Python object_detection.box_coders.mean_stddev_box_coder.MeanStddevBoxCoder() Examples

The following are 30 code examples of object_detection.box_coders.mean_stddev_box_coder.MeanStddevBoxCoder(). 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.box_coders.mean_stddev_box_coder , or try the search function .
Example #1
Source File: target_assigner_test.py    From object_detector_app 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 #2
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 #3
Source File: target_assigner_test.py    From DOTA_models 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 #4
Source File: target_assigner_test.py    From garbage-object-detection-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()
    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: 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 #7
Source File: mean_stddev_box_coder_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testGetCorrectBoxesAfterDecoding(self):
    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    decoded_boxes = coder.decode(rel_codes, priors)
    decoded_box_corners = decoded_boxes.get()
    with self.test_session() as sess:
      decoded_out = sess.run(decoded_box_corners)
      self.assertAllClose(decoded_out, expected_box_corners)
Example #8
Source File: mean_stddev_box_coder_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def testGetCorrectBoxesAfterDecoding(self):
    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    decoded_boxes = coder.decode(rel_codes, priors)
    decoded_box_corners = decoded_boxes.get()
    with self.test_session() as sess:
      decoded_out = sess.run(decoded_box_corners)
      self.assertAllClose(decoded_out, expected_box_corners)
Example #9
Source File: mean_stddev_box_coder_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def testGetCorrectRelativeCodesAfterEncoding(self):
    box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    rel_codes = coder.encode(boxes, priors)
    with self.test_session() as sess:
      rel_codes_out = sess.run(rel_codes)
      self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #10
Source File: box_coder_builder.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def build(box_coder_config):
  """Builds a box coder object based on the box coder config.

  Args:
    box_coder_config: A box_coder.proto object containing the config for the
      desired box coder.

  Returns:
    BoxCoder based on the config.

  Raises:
    ValueError: On empty box coder proto.
  """
  if not isinstance(box_coder_config, box_coder_pb2.BoxCoder):
    raise ValueError('box_coder_config not of type box_coder_pb2.BoxCoder.')

  if box_coder_config.WhichOneof('box_coder_oneof') == 'faster_rcnn_box_coder':
    return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[
        box_coder_config.faster_rcnn_box_coder.y_scale,
        box_coder_config.faster_rcnn_box_coder.x_scale,
        box_coder_config.faster_rcnn_box_coder.height_scale,
        box_coder_config.faster_rcnn_box_coder.width_scale
    ])
  if (box_coder_config.WhichOneof('box_coder_oneof') ==
      'mean_stddev_box_coder'):
    return mean_stddev_box_coder.MeanStddevBoxCoder()
  if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder':
    return square_box_coder.SquareBoxCoder(scale_factors=[
        box_coder_config.square_box_coder.y_scale,
        box_coder_config.square_box_coder.x_scale,
        box_coder_config.square_box_coder.length_scale
    ])
  raise ValueError('Empty box coder.')
Example #11
Source File: box_coder_builder_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def test_build_mean_stddev_box_coder(self):
    box_coder_text_proto = """
      mean_stddev_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(
        isinstance(box_coder_object,
                   mean_stddev_box_coder.MeanStddevBoxCoder))
Example #12
Source File: mean_stddev_box_coder_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def testGetCorrectRelativeCodesAfterEncoding(self):
    box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    priors = box_list.BoxList(prior_means)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
    rel_codes = coder.encode(boxes, priors)
    with self.test_session() as sess:
      rel_codes_out = sess.run(rel_codes)
      self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #13
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 #14
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.5)
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)
Example #15
Source File: mean_stddev_box_coder_test.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def testGetCorrectBoxesAfterDecoding(self):
    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    decoded_boxes = coder.decode(rel_codes, priors)
    decoded_box_corners = decoded_boxes.get()
    with self.test_session() as sess:
      decoded_out = sess.run(decoded_box_corners)
      self.assertAllClose(decoded_out, expected_box_corners)
Example #16
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(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,
        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]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.0, 0.0, 0.5, 0.8],
                   [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)
    with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #17
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.5)
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)
Example #18
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_assign_agnostic(self):
    def graph_fn(anchor_means, groundtruth_box_corners):
      similarity_calc = region_similarity_calculator.IouSimilarity()
      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                             unmatched_threshold=0.5)
      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
      target_assigner = targetassigner.TargetAssigner(
          similarity_calc, matcher, box_coder, unmatched_cls_target=None)
      anchors_boxlist = box_list.BoxList(anchor_means)
      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
      result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist)
      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
      return (cls_targets, cls_weights, reg_targets, reg_weights)

