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=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 _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 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_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_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=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 __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 _load_annotations(item):
parsed = item['annotations']
loaded = []
for ann in parsed:
ann_id = ann.get('id')
ann_type = AnnotationType[ann['type']]
attributes = ann.get('attributes')
group = ann.get('group')
label_id = ann.get('label_id')
z_order = ann.get('z_order')
points = ann.get('points')
if ann_type == AnnotationType.label:
loaded.append(Label(label=label_id,
id=ann_id, attributes=attributes, group=group))
elif ann_type == AnnotationType.mask:
rle = ann['rle']
rle['counts'] = rle['counts'].encode('ascii')
loaded.append(RleMask(rle=rle, label=label_id,
id=ann_id, attributes=attributes, group=group,
z_order=z_order))
elif ann_type == AnnotationType.polyline:
loaded.append(PolyLine(points, label=label_id,
id=ann_id, attributes=attributes, group=group,
z_order=z_order))
elif ann_type == AnnotationType.polygon:
loaded.append(Polygon(points, label=label_id,
id=ann_id, attributes=attributes, group=group,
z_order=z_order))
elif ann_type == AnnotationType.bbox:
x, y, w, h = ann['bbox']
loaded.append(Bbox(x, y, w, h, label=label_id,
id=ann_id, attributes=attributes, group=group,
z_order=z_order))
elif ann_type == AnnotationType.points:
loaded.append(Points(points, label=label_id,
id=ann_id, attributes=attributes, group=group,
z_order=z_order))
elif ann_type == AnnotationType.caption:
caption = ann.get('caption')
loaded.append(Caption(caption,
id=ann_id, attributes=attributes, group=group))
else:
raise NotImplementedError()
return loaded
def __iter__(self):
return iter([
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)
]),
])
def __iter__(self):
return iter([
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], label=None)
]),
])
def test_dataset(self):
label_categories = LabelCategories()
for i in range(5):
label_categories.add('cat' + str(i))
mask_categories = MaskCategories(
generate_colormap(len(label_categories.items)))
points_categories = PointsCategories()
for index, _ in enumerate(label_categories.items):
points_categories.add(index, ['cat1', 'cat2'], joints=[[0, 1]])
return Dataset.from_iterable([
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, label=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))),
], categories={
AnnotationType.label: label_categories,
AnnotationType.mask: mask_categories,
AnnotationType.points: points_categories,
})
def __iter__(self):
return iter([
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),
]),
])
def test_item_representations():
item = DatasetItem(id=1, subset='subset', path=['a', 'b'],
image=np.ones((5, 4, 3)),
annotations=[
Label(0, attributes={'a1': 1, 'a2': '2'}, id=1, group=2),
Caption('hello', id=1),
Caption('world', group=5),
Label(2, id=3, attributes={ 'x': 1, 'y': '2' }),
Bbox(1, 2, 3, 4, label=4, id=4, attributes={ 'a': 1.0 }),
Bbox(5, 6, 7, 8, id=5, group=5),
Points([1, 2, 2, 0, 1, 1], label=0, id=5),
Mask(id=5, image=np.ones((3, 2))),
Mask(label=3, id=5, image=np.ones((2, 3))),
PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11),
Polygon([1, 2, 3, 4, 5, 6, 7, 8]),
]
)
encoded = DatasetItemEncoder.encode(item)
DatasetItemEncoder.to_string(encoded)
def test_can_match_shapes(self):
source0 = Dataset.from_iterable(
[
DatasetItem(
1,
annotations=[
# unique
Bbox(1, 2, 3, 4, label=1),
# common
Mask(label=2,
z_order=2,
image=np.array([
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 0],
])),
Polygon([1, 0, 3, 2, 1, 2]),
# an instance with keypoints
Bbox(4, 5, 2, 4, label=2, z_order=1, group=1),
Points([5, 6], label=0, group=1),
Points([6, 8], label=1, group=1),
PolyLine([1, 1, 2, 1, 3, 1]),
]),
],
categories=['a', 'b', 'c'])
source1 = Dataset.from_iterable(
[
DatasetItem(
1,
annotations=[
# common
Mask(label=2,
image=np.array([
[0, 0, 0, 0],
[0, 1, 1, 1],
[0, 1, 1, 1],
[0, 1, 1, 1],
])),
Polygon([0, 2, 2, 0, 2, 1]),
# an instance with keypoints
Bbox(4, 4, 2, 5, label=2, z_order=1, group=2),
Points([5.5, 6.5], label=0, group=2),
Points([6, 8], label=1, group=2),
PolyLine([1, 1.5, 2, 1.5]),
]),
],
categories=['a', 'b', 'c'])
source2 = Dataset.from_iterable(
[
DatasetItem(
1,
annotations=[
# common
Mask(label=2,
z_order=3,
image=np.array([
[0, 0, 1, 1],
[0, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 0],
