def _image_wider_than_requested_aspect_ratio(): crop_height = tf.cast( tf.rint(crop_proportion * image_height_float), tf.int32) crop_width = tf.cast(tf.rint( crop_proportion * aspect_ratio * image_height_float), tf.int32) return crop_height, crop_width
def _smallest_size_at_least(height, width, smallest_side): """Computes new shape with the smallest side equal to `smallest_side`. Computes new shape with the smallest side equal to `smallest_side` while preserving the original aspect ratio. Args: height: an int32 scalar tensor indicating the current height. width: an int32 scalar tensor indicating the current width. smallest_side: A python integer or scalar `Tensor` indicating the size of the smallest side after resize. Returns: new_height: an int32 scalar tensor indicating the new height. new_width: and int32 scalar tensor indicating the new width. """ smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32) height = tf.to_float(height) width = tf.to_float(width) smallest_side = tf.to_float(smallest_side) scale = tf.cond(tf.greater(height, width), lambda: smallest_side / width, lambda: smallest_side / height) new_height = tf.to_int32(tf.rint(height * scale)) new_width = tf.to_int32(tf.rint(width * scale)) return new_height, new_width