Python region_similarity_calculator.IouSimilarity方法代码示例

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
def __init__(self):
    similarity_calc = region_similarity_calculator.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(
        matched_threshold=ssd_constants.MATCH_THRESHOLD,
        unmatched_threshold=ssd_constants.MATCH_THRESHOLD,
        negatives_lower_than_unmatched=True,
        force_match_for_each_row=True)

    box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=ssd_constants.BOX_CODER_SCALES)

    self.default_boxes = DefaultBoxes()('ltrb')
    self.default_boxes = box_list.BoxList(
        tf.convert_to_tensor(self.default_boxes))
    self.assigner = target_assigner.TargetAssigner(
        similarity_calc, matcher, box_coder) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
def test_get_correct_pairwise_similarity_based_on_iou(self):
    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
                            [0.0, 0.0, 20.0, 20.0]])
    exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]]
    boxes1 = box_list.BoxList(corners1)
    boxes2 = box_list.BoxList(corners2)
    iou_similarity_calculator = region_similarity_calculator.IouSimilarity()
    iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2)
    with self.test_session() as sess:
      iou_output = sess.run(iou_similarity)
      self.assertAllClose(iou_output, exp_output) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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.') 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
def testBuildIouSimilarityCalculator(self):
    similarity_calc_text_proto = """
      iou_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.IouSimilarity)) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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)
      anchors_boxlist = box_list.BoxList(anchor_means)
      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
      result = target_assigner.assign(
          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
      (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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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()
  if similarity_calculator == 'thresholded_iou_similarity':
    return region_similarity_calculator.ThresholdedIouSimilarity(
        region_similarity_calculator_config.thresholded_iou_similarity.threshold
    )

  raise ValueError('Unknown region similarity calculator.') 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
def _get_agnostic_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()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=None) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
def _get_agnostic_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,
        unmatched_cls_target=None) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import IouSimilarity [as 别名]
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)