def test_dataset_with_save_dataset_meta_file(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
subset='train',
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0,
1,
2,
3,
label=1,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
]),
],
categories=['face', 'label_0', 'label_1'])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True,
save_dataset_meta=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
self.assertTrue(osp.isfile(osp.join(test_dir,
'dataset_meta.json')))
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='кириллица с пробелом',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0,
1,
2,
3,
label=0,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
]),
],
categories=['face'])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_with_no_save_images(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=1),
Bbox(0,
1,
2,
3,
label=0,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
Label(1),
])
],
categories=['face', 'label_0'])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=False)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_no_subsets(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2),
Bbox(0,
1,
2,
3,
attributes={
'blur': 2,
'expression': 0,
'illumination': 0,
'occluded': 0,
'pose': 2,
'invalid': 0
}),
]),
],
categories=[])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_no_subsets(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0,
1,
2,
3,
label=1,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(3)),
})
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_non_widerface_attributes(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0),
Bbox(0,
1,
2,
3,
label=0,
attributes={
'non-widerface attribute': '0',
'blur': 1,
'invalid': '1'
}),
Bbox(1,
1,
2,
2,
label=0,
attributes={'non-widerface attribute': '0'}),
]),
],
categories=['face'])
target_dataset = Dataset.from_iterable([
DatasetItem(id='a/b/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0),
Bbox(0,
1,
2,
3,
label=0,
attributes={
'blur': '1',
'invalid': '1'
}),
Bbox(1, 1, 2, 2, label=0),
]),
],
categories=['face'])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, target_dataset, parsed_dataset)
def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable([
DatasetItem('q/1',
image=Image(path='q/1.JPEG', data=np.zeros(
(4, 3, 3)))),
DatasetItem('a/b/c/2',
image=Image(path='a/b/c/2.bmp',
data=np.zeros((3, 4, 3)))),
],
categories=[])
with TestDir() as test_dir:
WiderFaceConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0),
Bbox(0,
1,
2,
3,
label=0,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
Label(1),
]),
DatasetItem(id='2',
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Bbox(0,
2,
4,
2,
label=0,
attributes={
'blur': '2',
'expression': '0',
'illumination': '1',
'occluded': '0',
'pose': '1',
'invalid': '0'
}),
Bbox(3,
3,
2,
3,
label=0,
attributes={
'blur': '0',
'expression': '1',
'illumination': '0',
'occluded': '0',
'pose': '2',
'invalid': '0'
}),
Bbox(2,
1,
2,
3,
label=0,
attributes={
'blur': '2',
'expression': '0',
'illumination': '0',
'occluded': '0',
'pose': '0',
'invalid': '1'
}),
Label(2),
]),
DatasetItem(
id='3',
subset='val',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(
0,
1.1,
5.3,
2.1,
label=0,
attributes={
'blur':
'2',
'expression': '1',
'illumination': '0',
'occluded':
'0',
'pose':
'1',
'invalid': '0'
}),
Bbox(0, 2, 3, 2, label=0, attributes={'occluded': False}),
Bbox(0, 3, 4, 2, label=0, attributes={'occluded': True}),
Bbox(0, 2, 4, 2, label=0),
Bbox(0,
7,
3,
2,
label=0,
attributes={
'blur': '2',
'expression': '1',
'illumination': '0',
'occluded': '0',
'pose': '1',
'invalid': '0'
}),
]),
DatasetItem(id='4', subset='val', image=np.ones((8, 8, 3))),
],
categories=[
'face', 'label_0', 'label_1'
])
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='1',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2),
Bbox(0,
1,
2,
3,
attributes={
'blur': 2,
'expression': 0,
'illumination': 0,
'occluded': 0,
'pose': 2,
'invalid': 0
}),
Label(0),
]),
DatasetItem(id='2',
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Bbox(0,
2,
4,
2,
attributes={
'blur': 2,
'expression': 0,
'illumination': 1,
'occluded': 0,
'pose': 1,
'invalid': 0
}),
Bbox(3,
3,
2,
3,
attributes={
'blur': 0,
'expression': 1,
'illumination': 0,
'occluded': 0,
'pose': 2,
'invalid': 0
}),
Bbox(2,
1,
2,
3,
attributes={
'blur': 2,
'expression': 0,
'illumination': 0,
'occluded': 0,
'pose': 0,
'invalid': 1
}),
Label(1),
]),
DatasetItem(id='3',
subset='val',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0,
1,
5,
2,
attributes={
'blur': 2,
'expression': 1,
'illumination': 0,
'occluded': 0,
'pose': 1,
'invalid': 0
}),
Bbox(0, 2, 3, 2),
Bbox(0, 2, 4, 2),
Bbox(0,
7,
3,
2,
attributes={
'blur': 2,
'expression': 1,
'illumination': 0,
'occluded': 0,
'pose': 1,
'invalid': 0
}),
]),
DatasetItem(id='4', subset='val', image=np.ones((8, 8, 3))),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(3)),
})
with TestDir() as test_dir:
WiderFaceConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'wider_face')
compare_datasets(self, source_dataset, parsed_dataset)