def test_can_load_image(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='img0', subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0, z_order=1,
attributes={
'occluded': True,
'a1': True, 'a2': 'v3'
}),
PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
attributes={'occluded': False}),
], attributes={'frame': 0}),
DatasetItem(id='img1', subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Polygon([1, 2, 3, 4, 6, 5], z_order=1,
attributes={'occluded': False}),
Points([1, 2, 3, 4, 5, 6], label=1, z_order=2,
attributes={'occluded': False}),
], attributes={'frame': 1}),
], categories={
AnnotationType.label: LabelCategories.from_iterable([
['label1', '', {'a1', 'a2'}],
['label2'],
])
})
parsed_dataset = CvatImporter()(DUMMY_IMAGE_DATASET_DIR).make_dataset()
compare_datasets(self, expected_dataset, parsed_dataset)
def test_can_load(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=0,
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0,
2,
4,
2,
label=0,
attributes={
'occluded': True,
'z_order': 1,
'a1': True,
'a2': 'v3'
}),
PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
attributes={
'occluded': False,
'z_order': 0
}),
]),
DatasetItem(id=1,
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Polygon([1, 2, 3, 4, 6, 5],
attributes={
'occluded': False,
'z_order': 1
}),
Points([1, 2, 3, 4, 5, 6],
label=1,
attributes={
'occluded': False,
'z_order': 2
}),
]),
])
def categories(self):
label_categories = LabelCategories()
label_categories.add('label1', attributes={'a1', 'a2'})
label_categories.add('label2')
return {
AnnotationType.label: label_categories,
}
with TestDir() as test_dir:
self.generate_dummy_cvat(test_dir)
source_dataset = TestExtractor()
parsed_dataset = CvatImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def _test_save_and_load(self, source_dataset, converter, test_dir,
target_dataset=None, importer_args=None):
converter(source_dataset, test_dir)
if importer_args is None:
importer_args = {}
parsed_dataset = CvatImporter()(test_dir, **importer_args).make_dataset()
if target_dataset is None:
target_dataset = source_dataset
compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
def test_can_load_image(self):
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id='img0',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0,
2,
4,
2,
label=0,
z_order=1,
attributes={
'occluded': True,
'a1': True,
'a2': 'v3'
}),
PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
attributes={'occluded': False}),
],
attributes={'frame': 0}),
DatasetItem(id='img1',
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Polygon([1, 2, 3, 4, 6, 5],
z_order=1,
attributes={'occluded': False}),
Points([1, 2, 3, 4, 5, 6],
label=1,
z_order=2,
attributes={'occluded': False}),
],
attributes={'frame': 1}),
])
def categories(self):
label_categories = LabelCategories()
label_categories.add('label1', attributes={'a1', 'a2'})
label_categories.add('label2')
return {AnnotationType.label: label_categories}
parsed_dataset = CvatImporter()(DUMMY_IMAGE_DATASET_DIR).make_dataset()
compare_datasets(self, DstExtractor(), parsed_dataset)
def test_can_detect(self):
with TestDir() as test_dir:
generate_dummy_cvat(test_dir)
self.assertTrue(CvatImporter.detect(test_dir))
def test_can_load_video(self):
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id='frame_000010',
subset='annotations',
image=np.ones((20, 25, 3)),
annotations=[
Bbox(3,
4,
7,
1,
label=2,
id=0,
attributes={
'occluded': True,
'outside': False,
'keyframe': True,
'track_id': 0
}),
Points(
[21.95, 8.00, 2.55, 15.09, 2.23, 3.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': False,
'keyframe': True,
'track_id': 1,
'hgl': 'hgkf',
}),
],
attributes={'frame': 10}),
DatasetItem(id='frame_000013',
subset='annotations',
image=np.ones((20, 25, 3)),
annotations=[
Bbox(7,
6,
7,
2,
label=2,
id=0,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 0
}),
Points(
[21.95, 8.00, 9.55, 15.09, 5.23, 1.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 1,
'hgl': 'jk',
}),
PolyLine(
[
7.85, 13.88, 3.50, 6.67, 15.90,
2.00, 13.31, 7.21
],
label=2,
id=2,
attributes={
'occluded': False,
'outside': False,
'keyframe': True,
'track_id': 2,
}),
],
attributes={'frame': 13}),
DatasetItem(id='frame_000016',
subset='annotations',
image=Image(path='frame_0000016.png',
size=(20, 25)),
annotations=[
Bbox(8,
7,
6,
10,
label=2,
id=0,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 0
}),
PolyLine(
[
7.85, 13.88, 3.50, 6.67, 15.90,
2.00, 13.31, 7.21
],
label=2,
id=2,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 2,
}),
],
attributes={'frame': 16}),
])
def categories(self):
label_categories = LabelCategories()
label_categories.add('klhg', attributes={'hgl'})
label_categories.add('z U k')
label_categories.add('II')
return {AnnotationType.label: label_categories}
parsed_dataset = CvatImporter()(DUMMY_VIDEO_DATASET_DIR).make_dataset()
compare_datasets(self, DstExtractor(), parsed_dataset)
def test_can_detect_video(self):
self.assertTrue(CvatImporter.detect(DUMMY_VIDEO_DATASET_DIR))
def test_can_detect_image(self):
self.assertTrue(CvatImporter.detect(DUMMY_IMAGE_DATASET_DIR))
def test_can_load_video(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='frame_000010',
subset='annotations',
image=np.ones((20, 25, 3)),
annotations=[
Bbox(3,
4,
7,
1,
label=2,
id=0,
attributes={
'occluded': True,
'outside': False,
'keyframe': True,
'track_id': 0
}),
Points(
[21.95, 8.00, 2.55, 15.09, 2.23, 3.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': False,
'keyframe': True,
'track_id': 1,
'hgl': 'hgkf',
}),
],
attributes={'frame': 10}),
DatasetItem(id='frame_000013',
subset='annotations',
image=np.ones((20, 25, 3)),
annotations=[
Bbox(7,
6,
7,
2,
label=2,
id=0,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 0
}),
Points(
[21.95, 8.00, 9.55, 15.09, 5.23, 1.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 1,
'hgl': 'jk',
}),
PolyLine(
[
7.85, 13.88, 3.50, 6.67, 15.90, 2.00,
13.31, 7.21
],
label=2,
id=2,
attributes={
'occluded': False,
'outside': False,
'keyframe': True,
'track_id': 2,
}),
],
attributes={'frame': 13}),
DatasetItem(id='frame_000016',
subset='annotations',
image=Image(path='frame_0000016.png',
size=(20, 25)),
annotations=[
Bbox(8,
7,
6,
10,
label=2,
id=0,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 0
}),
PolyLine(
[
7.85, 13.88, 3.50, 6.67, 15.90, 2.00,
13.31, 7.21
],
label=2,
id=2,
attributes={
'occluded': False,
'outside': True,
'keyframe': True,
'track_id': 2,
}),
],
attributes={'frame': 16}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable([['klhg', '', {'hgl'}],
['z U k'], ['II']]),
})
parsed_dataset = CvatImporter()(DUMMY_VIDEO_DATASET_DIR).make_dataset()
compare_datasets(self, expected_dataset, parsed_dataset)
