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108 lines
4.1 KiB
����on
108 lines
4.1 KiB
����on
import numpy as np
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from unittest import TestCase
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from datumaro.components.project import Project
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from datumaro.components.extractor import (Extractor, DatasetItem,
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AnnotationType, Label, Mask, Points, Polygon,
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PolyLine, Bbox, Caption,
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LabelCategories, MaskCategories, PointsCategories
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)
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from datumaro.plugins.datumaro_format.importer import DatumaroImporter
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from datumaro.plugins.datumaro_format.converter import DatumaroConverter
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from datumaro.util.mask_tools import generate_colormap
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from datumaro.util.image import Image
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from datumaro.util.test_utils import TestDir, item_to_str
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class DatumaroConverterTest(TestCase):
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)),
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annotations=[
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Caption('hello', id=1),
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Caption('world', id=2, group=5),
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Label(2, id=3, attributes={
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'x': 1,
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'y': '2',
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}),
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Bbox(1, 2, 3, 4, label=4, id=4, attributes={
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'score': 1.0,
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}),
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Bbox(5, 6, 7, 8, id=5, group=5),
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Points([1, 2, 2, 0, 1, 1], label=0, id=5),
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Mask(label=3, id=5, image=np.ones((2, 3))),
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]),
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DatasetItem(id=21, subset='train',
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annotations=[
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Caption('test'),
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Label(2),
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Bbox(1, 2, 3, 4, 5, id=42, group=42)
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]),
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DatasetItem(id=2, subset='val',
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annotations=[
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PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11),
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Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12),
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]),
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DatasetItem(id=42, subset='test'),
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DatasetItem(id=42),
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DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))),
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])
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def categories(self):
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label_categories = LabelCategories()
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for i in range(5):
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label_categories.add('cat' + str(i))
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mask_categories = MaskCategories(
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generate_colormap(len(label_categories.items)))
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points_categories = PointsCategories()
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for index, _ in enumerate(label_categories.items):
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points_categories.add(index, ['cat1', 'cat2'], adjacent=[0, 1])
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return {
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AnnotationType.label: label_categories,
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AnnotationType.mask: mask_categories,
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AnnotationType.points: points_categories,
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}
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def test_can_save_and_load(self):
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with TestDir() as test_dir:
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source_dataset = self.TestExtractor()
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converter = DatumaroConverter(save_images=True)
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converter(source_dataset, test_dir)
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project = Project.import_from(test_dir, 'datumaro')
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parsed_dataset = project.make_dataset()
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self.assertListEqual(
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sorted(source_dataset.subsets()),
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sorted(parsed_dataset.subsets()),
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)
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self.assertEqual(len(source_dataset), len(parsed_dataset))
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for subset_name in source_dataset.subsets():
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source_subset = source_dataset.get_subset(subset_name)
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parsed_subset = parsed_dataset.get_subset(subset_name)
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self.assertEqual(len(source_subset), len(parsed_subset))
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for idx, (item_a, item_b) in enumerate(
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zip(source_subset, parsed_subset)):
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self.assertEqual(item_a, item_b, '%s:\n%s\nvs.\n%s\n' % \
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(idx, item_to_str(item_a), item_to_str(item_b)))
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self.assertEqual(
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source_dataset.categories(),
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parsed_dataset.categories())
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def test_can_detect(self):
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with TestDir() as test_dir:
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DatumaroConverter()(self.TestExtractor(), save_dir=test_dir)
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self.assertTrue(DatumaroImporter.detect(test_dir)) |