# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A module for helper tensorflow ops."""

import tensorflow as tf


def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
                                     image_width):
    """Transforms the box masks back to full image masks.

  Embeds masks in bounding boxes of larger masks whose shapes correspond to
  image shape.

  Args:
    box_masks: A tf.float32 tensor of size [num_masks, mask_height, mask_width].
    boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
           corners. Row i contains [ymin, xmin, ymax, xmax] of the box
           corresponding to mask i. Note that the box corners are in
           normalized coordinates.
    image_height: Image height. The output mask will have the same height as
                  the image height.
    image_width: Image width. The output mask will have the same width as the
                 image width.

  Returns:
    A tf.float32 tensor of size [num_masks, image_height, image_width].
  """

    # TODO(rathodv): Make this a public function.
    def reframe_box_masks_to_image_masks_default():
        """The default function when there are more than 0 box masks."""

        def transform_boxes_relative_to_boxes(boxes, reference_boxes):
            boxes = tf.reshape(boxes, [-1, 2, 2])
            min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)
            max_corner = tf.expand_dims(reference_boxes[:, 2:4], 1)
            transformed_boxes = (boxes - min_corner) / (max_corner - min_corner)
            return tf.reshape(transformed_boxes, [-1, 4])

        box_masks_expanded = tf.expand_dims(box_masks, axis=3)
        num_boxes = tf.shape(box_masks_expanded)[0]
        unit_boxes = tf.concat(
            [tf.zeros([num_boxes, 2]), tf.ones([num_boxes, 2])], axis=1)
        reverse_boxes = transform_boxes_relative_to_boxes(unit_boxes, boxes)
        return tf.image.crop_and_resize(
            image=box_masks_expanded,
            boxes=reverse_boxes,
            box_ind=tf.range(num_boxes),
            crop_size=[image_height, image_width],
            extrapolation_value=0.0)

    image_masks = tf.cond(
        tf.shape(box_masks)[0] > 0,
        reframe_box_masks_to_image_masks_default,
        lambda: tf.zeros([0, image_height, image_width, 1], dtype=tf.float32))
    return tf.squeeze(image_masks, axis=3)