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())