| from functools import partial |
| import numpy as np |
|
|
| from unittest import TestCase |
| from datumaro.components.project import Dataset |
| from datumaro.components.extractor import (DatasetItem, |
| AnnotationType, Label, Mask, Points, Polygon, |
| PolyLine, Bbox, Caption, |
| LabelCategories, MaskCategories, PointsCategories |
| ) |
| from datumaro.plugins.datumaro_format.extractor import DatumaroImporter |
| from datumaro.plugins.datumaro_format.converter import DatumaroConverter |
| from datumaro.util.mask_tools import generate_colormap |
| from datumaro.util.image import Image |
| from datumaro.util.test_utils import (TestDir, compare_datasets_strict, |
| test_save_and_load) |
|
|
|
|
| class DatumaroConverterTest(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='datumaro', |
| target_dataset=target_dataset, importer_args=importer_args, |
| compare=compare_datasets_strict) |
|
|
| @property |
| def test_dataset(self): |
| label_categories = LabelCategories() |
| for i in range(5): |
| label_categories.add('cat' + str(i)) |
|
|
| mask_categories = MaskCategories( |
| generate_colormap(len(label_categories.items))) |
|
|
| points_categories = PointsCategories() |
| for index, _ in enumerate(label_categories.items): |
| points_categories.add(index, ['cat1', 'cat2'], joints=[[0, 1]]) |
|
|
| return Dataset.from_iterable([ |
| DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)), |
| annotations=[ |
| Caption('hello', id=1), |
| Caption('world', id=2, group=5), |
| Label(2, id=3, attributes={ |
| 'x': 1, |
| 'y': '2', |
| }), |
| Bbox(1, 2, 3, 4, label=4, id=4, z_order=1, attributes={ |
| 'score': 1.0, |
| }), |
| Bbox(5, 6, 7, 8, id=5, group=5), |
| Points([1, 2, 2, 0, 1, 1], label=0, id=5, z_order=4), |
| Mask(label=3, id=5, z_order=2, image=np.ones((2, 3))), |
| ]), |
| DatasetItem(id=21, subset='train', |
| annotations=[ |
| Caption('test'), |
| Label(2), |
| Bbox(1, 2, 3, 4, label=5, id=42, group=42) |
| ]), |
|
|
| DatasetItem(id=2, subset='val', |
| annotations=[ |
| PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11, z_order=1), |
| Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12, z_order=4), |
| ]), |
|
|
| DatasetItem(id=42, subset='test', |
| attributes={'a1': 5, 'a2': '42'}), |
|
|
| DatasetItem(id=42), |
| DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))), |
| ], categories={ |
| AnnotationType.label: label_categories, |
| AnnotationType.mask: mask_categories, |
| AnnotationType.points: points_categories, |
| }) |
|
|
| def test_can_save_and_load(self): |
| with TestDir() as test_dir: |
| self._test_save_and_load(self.test_dataset, |
| partial(DatumaroConverter.convert, save_images=True), test_dir) |
|
|
| def test_can_detect(self): |
| with TestDir() as test_dir: |
| DatumaroConverter.convert(self.test_dataset, save_dir=test_dir) |
|
|
| self.assertTrue(DatumaroImporter.detect(test_dir)) |
|
|
| def test_relative_paths(self): |
| test_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))), |
| ]) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(test_dataset, |
| partial(DatumaroConverter.convert, save_images=True), test_dir) |
|
|
