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117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
import numpy as np
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import os.path as osp
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from unittest import TestCase
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from datumaro.components.extractor import (Extractor, DatasetItem,
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AnnotationType, Bbox, LabelCategories,
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)
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from datumaro.plugins.yolo_format.importer import YoloImporter
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from datumaro.plugins.yolo_format.converter import YoloConverter
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from datumaro.util.image import Image, save_image
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from datumaro.util.test_utils import TestDir, compare_datasets
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class YoloFormatTest(TestCase):
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def test_can_save_and_load(self):
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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=1, subset='train', image=np.ones((8, 8, 3)),
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annotations=[
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Bbox(0, 2, 4, 2, label=2),
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Bbox(0, 1, 2, 3, label=4),
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]),
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DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
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annotations=[
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Bbox(0, 2, 4, 2, label=2),
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Bbox(3, 3, 2, 3, label=4),
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Bbox(2, 1, 2, 3, label=4),
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]),
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DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)),
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annotations=[
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Bbox(0, 1, 5, 2, label=2),
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Bbox(0, 2, 3, 2, label=5),
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Bbox(0, 2, 4, 2, label=6),
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Bbox(0, 7, 3, 2, label=7),
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]),
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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(10):
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label_categories.add('label_' + str(i))
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return {
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AnnotationType.label: label_categories,
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}
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with TestDir() as test_dir:
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source_dataset = TestExtractor()
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YoloConverter(save_images=True)(source_dataset, test_dir)
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parsed_dataset = YoloImporter()(test_dir).make_dataset()
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compare_datasets(self, source_dataset, parsed_dataset)
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def test_can_save_dataset_with_image_info(self):
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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=1, subset='train',
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image=Image(path='1.jpg', size=(10, 15)),
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annotations=[
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Bbox(0, 2, 4, 2, label=2),
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Bbox(3, 3, 2, 3, label=4),
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]),
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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(10):
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label_categories.add('label_' + str(i))
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return {
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AnnotationType.label: label_categories,
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}
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with TestDir() as test_dir:
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source_dataset = TestExtractor()
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YoloConverter()(source_dataset, test_dir)
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save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'),
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np.ones((10, 15, 3))) # put the image for dataset
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parsed_dataset = YoloImporter()(test_dir).make_dataset()
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compare_datasets(self, source_dataset, parsed_dataset)
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def test_can_load_dataset_with_exact_image_info(self):
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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=1, subset='train',
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image=Image(path='1.jpg', size=(10, 15)),
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annotations=[
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Bbox(0, 2, 4, 2, label=2),
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Bbox(3, 3, 2, 3, label=4),
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]),
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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(10):
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label_categories.add('label_' + str(i))
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return {
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AnnotationType.label: label_categories,
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}
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with TestDir() as test_dir:
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source_dataset = TestExtractor()
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YoloConverter()(source_dataset, test_dir)
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parsed_dataset = YoloImporter()(test_dir,
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image_info={'1': (10, 15)}).make_dataset()
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compare_datasets(self, source_dataset, parsed_dataset)
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