def test_remap_labels(self):
src_dataset = Dataset.from_iterable(
[
DatasetItem(
id=1,
annotations=[
# Should be remapped
Label(1),
Bbox(1, 2, 3, 4, label=2),
Mask(image=np.array([1]), label=3),
# Should be kept
Polygon([1, 1, 2, 2, 3, 4], label=4),
PolyLine([1, 3, 4, 2, 5, 6])
])
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label%s' % i for i in range(5)),
AnnotationType.mask:
MaskCategories(colormap=mask_tools.generate_colormap(5)),
})
dst_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
annotations=[
Label(1),
Bbox(1, 2, 3, 4, label=0),
Mask(image=np.array([1]), label=1),
Polygon([1, 1, 2, 2, 3, 4], label=2),
PolyLine([1, 3, 4, 2, 5, 6], label=None)
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['label0', 'label9', 'label4']),
AnnotationType.mask:
MaskCategories(
colormap={
k: v
for k, v in mask_tools.generate_colormap(5).items()
if k in {0, 1, 3, 4}
})
})
actual = transforms.RemapLabels(src_dataset,
mapping={
'label1': 'label9',
'label2': 'label0',
'label3': 'label9',
},
default='keep')
compare_datasets(self, dst_dataset, actual)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='кириллица с пробелом',
subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Polygon(
[0, 4, 4, 4, 5, 6],
label=3,
attributes={
'occluded': True,
'a1': 'qwe',
'a2': True,
'a3': 123,
}),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
target_dataset = Dataset.from_iterable(
[
DatasetItem(id='кириллица с пробелом',
subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Polygon(
[0, 4, 4, 4, 5, 6],
label=0,
id=0,
attributes={
'occluded': True,
'username': '',
'a1': 'qwe',
'a2': True,
'a3': 123,
}),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['label_3']),
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(LabelMeConverter.convert,
save_images=True),
test_dir,
target_dataset=target_dataset,
require_images=True)
def from_iterable(cls, iterable, categories=None):
"""Generation of Dataset from iterable object
Args:
iterable: Iterable object contains DatasetItems
categories (dict, optional): You can pass dict of categories or
you can pass list of names. It'll interpreted as list of names of
LabelCategories. Defaults to {}.
Returns:
Dataset: Dataset object
"""
if isinstance(categories, list):
categories = {
AnnotationType.label: LabelCategories.from_iterable(categories)
}
if not categories:
categories = {}
class tmpExtractor(Extractor):
def __iter__(self):
return iter(iterable)
def categories(self):
return categories
return cls.from_extractors(tmpExtractor())
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_dataset_with_no_subsets(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0,
1,
2,
3,
label=1,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(3)),
})
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, source_dataset, parsed_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=2),
Bbox(0, 1, 2, 3, label=4),
]),
DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
Bbox(2, 1, 2, 3, label=4),
]),
DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 1, 5, 2, label=2),
Bbox(0, 2, 3, 2, label=5),
Bbox(0, 2, 4, 2, label=6),
Bbox(0, 7, 3, 2, label=7),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(i) for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_and_load_with_multiple_labels(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='1',
subset='train',
annotations=[Label(1), Label(3)]),
DatasetItem(id='2',
subset='train',
image=np.zeros((8, 6, 3)),
annotations=[Label(0)]),
DatasetItem(
id='3',
subset='train',
image=np.zeros((2, 8, 3)),
),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset()
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_dataset_with_image_info(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'),
np.ones((10, 15, 3))) # put the image for dataset
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, source_dataset, parsed_dataset)
def _parse_categories(self, path):
if osp.isfile(path):
with open(path) as f:
labels = [l.strip() for l in f]
else:
labels = [l.name for l in MotsLabels]
return {AnnotationType.label: LabelCategories.from_iterable(labels)}
def test_can_import(self):
target_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2),
Bbox(0, 4, 4, 4, label=3),
Bbox(2, 4, 4, 4),
],
attributes={'source_id': '1'}),
DatasetItem(id=2,
subset='val',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(1, 2, 4, 2, label=3),
],
attributes={'source_id': '2'}),
DatasetItem(id=3,
subset='test',
image=np.ones((5, 4, 3)) * 3,
attributes={'source_id': '3'}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
