| import os.path as osp |
| from collections import OrderedDict |
| from functools import partial |
| from unittest import TestCase |
|
|
| import datumaro.plugins.camvid_format as Camvid |
| import numpy as np |
| from datumaro.components.extractor import (AnnotationType, DatasetItem, |
| Extractor, LabelCategories, Mask) |
| from datumaro.components.project import Dataset, Project |
| from datumaro.plugins.camvid_format import CamvidConverter, CamvidImporter |
| from datumaro.util.test_utils import (TestDir, compare_datasets, |
| test_save_and_load) |
|
|
|
|
| class CamvidFormatTest(TestCase): |
| def test_can_write_and_parse_labelmap(self): |
| src_label_map = Camvid.CamvidLabelMap |
|
|
| with TestDir() as test_dir: |
| file_path = osp.join(test_dir, 'label_colors.txt') |
| Camvid.write_label_map(file_path, src_label_map) |
| dst_label_map = Camvid.parse_label_map(file_path) |
|
|
| self.assertEqual(src_label_map, dst_label_map) |
|
|
| DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'camvid_dataset') |
|
|
| class TestExtractorBase(Extractor): |
| def _label(self, camvid_label): |
| return self.categories()[AnnotationType.label].find(camvid_label)[0] |
|
|
| def categories(self): |
| return Camvid.make_camvid_categories() |
|
|
| class CamvidImportTest(TestCase): |
| def test_can_import(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='0001TP_008550', subset='test', |
| image=np.ones((1, 5, 3)), |
| annotations=[ |
| Mask(image=np.array([[1, 1, 0, 0, 0]]), label=1), |
| Mask(image=np.array([[0, 0, 1, 0, 0]]), label=18), |
| Mask(image=np.array([[0, 0, 0, 1, 1]]), label=22), |
| ] |
| ), |
| DatasetItem(id='0001TP_008580', subset='test', |
| image=np.ones((1, 5, 3)), |
| annotations=[ |
| Mask(image=np.array([[1, 1, 0, 0, 0]]), label=2), |
| Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4), |
| Mask(image=np.array([[0, 0, 0, 1, 1]]), label=27), |
| ] |
| ), |
| DatasetItem(id='0001TP_006690', subset='train', |
| image=np.ones((1, 5, 3)), |
| annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 1]]), label=3), |
| Mask(image=np.array([[0, 0, 1, 0, 0]]), label=18), |
| ] |
| ), |
| DatasetItem(id='0016E5_07959', subset = 'val', |
| image=np.ones((1, 5, 3)), |
| annotations=[ |
| Mask(image=np.array([[1, 1, 1, 0, 0]]), label=1), |
| Mask(image=np.array([[0, 0, 0, 1, 1]]), label=8), |
| ] |
| ), |
| ], categories=Camvid.make_camvid_categories()) |
|
|
| parsed_dataset = Project.import_from(DUMMY_DATASET_DIR, 'camvid').make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset) |
|
|
| def test_can_detect_camvid(self): |
| self.assertTrue(CamvidImporter.detect(DUMMY_DATASET_DIR)) |
|
|
| class CamvidConverterTest(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='camvid', |
| target_dataset=target_dataset, importer_args=importer_args) |
|
|
| def test_can_save_camvid_segm(self): |
| class TestExtractor(TestExtractorBase): |
| def __iter__(self): |
| return iter([ |
| DatasetItem(id='a/b/1', subset='test', |
| image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0), |
| Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3), |
| Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4), |
| ]), |
| ]) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(TestExtractor(), |
| partial(CamvidConverter.convert, label_map='camvid'), |
| test_dir) |
|
|
| def test_can_save_camvid_segm_unpainted(self): |
| class TestExtractor(TestExtractorBase): |
| def __iter__(self): |
| return iter([ |
| DatasetItem(id=1, subset='a', image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0), |
| Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3), |
| Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4), |
| ]), |
| ]) |
|
|
