data_handler.py 文件源码

def image_reading(path: str, resized_size: Tuple[int, int]=None, data_augmentation: bool=False,
                  padding: bool=False) -> Tuple[tf.Tensor, tf.Tensor]:
    # Read image
    image_content = tf.read_file(path, name='image_reader')
    image = tf.cond(tf.equal(tf.string_split([path], '.').values[1], tf.constant('jpg', dtype=tf.string)),
                    true_fn=lambda: tf.image.decode_jpeg(image_content, channels=1, try_recover_truncated=True), # TODO channels = 3 ?
                    false_fn=lambda: tf.image.decode_png(image_content, channels=1), name='image_decoding')

    # Data augmentation
    if data_augmentation:
        image = augment_data(image)

    # Padding
    if padding:
        with tf.name_scope('padding'):
            image, img_width = padding_inputs_width(image, resized_size, increment=CONST.DIMENSION_REDUCTION_W_POOLING)
    # Resize
    else:
        image = tf.image.resize_images(image, size=resized_size)
        img_width = tf.shape(image)[1]

    with tf.control_dependencies([tf.assert_equal(image.shape[:2], resized_size)]):
        return image, img_width