Python object_detection.core.region_similarity_calculator.NegSqDistSimilarity() Examples

The following are 30 code examples of object_detection.core.region_similarity_calculator.NegSqDistSimilarity(). 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.region_similarity_calculator , or try the search function .
Example #1
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=1.0)
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #2
Source File: target_assigner_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    prior_stddevs = tf.constant([[1.0, 1.0, 1.0, 1.0]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #3
Source File: target_assigner_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=1.0)
    unmatched_class_label = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(
          priors,
          boxes,
          groundtruth_labels,
          unmatched_class_label=unmatched_class_label)
Example #4
Source File: target_assigner_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    prior_stddevs = tf.constant([[1.0, 1.0, 1.0, 1.0]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #5
Source File: target_assigner_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    prior_stddevs = tf.constant([[1.0, 1.0, 1.0, 1.0]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #6
Source File: region_similarity_calculator_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
                            [1.0, 1.0, 0.0, 2.0]])
    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
                            [-4.0, 0.0, 0.0, 3.0],
                            [0.0, 0.0, 0.0, 0.0]])
    exp_output = [[-26, -25, 0], [-18, -27, -6]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
    with self.test_session() as sess:
      dist_output = sess.run(dist_similarity)
      self.assertAllClose(dist_output, exp_output)
Example #7
Source File: target_assigner_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def _get_agnostic_target_assigner(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=None)
Example #8
Source File: target_assigner_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #9
Source File: target_assigner_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #10
Source File: target_assigner_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def _get_agnostic_target_assigner(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=None)
Example #11
Source File: region_similarity_calculator_builder.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def build(region_similarity_calculator_config):
  """Builds region similarity calculator based on the configuration.

  Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
  core/region_similarity_calculator.proto for details.

  Args:
    region_similarity_calculator_config: RegionSimilarityCalculator
      configuration proto.

  Returns:
    region_similarity_calculator: RegionSimilarityCalculator object.

  Raises:
    ValueError: On unknown region similarity calculator.
  """

  if not isinstance(
      region_similarity_calculator_config,
      region_similarity_calculator_pb2.RegionSimilarityCalculator):
    raise ValueError(
        'region_similarity_calculator_config not of type '
        'region_similarity_calculator_pb2.RegionsSimilarityCalculator')

  similarity_calculator = region_similarity_calculator_config.WhichOneof(
      'region_similarity')
  if similarity_calculator == 'iou_similarity':
    return region_similarity_calculator.IouSimilarity()
  if similarity_calculator == 'ioa_similarity':
    return region_similarity_calculator.IoaSimilarity()
  if similarity_calculator == 'neg_sq_dist_similarity':
    return region_similarity_calculator.NegSqDistSimilarity()

  raise ValueError('Unknown region similarity calculator.')
Example #12
Source File: target_assigner_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def test_assign_multiclass_unequal_class_weights(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0, negative_class_weight=0.5,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
                               [0.5, 0.5, 1.0, 0.8],
                               [0, 0.5, .5, 1.0],
                               [.75, 0, 1.0, .25]])
    prior_stddevs = tf.constant(4 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
                                      [0, 0, 0, 0, 0, 1, 0],
                                      [0, 0, 0, 1, 0, 0, 0]], tf.float32)

    exp_cls_weights = [1, 1, .5, 1]
    result = target_assigner.assign(priors, boxes, groundtruth_labels,
                                    num_valid_rows=3)
    (_, cls_weights, _, _, _) = result
    with self.test_session() as sess:
      cls_weights_out = sess.run(cls_weights)
      self.assertAllClose(cls_weights_out, exp_cls_weights)
Example #13
Source File: target_assigner_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def _get_agnostic_target_assigner(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=None)
Example #14
Source File: target_assigner_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #15
Source File: target_assigner_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #16
Source File: region_similarity_calculator_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
                            [1.0, 1.0, 0.0, 2.0]])
    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
                            [-4.0, 0.0, 0.0, 3.0],
                            [0.0, 0.0, 0.0, 0.0]])
    exp_output = [[-26, -25, 0], [-18, -27, -6]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
    with self.test_session() as sess:
      dist_output = sess.run(dist_similarity)
      self.assertAllClose(dist_output, exp_output)
Example #17
Source File: region_similarity_calculator_builder.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def build(region_similarity_calculator_config):
  """Builds region similarity calculator based on the configuration.

  Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
  core/region_similarity_calculator.proto for details.

  Args:
    region_similarity_calculator_config: RegionSimilarityCalculator
      configuration proto.

  Returns:
    region_similarity_calculator: RegionSimilarityCalculator object.

  Raises:
    ValueError: On unknown region similarity calculator.
  """

  if not isinstance(
      region_similarity_calculator_config,
      region_similarity_calculator_pb2.RegionSimilarityCalculator):
    raise ValueError(
        'region_similarity_calculator_config not of type '
        'region_similarity_calculator_pb2.RegionsSimilarityCalculator')

  similarity_calculator = region_similarity_calculator_config.WhichOneof(
      'region_similarity')
  if similarity_calculator == 'iou_similarity':
    return region_similarity_calculator.IouSimilarity()
  if similarity_calculator == 'ioa_similarity':
    return region_similarity_calculator.IoaSimilarity()
  if similarity_calculator == 'neg_sq_dist_similarity':
    return region_similarity_calculator.NegSqDistSimilarity()

  raise ValueError('Unknown region similarity calculator.')
Example #18
Source File: region_similarity_calculator_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
                            [1.0, 1.0, 0.0, 2.0]])
    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
                            [-4.0, 0.0, 0.0, 3.0],
                            [0.0, 0.0, 0.0, 0.0]])
    exp_output = [[-26, -25, 0], [-18, -27, -6]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
    with self.test_session() as sess:
      dist_output = sess.run(dist_similarity)
      self.assertAllClose(dist_output, exp_output)
Example #19
Source File: target_assigner_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #20
Source File: target_assigner_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #21
Source File: target_assigner_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_assign_multiclass_unequal_class_weights(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0, negative_class_weight=0.5,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
                               [0.5, 0.5, 1.0, 0.8],
                               [0, 0.5, .5, 1.0],
                               [.75, 0, 1.0, .25]])
    prior_stddevs = tf.constant(4 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
                                      [0, 0, 0, 0, 0, 1, 0],
                                      [0, 0, 0, 1, 0, 0, 0]], tf.float32)

    exp_cls_weights = [1, 1, .5, 1]
    result = target_assigner.assign(priors, boxes, groundtruth_labels,
                                    num_valid_rows=3)
    (_, cls_weights, _, _, _) = result
    with self.test_session() as sess:
      cls_weights_out = sess.run(cls_weights)
      self.assertAllClose(cls_weights_out, exp_cls_weights)
Example #22
Source File: target_assigner_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #23
Source File: target_assigner_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def _get_agnostic_target_assigner(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=None)
Example #24
Source File: region_similarity_calculator_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
                            [1.0, 1.0, 0.0, 2.0]])
    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
                            [-4.0, 0.0, 0.0, 3.0],
                            [0.0, 0.0, 0.0, 0.0]])
    exp_output = [[-26, -25, 0], [-18, -27, -6]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
    with self.test_session() as sess:
      dist_output = sess.run(dist_similarity)
      self.assertAllClose(dist_output, exp_output)
Example #25
Source File: target_assigner_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def test_assign_multiclass_unequal_class_weights(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0, negative_class_weight=0.5,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
                               [0.5, 0.5, 1.0, 0.8],
                               [0, 0.5, .5, 1.0],
                               [.75, 0, 1.0, .25]])
    prior_stddevs = tf.constant(4 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
                                      [0, 0, 0, 0, 0, 1, 0],
                                      [0, 0, 0, 1, 0, 0, 0]], tf.float32)

    exp_cls_weights = [1, 1, .5, 1]
    result = target_assigner.assign(priors, boxes, groundtruth_labels,
                                    num_valid_rows=3)
    (_, cls_weights, _, _, _) = result
    with self.test_session() as sess:
      cls_weights_out = sess.run(cls_weights)
      self.assertAllClose(cls_weights_out, exp_cls_weights)
Example #26
Source File: region_similarity_calculator_builder.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def build(region_similarity_calculator_config):
  """Builds region similarity calculator based on the configuration.

  Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
  core/region_similarity_calculator.proto for details.

  Args:
    region_similarity_calculator_config: RegionSimilarityCalculator
      configuration proto.

  Returns:
    region_similarity_calculator: RegionSimilarityCalculator object.

  Raises:
    ValueError: On unknown region similarity calculator.
  """

  if not isinstance(
      region_similarity_calculator_config,
      region_similarity_calculator_pb2.RegionSimilarityCalculator):
    raise ValueError(
        'region_similarity_calculator_config not of type '
        'region_similarity_calculator_pb2.RegionsSimilarityCalculator')

  similarity_calculator = region_similarity_calculator_config.WhichOneof(
      'region_similarity')
  if similarity_calculator == 'iou_similarity':
    return region_similarity_calculator.IouSimilarity()
  if similarity_calculator == 'ioa_similarity':
    return region_similarity_calculator.IoaSimilarity()
  if similarity_calculator == 'neg_sq_dist_similarity':
    return region_similarity_calculator.NegSqDistSimilarity()

  raise ValueError('Unknown region similarity calculator.')
Example #27
Source File: region_similarity_calculator_builder.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def build(region_similarity_calculator_config):
  """Builds region similarity calculator based on the configuration.

  Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
  core/region_similarity_calculator.proto for details.

  Args:
    region_similarity_calculator_config: RegionSimilarityCalculator
      configuration proto.

  Returns:
    region_similarity_calculator: RegionSimilarityCalculator object.

  Raises:
    ValueError: On unknown region similarity calculator.
  """

  if not isinstance(
      region_similarity_calculator_config,
      region_similarity_calculator_pb2.RegionSimilarityCalculator):
    raise ValueError(
        'region_similarity_calculator_config not of type '
        'region_similarity_calculator_pb2.RegionsSimilarityCalculator')

  similarity_calculator = region_similarity_calculator_config.WhichOneof(
      'region_similarity')
  if similarity_calculator == 'iou_similarity':
    return region_similarity_calculator.IouSimilarity()
  if similarity_calculator == 'ioa_similarity':
    return region_similarity_calculator.IoaSimilarity()
  if similarity_calculator == 'neg_sq_dist_similarity':
    return region_similarity_calculator.NegSqDistSimilarity()

  raise ValueError('Unknown region similarity calculator.')
Example #28
Source File: region_similarity_calculator_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
                            [1.0, 1.0, 0.0, 2.0]])
    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
                            [-4.0, 0.0, 0.0, 3.0],
                            [0.0, 0.0, 0.0, 0.0]])
    exp_output = [[-26, -25, 0], [-18, -27, -6]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
    with self.test_session() as sess:
      dist_output = sess.run(dist_similarity)
      self.assertAllClose(dist_output, exp_output)
Example #29
Source File: target_assigner_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target)
Example #30
Source File: target_assigner_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def test_assign_multiclass_unequal_class_weights(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0, negative_class_weight=0.5,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
                               [0.5, 0.5, 1.0, 0.8],
                               [0, 0.5, .5, 1.0],
                               [.75, 0, 1.0, .25]])
    prior_stddevs = tf.constant(4 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.5, 0.5, 0.9, 0.9],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
                                      [0, 0, 0, 0, 0, 1, 0],
                                      [0, 0, 0, 1, 0, 0, 0]], tf.float32)

    exp_cls_weights = [1, 1, .5, 1]
    result = target_assigner.assign(priors, boxes, groundtruth_labels,
                                    num_valid_rows=3)
    (_, cls_weights, _, _, _) = result
    with self.test_session() as sess:
      cls_weights_out = sess.run(cls_weights)
      self.assertAllClose(cls_weights_out, exp_cls_weights)