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"""Tests for object_detection.utils.per_image_evaluation.""" |
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import numpy as np |
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import tensorflow as tf |
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from object_detection.utils import per_image_evaluation |
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class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase): |
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def setUp(self): |
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num_groundtruth_classes = 1 |
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matching_iou_threshold = 0.5 |
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nms_iou_threshold = 1.0 |
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nms_max_output_boxes = 10000 |
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self.eval = per_image_evaluation.PerImageEvaluation( |
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num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, |
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nms_max_output_boxes) |
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self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], |
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dtype=float) |
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self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float) |
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detected_masks_0 = np.array([[0, 1, 1, 0], |
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[0, 0, 1, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_1 = np.array([[1, 0, 0, 0], |
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[1, 1, 0, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_2 = np.array([[0, 0, 0, 0], |
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[0, 1, 1, 0], |
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[0, 1, 0, 0]], dtype=np.uint8) |
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self.detected_masks = np.stack( |
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[detected_masks_0, detected_masks_1, detected_masks_2], axis=0) |
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self.groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 10, 10]], |
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dtype=float) |
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groundtruth_masks_0 = np.array([[1, 1, 0, 0], |
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[1, 1, 0, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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groundtruth_masks_1 = np.array([[0, 0, 0, 1], |
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[0, 0, 0, 1], |
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[0, 0, 0, 1]], dtype=np.uint8) |
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self.groundtruth_masks = np.stack( |
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[groundtruth_masks_0, groundtruth_masks_1], axis=0) |
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def test_match_to_gt_box_0(self): |
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groundtruth_groundtruth_is_difficult_list = np.array([False, True], |
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dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, False], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False, True, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_to_gt_mask_0(self): |
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groundtruth_groundtruth_is_difficult_list = np.array([False, True], |
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dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, False], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([True, False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_match_to_gt_box_1(self): |
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groundtruth_groundtruth_is_difficult_list = np.array([True, False], |
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dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, False], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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expected_scores = np.array([0.8, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_to_gt_mask_1(self): |
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groundtruth_groundtruth_is_difficult_list = np.array([True, False], |
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dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, False], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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expected_scores = np.array([0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase): |
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def setUp(self): |
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num_groundtruth_classes = 1 |
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matching_iou_threshold = 0.5 |
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nms_iou_threshold = 1.0 |
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nms_max_output_boxes = 10000 |
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self.eval = per_image_evaluation.PerImageEvaluation( |
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num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, |
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nms_max_output_boxes) |
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self.detected_boxes = np.array( |
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[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float) |
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self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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detected_masks_0 = np.array([[0, 1, 1, 0], |
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[0, 0, 1, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_1 = np.array([[1, 0, 0, 0], |
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[1, 1, 0, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_2 = np.array([[0, 0, 0, 0], |
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[0, 1, 1, 0], |
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[0, 1, 0, 0]], dtype=np.uint8) |
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self.detected_masks = np.stack( |
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[detected_masks_0, detected_masks_1, detected_masks_2], axis=0) |
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self.groundtruth_boxes = np.array( |
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[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float) |
