Python object_detection.utils.np_box_list_ops.multi_class_non_max_suppression() Examples

The following are 30 code examples of object_detection.utils.np_box_list_ops.multi_class_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 hands-detection with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #2
Source File: np_box_list_ops_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #3
Source File: np_box_list_ops_test.py    From mtl-ssl with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #4
Source File: np_box_list_ops_test.py    From motion-rcnn with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #5
Source File: np_box_list_ops_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #6
Source File: np_box_list_ops_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #7
Source File: np_box_list_ops_test.py    From open-solution-googleai-object-detection with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #8
Source File: np_box_list_ops_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #9
Source File: np_box_list_ops_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #10
Source File: np_box_list_ops_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #11
Source File: np_box_list_ops_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #12
Source File: np_box_list_ops_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #13
Source File: np_box_list_ops_test.py    From MBMD with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #14
Source File: np_box_list_ops_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #15
Source File: np_box_list_ops_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #16
Source File: np_box_list_ops_test.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #17
Source File: np_box_list_ops_test.py    From moveo_ros with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #18
Source File: np_box_list_ops_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #19
Source File: np_box_list_ops_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #20
Source File: np_box_list_ops_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #21
Source File: np_box_list_ops_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #22
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_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #23
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_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #24
Source File: np_box_list_ops_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, 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_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
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_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #27
Source File: np_box_list_ops_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #28
Source File: np_box_list_ops_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #29
Source File: np_box_list_ops_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)
Example #30
Source File: np_box_list_ops_test.py    From object_detector_app with MIT License 5 votes vote down vote up 
def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes)