| from functools import partial |
| import numpy as np |
| import os.path as osp |
|
|
| from unittest import TestCase |
| from datumaro.components.project import Dataset |
| from datumaro.components.extractor import (DatasetItem, |
| AnnotationType, Points, Polygon, PolyLine, Bbox, Label, |
| LabelCategories, |
| ) |
| from datumaro.plugins.cvat_format.extractor import CvatImporter |
| from datumaro.plugins.cvat_format.converter import CvatConverter |
| from datumaro.util.image import Image |
| from datumaro.util.test_utils import (TestDir, compare_datasets, |
| test_save_and_load) |
|
|
|
|
| DUMMY_IMAGE_DATASET_DIR = osp.join(osp.dirname(__file__), |
| 'assets', 'cvat_dataset', 'for_images') |
|
|
| DUMMY_VIDEO_DATASET_DIR = osp.join(osp.dirname(__file__), |
| 'assets', 'cvat_dataset', 'for_video') |
|
|
| class CvatImporterTest(TestCase): |
| def test_can_detect_image(self): |
| self.assertTrue(CvatImporter.detect(DUMMY_IMAGE_DATASET_DIR)) |
|
|
| def test_can_detect_video(self): |
| self.assertTrue(CvatImporter.detect(DUMMY_VIDEO_DATASET_DIR)) |
|
|
| def test_can_load_image(self): |
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id='img0', subset='train', |
| image=np.ones((8, 8, 3)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=0, z_order=1, |
| attributes={ |
| 'occluded': True, |
| 'a1': True, 'a2': 'v3' |
| }), |
| PolyLine([1, 2, 3, 4, 5, 6, 7, 8], |
| attributes={'occluded': False}), |
| ], attributes={'frame': 0}), |
| DatasetItem(id='img1', subset='train', |
| image=np.ones((10, 10, 3)), |
| annotations=[ |
| Polygon([1, 2, 3, 4, 6, 5], z_order=1, |
| attributes={'occluded': False}), |
| Points([1, 2, 3, 4, 5, 6], label=1, z_order=2, |
| attributes={'occluded': False}), |
| ], attributes={'frame': 1}), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable([ |
| ['label1', '', {'a1', 'a2'}], |
| ['label2'], |
| ]) |
| }) |
|
|
| parsed_dataset = CvatImporter()(DUMMY_IMAGE_DATASET_DIR).make_dataset() |
|
|
| compare_datasets(self, expected_dataset, parsed_dataset) |
|
|
| def test_can_load_video(self): |
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id='frame_000010', subset='annotations', |
| image=255 * np.ones((20, 25, 3)), |
| annotations=[ |
| Bbox(3, 4, 7, 1, label=2, |
| id=0, |
| attributes={ |
| 'occluded': True, |
| 'outside': False, 'keyframe': True, |
| 'track_id': 0 |
| }), |
| Points([21.95, 8.00, 2.55, 15.09, 2.23, 3.16], |
| label=0, |
| id=1, |
| attributes={ |
| 'occluded': False, |
| 'outside': False, 'keyframe': True, |
| 'track_id': 1, 'hgl': 'hgkf', |
| }), |
| ], attributes={'frame': 10}), |
| DatasetItem(id='frame_000013', subset='annotations', |
| image=255 * np.ones((20, 25, 3)), |
| annotations=[ |
| Bbox(7, 6, 7, 2, label=2, |
| id=0, |
| attributes={ |
| 'occluded': False, |
| 'outside': True, 'keyframe': True, |
| 'track_id': 0 |
| }), |
| Points([21.95, 8.00, 9.55, 15.09, 5.23, 1.16], |
| label=0, |
| id=1, |
| attributes={ |
| 'occluded': False, |
| 'outside': True, 'keyframe': True, |
| 'track_id': 1, 'hgl': 'jk', |
| }), |
| PolyLine([7.85, 13.88, 3.50, 6.67, 15.90, 2.00, 13.31, 7.21], |
| label=2, |
| id=2, |
| attributes={ |
| 'occluded': False, |
| 'outside': False, 'keyframe': True, |
| 'track_id': 2, |
| }), |
| ], attributes={'frame': 13}), |
| DatasetItem(id='frame_000016', subset='annotations', |
| image=Image(path='frame_0000016.png', size=(20, 25)), |
| annotations=[ |
| Bbox(8, 7, 6, 10, label=2, |
| id=0, |
| attributes={ |
| 'occluded': False, |
| 'outside': True, 'keyframe': True, |
| 'track_id': 0 |
| }), |
| PolyLine([7.85, 13.88, 3.50, 6.67, 15.90, 2.00, 13.31, 7.21], |
| label=2, |
| id=2, |
| attributes={ |
| 'occluded': False, |
| 'outside': True, 'keyframe': True, |
| 'track_id': 2, |
| }), |
| ], attributes={'frame': 16}), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable([ |
| ['klhg', '', {'hgl'}], |
| ['z U k'], |
| ['II'] |
| ]), |
| }) |
|
|
| parsed_dataset = CvatImporter()(DUMMY_VIDEO_DATASET_DIR).make_dataset() |
|
|
| compare_datasets(self, expected_dataset, parsed_dataset) |
|
|
| class CvatConverterTest(TestCase): |
| def _test_save_and_load(self, source_dataset, converter, test_dir, |
| target_dataset=None, importer_args=None): |
| return test_save_and_load(self, source_dataset, converter, test_dir, |
| importer='cvat', |