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groundtruth_masks_0 = np.array([[1, 0, 0, 0], |
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[1, 0, 0, 0], |
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[1, 0, 0, 0]], dtype=np.uint8) |
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groundtruth_masks_1 = np.array([[0, 0, 1, 0], |
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[0, 0, 1, 0], |
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[0, 0, 1, 0]], dtype=np.uint8) |
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groundtruth_masks_2 = np.array([[0, 1, 0, 0], |
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[0, 1, 0, 0], |
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[0, 1, 0, 0]], dtype=np.uint8) |
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self.groundtruth_masks = np.stack( |
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[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0) |
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def test_match_to_non_group_of_and_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, True, True], dtype=bool) |
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expected_scores = np.array([0.8], dtype=float) |
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expected_tp_fp_labels = np.array([True], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_to_non_group_of_and_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, True, True], dtype=bool) |
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expected_scores = np.array([0.6], dtype=float) |
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expected_tp_fp_labels = np.array([True], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_match_two_to_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[True, False, True], dtype=bool) |
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expected_scores = np.array([0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_two_to_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[True, False, True], dtype=bool) |
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expected_scores = np.array([0.8], dtype=float) |
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expected_tp_fp_labels = np.array([True], dtype=bool) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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class SingleClassTpFpWithGroupOfBoxesTestWeighted(tf.test.TestCase): |
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def setUp(self): |
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num_groundtruth_classes = 1 |
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matching_iou_threshold = 0.5 |
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nms_iou_threshold = 1.0 |
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nms_max_output_boxes = 10000 |
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self.group_of_weight = 0.5 |
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self.eval = per_image_evaluation.PerImageEvaluation( |
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num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, |
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nms_max_output_boxes, self.group_of_weight) |
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self.detected_boxes = np.array( |
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[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float) |
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self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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detected_masks_0 = np.array( |
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[[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_1 = np.array( |
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[[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_2 = np.array( |
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[[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], dtype=np.uint8) |
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self.detected_masks = np.stack( |
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[detected_masks_0, detected_masks_1, detected_masks_2], axis=0) |
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self.groundtruth_boxes = np.array( |
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[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float) |
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groundtruth_masks_0 = np.array( |
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[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]], dtype=np.uint8) |
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groundtruth_masks_1 = np.array( |
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[[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]], dtype=np.uint8) |
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groundtruth_masks_2 = np.array( |
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[[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8) |
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self.groundtruth_masks = np.stack( |
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[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0) |
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def test_match_to_non_group_of_and_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, True, True], dtype=bool) |
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expected_scores = np.array([0.8, 0.6], dtype=float) |
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expected_tp_fp_labels = np.array([1.0, self.group_of_weight], dtype=float) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_to_non_group_of_and_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[False, True, True], dtype=bool) |
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expected_scores = np.array([0.6, 0.8, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array( |
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[1.0, self.group_of_weight, self.group_of_weight], dtype=float) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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tf.logging.info( |
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"test_mask_match_to_non_group_of_and_group_of_box {} {}".format( |
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tp_fp_labels, expected_tp_fp_labels)) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_match_two_to_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[True, False, True], dtype=bool) |
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expected_scores = np.array([0.5, 0.8], dtype=float) |
