def prepare_visdrone()

in scripts/data_prepare.py [0:0]

• 46 lines of code
• 14 McCabe index (conditional complexity)

def prepare_visdrone():
    
    name_dict = {'0': 'ignored regions', '1': 'pedestrian', '2': 'people',
                 '3': 'bicycle', '4': 'car', '5': 'van', '6': 'truck',
                 '7': 'tricycle', '8': 'awning-tricycle', '9': 'bus',
                 '10': 'motor', '11': 'others'}
    split_dict = {'test-dev': 'test-dev.txt', 'val': 'val.txt', 'train': 'train.txt'}
    root = opt.dataset

    os.makedirs(join(root, 'split'), exist_ok=True)
    for sub_dir in glob(join(root, 'VisDrone2019-DET-*')):
        os.makedirs(join(sub_dir, 'labels'), exist_ok=True)
        images = sorted(glob(join(sub_dir, 'images', '*.jpg')))
        if 'test-challenge' in sub_dir:
            with open(join(root, 'split', 'test-challenge.txt'), 'w+') as f:
                f.writelines([line + '\n' for line in images])
            continue

        data_paths = []
        for image_path in tqdm(images):
            image = cv2.imread(image_path)
            height, width, _ = image.shape
            label_path = image_path.replace('images', 'annotations').replace('.jpg', '.txt')
            # if "masked" in label_path:  # avoid repeated processing
            #     continue
            assert exists(label_path)
            label_lines = []
            masked = False
            # <bbox_left>,<bbox_top>,<bbox_width>,<bbox_height>,<score>,<object_category>,<truncation>,<occlusion>
            for line in _readlines(label_path):
                if line[-1] == ',':
                    line = line[:-1]
                x1, y1, w, h, score, cls, truncation, occlusion = list(map(int, line.split(',')))
                if cls in [0, 11]:
                    image[y1:y1 + h, x1:x1 + w, :] = 85
                    masked = True
                elif truncation < 2 and (occlusion < 2 or True):
                    xc, yc = x1 + w / 2., y1 + h / 2.
                    label_lines.append(('%d' + ' %.6f' * 4 + '\n') %
                                       (cls - 1, xc / width, yc / height, w / width, h / height))
                else:
                    pass
            if masked:
                image_path = image_path.replace('.jpg', '_masked.jpg')
                cv2.imwrite(image_path, image)
            # for consistency
            label_path = image_path.replace('images', 'annotations').replace('.jpg', '.txt')
            with open(label_path, 'w+') as f:
                f.writelines(label_lines)

            gen_mask(label_path, image, cls_ratio=True)

            data_paths.append(image_path + '\n')
        
        with open(join(root, 'split', split_dict[basename(sub_dir)[17:]]), 'w+') as f:
            f.writelines(data_paths)