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| # Copyright 2017 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Tests for object_detection.utils.per_image_evaluation.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| from six.moves import range | |
| import tensorflow.compat.v1 as tf | |
| from object_detection.utils import per_image_evaluation | |
| class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase): | |
| def setUp(self): | |
| num_groundtruth_classes = 1 | |
| matching_iou_threshold = 0.5 | |
| nms_iou_threshold = 1.0 | |
| nms_max_output_boxes = 10000 | |
| self.eval = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, | |
| nms_max_output_boxes) | |
| self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], | |
| dtype=float) | |
| self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], | |
| [0, 0, 1, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_1 = np.array([[1, 0, 0, 0], | |
| [1, 1, 0, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_2 = np.array([[0, 0, 0, 0], | |
| [0, 1, 1, 0], | |
| [0, 1, 0, 0]], dtype=np.uint8) | |
| self.detected_masks = np.stack( | |
| [detected_masks_0, detected_masks_1, detected_masks_2], axis=0) | |
| self.groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 10, 10]], | |
| dtype=float) | |
| groundtruth_masks_0 = np.array([[1, 1, 0, 0], | |
| [1, 1, 0, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| groundtruth_masks_1 = np.array([[0, 0, 0, 1], | |
| [0, 0, 0, 1], | |
| [0, 0, 0, 1]], dtype=np.uint8) | |
| self.groundtruth_masks = np.stack( | |
| [groundtruth_masks_0, groundtruth_masks_1], axis=0) | |
| def test_match_to_gt_box_0(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array([False, True], | |
| dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, False], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.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) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_to_gt_mask_0(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array([False, True], | |
| dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, False], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.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_match_to_gt_box_1(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array([True, False], | |
| dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, False], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list) | |
| expected_scores = np.array([0.8, 0.5], dtype=float) | |
| expected_tp_fp_labels = np.array([False, False], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_to_gt_mask_1(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array([True, False], | |
| dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, False], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.groundtruth_masks) | |
| expected_scores = np.array([0.6, 0.5], dtype=float) | |
| expected_tp_fp_labels = np.array([False, False], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase): | |
| def setUp(self): | |
| num_groundtruth_classes = 1 | |
| matching_iou_threshold = 0.5 | |
| nms_iou_threshold = 1.0 | |
| nms_max_output_boxes = 10000 | |
| self.eval = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, | |
| nms_max_output_boxes) | |
| self.detected_boxes = np.array( | |
| [[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float) | |
| self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], | |
| [0, 0, 1, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_1 = np.array([[1, 0, 0, 0], | |
| [1, 1, 0, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_2 = np.array([[0, 0, 0, 0], | |
| [0, 1, 1, 0], | |
| [0, 1, 0, 0]], dtype=np.uint8) | |
| self.detected_masks = np.stack( | |
| [detected_masks_0, detected_masks_1, detected_masks_2], axis=0) | |
| self.groundtruth_boxes = np.array( | |
| [[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float) | |
| groundtruth_masks_0 = np.array([[1, 0, 0, 0], | |
| [1, 0, 0, 0], | |
| [1, 0, 0, 0]], dtype=np.uint8) | |
| groundtruth_masks_1 = np.array([[0, 0, 1, 0], | |
| [0, 0, 1, 0], | |
| [0, 0, 1, 0]], dtype=np.uint8) | |
| groundtruth_masks_2 = np.array([[0, 1, 0, 0], | |
| [0, 1, 0, 0], | |
| [0, 1, 0, 0]], dtype=np.uint8) | |
| self.groundtruth_masks = np.stack( | |
| [groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0) | |
| def test_match_to_non_group_of_and_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, True, True], dtype=bool) | |
| expected_scores = np.array([0.8], dtype=float) | |
| expected_tp_fp_labels = np.array([True], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_to_non_group_of_and_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, True, True], dtype=bool) | |
| expected_scores = np.array([0.6], dtype=float) | |
| expected_tp_fp_labels = np.array([True], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.groundtruth_masks) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_match_two_to_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [True, False, True], dtype=bool) | |
| expected_scores = np.array([0.5], dtype=float) | |
| expected_tp_fp_labels = np.array([False], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_two_to_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [True, False, True], dtype=bool) | |
