def setUp(self):
self.vrd_eval = vrd_evaluation._VRDDetectionEvaluation(
matching_iou_threshold=0.5)
image_key1 = 'img1'
groundtruth_box_tuples1 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 2, 2, 3])],
dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples1 = np.array(
[(1, 2, 3), (1, 4, 3)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key1, groundtruth_box_tuples1, groundtruth_class_tuples1)
image_key2 = 'img2'
groundtruth_box_tuples2 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples2 = np.array(
[(1, 4, 3)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key2, groundtruth_box_tuples2, groundtruth_class_tuples2)
image_key3 = 'img3'
groundtruth_box_tuples3 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples3 = np.array(
[(1, 2, 4)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key3, groundtruth_box_tuples3, groundtruth_class_tuples3)
image_key = 'img1'
detected_box_tuples = np.array(
[([0, 0.3, 1, 1], [1.1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2])],
dtype=vrd_evaluation.vrd_box_data_type)
detected_class_tuples = np.array(
[(1, 2, 3), (1, 2, 3)], dtype=vrd_evaluation.label_data_type)
detected_scores = np.array([0.7, 0.8], dtype=float)
self.vrd_eval.add_single_detected_image_info(
image_key, detected_box_tuples, detected_scores, detected_class_tuples)
metrics = self.vrd_eval.evaluate()
expected_weighted_average_precision = 0.25
expected_mean_average_precision = 0.16666666666666
expected_precision = np.array([1., 0.5], dtype=float)
expected_recall = np.array([0.25, 0.25], dtype=float)
expected_recall_50 = 0.25
expected_recall_100 = 0.25
expected_median_rank_50 = 0
expected_median_rank_100 = 0
self.assertAlmostEqual(expected_weighted_average_precision,
metrics.weighted_average_precision)
self.assertAlmostEqual(expected_mean_average_precision,
metrics.mean_average_precision)
self.assertAlmostEqual(expected_mean_average_precision,
metrics.mean_average_precision)
self.assertAllClose(expected_precision, metrics.precisions)
self.assertAllClose(expected_recall, metrics.recalls)
self.assertAlmostEqual(expected_recall_50, metrics.recall_50)
self.assertAlmostEqual(expected_recall_100, metrics.recall_100)
self.assertAlmostEqual(expected_median_rank_50, metrics.median_rank_50)
self.assertAlmostEqual(expected_median_rank_100, metrics.median_rank_100)
def setUp(self):
self.vrd_eval = vrd_evaluation._VRDDetectionEvaluation(
matching_iou_threshold=0.5)
image_key1 = 'img1'
groundtruth_box_tuples1 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 2, 2, 3])],
dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples1 = np.array(
[(1, 2, 3), (1, 4, 3)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key1, groundtruth_box_tuples1, groundtruth_class_tuples1)
image_key2 = 'img2'
groundtruth_box_tuples2 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples2 = np.array(
[(1, 4, 3)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key2, groundtruth_box_tuples2, groundtruth_class_tuples2)
image_key3 = 'img3'
groundtruth_box_tuples3 = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
groundtruth_class_tuples3 = np.array(
[(1, 2, 4)], dtype=vrd_evaluation.label_data_type)
self.vrd_eval.add_single_ground_truth_image_info(
image_key3, groundtruth_box_tuples3, groundtruth_class_tuples3)
image_key = 'img1'
detected_box_tuples = np.array(
[([0, 0.3, 1, 1], [1.1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2])],
dtype=vrd_evaluation.vrd_box_data_type)
detected_class_tuples = np.array(
[(1, 2, 3), (1, 2, 3)], dtype=vrd_evaluation.label_data_type)
detected_scores = np.array([0.7, 0.8], dtype=float)
self.vrd_eval.add_single_detected_image_info(
image_key, detected_box_tuples, detected_scores, detected_class_tuples)
metrics = self.vrd_eval.evaluate()
expected_weighted_average_precision = 0.25
expected_mean_average_precision = 0.16666666666666
expected_precision = np.array([1., 0.5], dtype=float)
expected_recall = np.array([0.25, 0.25], dtype=float)
expected_recall_50 = 0.25
expected_recall_100 = 0.25
expected_median_rank_50 = 0
expected_median_rank_100 = 0
self.assertAlmostEqual(expected_weighted_average_precision,
metrics.weighted_average_precision)
self.assertAlmostEqual(expected_mean_average_precision,
metrics.mean_average_precision)
self.assertAlmostEqual(expected_mean_average_precision,
metrics.mean_average_precision)
self.assertAllClose(expected_precision, metrics.precisions)
self.assertAllClose(expected_recall, metrics.recalls)
self.assertAlmostEqual(expected_recall_50, metrics.recall_50)
self.assertAlmostEqual(expected_recall_100, metrics.recall_100)
self.assertAlmostEqual(expected_median_rank_50, metrics.median_rank_50)
self.assertAlmostEqual(expected_median_rank_100, metrics.median_rank_100)