def test_flushes_changes_on_save(self):
dataset = Dataset.from_iterable([])
dataset.put(DatasetItem(1))
self.assertTrue(dataset.is_modified)
with TestDir() as test_dir:
dataset.save(test_dir)
self.assertFalse(dataset.is_modified)
def setUp(self):
self.source = Dataset.from_iterable([
DatasetItem(id='1', subset='val',
attributes={'qq': 1, 'x': 2},
annotations=[ Label(0, attributes={ 'x': 1, 'y': 2 }) ]),
DatasetItem(id='2', subset='val',
attributes={'qq': 2},
annotations=[ Label(0, attributes={ 'x': 1, 'y': 2 }) ]),
], categories=['a'])
def _export(dst_file, instance_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
instance_data, include_images=save_images), env=dm_env)
with TemporaryDirectory() as temp_dir:
if save_images:
dataset.export(temp_dir, 'imagenet', save_images=save_images)
else:
dataset.export(temp_dir, 'imagenet_txt', save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def test_remap_labels_delete_unspecified(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(0, id=0), # will be removed
Label(1, id=1),
Bbox(1, 2, 3, 4, label=None),
])
], categories=['label0', 'label1'])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(0, id=1),
]),
], categories=['label1'])
actual = transforms.RemapLabels(source_dataset,
mapping={ 'label1': 'label1' }, default='delete')
compare_datasets(self, target_dataset, actual)
def test_project_labels(self):
source = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(1), # Label must be remapped
Label(3), # Must be removed (extra label)
Bbox(1, 2, 3, 4, label=None), # Must be kept (no label)
])
], categories=['a', 'b', 'c', 'd'])
expected = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(2),
Bbox(1, 2, 3, 4, label=None),
]),
], categories=['c', 'a', 'b'])
actual = transforms.ProjectLabels(source, dst_labels=['c', 'a', 'b'])
compare_datasets(self, expected, actual)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='кириллица с пробелом',
subset='train',
attributes={'id': 1}),
])
with TestDir() as test_dir:
self._test_save_and_load(expected_dataset,
CocoImageInfoConverter.convert, test_dir)
def test_can_save_dataset_with_image_info(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=Image(path='1.jpg', size=(10, 15)),
attributes={'id': 1}),
])
with TestDir() as test_dir:
self._test_save_and_load(expected_dataset,
CocoImageInfoConverter.convert, test_dir)
def test_can_import(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]),
DatasetItem(
id='2', image=np.ones(
(10, 10, 3)), annotations=[Label(0)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(2)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'imagenet')
compare_datasets(self, expected_dataset, dataset, require_images=True)
def test_relative_paths(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.ones((4, 2, 3))),
DatasetItem(
id='subdir1/1', subset='train', image=np.ones((2, 6, 3))),
DatasetItem(
id='subdir2/1', subset='train', image=np.ones((5, 4, 3))),
],
categories=[])
for save_images in {True, False}:
with self.subTest(save_images=save_images):
with TestDir() as test_dir:
YoloConverter.convert(source_dataset,
test_dir,
save_images=save_images)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, source_dataset, parsed_dataset)
def test_dataset_with_save_dataset_meta_file(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='1', subset='train', annotations=[Label(0)]),
DatasetItem(id='2', subset='train', annotations=[Label(1)]),
],
categories=['label_0', 'label_1'])
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset,
test_dir,
save_images=False,
save_dataset_meta=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet_txt')
self.assertTrue(osp.isfile(osp.join(test_dir,
'dataset_meta.json')))
compare_datasets(self, source_dataset, parsed_dataset)
def _export_recognition(dst_file, instance_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
instance_data, include_images=save_images),
env=dm_env)
dataset.transform(LabelToCaption)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir,
'icdar_word_recognition',
save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def test_can_import_without_people_file(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='name0_0001',
subset='test',
image=np.ones((2, 5, 3)),
annotations=[
Label(0,
attributes={
'negative_pairs':
['name1/name1_0001', 'name1/name1_0002']
}),
Points([0, 4, 3, 3, 2, 2, 1, 0, 3, 0], label=0),
]),
DatasetItem(id='name1_0001',
subset='test',
image=np.ones((2, 5, 3)),
annotations=[
Label(1,
attributes={
'positive_pairs': ['name1/name1_0002'],
}),
Points([1, 6, 4, 6, 3, 3, 2, 1, 4, 1], label=1),
]),
DatasetItem(id='name1_0002',
subset='test',
image=np.ones((2, 5, 3)),
annotations=[
Label(1),
