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 = 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):
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 = YoloImporter()(test_dir).make_dataset()
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=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 = 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={
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_detect(self):
self.assertTrue(YoloImporter.detect(DUMMY_DATASET_DIR))