| from functools import partial |
| import numpy as np |
| import os.path as osp |
|
|
| from unittest import TestCase |
|
|
| from datumaro.components.extractor import DatasetItem, Mask |
| from datumaro.components.project import Dataset, Project |
| from datumaro.plugins.mots_format import MotsPngConverter, MotsImporter |
| from datumaro.util.test_utils import (TestDir, compare_datasets, |
| test_save_and_load) |
|
|
| DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mots_dataset') |
|
|
|
|
| class MotsPngConverterTest(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='mots', |
| target_dataset=target_dataset, importer_args=importer_args) |
|
|
| def test_can_save_masks(self): |
| source = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='a', image=np.ones((5, 1)), annotations=[ |
| |
| |
| Mask(np.array([[0, 0, 0, 1, 0]]), label=3, z_order=3, |
| attributes={'track_id': 1}), |
| Mask(np.array([[0, 1, 1, 1, 0]]), label=2, z_order=1, |
| attributes={'track_id': 2}), |
| Mask(np.array([[1, 1, 0, 0, 0]]), label=3, z_order=2, |
| attributes={'track_id': 3}), |
| ]), |
| DatasetItem(id=2, subset='a', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[1, 0, 0, 0, 0]]), label=3, |
| attributes={'track_id': 2}), |
| ]), |
| DatasetItem(id=3, subset='b', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[0, 1, 0, 0, 0]]), label=0, |
| attributes={'track_id': 1}), |
| ]), |
| ], categories=['a', 'b', 'c', 'd']) |
|
|
| target = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='a', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[0, 0, 0, 1, 0]]), label=3, |
| attributes={'track_id': 1}), |
| Mask(np.array([[0, 0, 1, 0, 0]]), label=2, |
| attributes={'track_id': 2}), |
| Mask(np.array([[1, 1, 0, 0, 0]]), label=3, |
| attributes={'track_id': 3}), |
| ]), |
| DatasetItem(id=2, subset='a', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[1, 0, 0, 0, 0]]), label=3, |
| attributes={'track_id': 2}), |
| ]), |
| DatasetItem(id=3, subset='b', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[0, 1, 0, 0, 0]]), label=0, |
| attributes={'track_id': 1}), |
| ]), |
| ], categories=['a', 'b', 'c', 'd']) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(source, |
| partial(MotsPngConverter.convert, save_images=True), |
| test_dir, target_dataset=target) |
|
|
| class MotsImporterTest(TestCase): |
| def test_can_detect(self): |
| self.assertTrue(MotsImporter.detect(DUMMY_DATASET_DIR)) |
|
|
| def test_can_import(self): |
| target = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='train', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[0, 0, 0, 1, 0]]), label=3, |
| attributes={'track_id': 1}), |
| Mask(np.array([[0, 0, 1, 0, 0]]), label=2, |
| attributes={'track_id': 2}), |
| Mask(np.array([[1, 1, 0, 0, 0]]), label=3, |
| attributes={'track_id': 3}), |
| ]), |
| DatasetItem(id=2, subset='train', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[1, 0, 0, 0, 0]]), label=3, |
| attributes={'track_id': 2}), |
| ]), |
| DatasetItem(id=3, subset='val', image=np.ones((5, 1)), annotations=[ |
| Mask(np.array([[0, 1, 0, 0, 0]]), label=0, |
| attributes={'track_id': 1}), |
| ]), |
| ], categories=['a', 'b', 'c', 'd']) |
|
|
| parsed = Project.import_from(DUMMY_DATASET_DIR, 'mots').make_dataset() |
| compare_datasets(self, expected=target, actual=parsed) |