Points([0, 5, 3, 5, 2, 2, 1, 0, 3, 0], label=1),
]),
],
categories=['name0', 'name1'])
with TestDir() as test_dir:
dataset_path = osp.join(test_dir, 'dataset')
shutil.copytree(DUMMY_DATASET_DIR, dataset_path)
os.remove(
osp.join(dataset_path, 'test', 'annotations', 'people.txt'))
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'lfw')
compare_datasets(self, expected_dataset, dataset)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=0, group=1),
]
),
DatasetItem(id='2', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Points([4.23, 4.32, 5.34, 4.45, 3.54,
3.56, 4.52, 3.51, 4.78, 3.34], label=1, group=1),
]
),
DatasetItem(id='3', subset='train', image=np.ones((8, 8, 3)),
annotations=[Label(2, group=1)]
),
DatasetItem(id='4', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=3, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=3, group=1),
]
),
DatasetItem(id='a/5', subset='train', image=np.ones((8, 8, 3)),
annotations=[
Bbox(2, 2, 2, 2, group=1),
]
),
DatasetItem(id='label_0', subset='train', image=np.ones((8, 8, 3)),
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
[('label_%s' % i, 'class_%s' % i) for i in range(5)]),
})
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_extract_coco(self):
tfds_example = {
'image': encode_image(np.ones((20, 10)), '.png'),
'image/filename': 'test.png',
'image/id': 123,
'objects': {
'bbox': [[0.1, 0.2, 0.3, 0.4]],
'label': [5],
'is_crowd': [True],
}
}
with mock_tfds_data(example=tfds_example):
tfds_info = tfds.builder('coco/2014').info
expected_dataset = Dataset.from_iterable(
[
DatasetItem(
id='test',
subset='train',
image=np.ones((20, 10)),
annotations=[
Bbox(2,
2,
2,
4,
label=5,
attributes={'is_crowd': True}),
],
attributes={'id': 123},
),
],
categories=tfds_info.features['objects'].feature['label'].names
)
extractor = make_tfds_extractor('coco/2014')
actual_dataset = Dataset(extractor)
compare_datasets(self,
expected_dataset,
actual_dataset,
require_images=True)
def test_export_to_voc_format(self):
label_map = OrderedDict(('label_%s' % i, [None, [], []]) for i in range(10))
label_map['background'] = [None, [], []]
label_map.move_to_end('background', last=False)
expected_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.ones((10, 15, 3)),
annotations=[
Bbox(0.0, 2.0, 4.0, 2.0,
attributes={
'difficult': False,
'truncated': False,
'occluded': False
},
id=1, label=3, group=1
),
Bbox(3.0, 3.0, 2.0, 3.0,
attributes={
'difficult': False,
'truncated': False,
'occluded': False
},
id=2, label=5, group=2
)
]
)
], categories=VOC.make_voc_categories(label_map))
with TestDir() as test_dir:
yolo_dir = osp.join(__file__[:__file__.rfind(osp.join('tests', ''))],
'tests', 'assets', 'yolo_dataset')
run(self, 'create', '-o', test_dir)
run(self, 'import', '-p', test_dir, '-f', 'yolo', yolo_dir)
voc_export = osp.join(test_dir, 'voc_export')
run(self, 'export', '-p', test_dir, '-f', 'voc',
'-o', voc_export, '--', '--save-images')
parsed_dataset = Dataset.import_from(voc_export, format='voc')
compare_datasets(self, expected_dataset, parsed_dataset,
require_images=True)
def test_can_crop_covered_segments(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[1, 1, 0, 1, 1],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0]],
),
label=2, id=1, z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
label=1, id=2, z_order=1),
]
),
], categories=[str(i) for i in range(10)])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]],
),
attributes={ 'is_crowd': True },
label=2, id=1, group=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
label=1, id=2, group=2,
attributes={ 'is_crowd': False }),
], attributes={'id': 1}
),
], categories=[str(i) for i in range(10)])
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CocoInstancesConverter.convert, crop_covered=True),
test_dir, target_dataset=target_dataset)
def _export(dst_file, instance_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
instance_data, include_images=save_images),
env=dm_env)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir,
'voc',
save_images=save_images,
label_map='source')
make_zip_archive(temp_dir, dst_file)
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2, attributes={
'occluded': False,
'visibility': 1.0,
'ignored': False,
}),
]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'mot_seq')
compare_datasets(self, expected_dataset, dataset)
def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable([
DatasetItem(id='a/1',
image=Image(path='a/1.JPEG', data=np.zeros(
(4, 3, 3)))),
DatasetItem(id='b/c/d/2',
image=Image(path='b/c/d/2.bmp',
data=np.zeros((3, 4, 3)))),
],
categories=[])
with TestDir() as test_dir:
OpenImagesConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'open_images')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def test_can_check_item_existence(self):
dataset = Dataset.from_iterable(
[DatasetItem(0, subset='a'),
DatasetItem(1)])
self.assertTrue(DatasetItem(0, subset='a') in dataset)
self.assertFalse(DatasetItem(0, subset='b') in dataset)
self.assertTrue((0, 'a') in dataset)
self.assertFalse((0, 'b') in dataset)
self.assertTrue(1 in dataset)
self.assertFalse(0 in dataset)
def test_binds_on_save(self):
dataset = Dataset.from_iterable([DatasetItem(1)])
self.assertFalse(dataset.is_bound)
with TestDir() as test_dir:
dataset.save(test_dir)
self.assertTrue(dataset.is_bound)
self.assertEqual(dataset.data_path, test_dir)
self.assertEqual(dataset.format, DEFAULT_FORMAT)
def test_can_track_modifications_on_addition(self):
dataset = Dataset.from_iterable([
DatasetItem(1),
DatasetItem(2),
])
self.assertFalse(dataset.is_modified)
dataset.put(DatasetItem(3, subset='a'))
self.assertTrue(dataset.is_modified)
def test_can_track_modifications_on_removal(self):
dataset = Dataset.from_iterable([
DatasetItem(1),
DatasetItem(2),
])
self.assertFalse(dataset.is_modified)
dataset.remove(1)
self.assertTrue(dataset.is_modified)
def _export(dst_file, instance_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
instance_data=instance_data, include_images=save_images,
dimension=DimensionType.DIM_3D), env=dm_env)
if not save_images:
dataset.transform(DeleteImagePath)
with TemporaryDirectory() as tmp_dir:
dataset.export(tmp_dir, 'datumaro', save_images=save_images)
make_zip_archive(tmp_dir, dst_file)
def test_can_save_and_load_without_saving_images(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=0, subset='train', annotations=[Label(0)]),
DatasetItem(id=1, subset='train', annotations=[Label(1)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(
str(label) for label in range(10)),
})
with TestDir() as test_dir:
MnistConverter.convert(source_dataset, test_dir, save_images=False)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_import_with_meta_file(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='img0001',
subset='test',
image=np.ones((5, 5, 3)),
annotations=[Bbox(10, 5, 10, 2, label=0)]),
DatasetItem(id='img0002',
subset='test',
image=np.ones((5, 5, 3)),
annotations=[
Bbox(11.5, 12, 10.2, 20.5, label=1),
])
],
categories=['helmet', 'person'])
dataset = Dataset.import_from(DUMMY_DATASET_DIR_WITH_META_FILE,
'vott_csv')
compare_datasets(self, expected_dataset, dataset, require_images=True)
def test_relative_paths(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3))),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id='1', image=np.ones((4, 2, 3)),
attributes={'frame': 0}),
DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3)),
attributes={'frame': 1}),
DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3)),
attributes={'frame': 2}),
], categories=[])
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CvatConverter.convert, save_images=True), test_dir,
target_dataset=target_dataset, require_images=True)
def _export(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_data, include_images=save_images),
env=dm_env)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir,
'coco_instances',
save_images=save_images,
merge_images=True)
make_zip_archive(temp_dir, dst_file)
def test_can_transform_dataset_inplace(self):
test_dir = scope_add(TestDir())
Dataset.from_iterable([
DatasetItem(1, annotations=[Label(0)]),
DatasetItem(2, annotations=[Label(1)]),
],
categories=['a', 'b']).export(test_dir, 'coco')
run(self, 'transform', '-t', 'remap_labels', '--overwrite',
test_dir + ':coco', '--', '-l', 'a:cat', '-l', 'b:dog')
expected_dataset = Dataset.from_iterable([
DatasetItem(1, annotations=[Label(0, id=1, group=1)]),
DatasetItem(2, annotations=[Label(1, id=2, group=2)]),
],
categories=['cat', 'dog'])
compare_datasets(self,
expected_dataset,
Dataset.import_from(test_dir, 'coco'),
ignored_attrs='*')
def test_can_save_and_load_image_with_arbitrary_extension(self):
expected = Dataset.from_iterable([
DatasetItem(id='c/0001_c1s1_000000_00', image=Image(
path='c/0001_c1s1_0000_00.JPEG', data=np.zeros((4, 3, 3))),
attributes={'camera_id': 0, 'person_id': '0001', 'track_id': 1,
'frame_id': 0, 'bbox_id': 0, 'query': False}
),
DatasetItem(id='a/b/0002_c2s2_000001_00', image=Image(
path='a/b/0002_c2s2_0001_00.bmp', data=np.zeros((3, 4, 3))),
attributes={'camera_id': 1, 'person_id': '0002', 'track_id': 2,
'frame_id': 1, 'bbox_id': 0, 'query': False}
),
])
with TestDir() as test_dir:
Market1501Converter.convert(expected, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'market1501')
compare_datasets(self, expected, parsed_dataset,
require_images=True)
