Python np_box_list_ops.non_max_suppression方法代码示例

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_with_no_scores_field(self):
    boxlist = np_box_list.BoxList(self._boxes)
    max_output_size = 3
    iou_threshold = 0.5

    with self.assertRaises(ValueError):
      np_box_list_ops.non_max_suppression(
          boxlist, max_output_size, iou_threshold) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_nms_disabled_max_output_size_equals_three(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
    max_output_size = 3
    iou_threshold = 1.  # No NMS

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_select_from_three_clusters(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
    max_output_size = 3
    iou_threshold = 0.5

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_select_at_most_two_from_three_clusters(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .5, .3], dtype=float))
    max_output_size = 2
    iou_threshold = 0.5

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_select_from_ten_indentical_boxes(self):
    boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float)
    boxlist = np_box_list.BoxList(boxes)
    boxlist.add_field('scores', np.array(10 * [0.8]))
    iou_threshold = .5
    max_output_size = 3
    expected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_different_iou_threshold(self):
    boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300],
                      [200, 200, 210, 250]],
                     dtype=float)
    boxlist = np_box_list.BoxList(boxes)
    boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6]))
    max_output_size = 4

    iou_threshold = .4
    expected_boxes = np.array([[0, 0, 20, 100],
                               [200, 200, 210, 300],],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes)

    iou_threshold = .5
    expected_boxes = np.array([[0, 0, 20, 100], [200, 200, 210, 300],
                               [200, 200, 210, 250]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes)

    iou_threshold = .8
    expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
                               [200, 200, 210, 300], [200, 200, 210, 250]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def _get_overlaps_and_scores_box_mode(
      self,
      detected_boxes,
      detected_scores,
      groundtruth_boxes,
      groundtruth_is_group_of_list):
    """Computes overlaps and scores between detected and groudntruth boxes.

    Args:
      detected_boxes: A numpy array of shape [N, 4] representing detected box
          coordinates
      detected_scores: A 1-d numpy array of length N representing classification
          score
      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
          box coordinates
      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
          whether a ground truth box has group-of tag. If a groundtruth box
          is group-of box, every detection matching this box is ignored.

    Returns:
      iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
      ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_group_of_boxlist.num_boxes() == 0 it will be None.
      scores: The score of the detected boxlist.
      num_boxes: Number of non-maximum suppressed detected boxes.
    """
    detected_boxlist = np_box_list.BoxList(detected_boxes)
    detected_boxlist.add_field('scores', detected_scores)
    detected_boxlist = np_box_list_ops.non_max_suppression(
        detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
    gt_non_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[~groundtruth_is_group_of_list])
    gt_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[groundtruth_is_group_of_list])
    iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
    ioa = np.transpose(
        np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist))
    scores = detected_boxlist.get_field('scores')
    num_boxes = detected_boxlist.num_boxes()
    return iou, ioa, scores, num_boxes 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def test_select_at_most_thirty_from_three_clusters(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .5, .3], dtype=float))
    max_output_size = 30
    iou_threshold = 0.5

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import non_max_suppression [as 别名]
def _get_overlaps_and_scores_box_mode(
      self,
      detected_boxes,
      detected_scores,
      groundtruth_boxes,
      groundtruth_is_group_of_list):
    """Computes overlaps and scores between detected and groudntruth boxes.

    Args:
      detected_boxes: A numpy array of shape [N, 4] representing detected box
          coordinates
      detected_scores: A 1-d numpy array of length N representing classification
          score
      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
          box coordinates
      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
          whether a ground truth box has group-of tag. If a groundtruth box
          is group-of box, every detection matching this box is ignored.

    Returns:
      iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
      ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_group_of_boxlist.num_boxes() == 0 it will be None.
      scores: The score of the detected boxlist.
      num_boxes: Number of non-maximum suppressed detected boxes.
    """
    detected_boxlist = np_box_list.BoxList(detected_boxes)
    detected_boxlist.add_field('scores', detected_scores)
    detected_boxlist = np_box_list_ops.non_max_suppression(
        detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
    gt_non_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[~groundtruth_is_group_of_list])
    gt_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[groundtruth_is_group_of_list])
    iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
    ioa = np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist)
    scores = detected_boxlist.get_field('scores')
    num_boxes = detected_boxlist.num_boxes()
    return iou, ioa, scores, num_boxes