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 test_reindex(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=2,
image=np.ones((4, 2, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4], label=0, id=5),
],
attributes={'id': 22})
],
categories=[str(i) for i in range(10)])
target_dataset = Dataset.from_iterable(
[
DatasetItem(id=2,
image=np.ones((4, 2, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=0,
id=1,
group=1,
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(CocoConverter.convert,
reindex=True),
test_dir,
target_dataset=target_dataset)
def __iter__(self):
return iter([
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),
])
])
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_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 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_and_load_bboxes(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='a/b/1',
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Bbox(1, 3, 6, 10),
Bbox(0, 1, 3, 5, attributes={'text': 'word 0'}),
]),
DatasetItem(id=2,
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Polygon([0, 0, 3, 0, 4, 7, 1, 8],
attributes={'text': 'word 1'}),
Polygon([1, 2, 5, 3, 6, 8, 0, 7]),
]),
DatasetItem(id=3,
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Polygon([2, 2, 8, 3, 7, 10, 2, 9],
attributes={'text': 'word_2'}),
Bbox(0, 2, 5, 9, attributes={'text': 'word_3'}),
]),
])
with TestDir() as test_dir:
self._test_save_and_load(
expected_dataset,
partial(IcdarTextLocalizationConverter.convert,
save_images=True), test_dir, 'icdar_text_localization')
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4]),
Polygon([5, 0, 9, 0, 5, 5]),
]),
])
def __iter__(self):
return iter([
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]),
]
),
])
def __iter__(self):
return iter([
DatasetItem(id=0,
subset='s1',
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=1,
group=4,
attributes={'occluded': True}),
Points([1, 1, 3, 2, 2, 3],
label=2,
attributes={
'occluded': False,
'a1': 'x',
'a2': 42
}),
Label(1),
Label(2, attributes={
'a1': 'y',
'a2': 44
}),
],
attributes={'frame': 0}),
DatasetItem(id=1,
subset='s1',
annotations=[
PolyLine([0, 0, 4, 0, 4, 4],
label=3,
group=4,
attributes={'occluded': False}),
Bbox(5,
0,
1,
9,
label=3,
group=4,
attributes={'occluded': False}),
],
attributes={'frame': 1}),
DatasetItem(id=2,
subset='s2',
image=np.ones((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
z_order=1,
label=3,
group=4,
attributes={'occluded': False}),
],
attributes={'frame': 0}),
DatasetItem(id=3,
subset='s3',
image=Image(path='3.jpg', size=(2, 4)),
attributes={'frame': 0}),
])
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 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=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),
]
),
])
def __iter__(self):
return iter([
DatasetItem(id=0,
subset='s1',
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=1,
group=4,
attributes={'occluded': True}),
Polygon([5, 0, 9, 0, 5, 5],
label=2,
group=4,
attributes={'unknown': 'bar'}),
Points([1, 1, 3, 2, 2, 3],
label=2,
attributes={
'a1': 'x',
'a2': 42
}),
Label(1),
Label(2, attributes={
'a1': 'y',
'a2': 44
}),
]),
DatasetItem(id=1,
subset='s1',
annotations=[
PolyLine([0, 0, 4, 0, 4, 4],
label=3,
id=4,
group=4),
Bbox(5, 0, 1, 9, label=3, id=4, group=4),
]),
DatasetItem(
id=2,
subset='s2',
image=np.ones((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=3,
group=4,
attributes={
'z_order': 1,
'occluded': False
}),
PolyLine([5, 0, 9, 0, 5,
5]), # will be skipped as no label
]),
DatasetItem(id=3,
subset='s3',
image=Image(path='3.jpg', size=(2, 4))),
])
def __iter__(self):
return iter([
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}),
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
group=2,
id=2,
attributes={'is_crowd': False}),
]),
DatasetItem(id=2,
subset='train',
annotations=[
Points([1, 2, 0, 2, 4, 1],
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
Polygon([0, 1, 4, 1, 4, 2, 0, 2],
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
]),
DatasetItem(id=3,
subset='val',
annotations=[
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=3,
id=3,
attributes={'is_crowd': False}),
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
group=3,
id=3,
attributes={'is_crowd': False}),
]),
])
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=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],
label=3, id=4, group=4,
attributes={ 'is_crowd': False }),
Polygon(
[5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5],
label=3, id=4, group=4,
attributes={ 'is_crowd': False }),
]
),
])
def _parse_shape_ann(cls, ann, categories):
ann_id = ann.get('id')
ann_type = ann['type']
attributes = ann.get('attributes', {})
if 'occluded' in categories[AnnotationType.label].attributes:
attributes['occluded'] = ann.get('occluded', False)