    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
                             [0.5, 0.5, 1.0, 0.8],
                             [0, 0.5, .5, 1.0]], dtype=np.float32)
    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
                                        [0.5, 0.5, 0.9, 0.9]],
                                       dtype=np.float32)
    exp_cls_targets = [[1], [1], [0]]
    exp_cls_weights = [1, 1, 1]
    exp_reg_targets = [[0, 0, 0, 0],
                       [0, 0, -1, 1],
                       [0, 0, 0, 0]]
    exp_reg_weights = [1, 1, 0]

    (cls_targets_out,
     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
         graph_fn, [anchor_means, groundtruth_box_corners])
    self.assertAllClose(cls_targets_out, exp_cls_targets)
    self.assertAllClose(cls_weights_out, exp_cls_weights)
    self.assertAllClose(reg_targets_out, exp_reg_targets)
    self.assertAllClose(reg_weights_out, exp_reg_weights)
    self.assertEquals(cls_targets_out.dtype, np.float32)
    self.assertEquals(cls_weights_out.dtype, np.float32)
    self.assertEquals(reg_targets_out.dtype, np.float32)
    self.assertEquals(reg_weights_out.dtype, np.float32)
Example #19
Source File: mean_stddev_box_coder_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def testGetCorrectBoxesAfterDecoding(self):
    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    priors = box_list.BoxList(prior_means)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
    decoded_boxes = coder.decode(rel_codes, priors)
    decoded_box_corners = decoded_boxes.get()
    with self.test_session() as sess:
      decoded_out = sess.run(decoded_box_corners)
      self.assertAllClose(decoded_out, expected_box_corners)
Example #20
Source File: target_assigner_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_assign_class_agnostic_with_ignored_matches(self):
    # Note: test is very similar to above. The third box matched with an IOU
    # of 0.35, which is between the matched and unmatched threshold. This means
    # That like above the expected classification targets are [1, 1, 0].
    # Unlike above, the third target is ignored and therefore expected
    # classification weights are [1, 1, 0].
    def graph_fn(anchor_means, groundtruth_box_corners):
      similarity_calc = region_similarity_calculator.IouSimilarity()
      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                             unmatched_threshold=0.3)
      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
      target_assigner = targetassigner.TargetAssigner(
          similarity_calc, matcher, box_coder, unmatched_cls_target=None)
      anchors_boxlist = box_list.BoxList(anchor_means)
      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
      result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist)
      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
      return (cls_targets, cls_weights, reg_targets, reg_weights)

    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
                             [0.5, 0.5, 1.0, 0.8],
                             [0.0, 0.5, .9, 1.0]], dtype=np.float32)
    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
                                        [0.5, 0.5, 0.9, 0.9]], dtype=np.float32)
    exp_cls_targets = [[1], [1], [0]]
    exp_cls_weights = [1, 1, 0]
    exp_reg_targets = [[0, 0, 0, 0],
                       [0, 0, -1, 1],
                       [0, 0, 0, 0]]
    exp_reg_weights = [1, 1, 0]
    (cls_targets_out,
     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
         graph_fn, [anchor_means, groundtruth_box_corners])
    self.assertAllClose(cls_targets_out, exp_cls_targets)
    self.assertAllClose(cls_weights_out, exp_cls_weights)
    self.assertAllClose(reg_targets_out, exp_reg_targets)
    self.assertAllClose(reg_weights_out, exp_reg_weights)
    self.assertEquals(cls_targets_out.dtype, np.float32)
    self.assertEquals(cls_weights_out.dtype, np.float32)
    self.assertEquals(reg_targets_out.dtype, np.float32)
    self.assertEquals(reg_weights_out.dtype, np.float32)
Example #21
Source File: box_coder_builder_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_build_mean_stddev_box_coder(self):
    box_coder_text_proto = """
      mean_stddev_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(
        isinstance(box_coder_object,
                   mean_stddev_box_coder.MeanStddevBoxCoder))
Example #22
Source File: target_assigner_test.py    From ros_people_object_detection_tensorflow 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.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.5)
    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,
        unmatched_cls_target=unmatched_cls_target)
Example #23
Source File: target_assigner_test.py    From ros_people_object_detection_tensorflow 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.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.5)
    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,
        unmatched_cls_target=unmatched_cls_target)
Example #24
Source File: target_assigner_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(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,
        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],
                   [0.0, 0.0, 0.5, 0.8],
                   [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)
    with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3)
Example #25
Source File: target_assigner_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_assign_agnostic(self):
    def graph_fn(anchor_means, anchor_stddevs, groundtruth_box_corners):
      similarity_calc = region_similarity_calculator.IouSimilarity()
      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                             unmatched_threshold=0.5)
      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
      target_assigner = targetassigner.TargetAssigner(
          similarity_calc, matcher, box_coder, unmatched_cls_target=None)
      anchors_boxlist = box_list.BoxList(anchor_means)
      anchors_boxlist.add_field('stddev', anchor_stddevs)
      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
      result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist)
      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
      return (cls_targets, cls_weights, reg_targets, reg_weights)

