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# coding=utf-8 | |
# Copyright 2021 The Deeplab2 Authors. | |
# | |
# 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 coco_tools.""" | |
from absl.testing import absltest | |
import numpy as np | |
from pycocotools import mask | |
from deeplab2.utils import coco_tools | |
class CocoToolsTest(absltest.TestCase): | |
def testSingleImageDetectionMaskExport(self): | |
masks = np.array( | |
[[[1, 1,], [1, 1]], | |
[[0, 0], [0, 1]], | |
[[0, 0], [0, 0]]], dtype=np.uint8) | |
classes = np.array([1, 2, 3], dtype=np.int32) | |
scores = np.array([0.8, 0.2, 0.7], dtype=np.float32) | |
coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco( | |
image_id='first_image', | |
category_id_set=set([1, 2, 3]), | |
detection_classes=classes, | |
detection_scores=scores, | |
detection_masks=masks) | |
expected_counts = ['04', '31', '4'] | |
for i, mask_annotation in enumerate(coco_annotations): | |
self.assertEqual(mask_annotation['segmentation']['counts'], | |
expected_counts[i]) | |
self.assertTrue(np.all(np.equal(mask.decode( | |
mask_annotation['segmentation']), masks[i]))) | |
self.assertEqual(mask_annotation['image_id'], 'first_image') | |
self.assertEqual(mask_annotation['category_id'], classes[i]) | |
self.assertAlmostEqual(mask_annotation['score'], scores[i]) | |
def testSingleImageGroundtruthExport(self): | |
masks = np.array( | |
[[[1, 1,], [1, 1]], | |
[[0, 0], [0, 1]], | |
[[0, 0], [0, 0]]], dtype=np.uint8) | |
boxes = np.array([[0, 0, 1, 1], | |
[0, 0, .5, .5], | |
[.5, .5, 1, 1]], dtype=np.float32) | |
coco_boxes = np.array([[0, 0, 1, 1], | |
[0, 0, .5, .5], | |
[.5, .5, .5, .5]], dtype=np.float32) | |
classes = np.array([1, 2, 3], dtype=np.int32) | |
is_crowd = np.array([0, 1, 0], dtype=np.int32) | |
next_annotation_id = 1 | |
expected_counts = ['04', '31', '4'] | |
# Tests exporting without passing in is_crowd (for backward compatibility). | |
coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco( | |
image_id='first_image', | |
category_id_set=set([1, 2, 3]), | |
next_annotation_id=next_annotation_id, | |
groundtruth_boxes=boxes, | |
groundtruth_classes=classes, | |
groundtruth_masks=masks) | |
for i, annotation in enumerate(coco_annotations): | |
self.assertEqual(annotation['segmentation']['counts'], | |
expected_counts[i]) | |
self.assertTrue(np.all(np.equal(mask.decode( | |
annotation['segmentation']), masks[i]))) | |
self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i]))) | |
self.assertEqual(annotation['image_id'], 'first_image') | |
self.assertEqual(annotation['category_id'], classes[i]) | |
self.assertEqual(annotation['id'], i + next_annotation_id) | |
# Tests exporting with is_crowd. | |
coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco( | |
image_id='first_image', | |
category_id_set=set([1, 2, 3]), | |
next_annotation_id=next_annotation_id, | |
groundtruth_boxes=boxes, | |
groundtruth_classes=classes, | |
groundtruth_masks=masks, | |
groundtruth_is_crowd=is_crowd) | |
for i, annotation in enumerate(coco_annotations): | |
self.assertEqual(annotation['segmentation']['counts'], | |
expected_counts[i]) | |
self.assertTrue(np.all(np.equal(mask.decode( | |
annotation['segmentation']), masks[i]))) | |
self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i]))) | |
self.assertEqual(annotation['image_id'], 'first_image') | |
self.assertEqual(annotation['category_id'], classes[i]) | |
self.assertEqual(annotation['iscrowd'], is_crowd[i]) | |
self.assertEqual(annotation['id'], i + next_annotation_id) | |
if __name__ == '__main__': | |
absltest.main() | |