| class DstExtractor(TestExtractorBase): |
| def __iter__(self): |
| return iter([ |
| DatasetItem(id=1, subset='a', image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0), |
| Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3), |
| Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4), |
| ]), |
| ]) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(TestExtractor(), |
| partial(CamvidConverter.convert, |
| label_map='camvid', apply_colormap=False), |
| test_dir, target_dataset=DstExtractor()) |
|
|
| def test_can_save_dataset_with_no_subsets(self): |
| class TestExtractor(TestExtractorBase): |
| def __iter__(self): |
| return iter([ |
| DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 0, 0, 1, 0]]), label=0), |
| Mask(image=np.array([[0, 1, 1, 0, 1]]), label=3), |
| ]), |
|
|
| DatasetItem(id=2, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 0]]), label=1), |
| Mask(image=np.array([[0, 0, 1, 0, 1]]), label=2), |
| ]), |
| ]) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(TestExtractor(), |
| partial(CamvidConverter.convert, label_map='camvid'), test_dir) |
|
|
| def test_can_save_with_no_masks(self): |
| class TestExtractor(TestExtractorBase): |
| def __iter__(self): |
| return iter([ |
| DatasetItem(id='a/b/1', subset='test', |
| image=np.ones((2, 5, 3)), |
| ), |
| ]) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(TestExtractor(), |
| partial(CamvidConverter.convert, label_map='camvid'), |
| test_dir) |
|
|
| def test_dataset_with_source_labelmap_undefined(self): |
| class SrcExtractor(TestExtractorBase): |
| def __iter__(self): |
| yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 0]]), label=0), |
| Mask(image=np.array([[0, 0, 1, 0, 0]]), label=1), |
| ]) |
|
|
| def categories(self): |
| label_cat = LabelCategories() |
| label_cat.add('Label_1') |
| label_cat.add('label_2') |
| return { |
| AnnotationType.label: label_cat, |
| } |
|
|
| class DstExtractor(TestExtractorBase): |
| def __iter__(self): |
| yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 0]]), label=self._label('Label_1')), |
| Mask(image=np.array([[0, 0, 1, 0, 0]]), label=self._label('label_2')), |
| ]) |
|
|
| def categories(self): |
| label_map = OrderedDict() |
| label_map['background'] = None |
| label_map['Label_1'] = None |
| label_map['label_2'] = None |
| return Camvid.make_camvid_categories(label_map) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(SrcExtractor(), |
| partial(CamvidConverter.convert, label_map='source'), |
| test_dir, target_dataset=DstExtractor()) |
|
|
| def test_dataset_with_source_labelmap_defined(self): |
| class SrcExtractor(TestExtractorBase): |
| def __iter__(self): |
| yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 0]]), label=1), |
| Mask(image=np.array([[0, 0, 1, 0, 1]]), label=2), |
| ]) |
|
|
| def categories(self): |
| label_map = OrderedDict() |
| label_map['background'] = (0, 0, 0) |
| label_map['label_1'] = (1, 2, 3) |
| label_map['label_2'] = (3, 2, 1) |
| return Camvid.make_camvid_categories(label_map) |
|
|
| class DstExtractor(TestExtractorBase): |
| def __iter__(self): |
| yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[ |
| Mask(image=np.array([[1, 1, 0, 1, 0]]), label=self._label('label_1')), |
| Mask(image=np.array([[0, 0, 1, 0, 1]]), label=self._label('label_2')), |
| ]) |
|
|
| def categories(self): |
| label_map = OrderedDict() |
| label_map['background'] = (0, 0, 0) |
| label_map['label_1'] = (1, 2, 3) |
| label_map['label_2'] = (3, 2, 1) |
| return Camvid.make_camvid_categories(label_map) |
|
|
| with TestDir() as test_dir: |
| self._test_save_and_load(SrcExtractor(), |
| partial(CamvidConverter.convert, label_map='source'), |
| test_dir, target_dataset=DstExtractor()) |
|
|
