def test_polygons_to_masks(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4]),
Polygon([5, 0, 9, 0, 5, 5]),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Mask(
np.array([
[0, 0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]), ),
Mask(
np.array([
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]), ),
]),
])
actual = transforms.PolygonsToMasks(source_dataset)
compare_datasets(self, target_dataset, actual)
def __iter__(self):
return iter([
DatasetItem(id=1, subset='train', image=np.ones((4, 4, 3)),
annotations=[
Polygon([0, 1, 2, 1, 2, 3, 0, 3],
attributes={ 'is_crowd': False },
label=2, group=1, id=1),
]),
DatasetItem(id=2, subset='train', image=np.ones((4, 4, 3)),
annotations=[
Mask(np.array([
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 1, 1],
[0, 0, 0, 0]],
),
attributes={ 'is_crowd': True },
label=4, group=3, id=3),
]),
DatasetItem(id=3, subset='val', image=np.ones((4, 4, 3)),
annotations=[
Mask(np.array([
[0, 0, 0, 0],
[1, 1, 1, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]],
),
attributes={ 'is_crowd': True },
label=4, group=3, id=3),
]),
])
def test_annotation_comparison(self):
a = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Caption('hello'), # unmatched
Caption('world', group=5),
Label(2, attributes={ 'x': 1, 'y': '2', }),
Bbox(1, 2, 3, 4, label=4, z_order=1, attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, group=5),
Points([1, 2, 2, 0, 1, 1], label=0, z_order=4),
Mask(label=3, z_order=2, image=np.ones((2, 3))),
]),
], categories=['a', 'b', 'c', 'd'])
b = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Caption('world', group=5),
Label(2, attributes={ 'x': 1, 'y': '2', }),
Bbox(1, 2, 3, 4, label=4, z_order=1, attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, group=5),
Bbox(5, 6, 7, 8, group=5), # unmatched
Points([1, 2, 2, 0, 1, 1], label=0, z_order=4),
Mask(label=3, z_order=2, image=np.ones((2, 3))),
]),
], categories=['a', 'b', 'c', 'd'])
comp = ExactComparator()
matched, unmatched, _, _, errors = comp.compare_datasets(a, b)
self.assertEqual(6, len(matched), matched)
self.assertEqual(2, len(unmatched), unmatched)
self.assertEqual(0, len(errors), errors)
def test_crop_covered_segments(self):
source_dataset = Dataset.from_iterable([
DatasetItem(
id=1,
image=np.zeros((5, 5, 3)),
annotations=[
# The mask is partially covered by the polygon
Mask(np.array(
[[0, 0, 1, 1, 1], [0, 0, 1, 1, 1], [1, 1, 1, 1, 1],
[1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], ),
z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1),
]),
])
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]], ),
z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1),
]),
])
actual = transforms.CropCoveredSegments(source_dataset)
compare_datasets(self, target_dataset, actual)
def __iter__(self):
return iter([
DatasetItem(id=1, subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=0, group=2, id=0,
attributes={
'occluded': False, 'username': '',
}
),
Polygon([0, 4, 4, 4, 5, 6], label=1, id=1,
attributes={
'occluded': True, 'username': '',
'a1': 'qwe',
'a2': True,
'a3': 123,
}
),
Mask(np.array([[0, 1], [1, 0], [1, 1]]), group=2,
id=2, attributes={
'occluded': False, 'username': '******'
}
),
Bbox(1, 2, 3, 4, group=1, id=3, attributes={
'occluded': False, 'username': '',
}),
Mask(np.array([[0, 0], [0, 0], [1, 1]]), group=1,
id=4, attributes={
'occluded': True, 'username': ''
}
),
]
),
])
def test_can_import_masks(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
image=np.ones((2, 5, 3)),
annotations=[
Mask(group=0,
image=np.array([[0, 1, 1, 0, 0], [0, 0, 0, 0, 0]]),
attributes={ 'index': 0, 'color': '108 225 132',
'text': 'F', 'center': '0 1'
}),
Mask(group=1,
image=np.array([[0, 0, 0, 1, 0], [0, 0, 0, 1, 0]]),
attributes={ 'index': 1, 'color': '82 174 214',
'text': 'T', 'center': '1 3'
}),
Mask(group=1,
image=np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 1]]),
attributes={ 'index': 2, 'color': '241 73 144',
'text': 'h', 'center': '1 4'
}),
]
),
])
dataset = Dataset.import_from(
osp.join(DUMMY_DATASET_DIR, 'text_segmentation'), 'icdar')
compare_datasets(self, expected_dataset, dataset)
def test_can_save_and_load_image_with_arbitrary_extension(self):
