def test_can_detect(self):
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2),
]),
])
def categories(self):
label_cat = LabelCategories()
for label in range(10):
label_cat.add('label_' + str(label))
return {
AnnotationType.label: label_cat,
}
def generate_dummy_tfrecord(path):
TfDetectionApiConverter()(TestExtractor(), save_dir=path)
with TestDir() as test_dir:
generate_dummy_tfrecord(test_dir)
self.assertTrue(TfDetectionApiImporter.detect(test_dir))
def test_can_detect(self):
self.assertTrue(TfDetectionApiImporter.detect(DUMMY_DATASET_DIR))