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561 lines
27 KiB
561 lines
27 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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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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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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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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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],
|
|
[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()
|