c69da5ec32a1af288c70af7c37e08f6a8a19fe82,tensornets/utils.py,,load_img,#,169

Before Change

            hw_tuple = (target_size[1], target_size[0])
        if img.shape[1::-1] != hw_tuple:
            img = cv2.resize(img, hw_tuple, interpolation=interp)
    img = np.array([img[:, :, ::-1]], dtype=np.float32)
    if len(img.shape) == 3:
        img = np.expand_dims(img, -1)
    if crop_size is not None:

After Change

        interp = cv2.INTER_CUBIC
    if not isinstance(paths, list):
        paths = [paths]
    if len(paths) > 1 and (target_size is None or
                           isinstance(target_size, int)):
        raise ValueError("A tuple `target_size` should be provided "
                         "when loading multiple images.")

    def _load_img(path):
        img = cv2.imread(path)
        if target_size:
            if isinstance(target_size, int):
                hw_tuple = tuple([x * target_size // min(img.shape[:2])
                                  for x in img.shape[1::-1]])
            else:
                hw_tuple = (target_size[1], target_size[0])
            if img.shape[1::-1] != hw_tuple:
                img = cv2.resize(img, hw_tuple, interpolation=interp)
        img = img[:, :, ::-1]
        if len(img.shape) == 2:
            img = np.expand_dims(img, -1)
        return img

    if len(paths) > 1:
        imgs = np.zeros((len(paths),) + target_size + (3,), dtype=np.float32)
        for (i, path) in enumerate(paths):
            imgs[i] = _load_img(path)
    else:
        imgs = np.array([_load_img(paths[0])], dtype=np.float32)

    if crop_size is not None:
        imgs = crop(imgs, crop_size)

    return imgs

Instances