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| # Lint as: python2, python3 | |
| # Copyright 2019 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 Parsing Covering metric.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from absl.testing import absltest | |
| import numpy as np | |
| from deeplab.evaluation import parsing_covering | |
| from deeplab.evaluation import test_utils | |
| # See the definition of the color names at: | |
| # https://en.wikipedia.org/wiki/Web_colors. | |
| _CLASS_COLOR_MAP = { | |
| (0, 0, 0): 0, | |
| (0, 0, 255): 1, # Person (blue). | |
| (255, 0, 0): 2, # Bear (red). | |
| (0, 255, 0): 3, # Tree (lime). | |
| (255, 0, 255): 4, # Bird (fuchsia). | |
| (0, 255, 255): 5, # Sky (aqua). | |
| (255, 255, 0): 6, # Cat (yellow). | |
| } | |
| class CoveringConveringTest(absltest.TestCase): | |
| def test_perfect_match(self): | |
| categories = np.zeros([6, 6], np.uint16) | |
| instances = np.array([ | |
| [2, 2, 2, 2, 2, 2], | |
| [2, 4, 4, 4, 4, 2], | |
| [2, 4, 4, 4, 4, 2], | |
| [2, 4, 4, 4, 4, 2], | |
| [2, 4, 4, 2, 2, 2], | |
| [2, 4, 2, 2, 2, 2], | |
| ], | |
| dtype=np.uint16) | |
| pc = parsing_covering.ParsingCovering( | |
| num_categories=3, | |
| ignored_label=2, | |
| max_instances_per_category=2, | |
| offset=16, | |
| normalize_by_image_size=False) | |
| pc.compare_and_accumulate(categories, instances, categories, instances) | |
| np.testing.assert_array_equal(pc.weighted_iou_per_class, [0.0, 21.0, 0.0]) | |
| np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 21.0, 0.0]) | |
| np.testing.assert_array_equal(pc.result_per_category(), [0.0, 1.0, 0.0]) | |
| self.assertEqual(pc.result(), 1.0) | |
| def test_totally_wrong(self): | |
| categories = np.zeros([6, 6], np.uint16) | |
| gt_instances = np.array([ | |
| [0, 0, 0, 0, 0, 0], | |
| [0, 1, 0, 0, 1, 0], | |
| [0, 1, 1, 1, 1, 0], | |
| [0, 1, 1, 1, 1, 0], | |
| [0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0], | |
| ], | |
| dtype=np.uint16) | |
| pred_instances = 1 - gt_instances | |
| pc = parsing_covering.ParsingCovering( | |
| num_categories=2, | |
| ignored_label=0, | |
| max_instances_per_category=1, | |
| offset=16, | |
| normalize_by_image_size=False) | |
| pc.compare_and_accumulate(categories, gt_instances, categories, | |
| pred_instances) | |
| np.testing.assert_array_equal(pc.weighted_iou_per_class, [0.0, 0.0]) | |
| np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 10.0]) | |
| np.testing.assert_array_equal(pc.result_per_category(), [0.0, 0.0]) | |
| self.assertEqual(pc.result(), 0.0) | |
| def test_matches_expected(self): | |
| pred_classes = test_utils.read_segmentation_with_rgb_color_map( | |
| 'team_pred_class.png', _CLASS_COLOR_MAP) | |
| pred_instances = test_utils.read_test_image( | |
| 'team_pred_instance.png', mode='L') | |
| instance_class_map = { | |
| 0: 0, | |
| 47: 1, | |
| 97: 1, | |
| 133: 1, | |
| 150: 1, | |
| 174: 1, | |
| 198: 2, | |
| 215: 1, | |
| 244: 1, | |
| 255: 1, | |
| } | |
| gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map( | |
| 'team_gt_instance.png', instance_class_map) | |
| pc = parsing_covering.ParsingCovering( | |
| num_categories=3, | |
| ignored_label=0, | |
| max_instances_per_category=256, | |
| offset=256 * 256, | |
| normalize_by_image_size=False) | |
| pc.compare_and_accumulate(gt_classes, gt_instances, pred_classes, | |
| pred_instances) | |
| np.testing.assert_array_almost_equal( | |
| pc.weighted_iou_per_class, [0.0, 39864.14634, 3136], decimal=4) | |
| np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 56870, 5800]) | |
| np.testing.assert_array_almost_equal( | |
| pc.result_per_category(), [0.0, 0.70097, 0.54069], decimal=4) | |
| self.assertAlmostEqual(pc.result(), 0.6208296732) | |
| def test_matches_expected_normalize_by_size(self): | |
| pred_classes = test_utils.read_segmentation_with_rgb_color_map( | |
| 'team_pred_class.png', _CLASS_COLOR_MAP) | |
| pred_instances = test_utils.read_test_image( | |
| 'team_pred_instance.png', mode='L') | |
| instance_class_map = { | |
| 0: 0, | |
| 47: 1, | |
| 97: 1, | |
| 133: 1, | |
| 150: 1, | |
| 174: 1, | |
| 198: 2, | |
| 215: 1, | |
| 244: 1, | |
| 255: 1, | |
| } | |
| gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map( | |
| 'team_gt_instance.png', instance_class_map) | |
| pc = parsing_covering.ParsingCovering( | |
| num_categories=3, | |
| ignored_label=0, | |
| max_instances_per_category=256, | |
| offset=256 * 256, | |
| normalize_by_image_size=True) | |
| pc.compare_and_accumulate(gt_classes, gt_instances, pred_classes, | |
| pred_instances) | |
| np.testing.assert_array_almost_equal( | |
| pc.weighted_iou_per_class, [0.0, 0.5002088756, 0.03935002196], | |
| decimal=4) | |
| np.testing.assert_array_almost_equal( | |
| pc.gt_area_per_class, [0.0, 0.7135955832, 0.07277746408], decimal=4) | |
| # Note that the per-category and overall PCs are identical to those without | |
| # normalization in the previous test, because we only have a single image. | |
| np.testing.assert_array_almost_equal( | |
| pc.result_per_category(), [0.0, 0.70097, 0.54069], decimal=4) | |
| self.assertAlmostEqual(pc.result(), 0.6208296732) | |
| if __name__ == '__main__': | |
| absltest.main() | |