data_augmentation.py 文件源码

def random_crop(image, crop_size, padding=None):
    """Randmly crop a image.

    Args:
        image: 3-D float Tensor of image
        crop_size:int/tuple, output image height, width, for deep network we prefer same width and height
        padding: int, padding use to restore original image size, padded with 0's

    Returns:
        3-D float Tensor of randomly flipped updown image used for training.
    """
    if isinstance(crop_size, int):
        crop_size = (crop_size, crop_size)
    oshape = np.shape(image)
    if padding:
        oshape = (oshape[0] + 2 * padding, oshape[1] + 2 * padding)
    npad = ((padding, padding), (padding, padding), (0, 0))
    modified_image = image
    if padding:
        modified_image = np.lib.pad(
            image, pad_width=npad, mode='constant', constant_values=0)
    nh = random.randint(0, oshape[0] - crop_size[0])
    nw = random.randint(0, oshape[1] - crop_size[1])
    modified_image = modified_image[nh:nh + crop_size[0], nw:nw + crop_size[1]]
    return modified_image