| target_dataset=target_dataset, importer_args=importer_args) |
|
|
| def test_can_save_and_load(self): |
| label_categories = LabelCategories() |
| for i in range(10): |
| label_categories.add(str(i)) |
| label_categories.items[2].attributes.update(['a1', 'a2', 'empty']) |
| label_categories.attributes.update(['occluded']) |
|
|
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)), |
| annotations=[ |
| Polygon([0, 0, 4, 0, 4, 4], |
| label=1, group=4, |
| attributes={ 'occluded': True}), |
| Points([1, 1, 3, 2, 2, 3], |
| label=2, |
| attributes={ 'a1': 'x', 'a2': 42, 'empty': '', |
| 'unknown': 'bar' }), |
| Label(1), |
| Label(2, attributes={ 'a1': 'y', 'a2': 44 }), |
| ] |
| ), |
| DatasetItem(id=1, subset='s1', |
| annotations=[ |
| PolyLine([0, 0, 4, 0, 4, 4], |
| label=3, id=4, group=4), |
| Bbox(5, 0, 1, 9, |
| label=3, id=4, group=4), |
| ] |
| ), |
|
|
| DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)), |
| annotations=[ |
| Polygon([0, 0, 4, 0, 4, 4], z_order=1, |
| label=3, group=4, |
| attributes={ 'occluded': False }), |
| PolyLine([5, 0, 9, 0, 5, 5]), |
| ] |
| ), |
|
|
| DatasetItem(id=3, subset='s3', image=Image( |
| path='3.jpg', size=(2, 4))), |
| ], categories={ |
| AnnotationType.label: label_categories, |
| }) |
|
|
| target_dataset = Dataset.from_iterable([ |
| DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)), |
| annotations=[ |
| Polygon([0, 0, 4, 0, 4, 4], |
| label=1, group=4, |
| attributes={ 'occluded': True }), |
| Points([1, 1, 3, 2, 2, 3], |
| label=2, |
| attributes={ 'occluded': False, 'empty': '', |
| 'a1': 'x', 'a2': 42 }), |
| Label(1), |
| Label(2, attributes={ 'a1': 'y', 'a2': 44 }), |
| ], attributes={'frame': 0} |
| ), |
| DatasetItem(id=1, subset='s1', |
| annotations=[ |
| PolyLine([0, 0, 4, 0, 4, 4], |
| label=3, group=4, |
| attributes={ 'occluded': False }), |
| Bbox(5, 0, 1, 9, |
| label=3, group=4, |
| attributes={ 'occluded': False }), |
| ], attributes={'frame': 1} |
| ), |
|
|
| DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)), |
| annotations=[ |
| Polygon([0, 0, 4, 0, 4, 4], z_order=1, |
| label=3, group=4, |
| attributes={ 'occluded': False }), |
| ], attributes={'frame': 0} |
| ), |
|
|
| DatasetItem(id=3, subset='s3', image=Image( |
| path='3.jpg', size=(2, 4)), |
| attributes={'frame': 0}), |
| ], categories={ |
| AnnotationType.label: label_categories, |
| }) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(source_dataset, |
| partial(CvatConverter.convert, save_images=True), test_dir, |
| target_dataset=target_dataset) |
|
|
| def test_relative_paths(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', image=np.ones((4, 2, 3))), |
| DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3))), |
| DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3))), |
| ], categories={ AnnotationType.label: LabelCategories() }) |
|
|
| target_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', image=np.ones((4, 2, 3)), |
| attributes={'frame': 0}), |
| DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3)), |
| attributes={'frame': 1}), |
| DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3)), |
| attributes={'frame': 2}), |
| ], categories={ |
| AnnotationType.label: LabelCategories() |
| }) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(source_dataset, |
| partial(CvatConverter.convert, save_images=True), test_dir, |
| target_dataset=target_dataset) |
|
|
| def test_preserve_frame_ids(self): |
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id='some/name1', image=np.ones((4, 2, 3)), |
| attributes={'frame': 40}), |
| ], categories={ |
| AnnotationType.label: LabelCategories() |
| }) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(expected_dataset, |
| CvatConverter.convert, test_dir) |
|
|
| def test_reindex(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='some/name1', image=np.ones((4, 2, 3)), |
| attributes={'frame': 40}), |
| ], categories={ AnnotationType.label: LabelCategories() }) |
|
|
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id='some/name1', image=np.ones((4, 2, 3)), |
| attributes={'frame': 0}), |
| ], categories={ AnnotationType.label: LabelCategories() }) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(source_dataset, |
| partial(CvatConverter.convert, reindex=True), test_dir, |
| target_dataset=expected_dataset) |
|
|
