preprocessor.py 文件源码

def _crop(self, image, offset_height, offset_width, crop_height, crop_width):
        """Crops the given image using the provided offsets and sizes.
        Note that the method doesn't assume we know the input image size but it does
        assume we know the input image rank.

        Args:
          image: an image of shape [height, width, channels].
          offset_height: a scalar tensor indicating the height offset.
          offset_width: a scalar tensor indicating the width offset.
          crop_height: the height of the cropped image.
          crop_width: the width of the cropped image.

        Returns:
          the cropped (and resized) image.

        Raises:
          InvalidArgumentError: if the rank is not 3 or if the image dimensions are
            less than the crop size.
        """
        original_shape = tf.shape(image)

        rank_assertion = tf.Assert(
            tf.equal(tf.rank(image), 3),
            ['Rank of image must be equal to 3.'])
        with tf.control_dependencies([rank_assertion]):
            cropped_shape = tf.stack(
                [crop_height, crop_width, original_shape[2]])

        size_assertion = tf.Assert(
            tf.logical_and(
                tf.greater_equal(original_shape[0], crop_height),
                tf.greater_equal(original_shape[1], crop_width)),
            ['Crop size greater than the image size.'])

        offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
        with tf.control_dependencies([size_assertion]):
            image = tf.slice(image, offsets, cropped_shape)
        return tf.reshape(image, cropped_shape)