    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
                             [0.5, 0.5, 1.0, 0.8],
                             [0, 0.5, .5, 1.0]], dtype=np.float32)
    anchor_stddevs = np.array(3 * [4 * [.1]], dtype=np.float32)
    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
                                        [0.5, 0.5, 0.9, 0.9]],
                                       dtype=np.float32)
    exp_cls_targets = [[1], [1], [0]]
    exp_cls_weights = [1, 1, 1]
    exp_reg_targets = [[0, 0, 0, 0],
                       [0, 0, -1, 1],
                       [0, 0, 0, 0]]
    exp_reg_weights = [1, 1, 0]

    (cls_targets_out, cls_weights_out, reg_targets_out,
     reg_weights_out) = self.execute(graph_fn, [anchor_means, anchor_stddevs,
                                                groundtruth_box_corners])
    self.assertAllClose(cls_targets_out, exp_cls_targets)
    self.assertAllClose(cls_weights_out, exp_cls_weights)
    self.assertAllClose(reg_targets_out, exp_reg_targets)
    self.assertAllClose(reg_weights_out, exp_reg_weights)
    self.assertEquals(cls_targets_out.dtype, np.float32)
    self.assertEquals(cls_weights_out.dtype, np.float32)
    self.assertEquals(reg_targets_out.dtype, np.float32)
    self.assertEquals(reg_weights_out.dtype, np.float32)
Example #26
Source File: mean_stddev_box_coder_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def testGetCorrectBoxesAfterDecoding(self):
    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    decoded_boxes = coder.decode(rel_codes, priors)
    decoded_box_corners = decoded_boxes.get()
    with self.test_session() as sess:
      decoded_out = sess.run(decoded_box_corners)
      self.assertAllClose(decoded_out, expected_box_corners)
Example #27
Source File: mean_stddev_box_coder_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def testGetCorrectRelativeCodesAfterEncoding(self):
    box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
    prior_stddevs = tf.constant(2 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    rel_codes = coder.encode(boxes, priors)
    with self.test_session() as sess:
      rel_codes_out = sess.run(rel_codes)
      self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #28
Source File: box_coder_builder_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_build_mean_stddev_box_coder(self):
    box_coder_text_proto = """
      mean_stddev_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(
        isinstance(box_coder_object,
                   mean_stddev_box_coder.MeanStddevBoxCoder))
Example #29
Source File: target_assigner_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def _get_target_assigner(self):
    similarity_calc = region_similarity_calculator.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.5)
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
    return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
Example #30
Source File: target_assigner_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_class_label = tf.constant([1, 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],
                               [0.5, 0.5, 1.0, 0.8],
                               [0, 0.5, .5, 1.0],
                               [.75, 0, 1.0, .25]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.0, 0.0, 0.5, 0.8],
                   [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)
    with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
      target_assigner.assign(
          priors,
          boxes,
          groundtruth_labels,
          unmatched_class_label=unmatched_class_label)