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=2),
Bbox(0, 1, 2, 3, label=4),
]),
DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
Bbox(2, 1, 2, 3, label=4),
]),
DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 1, 5, 2, label=2),
Bbox(0, 2, 3, 2, label=5),
Bbox(0, 2, 4, 2, label=6),
Bbox(0, 7, 3, 2, label=7),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(i) for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_load_dataset_with_exact_image_info(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
parsed_dataset = Dataset.import_from(test_dir,
'yolo',
image_info={'1': (10, 15)})
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_image_info(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'),
np.ones((10, 15, 3))) # put the image for dataset
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
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',
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0, 1, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable([
DatasetItem('q/1', subset='train',
image=Image(path='q/1.JPEG', data=np.zeros((4, 3, 3)))),
DatasetItem('a/b/c/2', subset='valid',
image=Image(path='a/b/c/2.bmp', data=np.zeros((3, 4, 3)))),
], categories=[])
with TestDir() as test_dir:
YoloConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, dataset, parsed_dataset, require_images=True)
def test_can_load_dataset_with_exact_image_info(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
])
def categories(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add('label_' + str(i))
return {
AnnotationType.label: label_categories,
}
with TestDir() as test_dir:
source_dataset = TestExtractor()
YoloConverter()(source_dataset, test_dir)
parsed_dataset = YoloImporter()(test_dir,
image_info={
'1': (10, 15)
}).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_dataset_with_image_info(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
])
def categories(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add('label_' + str(i))
return {
AnnotationType.label: label_categories,
}
with TestDir() as test_dir:
source_dataset = TestExtractor()
YoloConverter()(source_dataset, test_dir)
save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'),
np.ones((10, 15, 3))) # put the image for dataset
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_relative_paths(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id='1',
subset='train',
image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1',
subset='train',
image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1',
subset='train',
image=np.ones((5, 4, 3))),
])
def categories(self):
return {AnnotationType.label: LabelCategories()}
for save_images in {True, False}:
with self.subTest(save_images=save_images):
with TestDir() as test_dir:
source_dataset = TestExtractor()
YoloConverter(save_images=save_images)(source_dataset,
test_dir)
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_relative_paths(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1', subset='train',
image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1', subset='train',
image=np.ones((5, 4, 3))),
], categories=[])
for save_images in {True, False}:
with self.subTest(save_images=save_images):
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir,
save_images=save_images)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, source_dataset, parsed_dataset)
def test_relative_paths(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1', subset='train',
image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1', subset='train',
image=np.ones((5, 4, 3))),
], categories={
AnnotationType.label: LabelCategories(),
})
for save_images in {True, False}:
with self.subTest(save_images=save_images):
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir,
save_images=save_images)
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_save_and_load(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0, 1, 2, 3, label=4),
]),
DatasetItem(id=2,
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
Bbox(2, 1, 2, 3, label=4),
]),
DatasetItem(id=3,
subset='valid',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 1, 5, 2, label=2),
Bbox(0, 2, 3, 2, label=5),
Bbox(0, 2, 4, 2, label=6),
Bbox(0, 7, 3, 2, label=7),
]),
])
def categories(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add('label_' + str(i))
return {
AnnotationType.label: label_categories,
}
with TestDir() as test_dir:
source_dataset = TestExtractor()
YoloConverter(save_images=True)(source_dataset, test_dir)
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_detect(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
])
def categories(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add('label_' + str(i))
return {
AnnotationType.label: label_categories,
}
with TestDir() as test_dir:
YoloConverter()(TestExtractor(), save_dir=test_dir)
self.assertTrue(YoloImporter.detect(test_dir))
