def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable([
DatasetItem('no_label/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))),
annotations=[
Bbox(0, 2, 4, 2, label=0),
Points([
4.23, 4.32, 5.34, 4.45, 3.54, 3.56, 4.52, 3.51,
4.78, 3.34
],
label=0),
]),
],
categories=['a'])
with TestDir() as test_dir:
VggFace2Converter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def test_can_save_dataset_with_no_labels(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='no_label/1',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2),
Points([
4.23, 4.32, 5.34, 4.45, 3.54, 3.56, 4.52, 3.51,
4.78, 3.34
]),
]),
DatasetItem(id='no_label/2',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(2, 2, 4, 2),
]),
],
categories=[])
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset,
test_dir,
save_images=False)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self, source_dataset, parsed_dataset)
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, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=0, group=1),
]
),
DatasetItem(id='2', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Points([4.23, 4.32, 5.34, 4.45, 3.54,
3.56, 4.52, 3.51, 4.78, 3.34], label=1, group=1),
]
),
DatasetItem(id='3', subset='train', image=np.ones((8, 8, 3)),
annotations=[Label(2, group=1)]
),
DatasetItem(id='4', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=3, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=3, group=1),
]
),
DatasetItem(id='a/5', subset='train', image=np.ones((8, 8, 3)),
annotations=[
Bbox(2, 2, 2, 2, group=1),
]
),
DatasetItem(id='label_0', subset='train', image=np.ones((8, 8, 3)),
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
[('label_%s' % i, 'class_%s' % i) for i in range(5)]),
})
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_and_load_with_meta_file(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='class_0/1',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0),
Points([
3.2, 3.12, 4.11, 3.2, 2.11, 2.5, 3.5, 2.11,
3.8, 2.13
],
label=0),
]),
DatasetItem(id='class_1/2',
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Points([
4.23, 4.32, 5.34, 4.45, 3.54, 3.56, 4.52,
3.51, 4.78, 3.34
],
label=1),
])
],
categories={
AnnotationType.label:
LabelCategories.from_iterable([('class_%s' % i)
for i in range(5)]),
})
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset,
test_dir,
save_images=True,
save_dataset_meta=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
self.assertTrue(osp.isfile(osp.join(test_dir,
'dataset_meta.json')))
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_wrong_number_of_points(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[
Points([4.23, 4.32, 5.34, 3.51, 4.78, 3.34]),
]),
],
categories=[])
target_dataset = Dataset.from_iterable([
DatasetItem(id='1', image=np.ones((8, 8, 3)), annotations=[]),
],
categories=[])
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self, target_dataset, parsed_dataset)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='a/кириллица с пробелом',
image=np.ones((8, 8, 3)),
annotations=[
Points([
4.23, 4.32, 5.34, 4.45, 3.54, 3.56, 4.52, 3.51,
4.78, 3.34
],
label=0),
]),
],
categories=['a'])
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
