text: # 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.
  # ==============================================================================
  
  """Tests for object_detection.box_coder.keypoint_box_coder."""
  
  import tensorflow as tf
  
  from object_detection.box_coders import keypoint_box_coder
  from object_detection.core import box_list
  from object_detection.core import standard_fields as fields
  
  
  class KeypointBoxCoderTest(tf.test.TestCase):
  
    def test_get_correct_relative_codes_after_encoding(self):
      boxes = [[10., 10., 20., 15.],
               [0.2, 0.1, 0.5, 0.4]]
      keypoints = [[[15., 12.], [10., 15.]],
                   [[0.5, 0.3], [0.2, 0.4]]]
      num_keypoints = len(keypoints[0])
      anchors = [[15., 12., 30., 18.],
                 [0.1, 0.0, 0.7, 0.9]]
      expected_rel_codes = [
          [-0.5, -0.416666, -0.405465, -0.182321,
           -0.5, -0.5, -0.833333, 0.],
          [-0.083333, -0.222222, -0.693147, -1.098612,
           0.166667, -0.166667, -0.333333, -0.055556]
      ]
      boxes = box_list.BoxList(tf.constant(boxes))
      boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
      anchors = box_list.BoxList(tf.constant(anchors))
      coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
      rel_codes = coder.encode(boxes, anchors)
      with self.test_session() as sess:
        rel_codes_out, = sess.run([rel_codes])
        self.assertAllClose(rel_codes_out, expected_rel_codes)
  
    def test_get_correct_relative_codes_after_encoding_with_scaling(self):
      boxes = [[10., 10., 20., 15.],
               [0.2, 0.1, 0.5, 0.4]]
      keypoints = [[[15., 12.], [10., 15.]],
                   [[0.5, 0.3], [0.2, 0.4]]]
      num_keypoints = len(keypoints[0])
      anchors = [[15., 12., 30., 18.],
                 [0.1, 0.0, 0.7, 0.9]]
      scale_factors = [2, 3, 4, 5]
      expected_rel_codes = [
          [-1., -1.25, -1.62186, -0.911608,
           -1.0, -1.5, -1.666667, 0.],
          [-0.166667, -0.666667, -2.772588, -5.493062,
           0.333333, -0.5, -0.666667, -0.166667]
      ]
      boxes = box_list.BoxList(tf.constant(boxes))
      boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
      anchors = box_list.BoxList(tf.constant(anchors))
      coder = keypoint_box_coder.KeypointBoxCoder(
          num_keypoints, scale_factors=scale_factors)
      rel_codes = coder.encode(boxes, anchors)
      with self.test_session() as sess:
        rel_codes_out, = sess.run([rel_codes])
        self.assertAllClose(rel_codes_out, expected_rel_codes)
  
    def test_get_correct_boxes_after_decoding(self):
      anchors = [[15., 12., 30., 18.],
                 [0.1, 0.0, 0.7, 0.9]]
      rel_codes = [
          [-0.5, -0.416666, -0.405465, -0.182321,
           -0.5, -0.5, -0.833333, 0.],
          [-0.083333, -0.222222, -0.693147, -1.098612,
           0.166667, -0.166667, -0.333333, -0.055556]
      ]
      expected_boxes = [[10., 10., 20., 15.],
                        [0.2, 0.1, 0.5, 0.4]]
      expected_keypoints = [[[15., 12.], [10., 15.]],
                            [[0.5, 0.3], [0.2, 0.4]]]
      num_keypoints = len(expected_keypoints[0])
      anchors = box_list.BoxList(tf.constant(anchors))
      coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
      boxes = coder.decode(rel_codes, anchors)
      with self.test_session() as sess:
        boxes_out, keypoints_out = sess.run(
            [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
        self.assertAllClose(boxes_out, expected_boxes)
        self.assertAllClose(keypoints_out, expected_keypoints)
  
    def test_get_correct_boxes_after_decoding_with_scaling(self):
      anchors = [[15., 12., 30., 18.],
                 [0.1, 0.0, 0.7, 0.9]]
      rel_codes = [
          [-1., -1.25, -1.62186, -0.911608,
           -1.0, -1.5, -1.666667, 0.],
          [-0.166667, -0.666667, -2.772588, -5.493062,
           0.333333, -0.5, -0.666667, -0.166667]
      ]
      scale_factors = [2, 3, 4, 5]
      expected_boxes = [[10., 10., 20., 15.],
                        [0.2, 0.1, 0.5, 0.4]]
      expected_keypoints = [[[15., 12.], [10., 15.]],
                            [[0.5, 0.3], [0.2, 0.4]]]
      num_keypoints = len(expected_keypoints[0])
      anchors = box_list.BoxList(tf.constant(anchors))
      coder = keypoint_box_coder.KeypointBoxCoder(
          num_keypoints, scale_factors=scale_factors)
      boxes = coder.decode(rel_codes, anchors)
      with self.test_session() as sess:
        boxes_out, keypoints_out = sess.run(
            [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
        self.assertAllClose(boxes_out, expected_boxes)
        self.assertAllClose(keypoints_out, expected_keypoints)
  
    def test_very_small_width_nan_after_encoding(self):
      boxes = [[10., 10., 10.0000001, 20.]]
      keypoints = [[[10., 10.], [10.0000001, 20.]]]
      anchors = [[15., 12., 30., 18.]]
      expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826,
                             -0.833333, -0.833333, -0.833333, 0.833333]]
      boxes = box_list.BoxList(tf.constant(boxes))
      boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
      anchors = box_list.BoxList(tf.constant(anchors))
      coder = keypoint_box_coder.KeypointBoxCoder(2)
      rel_codes = coder.encode(boxes, anchors)
      with self.test_session() as sess:
        rel_codes_out, = sess.run([rel_codes])
        self.assertAllClose(rel_codes_out, expected_rel_codes)
  
  
  if __name__ == '__main__':
    tf.test.main()
  