def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=0,
subset='test',
image=np.ones((28, 28)),
annotations=[Label(0)]),
DatasetItem(id=1, subset='test', image=np.ones((28, 28))),
DatasetItem(id=2,
subset='test',
image=np.ones((28, 28)),
annotations=[Label(1)])
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(
str(label) for label in range(10)),
})
with TestDir() as test_dir:
MnistConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_empty_image(self):
dataset = Dataset.from_iterable(
[DatasetItem(id=0, annotations=[Label(0)]),
DatasetItem(id=1)],
categories={
AnnotationType.label:
LabelCategories.from_iterable(
str(label) for label in range(10)),
})
with TestDir() as test_dir:
MnistConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
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((28, 28)),
annotations=[Label(0)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(
str(label) for label in range(10)),
})
with TestDir() as test_dir:
MnistConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_with_other_labels(self):
dataset = Dataset.from_iterable(
[
DatasetItem(
id=0, image=np.ones((28, 28)), annotations=[Label(0)]),
DatasetItem(
id=1, image=np.ones((28, 28)), annotations=[Label(1)])
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_%s' % label
for label in range(2)),
})
with TestDir() as test_dir:
MnistConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable(
[
DatasetItem(id='q/1',
image=Image(path='q/1.JPEG',
data=np.zeros((28, 28)))),
DatasetItem(id='a/b/c/2',
image=Image(path='a/b/c/2.bmp',
data=np.zeros((28, 28)))),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(
str(label) for label in range(10)),
})
with TestDir() as test_dir:
MnistConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'mnist')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)