out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]} img_paths, label_paths = load_paths(DATA_FILE_PATH) image_cnt = 0 ann_cnt = 0 video_cnt = 0 for img_path, label_path in zip(img_paths, label_paths): image_cnt += 1 im = Image.open(img_path) image_info = {'file_name': img_path, 'id': image_cnt, 'height': im.size[1], 'width': im.size[0]} out['images'].append(image_info) # Load labels if os.path.isfile(label_path): labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6) # Normalized xywh to pixel xyxy format labels = labels0.copy() labels[:, 2] = image_info['width'] * (labels0[:, 2] - labels0[:, 4] / 2) labels[:, 3] = image_info['height'] * (labels0[:, 3] - labels0[:, 5] / 2) labels[:, 4] = image_info['width'] * labels0[:, 4] labels[:, 5] = image_info['height'] * labels0[:, 5] else: labels = np.array([]) for i in range(len(labels)): ann_cnt += 1 fbox = labels[i, 2:6].tolist() ann = {'id': ann_cnt, 'category_id': 1, 'image_id': image_cnt, 'track_id': -1, 'bbox': fbox, 'area': fbox[2] * fbox[3], 'iscrowd': 0} out['annotations'].append(ann) print('loaded train for {} images and {} samples'.format(len(out['images']), len(out['annotations']))) json.dump(out, open(out_path, 'w'))