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_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)
def test_can_merge_categories(self):
source0 = Dataset.from_iterable(
[
DatasetItem(1, annotations=[
Label(0),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['a', 'b']),
AnnotationType.points:
PointsCategories.from_iterable([
(0, ['l0', 'l1']),
(1, ['l2', 'l3']),
]),
AnnotationType.mask:
MaskCategories({
0: (0, 1, 2),
1: (1, 2, 3),
}),
})
source1 = Dataset.from_iterable(
[
DatasetItem(1, annotations=[
Label(0),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['c', 'b']),
AnnotationType.points:
PointsCategories.from_iterable([
(0, []),
(1, ['l2', 'l3']),
]),
AnnotationType.mask:
MaskCategories({
0: (0, 2, 4),
1: (1, 2, 3),
}),
})
expected = Dataset.from_iterable(
[
DatasetItem(1, annotations=[
Label(0),
Label(2),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['a', 'b', 'c']),
AnnotationType.points:
PointsCategories.from_iterable([
(0, ['l0', 'l1']),
(1, ['l2', 'l3']),
(2, []),
]),
AnnotationType.mask:
MaskCategories({
0: (0, 1, 2),
1: (1, 2, 3),
2: (0, 2, 4),
}),
})
merger = IntersectMerge()
merged = merger([source0, source1])
compare_datasets(self, expected, merged, ignored_attrs={'score'})