Python object_detection.utils.np_box_list_ops.non_max_suppression() Examples

The following are 30 code examples of object_detection.utils.np_box_list_ops.non_max_suppression(). 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.utils.np_box_list_ops , or try the search function .
Example #1
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #2
Source File: np_box_list_ops_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
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)
Example #3
Source File: np_box_list_ops_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
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)
Example #4
Source File: np_box_list_ops_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
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)
Example #5
Source File: np_box_list_ops_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
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)
Example #6
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #7
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #8
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #9
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #10
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #11
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
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)
Example #12
Source File: np_box_list_ops_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
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)
Example #13
Source File: per_image_evaluation.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
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
Example #14
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #15
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #16
Source File: np_box_list_ops_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
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)
Example #17
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #18
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #19
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #20
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #21
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #22
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #23
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #24
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #25
Source File: np_box_list_ops_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
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)
Example #26
Source File: np_box_list_ops_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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)
Example #27
Source File: np_box_list_ops_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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)
Example #28
Source File: np_box_list_ops_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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)
Example #29
Source File: np_box_list_ops_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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)
Example #30
Source File: np_box_list_ops_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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)