def test_remap_labels_ignore_missing_labels_in_secondary_categories(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(0),
])
], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b', 'c']),
AnnotationType.points: PointsCategories.from_iterable([]), # all missing
AnnotationType.mask: MaskCategories.generate(2) # no c color
})
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(0),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(['d', 'e', 'f']),
AnnotationType.points: PointsCategories.from_iterable([]),
AnnotationType.mask: MaskCategories.generate(2)
})
actual = transforms.RemapLabels(source_dataset,
mapping={ 'a': 'd', 'b': 'e', 'c': 'f' }, default='delete')
compare_datasets(self, target_dataset, actual)
def test_project_labels_maps_secondary_categories(self):
source = Dataset.from_iterable([], categories={
AnnotationType.label: LabelCategories.from_iterable([
'a', 'b', # no parents
('c', 'a'), ('d', 'b') # have parents
]),
AnnotationType.points: PointsCategories.from_iterable([
(0, ['a']), (1, ['b']), (2, ['c'])
]),
AnnotationType.mask: MaskCategories.generate(4)
})
expected = Dataset.from_iterable([], categories={
AnnotationType.label: LabelCategories.from_iterable([
('c', 'a'), # must keep parent
'a',
'd' # must drop parent because it was removed
]),
AnnotationType.points: PointsCategories.from_iterable([
(0, ['c']), (1, ['a'])
]),
AnnotationType.mask: MaskCategories(colormap={
i: v for i, v in {
{ 2: 0, 0: 1, 3: 2 }.get(k): v
for k, v in mask_tools.generate_colormap(4).items()
}.items()
if i is not None
}),
})
actual = transforms.ProjectLabels(source, dst_labels=['c', 'a', 'd'])
compare_datasets(self, expected, actual)
def test_project_labels_generates_colors_for_added_labels(self):
source = Dataset.from_iterable([], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b', 'c']),
AnnotationType.mask: MaskCategories.generate(2)
})
actual = transforms.ProjectLabels(source, dst_labels=['a', 'c', 'd'])
self.assertEqual((0, 0, 0), actual.categories()[AnnotationType.mask][0])
self.assertNotIn(1, actual.categories()[AnnotationType.mask])
self.assertIn(2, actual.categories()[AnnotationType.mask])
def test_inplace_save_writes_only_updated_data(self):
src_mask_cat = MaskCategories.generate(3, include_background=False)
expected = Dataset.from_iterable(
[
DatasetItem(1,
subset='a',
image=np.ones((2, 1, 3)),
annotations=[Mask(np.ones((2, 1)), label=2)]),
DatasetItem(2, subset='a', image=np.ones((3, 2, 3))),
DatasetItem(2, subset='b'),
],
categories=Camvid.make_camvid_categories(
OrderedDict([
('background', (0, 0, 0)),
('a', src_mask_cat.colormap[0]),
('b', src_mask_cat.colormap[1]),
])))
with TestDir() as path:
dataset = Dataset.from_iterable(
[
DatasetItem(1,
subset='a',
image=np.ones((2, 1, 3)),
annotations=[Mask(np.ones((2, 1)), label=1)]),
DatasetItem(2, subset='b'),
DatasetItem(3,
subset='c',
image=np.ones((2, 2, 3)),
annotations=[Mask(np.ones((2, 2)), label=0)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(['a', 'b']),
AnnotationType.mask: src_mask_cat
})
dataset.export(path, 'camvid', save_images=True)
dataset.put(DatasetItem(2, subset='a', image=np.ones((3, 2, 3))))
dataset.remove(3, 'c')
dataset.save(save_images=True)
self.assertEqual(
{'a', 'aannot', 'a.txt', 'b.txt', 'label_colors.txt'},
set(os.listdir(path)))
self.assertEqual({'1.jpg', '2.jpg'},
set(os.listdir(osp.join(path, 'a'))))
compare_datasets(self,
expected,
Dataset.import_from(path, 'camvid'),
require_images=True)
def test_inplace_save_writes_only_updated_data(self):
src_mask_cat = MaskCategories.generate(2, include_background=False)
expected = Dataset.from_iterable([
DatasetItem(1, subset='a', image=np.ones((2, 1, 3)),
annotations=[
Mask(np.ones((2, 1)), label=2, id=1)
]),
DatasetItem(2, subset='a', image=np.ones((3, 2, 3))),
DatasetItem(2, subset='b', image=np.ones((2, 2, 3)),
annotations=[
Mask(np.ones((2, 2)), label=1, id=1)
]),
], categories=Cityscapes.make_cityscapes_categories(OrderedDict([
('a', src_mask_cat.colormap[0]),
('b', src_mask_cat.colormap[1]),
])))
with TestDir() as path:
dataset = Dataset.from_iterable([
DatasetItem(1, subset='a', image=np.ones((2, 1, 3)),
annotations=[
Mask(np.ones((2, 1)), label=1)
]),
DatasetItem(2, subset='b', image=np.ones((2, 2, 3)),
annotations=[
Mask(np.ones((2, 2)), label=0)
]),
DatasetItem(3, subset='c', image=np.ones((2, 3, 3)),
annotations=[
Mask(np.ones((2, 2)), label=0)
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b']),
AnnotationType.mask: src_mask_cat
})
dataset.export(path, 'cityscapes', save_images=True)
dataset.put(DatasetItem(2, subset='a', image=np.ones((3, 2, 3))))
dataset.remove(3, 'c')
dataset.save(save_images=True)
self.assertEqual({'a', 'b'},
set(os.listdir(osp.join(path, 'gtFine'))))
self.assertEqual({
'1_gtFine_color.png', '1_gtFine_instanceIds.png',
'1_gtFine_labelIds.png'
},
set(os.listdir(osp.join(path, 'gtFine', 'a'))))
self.assertEqual({
'2_gtFine_color.png', '2_gtFine_instanceIds.png',
'2_gtFine_labelIds.png'
},
set(os.listdir(osp.join(path, 'gtFine', 'b'))))
self.assertEqual({'a', 'b'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit'))))
self.assertEqual({'1_leftImg8bit.png', '2_leftImg8bit.png'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit', 'a'))))
self.assertEqual({'2_leftImg8bit.png'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit', 'b'))))
compare_datasets(self, expected,
Dataset.import_from(path, 'cityscapes'),
require_images=True, ignored_attrs=IGNORE_ALL)
def test_can_compare_projects(self): # just a smoke test
label_categories1 = LabelCategories.from_iterable(['x', 'a', 'b', 'y'])
mask_categories1 = MaskCategories.generate(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.generate(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 DiffVisualizer(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)))