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147 lines
5.2 KiB
Python
147 lines
5.2 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.mot_format import MotSeqGtConverter, MotSeqImporter
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from datumaro.util.test_utils import TestDir, compare_datasets
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class MotConverterTest(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 = MotSeqImporter()(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 SrcExtractor(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, attributes={
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'occluded': True,
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}),
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Bbox(0, 4, 4, 4, label=3, attributes={
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'visibility': 0.4,
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}),
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Bbox(2, 4, 4, 4, attributes={
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'ignored': True
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}),
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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),
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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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class DstExtractor(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(0, 4, 4, 8, label=2, attributes={
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'occluded': True,
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'visibility': 0.0,
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'ignored': False,
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}),
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Bbox(0, 4, 4, 4, label=3, attributes={
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'occluded': False,
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'visibility': 0.4,
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'ignored': False,
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}),
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Bbox(2, 4, 4, 4, attributes={
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'occluded': False,
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'visibility': 1.0,
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'ignored': True,
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}),
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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)),
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annotations=[
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Bbox(1, 2, 4, 2, label=3, attributes={
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'occluded': False,
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'visibility': 1.0,
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'ignored': False,
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}),
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]
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),
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DatasetItem(id=3,
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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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SrcExtractor(), MotSeqGtConverter(save_images=True),
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test_dir, target_dataset=DstExtractor())
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class MotImporterTest(TestCase):
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def test_can_detect(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),
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]
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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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def generate_dummy_dataset(path):
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MotSeqGtConverter()(TestExtractor(), save_dir=path)
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with TestDir() as test_dir:
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generate_dummy_dataset(test_dir)
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self.assertTrue(MotSeqImporter.detect(test_dir))
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