| import numpy as np |
| import os.path as osp |
|
|
| from unittest import TestCase |
|
|
| from datumaro.components.extractor import (DatasetItem, |
| AnnotationType, Bbox, LabelCategories, |
| ) |
| from datumaro.components.project import Project, Dataset |
| from datumaro.plugins.yolo_format.extractor import YoloImporter |
| from datumaro.plugins.yolo_format.converter import YoloConverter |
| from datumaro.util.image import Image, save_image |
| from datumaro.util.test_utils import TestDir, compare_datasets |
|
|
|
|
| class YoloFormatTest(TestCase): |
| def test_can_save_and_load(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='train', image=np.ones((8, 8, 3)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=2), |
| Bbox(0, 1, 2, 3, label=4), |
| ]), |
| DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=2), |
| Bbox(3, 3, 2, 3, label=4), |
| Bbox(2, 1, 2, 3, label=4), |
| ]), |
|
|
| DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)), |
| annotations=[ |
| Bbox(0, 1, 5, 2, label=2), |
| Bbox(0, 2, 3, 2, label=5), |
| Bbox(0, 2, 4, 2, label=6), |
| Bbox(0, 7, 3, 2, label=7), |
| ]), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(i) for i in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| YoloConverter.convert(source_dataset, test_dir, save_images=True) |
| parsed_dataset = YoloImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset) |
|
|
| def test_can_save_dataset_with_image_info(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='train', |
| image=Image(path='1.jpg', size=(10, 15)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=2), |
| Bbox(3, 3, 2, 3, label=4), |
| ]), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(i) for i in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| YoloConverter.convert(source_dataset, test_dir) |
|
|
| save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'), |
| np.ones((10, 15, 3))) |
| parsed_dataset = YoloImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset) |
|
|
| def test_can_load_dataset_with_exact_image_info(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='train', |
| image=Image(path='1.jpg', size=(10, 15)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=2), |
| Bbox(3, 3, 2, 3, label=4), |
| ]), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(i) for i in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| YoloConverter.convert(source_dataset, test_dir) |
|
|
| parsed_dataset = YoloImporter()(test_dir, |
| image_info={'1': (10, 15)}).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset) |
|
|
| def test_relative_paths(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', subset='train', |
| image=np.ones((4, 2, 3))), |
| DatasetItem(id='subdir1/1', subset='train', |
| image=np.ones((2, 6, 3))), |
| DatasetItem(id='subdir2/1', subset='train', |
| image=np.ones((5, 4, 3))), |
| ], categories={ |
| AnnotationType.label: LabelCategories(), |
| }) |
|
|
| for save_images in {True, False}: |
| with self.subTest(save_images=save_images): |
| with TestDir() as test_dir: |
| YoloConverter.convert(source_dataset, test_dir, |
| save_images=save_images) |
| parsed_dataset = YoloImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset) |
|
|
|
|
| DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'yolo_dataset') |
|
|
| class YoloImporterTest(TestCase): |
| def test_can_detect(self): |
| self.assertTrue(YoloImporter.detect(DUMMY_DATASET_DIR)) |
|
|
| def test_can_import(self): |
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id=1, subset='train', |
| image=np.ones((10, 15, 3)), |
| annotations=[ |
| Bbox(0, 2, 4, 2, label=2), |
| Bbox(3, 3, 2, 3, label=4), |
| ]), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(i) for i in range(10)), |
| }) |
|
|
| dataset = Project.import_from(DUMMY_DATASET_DIR, 'yolo') \ |
| .make_dataset() |
|
|
| compare_datasets(self, expected_dataset, dataset) |
|
|