if 'outside' in categories[AnnotationType.label].attributes:
attributes['outside'] = ann.get('outside', False)
if 'keyframe' in categories[AnnotationType.label].attributes:
attributes['keyframe'] = ann.get('keyframe', False)
group = ann.get('group')
label = ann.get('label')
label_id = categories[AnnotationType.label].find(label)[0]
z_order = ann.get('z_order', 0)
points = ann.get('points', [])
if ann_type == 'polyline':
return PolyLine(points,
label=label_id,
z_order=z_order,
id=ann_id,
attributes=attributes,
group=group)
elif ann_type == 'polygon':
return Polygon(points,
label=label_id,
z_order=z_order,
id=ann_id,
attributes=attributes,
group=group)
elif ann_type == 'points':
return Points(points,
label=label_id,
z_order=z_order,
id=ann_id,
attributes=attributes,
group=group)
elif ann_type == 'box':
x, y = points[0], points[1]
w, h = points[2] - x, points[3] - y
return Bbox(x,
y,
w,
h,
label=label_id,
z_order=z_order,
id=ann_id,
attributes=attributes,
group=group)
else:
raise NotImplementedError("Unknown annotation type '%s'" %
ann_type)
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 download_labeled_image(task_api, task_id:int, frame_id:int, outdir:str):
filename, img = task_api.download_frame(task_id, frame_id, outdir, save=False)
labels = dict([(label.id, label) for label in task_api.get_id(task_id).labels])
jobs = task_api.get_jobs(task_id)
job_id_with_frame = [job.id for job in jobs if (int(job.start_frame) <= frame_id) & (frame_id <= int(job.stop_frame))]
job_annos_list = [job_api.get_annotations(job_id) for job_id in job_id_with_frame]
annos = [[anno for anno in job_annos.shapes if anno.frame==frame_id][0] for job_annos in job_annos_list]
categories=[label.name for label in labels.values()]
items = []
for anno in annos:
attriname = dict([ (attr.id, attr.name) for attr in labels[anno.label_id].attributes ])
attris = dict([ (attriname[attr.spec_id], attr.value) for attr in anno.attributes ])
if anno.type=='rectangle':
x,y,x2,y2 = anno.points
items.append(Bbox(x,y,x2-x, y2-y,attributes=attris, label=categories.index(labels[anno.label_id].name)))
elif anno.type=='polygon':
items.append(Polygon(anno.points,attributes=attris, label=categories.index(labels[anno.label_id].name)))
# image = {'data': np.asarray(img),
# 'path': str(Path(outdir)/filename)}
dataset = Dataset.from_iterable([
DatasetItem(id=filename, annotations=items, image=np.array(img))],
categories=categories)
customset = customDataset(dataset)
for image_data in customset._imageDatas:
image_data.drawItem('s','s').saveImg(Path(outdir)/filename)
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 __iter__(self):
return iter([
DatasetItem(id=0,
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],
z_order=0,
attributes={'occluded': False}),
]),
DatasetItem(id=1,
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}),
]),
])
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 __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_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 crop_segments(cls, segment_anns, img_width, img_height):
segment_anns = sorted(segment_anns, key=lambda x: x.z_order)
segments = []
for s in segment_anns:
if s.type == AnnotationType.polygon:
segments.append(s.points)
elif s.type == AnnotationType.mask:
if isinstance(s, RleMask):
rle = s.rle
else:
rle = mask_tools.mask_to_rle(s.image)
segments.append(rle)
segments = mask_tools.crop_covered_segments(
segments, img_width, img_height)
new_anns = []
for ann, new_segment in zip(segment_anns, segments):
fields = {'z_order': ann.z_order, 'label': ann.label,
'id': ann.id, 'group': ann.group, 'attributes': ann.attributes
}
if ann.type == AnnotationType.polygon:
if fields['group'] is None:
fields['group'] = cls._make_group_id(
segment_anns + new_anns, fields['id'])
for polygon in new_segment:
new_anns.append(Polygon(points=polygon, **fields))
else:
rle = mask_tools.mask_to_rle(new_segment)
rle = mask_utils.frPyObjects(rle, *rle['size'])
new_anns.append(RleMask(rle=rle, **fields))
return new_anns
def test_can_convert_masks_to_polygons(self):
source_dataset = 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],
]),
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((5, 10, 3)),
annotations=[
Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5],
label=3,
id=4,
group=4,
attributes={'is_crowd': False}),
Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5],
label=3,
id=4,
group=4,
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,
segmentation_mode='polygons'),
test_dir,
target_dataset=target_dataset)
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 convert_mask(mask):
polygons = mask_tools.mask_to_polygons(mask.image)
return [
Polygon(points=p, label=mask.label, z_order=mask.z_order,
id=mask.id, attributes=mask.attributes, group=mask.group)
for p in polygons
]
