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129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
import numpy as np
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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.tf_detection_api_format.importer import TfDetectionApiImporter
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from datumaro.plugins.tf_detection_api_format.extractor import TfDetectionApiExtractor
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from datumaro.plugins.tf_detection_api_format.converter import TfDetectionApiConverter
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from datumaro.util.test_utils import TestDir, compare_datasets
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class TfrecordConverterTest(TestCase):
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def _test_save_and_load(self, source_dataset, converter, test_dir,
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target_dataset=None, importer_args=None):
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converter(source_dataset, test_dir)
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if importer_args is None:
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importer_args = {}
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parsed_dataset = TfDetectionApiImporter()(test_dir, **importer_args) \
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.make_dataset()
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if target_dataset is None:
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target_dataset = source_dataset
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compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
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def test_can_save_bboxes(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=np.ones((16, 16, 3)),
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annotations=[
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Bbox(0, 4, 4, 8, label=2, id=0),
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Bbox(0, 4, 4, 4, label=3, id=1),
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Bbox(2, 4, 4, 4, id=2),
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]
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),
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DatasetItem(id=2, subset='val',
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image=np.ones((8, 8, 3)),
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annotations=[
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Bbox(1, 2, 4, 2, label=3, id=0),
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]
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),
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DatasetItem(id=3, subset='test',
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image=np.ones((5, 4, 3)) * 3,
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),
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])
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def categories(self):
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label_cat = LabelCategories()
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for label in range(10):
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label_cat.add('label_' + str(label))
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return {
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AnnotationType.label: label_cat,
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}
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with TestDir() as test_dir:
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self._test_save_and_load(
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TestExtractor(), TfDetectionApiConverter(save_images=True),
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test_dir)
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def test_can_save_dataset_with_no_subsets(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,
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image=np.ones((16, 16, 3)),
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annotations=[
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Bbox(2, 1, 4, 4, label=2, id=0),
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Bbox(4, 2, 8, 4, label=3, id=1),
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]
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),
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DatasetItem(id=2,
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image=np.ones((8, 8, 3)) * 2,
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annotations=[
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Bbox(4, 4, 4, 4, label=3, id=0),
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]
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),
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DatasetItem(id=3,
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image=np.ones((8, 4, 3)) * 3,
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),
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])
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def categories(self):
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label_cat = LabelCategories()
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for label in range(10):
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label_cat.add('label_' + str(label))
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return {
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AnnotationType.label: label_cat,
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}
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with TestDir() as test_dir:
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self._test_save_and_load(
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TestExtractor(), TfDetectionApiConverter(save_images=True),
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test_dir)
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def test_labelmap_parsing(self):
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text = """
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{
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id: 4
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name: 'qw1'
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}
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{
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id: 5 name: 'qw2'
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}
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{
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name: 'qw3'
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id: 6
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}
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{name:'qw4' id:7}
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"""
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expected = {
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'qw1': 4,
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'qw2': 5,
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'qw3': 6,
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'qw4': 7,
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}
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parsed = TfDetectionApiExtractor._parse_labelmap(text)
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self.assertEqual(expected, parsed)
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