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| # Copyright 2023 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 mask_ops.py.""" | |
| # Import libraries | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| from official.vision.ops import mask_ops | |
| class MaskUtilsTest(tf.test.TestCase): | |
| def testPasteInstanceMasks(self): | |
| image_height = 10 | |
| image_width = 10 | |
| mask_height = 6 | |
| mask_width = 6 | |
| masks = np.random.randint(0, 255, (1, mask_height, mask_width)) | |
| detected_boxes = np.array([[0.0, 2.0, mask_width, mask_height]]) | |
| _ = mask_ops.paste_instance_masks( | |
| masks, detected_boxes, image_height, image_width) | |
| def testPasteInstanceMasksV2(self): | |
| image_height = 10 | |
| image_width = 10 | |
| mask_height = 6 | |
| mask_width = 6 | |
| masks = np.random.randint(0, 255, (1, mask_height, mask_width)) | |
| detected_boxes = np.array([[0.0, 2.0, mask_width, mask_height]]) | |
| image_masks = mask_ops.paste_instance_masks_v2( | |
| masks, detected_boxes, image_height, image_width) | |
| self.assertNDArrayNear( | |
| image_masks[:, 2:8, 0:6], | |
| np.array(masks > 0.5, dtype=np.uint8), | |
| 1e-5) | |
| def testInstanceMasksOverlap(self): | |
| boxes = tf.constant([[[0, 0, 4, 4], [1, 1, 5, 5]]]) | |
| masks = tf.constant([[ | |
| [ | |
| [0.9, 0.8, 0.1, 0.2], | |
| [0.8, 0.7, 0.3, 0.2], | |
| [0.6, 0.7, 0.4, 0.3], | |
| [1.0, 0.7, 0.1, 0.0], | |
| ], | |
| [ | |
| [0.9, 0.8, 0.8, 0.7], | |
| [0.8, 0.7, 0.6, 0.8], | |
| [0.1, 0.2, 0.4, 0.3], | |
| [0.2, 0.1, 0.1, 0.0], | |
| ], | |
| ]]) | |
| gt_boxes = tf.constant([[[1, 1, 5, 5], [2, 2, 6, 6]]]) | |
| gt_masks = tf.constant([[ | |
| [ | |
| [1.0, 0.0, 0.0, 0.0], | |
| [1.0, 0.0, 0.0, 0.0], | |
| [1.0, 0.0, 0.0, 0.0], | |
| [1.0, 0.0, 0.0, 0.0], | |
| ], | |
| [ | |
| [1.0, 1.0, 1.0, 1.0], | |
| [1.0, 1.0, 1.0, 1.0], | |
| [1.0, 1.0, 1.0, 1.0], | |
| [1.0, 1.0, 1.0, 1.0], | |
| ], | |
| ]]) | |
| iou, ioa = mask_ops.instance_masks_overlap( | |
| boxes, | |
| masks, | |
| gt_boxes, | |
| gt_masks, | |
| output_size=[10, 10], | |
| ) | |
| self.assertAllClose(iou, [[[1 / 3, 0], [1 / 5, 1 / 7]]], atol=1e-4) | |
| self.assertAllClose(ioa, [[[3 / 8, 0], [1 / 4, 3 / 8]]], atol=1e-4) | |
| if __name__ == '__main__': | |
| tf.test.main() | |