expected = Dataset.from_iterable([
DatasetItem('q/1',
image=Image(path='q/1.JPEG', data=np.zeros((4, 3, 3))),
annotations=[
Mask(np.array([[0, 1, 0, 0, 0]]),
label=0,
attributes={'track_id': 1}),
]),
DatasetItem('a/b/c/2',
image=Image(path='a/b/c/2.bmp',
data=np.zeros((3, 4, 3))),
annotations=[
Mask(np.array([[0, 1, 0, 0, 0]]),
label=0,
attributes={'track_id': 1}),
]),
],
categories=['a'])
with TestDir() as test_dir:
self._test_save_and_load(expected,
partial(MotsPngConverter.convert,
save_images=True),
test_dir,
require_images=True)
def test_boxes_to_masks(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 3, 3, z_order=1),
Bbox(0, 0, 3, 1, z_order=2),
Bbox(0, 2, 3, 1, z_order=3),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(
id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array(
[[1, 1, 1, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=1),
Mask(np.array(
[[1, 1, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=2),
Mask(np.array(
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=3),
]),
])
actual = transforms.BoxesToMasks(source_dataset)
compare_datasets(self, target_dataset, actual)
def __iter__(self):
yield DatasetItem(id=1,
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]),
label=self._label('label_1')),
Mask(image=np.array([[0, 0, 1, 0, 1]]),
label=self._label('label_2')),
])
def _load_annotations(self, item_id):
item_annotations = []
class_mask = None
segm_path = osp.join(self._dataset_dir, VocPath.SEGMENTATION_DIR,
item_id + VocPath.SEGM_EXT)
if osp.isfile(segm_path):
inverse_cls_colormap = \
self._categories[AnnotationType.mask].inverse_colormap
class_mask = lazy_mask(segm_path, inverse_cls_colormap)
instances_mask = None
inst_path = osp.join(self._dataset_dir, VocPath.INSTANCES_DIR,
item_id + VocPath.SEGM_EXT)
if osp.isfile(inst_path):
instances_mask = lazy_mask(inst_path, _inverse_inst_colormap)
if instances_mask is not None:
compiled_mask = CompiledMask(class_mask, instances_mask)
if class_mask is not None:
label_cat = self._categories[AnnotationType.label]
instance_labels = compiled_mask.get_instance_labels(
class_count=len(label_cat.items))
else:
instance_labels = {
i: None
for i in range(compiled_mask.instance_count)
}
for instance_id, label_id in instance_labels.items():
image = compiled_mask.lazy_extract(instance_id)
attributes = {}
if label_id is not None:
actions = {
a: False
for a in label_cat.items[label_id].attributes
}
attributes.update(actions)
item_annotations.append(
Mask(image=image,
label=label_id,
attributes=attributes,
group=instance_id))
elif class_mask is not None:
log.warn("item '%s': has only class segmentation, "
"instance masks will not be available" % item_id)
class_mask = class_mask()
classes = np.unique(class_mask)
for label_id in classes:
image = self._lazy_extract_mask(class_mask, label_id)
item_annotations.append(Mask(image=image, label=label_id))
return item_annotations
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 __iter__(self):
return iter([
DatasetItem(id='кириллица с пробелом',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 0, 0, 1, 0]]),
label=0),
Mask(image=np.array([[0, 1, 1, 0, 1]]),
label=3),
]),
])
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4,
group=1),
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
group=2),
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
group=3),
]),
])
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
# overlapping masks, the first should be truncated
# the second and third are different instances
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
z_order=3),
Mask(image=np.array([[0, 1, 1, 1, 0]]), label=4,
z_order=1),
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
z_order=2),
]),
])
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='a',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[0, 0, 0, 1, 0]]),
label=0),
Mask(image=np.array([[0, 1, 1, 0, 0]]),
label=3),
Mask(image=np.array([[1, 0, 0, 0, 1]]),
label=4),
]),
])
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 __iter__(self):
return iter([
DatasetItem(id='q/1',
image=Image(path='q/1.JPEG',
data=np.zeros((4, 3, 3)))),
