def score_from_my_method_for_dataset()

in metric_utils.py [0:0]

• 23 lines of code
• 8 McCabe index (conditional complexity)

def score_from_my_method_for_dataset(my_scorer,
                                  input_path, dataset,
                                  score_type='clip'
                                  ):  # psnr, lpips
    scores = {}
    final_res = 0
    input_path = osp(input_path, dataset)
    ref_path = glob.glob(osp(input_path, "*_rgba.png"))
    novel_view = [osp(input_path, '%d.png' % i) for i in range(120)]
    # print(ref_path)
    # print(novel_view)
    for i in tqdm(range(120)):
        if os.path.exists(osp(input_path, '%d_color.png' % i)):
            continue
        img = cv2.imread(novel_view[i])
        H = img.shape[0]
        img = img[:, :H]
        cv2.imwrite(osp(input_path, '%d_color.png' % i), img)
    if score_type == 'clip' or score_type == 'cx':
        novel_view = [osp(input_path, '%d_color.png' % i) for i in range(120)]
    else:
        novel_view = [osp(input_path, '%d_color.png' % i) for i in range(1)]
    print(novel_view)
    scores['%s_average' % dataset] = my_scorer.score_gt(ref_path, novel_view)
    return scores