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expected_tp_fp_labels = np.array([0.0, self.group_of_weight], dtype=float) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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tf.logging.info("test_match_two_to_group_of_box {} {}".format( |
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tp_fp_labels, expected_tp_fp_labels)) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_mask_match_two_to_group_of_box(self): |
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groundtruth_groundtruth_is_difficult_list = np.array( |
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[False, False, False], dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array( |
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[True, False, True], dtype=bool) |
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expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array( |
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[1.0, self.group_of_weight, self.group_of_weight], dtype=float) |
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scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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self.groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=self.groundtruth_masks) |
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tf.logging.info("test_mask_match_two_to_group_of_box {} {}".format( |
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tp_fp_labels, expected_tp_fp_labels)) |
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|
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase): |
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def setUp(self): |
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num_groundtruth_classes = 1 |
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matching_iou_threshold_high_iou = 0.5 |
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matching_iou_threshold_low_iou = 0.1 |
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nms_iou_threshold = 1.0 |
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nms_max_output_boxes = 10000 |
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self.eval_high_iou = per_image_evaluation.PerImageEvaluation( |
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num_groundtruth_classes, matching_iou_threshold_high_iou, |
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nms_iou_threshold, nms_max_output_boxes) |
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|
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self.eval_low_iou = per_image_evaluation.PerImageEvaluation( |
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num_groundtruth_classes, matching_iou_threshold_low_iou, |
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nms_iou_threshold, nms_max_output_boxes) |
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|
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self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], |
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dtype=float) |
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self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float) |
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detected_masks_0 = np.array([[0, 1, 1, 0], |
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[0, 0, 1, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_1 = np.array([[1, 0, 0, 0], |
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[1, 1, 0, 0], |
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[0, 0, 0, 0]], dtype=np.uint8) |
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detected_masks_2 = np.array([[0, 0, 0, 0], |
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[0, 1, 1, 0], |
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[0, 1, 0, 0]], dtype=np.uint8) |
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self.detected_masks = np.stack( |
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[detected_masks_0, detected_masks_1, detected_masks_2], axis=0) |
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|
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def test_no_true_positives(self): |
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groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float) |
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groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool) |
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scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class( |
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self.detected_boxes, self.detected_scores, groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list) |
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expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False, False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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|
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def test_mask_no_true_positives(self): |
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groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float) |
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groundtruth_masks_0 = np.array([[1, 1, 1, 1], |
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[1, 1, 1, 1], |
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[1, 1, 1, 1]], dtype=np.uint8) |
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groundtruth_masks = np.stack([groundtruth_masks_0], axis=0) |
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groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool) |
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groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool) |
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scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class( |
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self.detected_boxes, |
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self.detected_scores, |
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groundtruth_boxes, |
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groundtruth_groundtruth_is_difficult_list, |
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groundtruth_groundtruth_is_group_of_list, |
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detected_masks=self.detected_masks, |
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groundtruth_masks=groundtruth_masks) |
|
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
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expected_tp_fp_labels = np.array([False, False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
|
|
|
def test_one_true_positives_with_large_iou_threshold(self): |
|
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float) |