])),
Polygon([3, 1, 2, 2, 0, 1]),
# an instance with keypoints, one is missing
Bbox(3, 6, 2, 3, label=2, z_order=4, group=3),
Points([4.5, 5.5], label=0, group=3),
PolyLine([1, 1.25, 3, 1, 4, 2]),
]),
],
categories=['a', 'b', 'c'])
expected = Dataset.from_iterable(
[
DatasetItem(
1,
annotations=[
# unique
Bbox(1, 2, 3, 4, label=1),
# common
# nearest to mean bbox
Mask(label=2,
z_order=3,
image=np.array([
[0, 0, 0, 0],
[0, 1, 1, 1],
[0, 1, 1, 1],
[0, 1, 1, 1],
])),
Polygon([1, 0, 3, 2, 1, 2]),
# an instance with keypoints
Bbox(4, 5, 2, 4, label=2, z_order=4, group=1),
Points([5, 6], label=0, group=1),
Points([6, 8], label=1, group=1),
PolyLine([1, 1.25, 3, 1, 4, 2]),
]),
],
categories=['a', 'b', 'c'])
merger = IntersectMerge(conf={'quorum': 1, 'pairwise_dist': 0.1})
merged = merger([source0, source1, source2])
compare_datasets(self, expected, merged, ignored_attrs={'score'})
self.assertEqual(
[
NoMatchingAnnError(item_id=('1', ''),
sources={2},
ann=source0.get('1').annotations[5]),
NoMatchingAnnError(item_id=('1', ''),
sources={1, 2},
ann=source0.get('1').annotations[0]),
],
sorted(
(e
for e in merger.errors if isinstance(e, NoMatchingAnnError)),
key=lambda e: len(e.sources)))
def test_can_load_video(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='frame_000010',
subset='annotations',
image=255 * 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=255 * 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 = Dataset.import_from(DUMMY_VIDEO_DATASET_DIR, 'cvat')
compare_datasets(self, expected_dataset, parsed_dataset)
def test_can_save_and_load(self):
src_label_cat = LabelCategories(attributes={'occluded', 'common'})
for i in range(10):
src_label_cat.add(str(i))
src_label_cat.items[2].attributes.update(['a1', 'a2', 'empty'])
source_dataset = Dataset.from_iterable(
[
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,
'common': 't'
}),
Points(
[1, 1, 3, 2, 2, 3],
label=2,
attributes={
'a1': 'x',
'a2': 42,
'empty': '',
'unknown': 'bar'
}),
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],
z_order=1,
label=3,
group=4,
attributes={'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))),
],
categories={AnnotationType.label: src_label_cat})
target_label_cat = LabelCategories(
attributes={'occluded'}) # unable to represent a common attribute
for i in range(10):
target_label_cat.add(str(i), attributes={'common'})
target_label_cat.items[2].attributes.update(
['a1', 'a2', 'empty', 'common'])
target_dataset = Dataset.from_iterable(
[
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,
'common': 't'
}),
Points(
[1, 1, 3, 2, 2, 3],
label=2,
attributes={
'occluded': False,
'empty': '',
'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}),
],
categories={AnnotationType.label: target_label_cat})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CvatConverter.convert,
save_images=True),
test_dir,
target_dataset=target_dataset)
def test_can_compare_projects(self): # just a smoke test
label_categories1 = LabelCategories.from_iterable(['x', 'a', 'b', 'y'])
mask_categories1 = MaskCategories.make_default(len(label_categories1))
point_categories1 = PointsCategories()
for index, _ in enumerate(label_categories1.items):
point_categories1.add(index, ['cat1', 'cat2'], joints=[[0, 1]])
dataset1 = Dataset.from_iterable(
[
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=0,
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, label=2, 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))),
],
categories={
AnnotationType.label: label_categories1,
AnnotationType.mask: mask_categories1,
AnnotationType.points: point_categories1,
})
label_categories2 = LabelCategories.from_iterable(['a', 'b', 'x', 'y'])
mask_categories2 = MaskCategories.make_default(len(label_categories2))
point_categories2 = PointsCategories()
for index, _ in enumerate(label_categories2.items):
point_categories2.add(index, ['cat1', 'cat2'], joints=[[0, 1]])
dataset2 = Dataset.from_iterable(
[
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=1,
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, label=3, 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))),
],
categories={
AnnotationType.label: label_categories2,
AnnotationType.mask: mask_categories2,
AnnotationType.points: point_categories2,
})
with TestDir() as test_dir:
with DatasetDiffVisualizer(
save_dir=test_dir,
comparator=DistanceComparator(iou_threshold=0.8),
) as visualizer:
visualizer.save(dataset1, dataset2)
self.assertNotEqual(0, os.listdir(osp.join(test_dir)))
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}),
])