| expected_scores = np.array([0.8], dtype=float) | |
| expected_tp_fp_labels = np.array([True], dtype=bool) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.groundtruth_masks) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| class SingleClassTpFpWithGroupOfBoxesTestWeighted(tf.test.TestCase): | |
| def setUp(self): | |
| num_groundtruth_classes = 1 | |
| matching_iou_threshold = 0.5 | |
| nms_iou_threshold = 1.0 | |
| nms_max_output_boxes = 10000 | |
| self.group_of_weight = 0.5 | |
| self.eval = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold, | |
| nms_max_output_boxes, self.group_of_weight) | |
| self.detected_boxes = np.array( | |
| [[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float) | |
| self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float) | |
| detected_masks_0 = np.array( | |
| [[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_1 = np.array( | |
| [[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_2 = np.array( | |
| [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], dtype=np.uint8) | |
| self.detected_masks = np.stack( | |
| [detected_masks_0, detected_masks_1, detected_masks_2], axis=0) | |
| self.groundtruth_boxes = np.array( | |
| [[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float) | |
| groundtruth_masks_0 = np.array( | |
| [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]], dtype=np.uint8) | |
| groundtruth_masks_1 = np.array( | |
| [[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]], dtype=np.uint8) | |
| groundtruth_masks_2 = np.array( | |
| [[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8) | |
| self.groundtruth_masks = np.stack( | |
| [groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0) | |
| def test_match_to_non_group_of_and_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, True, True], dtype=bool) | |
| expected_scores = np.array([0.8, 0.6], dtype=float) | |
| expected_tp_fp_labels = np.array([1.0, self.group_of_weight], dtype=float) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_to_non_group_of_and_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [False, True, True], dtype=bool) | |
| expected_scores = np.array([0.6, 0.8, 0.5], dtype=float) | |
| expected_tp_fp_labels = np.array( | |
| [1.0, self.group_of_weight, self.group_of_weight], dtype=float) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.groundtruth_masks) | |
| tf.logging.info( | |
| "test_mask_match_to_non_group_of_and_group_of_box {} {}".format( | |
| tp_fp_labels, expected_tp_fp_labels)) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_match_two_to_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [True, False, True], dtype=bool) | |
| expected_scores = np.array([0.5, 0.8], dtype=float) | |
| expected_tp_fp_labels = np.array([0.0, self.group_of_weight], dtype=float) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, self.detected_scores, self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list) | |
| tf.logging.info("test_match_two_to_group_of_box {} {}".format( | |
| tp_fp_labels, expected_tp_fp_labels)) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_match_two_to_group_of_box(self): | |
| groundtruth_groundtruth_is_difficult_list = np.array( | |
| [False, False, False], dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array( | |
| [True, False, True], dtype=bool) | |
| expected_scores = np.array([0.8, 0.6, 0.5], dtype=float) | |
| expected_tp_fp_labels = np.array( | |
| [1.0, self.group_of_weight, self.group_of_weight], dtype=float) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| self.detected_boxes, | |
| self.detected_scores, | |
| self.groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=self.detected_masks, | |
| groundtruth_masks=self.groundtruth_masks) | |
| tf.logging.info("test_mask_match_two_to_group_of_box {} {}".format( | |
| tp_fp_labels, expected_tp_fp_labels)) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase): | |
| def setUp(self): | |
| num_groundtruth_classes = 1 | |
| matching_iou_threshold_high_iou = 0.5 | |
| matching_iou_threshold_low_iou = 0.1 | |
| nms_iou_threshold = 1.0 | |
| nms_max_output_boxes = 10000 | |
| self.eval_high_iou = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold_high_iou, | |
| nms_iou_threshold, nms_max_output_boxes) | |
| self.eval_low_iou = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold_low_iou, | |
| nms_iou_threshold, nms_max_output_boxes) | |
| self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], | |
| dtype=float) | |
| self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], | |
| [0, 0, 1, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_1 = np.array([[1, 0, 0, 0], | |
| [1, 1, 0, 0], | |
| [0, 0, 0, 0]], dtype=np.uint8) | |
| detected_masks_2 = np.array([[0, 0, 0, 0], | |
| [0, 1, 1, 0], | |
| [0, 1, 0, 0]], dtype=np.uint8) | |
| self.detected_masks = np.stack( | |
| [detected_masks_0, detected_masks_1, detected_masks_2], axis=0) | |
| def test_no_true_positives(self): | |
| groundtruth_boxes = np.array([[100, 100, 105, 105]], 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_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, False, False], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_no_true_positives(self): | |
| groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float) | |
| groundtruth_masks_0 = np.array([[1, 1, 1, 1], | |
| [1, 1, 1, 1], | |
| [1, 1, 1, 1]], 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([False, 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_large_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_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) | |
| 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) | |
| 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 SingleClassTpFpEmptyMaskAndBoxesTest(tf.test.TestCase): | |
| def setUp(self): | |
| num_groundtruth_classes = 1 | |
| matching_iou_threshold_iou = 0.5 | |
| nms_iou_threshold = 1.0 | |
| nms_max_output_boxes = 10000 | |
| self.eval = per_image_evaluation.PerImageEvaluation( | |
| num_groundtruth_classes, matching_iou_threshold_iou, nms_iou_threshold, | |
| nms_max_output_boxes) | |
| def test_mask_tp_and_ignore(self): | |
| # GT: one box with mask, one without | |
| # Det: One mask matches gt1, one matches box gt2 and is ignored | |
| groundtruth_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float) | |
| groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1], | |
| axis=0) | |
| groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array([False, False], | |
| dtype=bool) | |
| detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float) | |
| detected_scores = np.array([0.6, 0.8], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks_1 = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| detected_boxes, detected_scores, groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, detected_masks, | |
| groundtruth_masks) | |
| expected_scores = np.array([0.6], dtype=float) | |
| expected_tp_fp_labels = np.array([True], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_mask_one_tp_one_fp(self): | |
| # GT: one box with mask, one without | |
| # Det: one mask matches gt1, one is fp (box does not match) | |
| groundtruth_boxes = np.array([[0, 0, 2, 3], [2, 2, 4, 4]], dtype=float) | |
| groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1], | |
| axis=0) | |
| groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array([False, False], | |
| dtype=bool) | |
| detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float) | |
| detected_scores = np.array([0.6, 0.8], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks_1 = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| detected_boxes, | |
| detected_scores, | |
| groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=detected_masks, | |
| groundtruth_masks=groundtruth_masks) | |
| expected_scores = np.array([0.8, 0.6], dtype=float) | |
| expected_tp_fp_labels = np.array([False, True], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_two_mask_one_gt_one_ignore(self): | |
| # GT: one box with mask, one without. | |
| # Det: two mask matches same gt, one is tp, one is passed down to box match | |
| # and ignored. | |
| groundtruth_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float) | |
| groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1], | |
| axis=0) | |
| groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array([False, False], | |
| dtype=bool) | |
| detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float) | |
| detected_scores = np.array([0.6, 0.8], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks_1 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| detected_boxes, | |
| detected_scores, | |
| groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=detected_masks, | |
| groundtruth_masks=groundtruth_masks) | |
| expected_scores = np.array([0.8], dtype=float) | |
| expected_tp_fp_labels = np.array([True], dtype=bool) | |
| self.assertTrue(np.allclose(expected_scores, scores)) | |
| self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) | |
| def test_two_mask_one_gt_one_fp(self): | |
| # GT: one box with mask, one without. | |
| # Det: two mask matches same gt, one is tp, one is passed down to box match | |
| # and is fp. | |
| groundtruth_boxes = np.array([[0, 0, 2, 3], [2, 3, 4, 6]], dtype=float) | |
| groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1], | |
| axis=0) | |
| groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool) | |
| groundtruth_groundtruth_is_group_of_list = np.array([False, False], | |
| dtype=bool) | |
| detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float) | |
| detected_scores = np.array([0.6, 0.8], dtype=float) | |
| detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks_1 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], | |
| dtype=np.uint8) | |
| detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0) | |
| scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class( | |
| detected_boxes, | |
| detected_scores, | |
| groundtruth_boxes, | |
| groundtruth_groundtruth_is_difficult_list, | |
| groundtruth_groundtruth_is_group_of_list, | |
| detected_masks=detected_masks, | |
| groundtruth_masks=groundtruth_masks) | |
| expected_scores = np.array([0.8, 0.6], dtype=float) | |
| expected_tp_fp_labels = np.array([True, False], 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() | |