dataset = Project.import_from(DUMMY_DATASET_DIR, 'tf_detection_api') \
.make_dataset()
compare_datasets(self, target_dataset, dataset)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
annotations=[Label(0)]
),
DatasetItem(id='2', subset='train',
annotations=[Label(0)]
),
DatasetItem(id='3', subset='train', image=np.zeros((8, 8, 3)),
annotations=[Label(0)]
),
DatasetItem(id='4', subset='train',
annotations=[Label(1)]
),
DatasetItem(id='5', subset='train', image=np.zeros((4, 8, 3)),
annotations=[Label(1)]
),
DatasetItem(id='6', subset='train',
annotations=[Label(5)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset, test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet_txt')
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
def test_can_save_masks(self):
test_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.ones((4, 5, 3)),
annotations=[
Mask(image=np.array([
[1, 0, 0, 1],
[0, 1, 1, 0],
[0, 1, 1, 0],
[1, 0, 0, 1],
]),
label=1),
],
attributes={'source_id': ''}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(
test_dataset,
partial(TfDetectionApiConverter.convert, save_masks=True),
test_dir)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0)]
),
DatasetItem(id='2',
image=np.ones((10, 10, 3)),
annotations=[Label(1)]
),
DatasetItem(id='3',
image=np.ones((10, 10, 3)),
annotations=[Label(0)]
),
DatasetItem(id='4',
image=np.ones((8, 8, 3)),
annotations=[Label(2)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(3)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = ImagenetImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
def test_can_save_dataset_with_no_subsets(self):
test_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(2, 1, 4, 4, label=2),
Bbox(4, 2, 8, 4, label=3),
],
attributes={'source_id': ''}),
DatasetItem(id=2,
image=np.ones((8, 8, 3)) * 2,
annotations=[
Bbox(4, 4, 4, 4, label=3),
],
attributes={'source_id': ''}),
DatasetItem(id=3,
image=np.ones((8, 4, 3)) * 3,
attributes={'source_id': ''}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(
test_dataset,
partial(TfDetectionApiConverter.convert, save_images=True),
test_dir)
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_import(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='0001_01',
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Bbox(2, 2, 1, 2, label=0),
Points([
2.787, 2.898, 2.965, 2.79, 2.8, 2.456,
2.81, 2.32, 2.89, 2.3
],
label=0),
]),
DatasetItem(id='0002_01',
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Bbox(1, 3, 1, 1, label=1),
Points([
1.2, 3.8, 1.8, 3.82, 1.51, 3.634, 1.43,
3.34, 1.65, 3.32
],
label=1)
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable([('n000001', 'car'),
('n000002', 'person')]),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'vgg_face2')
compare_datasets(self, expected_dataset, dataset)
def test_can_load_dataset_with_exact_image_info(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
parsed_dataset = Dataset.import_from(test_dir,
'yolo',
image_info={'1': (10, 15)})
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='кириллица с пробелом',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0, 1, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(i) for i in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'yolo')
compare_datasets(self, expected_dataset, dataset)
def test_remap_labels_delete_unspecified(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[ Label(0) ])
], categories={
AnnotationType.label: LabelCategories.from_iterable('label0'),
})
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[]),
], categories={
AnnotationType.label: LabelCategories(),
})
actual = transforms.RemapLabels(source_dataset,
mapping={}, default='delete')
compare_datasets(self, target_dataset, actual)
def test_can_save_dataset_with_no_subsets(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='a/b/c', image=np.zeros((8, 4, 3)),
annotations=[Label(1)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset, test_dir,
save_images=True)
parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
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_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id="кириллица с пробелом",
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(2)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet')
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
def from_iterable(cls, iterable: Iterable[DatasetItem],
categories: Union[Dict, List[str]] = None):
if isinstance(categories, list):
categories = { AnnotationType.label:
LabelCategories.from_iterable(categories)
}
if not categories:
categories = {}
class _extractor(Extractor):
def __iter__(self):
