Python object_detection.core.keypoint_ops.scale() Examples

The following are 30 code examples of object_detection.core.keypoint_ops.scale(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.core.keypoint_ops , or try the search function .
Example #1
Source File: keypoint_ops_test.py    From MBMD with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #2
Source File: keypoint_ops_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #3
Source File: keypoint_ops_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #4
Source File: keypoint_ops_test.py    From HereIsWally with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #5
Source File: keypoint_ops_test.py    From object_detector_app with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #6
Source File: keypoint_ops_test.py    From mtl-ssl with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #7
Source File: keypoint_ops_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    def graph_fn():
      keypoints = tf.constant([
          [[0.0, 0.0], [100.0, 200.0]],
          [[50.0, 120.0], [100.0, 140.0]]
      ])
      y_scale = tf.constant(1.0 / 100)
      x_scale = tf.constant(1.0 / 200)

      expected_keypoints = tf.constant([
          [[0., 0.], [1.0, 1.0]],
          [[0.5, 0.6], [1.0, 0.7]]
      ])
      output = keypoint_ops.scale(keypoints, y_scale, x_scale)
      return output, expected_keypoints
    output, expected_keypoints = self.execute(graph_fn, [])
    self.assertAllClose(output, expected_keypoints)
Example #8
Source File: keypoint_ops_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #9
Source File: keypoint_ops_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #10
Source File: keypoint_ops_test.py    From motion-rcnn with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #11
Source File: keypoint_ops_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #12
Source File: keypoint_ops_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #13
Source File: keypoint_ops_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #14
Source File: keypoint_ops_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #15
Source File: keypoint_ops_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #16
Source File: keypoint_ops_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #17
Source File: keypoint_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #18
Source File: keypoint_ops_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #19
Source File: keypoint_ops_test.py    From AniSeg with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #20
Source File: keypoint_ops_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #21
Source File: keypoint_ops_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #22
Source File: keypoint_ops_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #23
Source File: keypoint_ops_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #24
Source File: keypoint_ops_test.py    From moveo_ros with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #25
Source File: keypoint_ops_test.py    From hands-detection with MIT License 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #26
Source File: keypoint_ops_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #27
Source File: keypoint_ops_test.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #28
Source File: keypoint_ops_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #29
Source File: keypoint_ops_test.py    From Accident-Detection-on-Indian-Roads with GNU Affero General Public License v3.0 6 votes vote down vote up 
def test_scale(self):
    keypoints = tf.constant([
        [[0.0, 0.0], [100.0, 200.0]],
        [[50.0, 120.0], [100.0, 140.0]]
    ])
    y_scale = tf.constant(1.0 / 100)
    x_scale = tf.constant(1.0 / 200)

    expected_keypoints = tf.constant([
        [[0., 0.], [1.0, 1.0]],
        [[0.5, 0.6], [1.0, 0.7]]
    ])
    output = keypoint_ops.scale(keypoints, y_scale, x_scale)

    with self.test_session() as sess:
      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
      self.assertAllClose(output_, expected_keypoints_)
Example #30
Source File: preprocessor.py    From models with Apache License 2.0 5 votes vote down vote up 
def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None):
  """Scales boxes from normalized to pixel coordinates.

  Args:
    image: A 3D float32 tensor of shape [height, width, channels].
    boxes: A 2D float32 tensor of shape [num_boxes, 4] containing the bounding
      boxes in normalized coordinates. Each row is of the form
      [ymin, xmin, ymax, xmax].
    keypoints: (optional) rank 3 float32 tensor with shape
      [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
      coordinates.

  Returns:
    image: unchanged input image.
    scaled_boxes: a 2D float32 tensor of shape [num_boxes, 4] containing the
      bounding boxes in pixel coordinates.
    scaled_keypoints: a 3D float32 tensor with shape
      [num_instances, num_keypoints, 2] containing the keypoints in pixel
      coordinates.
  """
  boxlist = box_list.BoxList(boxes)
  image_height = tf.shape(image)[0]
  image_width = tf.shape(image)[1]
  scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get()
  result = [image, scaled_boxes]
  if keypoints is not None:
    scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width)
    result.append(scaled_keypoints)
  return tuple(result)


# TODO(alirezafathi): Investigate if instead the function should return None if
# masks is None.
# pylint: disable=g-doc-return-or-yield