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groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool) |
|
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool) |
|
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class( |
|
self.detected_boxes, self.detected_scores, groundtruth_boxes, |
|
groundtruth_groundtruth_is_difficult_list, |
|
groundtruth_groundtruth_is_group_of_list) |
|
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
|
expected_tp_fp_labels = np.array([False, True, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
|
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
|
|
|
def test_mask_one_true_positives_with_large_iou_threshold(self): |
|
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float) |
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groundtruth_masks_0 = np.array([[1, 0, 0, 0], |
|
[1, 1, 0, 0], |
|
[0, 0, 0, 0]], dtype=np.uint8) |
|
groundtruth_masks = np.stack([groundtruth_masks_0], axis=0) |
|
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool) |
|
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool) |
|
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class( |
|
self.detected_boxes, |
|
self.detected_scores, |
|
groundtruth_boxes, |
|
groundtruth_groundtruth_is_difficult_list, |
|
groundtruth_groundtruth_is_group_of_list, |
|
detected_masks=self.detected_masks, |
|
groundtruth_masks=groundtruth_masks) |
|
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
|
expected_tp_fp_labels = np.array([True, False, False], dtype=bool) |
|
self.assertTrue(np.allclose(expected_scores, scores)) |
|
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
|
|
|
def test_one_true_positives_with_very_small_iou_threshold(self): |
|
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float) |
|
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool) |
|
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool) |
|
scores, tp_fp_labels = self.eval_low_iou._compute_tp_fp_for_single_class( |
|
self.detected_boxes, self.detected_scores, groundtruth_boxes, |
|
groundtruth_groundtruth_is_difficult_list, |
|
groundtruth_groundtruth_is_group_of_list) |
|
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
|
expected_tp_fp_labels = np.array([True, False, False], dtype=bool) |
|
self.assertTrue(np.allclose(expected_scores, scores)) |
|
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
|
|
|
def test_two_true_positives_with_large_iou_threshold(self): |
|
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float) |
|
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool) |
|
groundtruth_groundtruth_is_group_of_list = np.array( |
|
[False, False], dtype=bool) |
|
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class( |
|
self.detected_boxes, self.detected_scores, groundtruth_boxes, |
|
groundtruth_groundtruth_is_difficult_list, |
|
groundtruth_groundtruth_is_group_of_list) |
|
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) |
|
expected_tp_fp_labels = np.array([False, True, True], dtype=bool) |
|
self.assertTrue(np.allclose(expected_scores, scores)) |
|
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
|
|
|
|
|
class MultiClassesTpFpTest(tf.test.TestCase): |
|
|
|
def test_tp_fp(self): |
|
num_groundtruth_classes = 3 |
|
matching_iou_threshold = 0.5 |
|
nms_iou_threshold = 1.0 |
|
nms_max_output_boxes = 10000 |
|
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes, |
|
matching_iou_threshold, |
|
nms_iou_threshold, |
|
nms_max_output_boxes) |
|
detected_boxes = np.array([[0, 0, 1, 1], [10, 10, 5, 5], [0, 0, 2, 2], |
|
[5, 10, 10, 5], [10, 5, 5, 10], [0, 0, 3, 3]], |
|
dtype=float) |
|
detected_scores = np.array([0.8, 0.1, 0.8, 0.9, 0.7, 0.8], dtype=float) |
|
detected_class_labels = np.array([0, 1, 1, 2, 0, 2], dtype=int) |
|
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float) |
|
groundtruth_class_labels = np.array([0, 2], dtype=int) |
|
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=float) |
|
groundtruth_groundtruth_is_group_of_list = np.array( |
|
[False, False], dtype=bool) |
|
scores, tp_fp_labels, _ = eval1.compute_object_detection_metrics( |
|
detected_boxes, detected_scores, detected_class_labels, |
|
groundtruth_boxes, groundtruth_class_labels, |
|
groundtruth_groundtruth_is_difficult_list, |
|
groundtruth_groundtruth_is_group_of_list) |
|
expected_scores = [np.array([0.8], dtype=float)] * 3 |
|
expected_tp_fp_labels = [np.array([True]), np.array([False]), np.array([True |
|
])] |
|
for i in range(len(expected_scores)): |
|
self.assertTrue(np.allclose(expected_scores[i], scores[i])) |
|
self.assertTrue(np.array_equal(expected_tp_fp_labels[i], tp_fp_labels[i])) |
|
|
|
|
|
class CorLocTest(tf.test.TestCase): |
|
|
|
def test_compute_corloc_with_normal_iou_threshold(self): |
|
num_groundtruth_classes = 3 |
|
matching_iou_threshold = 0.5 |
|
nms_iou_threshold = 1.0 |
|
nms_max_output_boxes = 10000 |
|
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes, |
|
matching_iou_threshold, |
|
nms_iou_threshold, |
|
nms_max_output_boxes) |
|
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3], |
|
[0, 0, 5, 5]], dtype=float) |
|
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float) |
|
detected_class_labels = np.array([0, 1, 0, 2], dtype=int) |
|
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]], |
|
dtype=float) |
|
groundtruth_class_labels = np.array([0, 0, 2], dtype=int) |
|
|
|
is_class_correctly_detected_in_image = eval1._compute_cor_loc( |
|
detected_boxes, detected_scores, detected_class_labels, |
|
groundtruth_boxes, groundtruth_class_labels) |
|
expected_result = np.array([1, 0, 1], dtype=int) |
|
self.assertTrue(np.array_equal(expected_result, |
|
is_class_correctly_detected_in_image)) |
|
|
|
def test_compute_corloc_with_very_large_iou_threshold(self): |
|
num_groundtruth_classes = 3 |
|
matching_iou_threshold = 0.9 |
|
nms_iou_threshold = 1.0 |
|
nms_max_output_boxes = 10000 |
|
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes, |
|
matching_iou_threshold, |
|
nms_iou_threshold, |
|
nms_max_output_boxes) |
|
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3], |
|
[0, 0, 5, 5]], dtype=float) |
|
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float) |
|
detected_class_labels = np.array([0, 1, 0, 2], dtype=int) |
|
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]], |
|
dtype=float) |
|
groundtruth_class_labels = np.array([0, 0, 2], dtype=int) |
|
|
|
is_class_correctly_detected_in_image = eval1._compute_cor_loc( |
|
detected_boxes, detected_scores, detected_class_labels, |
|
groundtruth_boxes, groundtruth_class_labels) |
|
expected_result = np.array([1, 0, 0], dtype=int) |
|
self.assertTrue(np.array_equal(expected_result, |
|
is_class_correctly_detected_in_image)) |
|
|
|
|
|
if __name__ == "__main__": |
|
tf.test.main() |
|
|