Python object_detection.utils.object_detection_evaluation.ObjectDetectionEvaluation() Examples

The following are 30 code examples of object_detection.utils.object_detection_evaluation.ObjectDetectionEvaluation(). 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.object_detection_evaluation , or try the search function .
Example #1
Source File: object_detection_evaluation_test.py    From open-solution-googleai-object-detection with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #2
Source File: object_detection_evaluation_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_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #3
Source File: object_detection_evaluation_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = "img1"
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = "img2"
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2)
    image_key3 = "img3"
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = "img2"
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #4
Source File: object_detection_evaluation_test.py    From MBMD with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = "img1"
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = "img2"
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2)
    image_key3 = "img3"
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = "img2"
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #5
Source File: object_detection_evaluation_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #6
Source File: object_detection_evaluation_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #7
Source File: object_detection_evaluation_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #8
Source File: object_detection_evaluation_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #9
Source File: object_detection_evaluation_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #10
Source File: object_detection_evaluation_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #11
Source File: object_detection_evaluation_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #12
Source File: object_detection_evaluation_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #13
Source File: object_detection_evaluation_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #14
Source File: object_detection_evaluation_test.py    From open-solution-googleai-object-detection with MIT License 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #15
Source File: object_detection_evaluation_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #16
Source File: object_detection_evaluation_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #17
Source File: object_detection_evaluation_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #18
Source File: object_detection_evaluation_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #19
Source File: object_detection_evaluation_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def test_merge_internal_state(self):
    # Test that if initial state is merged, the results of the evaluation are
    # the same.
    od_eval_state = self.od_eval.get_internal_state()
    copy_od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        self.od_eval.num_class)
    copy_od_eval.merge_internal_state(od_eval_state)

    (average_precision_per_class, mean_ap, precisions_per_class,
     recalls_per_class, corloc_per_class,
     mean_corloc) = self.od_eval.evaluate()

    (copy_average_precision_per_class, copy_mean_ap, copy_precisions_per_class,
     copy_recalls_per_class, copy_corloc_per_class,
     copy_mean_corloc) = copy_od_eval.evaluate()

    for i in range(self.od_eval.num_class):
      self.assertTrue(
          np.allclose(copy_precisions_per_class[i], precisions_per_class[i]))
      self.assertTrue(
          np.allclose(copy_recalls_per_class[i], recalls_per_class[i]))
    self.assertTrue(
        np.allclose(copy_average_precision_per_class,
                    average_precision_per_class))
    self.assertTrue(np.allclose(copy_corloc_per_class, corloc_per_class))
    self.assertAlmostEqual(copy_mean_ap, mean_ap)
    self.assertAlmostEqual(copy_mean_corloc, mean_corloc)
Example #20
Source File: object_detection_evaluation_test.py    From motion-rcnn with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = "img1"
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = "img2"
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2)
    image_key3 = "img3"
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = "img2"
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #21
Source File: object_detection_evaluation_test.py    From mtl-ssl with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = "img1"
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = "img2"
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    subset_list2 = np.array(['default', '', 'default'])
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        subset_list2)
    image_key3 = "img3"
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = "img2"
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #22
Source File: object_detection_evaluation_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #23
Source File: object_detection_evaluation_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #24
Source File: object_detection_evaluation_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #25
Source File: object_detection_evaluation_test.py    From object_detector_app with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = "img1"
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = "img2"
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2)
    image_key3 = "img3"
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = "img2"
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #26
Source File: object_detection_evaluation_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #27
Source File: object_detection_evaluation_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #28
Source File: object_detection_evaluation_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)
Example #29
Source File: object_detection_evaluation_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_value_error_on_zero_classes(self):
    with self.assertRaises(ValueError):
      object_detection_evaluation.ObjectDetectionEvaluation(
          num_groundtruth_classes=0)
Example #30
Source File: object_detection_evaluation_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def setUp(self):
    num_groundtruth_classes = 3
    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
        num_groundtruth_classes)

    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
    image_key2 = 'img2'
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
    self.od_eval.add_single_ground_truth_image_info(
        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)

    image_key3 = 'img3'
    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
    groundtruth_class_labels3 = np.array([1], dtype=int)
    self.od_eval.add_single_ground_truth_image_info(
        image_key3, groundtruth_boxes3, groundtruth_class_labels3)

    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([0, 0, 2], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.od_eval.add_single_detected_image_info(
        image_key, detected_boxes, detected_scores, detected_class_labels)