DatasetItem(id='a/b/c/2',
image=Image(path='a/b/c/2.bmp',
data=np.ones((1, 5, 3))),
annotations=[
Mask(np.array([[0, 0, 0, 1, 0]]),
label=self._label('a')),
Mask(np.array([[0, 1, 1, 0, 0]]),
label=self._label('b')),
])
])
def test_transform_labels(self):
src_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
annotations=[
Label(1),
Bbox(1, 2, 3, 4, label=2),
Bbox(1, 3, 3, 3),
Mask(image=np.array([1]), label=3),
Polygon([1, 1, 2, 2, 3, 4], label=4),
PolyLine([1, 3, 4, 2, 5, 6], label=5)
])
],
categories=['label%s' % i for i in range(6)])
dst_dataset = Dataset.from_iterable([
DatasetItem(
id=1,
annotations=[Label(1),
Label(2),
Label(3),
Label(4),
Label(5)]),
],
categories=[
'label%s' % i for i in range(6)
])
actual = transforms.AnnsToLabels(src_dataset)
compare_datasets(self, dst_dataset, actual)
def test_shapes_to_boxes(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, 0, 0, 1],
[1, 0, 0, 0, 1], [1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]], ),
id=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], id=2),
PolyLine([1, 1, 2, 1, 2, 2, 1, 2], id=3),
Points([2, 2, 4, 2, 4, 4, 2, 4], id=4),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 4, 4, id=1),
Bbox(1, 1, 3, 3, id=2),
Bbox(1, 1, 1, 1, id=3),
Bbox(2, 2, 2, 2, id=4),
]),
])
actual = transforms.ShapesToBoxes(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_can_convert_polygons_to_mask(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((6, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=3, id=4, group=4),
Polygon([5, 0, 9, 0, 5, 5],
label=3, id=4, group=4),
]
),
], categories=[str(i) for i in range(10)])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((6, 10, 3)),
annotations=[
Mask(np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
# only internal fragment (without the border),
# but not everywhere...
),
attributes={ 'is_crowd': True },
label=3, id=4, group=4),
], 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, segmentation_mode='mask'),
test_dir, target_dataset=target_dataset)
def test_mask_to_polygons(self):
source = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Mask(
np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]), ),
]),
])
expected = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5]),
Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5]),
]),
])
actual = transforms.MasksToPolygons(source)
compare_datasets(self, expected, actual)
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_import_mots_annotations_with_splited_masks(self):
#https://github.com/openvinotoolkit/cvat/issues/3360
format_name = 'MOTS PNG 1.0'
source_dataset = Dataset.from_iterable([
DatasetItem(id='image_0',
annotations=[
Mask(np.array([[1, 1, 1, 0, 1, 1, 1]] * 5),
label=0,
attributes={'track_id': 0})
])
],
categories=['label_0'])
with tempfile.TemporaryDirectory() as temp_dir:
dataset_dir = osp.join(temp_dir, 'dataset')
source_dataset.export(dataset_dir, 'mots_png')
dataset_path = osp.join(temp_dir, 'annotations.zip')
make_zip_archive(dataset_dir, dataset_path)
images = self._generate_task_images(1, size=(5, 7))
task = {
'name': 'test',
"overlap": 0,
"segment_size": 100,
"labels": [{
'name': 'label_0'
}]
}
task.update()
task = self._create_task(task, images)
dm.task.import_task_annotations(task['id'], dataset_path,
format_name)
self._test_can_import_annotations(task, format_name)
def test_can_import_instances(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='000000000001',
image=np.ones((10, 5, 3)),
subset='val',
attributes={'id': 1},
annotations=[
Polygon([0, 0, 1, 0, 1, 2, 0, 2],
label=0,
id=1,
group=1,
attributes={
'is_crowd': False,
'x': 1,
'y': 'hello'
}),
Mask(np.array([[1, 0, 0, 1, 0]] * 5 +
[[1, 1, 1, 1, 0]] * 5),
label=0,
id=2,
group=2,
attributes={'is_crowd': True}),
]),
],
categories=[
'TEST',
])
dataset = Dataset.import_from(
osp.join(DUMMY_DATASET_DIR, 'coco_instances'), 'coco')
compare_datasets(self, expected_dataset, dataset)
def __iter__(self):
return iter([
DatasetItem(
id='2007_000001',
subset='train',
image=Image(path='2007_000001.jpg', size=(10, 20)),
annotations=[
Label(self._label(l.name))
for l in VOC.VocLabel if l.value % 2 == 1
] + [
Bbox(
1,
2,
2,
2,
