# 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.""" import numpy as np import tensorflow 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 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()