return iter(iterable)
def categories(self):
return categories
return cls.from_extractors(_extractor())
def test_can_import_points(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=Image(path='1.jpg', size=(5, 5)),
annotations=[
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=1, group=1, id=1,
attributes={'is_crowd': False}),
Polygon([0, 0, 4, 0, 4, 4],
label=1, group=1, id=1,
attributes={'is_crowd': False}),
Points([1, 2, 3, 4, 2, 3],
group=2, id=2,
attributes={'is_crowd': False}),
Bbox(1, 2, 2, 2,
group=2, id=2,
attributes={'is_crowd': False}),
Points([1, 2, 0, 2, 4, 1],
label=0, group=3, id=3,
attributes={'is_crowd': False}),
Bbox(0, 1, 4, 1,
label=0, group=3, id=3,
attributes={'is_crowd': False}),
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=5, id=5,
attributes={'is_crowd': False}),
Bbox(1, 2, 2, 2,
group=5, id=5,
attributes={'is_crowd': False}),
], attributes={'id': 1}),
], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b']),
AnnotationType.points: PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(2)
),
})
dataset = Dataset.import_from(
osp.join(DUMMY_DATASET_DIR, 'coco_person_keypoints'), 'coco')
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_save_and_load_with_multiple_labels(self):
source_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), Label(1)]),
DatasetItem(id='3',
image=np.ones((10, 10, 3)),
annotations=[Label(0), Label(2)]),
DatasetItem(id='4',
image=np.ones((8, 8, 3)),
annotations=[Label(2), Label(4)]),
DatasetItem(id='5',
image=np.ones((10, 10, 3)),
annotations=[Label(3), Label(4)]),
DatasetItem(
id='6',
image=np.ones((10, 10, 3)),
),
DatasetItem(id='7', image=np.ones((8, 8, 3))),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(5)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.zeros((8, 6, 3)),
annotations=[Label(0)]
),
DatasetItem(id='2', subset='train', image=np.zeros((2, 8, 3)),
annotations=[Label(5)]
),
DatasetItem(id='3', subset='train',
annotations=[Label(3)]
),
DatasetItem(id='4', subset='train',
annotations=[Label(5)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_%s' % label for label in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'imagenet_txt')
compare_datasets(self, expected_dataset, dataset, require_images=True)
def from_iterable(cls,
iterable: Iterable[DatasetItem],
categories: Union[CategoriesInfo, List[str]] = None,
env: Environment = None):
if isinstance(categories, list):
categories = {
AnnotationType.label: LabelCategories.from_iterable(categories)
}
if not categories:
categories = {}
class _extractor(Extractor):
def __init__(self):
super().__init__(length=len(iterable) \
if hasattr(iterable, '__len__') else None)
def __iter__(self):
return iter(iterable)
def categories(self):
return categories
return cls.from_extractors(_extractor(), env=env)
def test_can_save_and_load_keypoints(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id=1,
subset='train',
image=np.zeros((5, 5, 3)),
annotations=[
# Full instance annotations: polygon + keypoints
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=3,
group=1,
id=1),
Polygon([0, 0, 4, 0, 4, 4], label=3, group=1, id=1),
# Full instance annotations: bbox + keypoints
Points([1, 2, 3, 4, 2, 3], group=2, id=2),
Bbox(1, 2, 2, 2, group=2, id=2),
# Solitary keypoints
Points([1, 2, 0, 2, 4, 1], label=5, id=3),
# Some other solitary annotations (bug #1387)
Polygon([0, 0, 4, 0, 4, 4], label=3, id=4),
# Solitary keypoints with no label
Points([0, 0, 1, 2, 3, 4], [0, 1, 2], id=5),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(str(i) for i in range(10)),
AnnotationType.points:
PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(10)),
})
target_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.zeros((5, 5, 3)),
annotations=[
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=3,
group=1,
id=1,
attributes={'is_crowd': False}),
Polygon([0, 0, 4, 0, 4, 4],
label=3,
group=1,
id=1,
attributes={'is_crowd': False}),
Points([1, 2, 3, 4, 2, 3],
group=2,
id=2,
attributes={'is_crowd': False}),
Bbox(1,
2,
2,
2,
group=2,
id=2,
attributes={'is_crowd': False}),
Points([1, 2, 0, 2, 4, 1],
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
Bbox(0,
1,
4,
1,
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=5,
id=5,
attributes={'is_crowd': False}),
Bbox(1,
2,
2,
2,
group=5,
id=5,
attributes={'is_crowd': False}),
],
attributes={'id': 1}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(str(i) for i in range(10)),
AnnotationType.points:
PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
CocoPersonKeypointsConverter.convert,
test_dir,
target_dataset=target_dataset)