label=self._label('cat'),
attributes={
'pose': VOC.VocPose(1).name,
'truncated': True,
'difficult': False,
'occluded': False,
},
id=1,
group=1,
),
Bbox(
4,
5,
2,
2,
label=self._label('person'),
attributes={
'truncated': False,
'difficult': False,
'occluded': False,
**{
a.name: a.value % 2 == 1
for a in VOC.VocAction
}
},
id=2,
group=2,
),
Bbox(5.5,
6,
2,
2,
label=self._label(VOC.VocBodyPart(1).name),
group=2),
Mask(
image=np.ones([5, 10]),
label=self._label(VOC.VocLabel(2).name),
group=1,
),
]),
DatasetItem(id='2007_000002',
subset='test',
image=np.ones((10, 20, 3))),
])
def test_merge_instance_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, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]],
),
z_order=0, group=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
z_order=1, group=1),
Polygon([0, 0, 0, 2, 2, 2, 2, 0],
z_order=1),
]
),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 0],
[1, 1, 1, 0, 0]],
),
z_order=0, group=1),
Mask(np.array([
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
),
z_order=1),
]
),
])
actual = transforms.MergeInstanceSegments(source_dataset,
include_polygons=True)
compare_datasets(self, target_dataset, actual)
def __iter__(self):
return iter([
DatasetItem(
id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([[0, 0, 1, 1, 1], [0, 1, 1, 1, 1],
[1, 1, 1, 1, 1], [1, 1, 1, 1, 0],
[1, 1, 1, 0, 0]], ),
z_order=0,
group=1),
Mask(np.array([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], ),
z_order=1),
]),
])
def __iter__(self):
return iter([
DatasetItem(id=100,
subset='train',
image=np.ones((10, 6, 3)),
annotations=[
Caption('hello', id=1),
Caption('world', id=2, group=5),
Label(2, id=3, attributes={
'x': 1,
'y': '2',
}),
Bbox(1,
2,
3,
4,
label=4,
id=4,
z_order=1,
attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, id=5, group=5),
Points([1, 2, 2, 0, 1, 1],
label=0,
id=5,
z_order=4),
Mask(label=3,
id=5,
z_order=2,
image=np.ones((2, 3))),
]),
DatasetItem(id=21,
subset='train',
annotations=[
Caption('test'),
Label(2),
Bbox(1, 2, 3, 4, 5, id=42, group=42)
]),
DatasetItem(id=2,
subset='val',
annotations=[
PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
id=11,
z_order=1),
Polygon([1, 2, 3, 4, 5, 6, 7, 8],
id=12,
z_order=4),
]),
DatasetItem(id=42,
subset='test',
attributes={
'a1': 5,
'a2': '42'
}),
DatasetItem(id=42),
DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))),
])
def test_can_save_masks(self):
source = Dataset.from_iterable([
DatasetItem(id=1, subset='a', image=np.ones((5, 1)), annotations=[
# overlapping masks, the first should be truncated
# the first and third are different instances
Mask(np.array([[0, 0, 0, 1, 0]]), label=3, z_order=3,
attributes={'track_id': 1}),
Mask(np.array([[0, 1, 1, 1, 0]]), label=2, z_order=1,
attributes={'track_id': 2}),
Mask(np.array([[1, 1, 0, 0, 0]]), label=3, z_order=2,
attributes={'track_id': 3}),
]),
DatasetItem(id=2, subset='a', image=np.ones((5, 1)), annotations=[
Mask(np.array([[1, 0, 0, 0, 0]]), label=3,
attributes={'track_id': 2}),
]),
DatasetItem(id=3, subset='b', image=np.ones((5, 1)), annotations=[
Mask(np.array([[0, 1, 0, 0, 0]]), label=0,
attributes={'track_id': 1}),
]),
], categories=['a', 'b', 'c', 'd'])
target = Dataset.from_iterable([
DatasetItem(id=1, subset='a', image=np.ones((5, 1)), annotations=[
Mask(np.array([[0, 0, 0, 1, 0]]), label=3,
attributes={'track_id': 1}),
Mask(np.array([[0, 0, 1, 0, 0]]), label=2,
attributes={'track_id': 2}),
Mask(np.array([[1, 1, 0, 0, 0]]), label=3,
attributes={'track_id': 3}),
]),
DatasetItem(id=2, subset='a', image=np.ones((5, 1)), annotations=[
Mask(np.array([[1, 0, 0, 0, 0]]), label=3,
attributes={'track_id': 2}),
]),
DatasetItem(id=3, subset='b', image=np.ones((5, 1)), annotations=[
Mask(np.array([[0, 1, 0, 0, 0]]), label=0,
attributes={'track_id': 1}),
]),
], categories=['a', 'b', 'c', 'd'])
with TestDir() as test_dir:
self._test_save_and_load(source,
partial(MotsPngConverter.convert, save_images=True),
test_dir, target_dataset=target)
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
Mask(image=bit(x, y, shape=[10, 10]),
label=self._label(VOC.VocLabel(3).name),
group=10 * y + x + 1
)
for y in range(10) for x in range(10)
]),
])
