MetaMOT/tools/convert_cityperson_to_coco_metaway.py

import os
import numpy as np
import json
from PIL import Image

DATA_PATH = '/media/external_10TB/10TB/vision/ByteTrackData/Citypersons/'
DATA_FILE_PATH = 'datasets/data_path/citypersons'
OUT_PATH = DATA_PATH + 'annotations/'
DATA_ROOT = '/media/external_10TB/10TB/vision/ByteTrackData'

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)

    files = os.listdir(DATA_FILE_PATH)
    for file in files:
        out_path = OUT_PATH + 'train_{}.json'.format(file.replace('.train', ''))
        out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}
        img_paths, label_paths = load_paths(os.path.join(DATA_FILE_PATH, file))
        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(os.path.join(DATA_ROOT, 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(os.path.join(DATA_ROOT, label_path)):
                labels0 = np.loadtxt(os.path.join(DATA_ROOT, 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'))