def load(file_object, annotations):
from pyunpack import Archive
from tempfile import TemporaryDirectory
from datumaro.plugins.tf_detection_api_format.importer import TfDetectionApiImporter
from cvat.apps.dataset_manager.bindings import import_dm_annotations
archive_file = file_object if isinstance(file_object, str) else getattr(
file_object, "name")
with TemporaryDirectory() as tmp_dir:
Archive(archive_file).extractall(tmp_dir)
dm_project = TfDetectionApiImporter()(tmp_dir)
dm_dataset = dm_project.make_dataset()
import_dm_annotations(dm_dataset, annotations)
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_save_and_load(self,
source_dataset,
converter,
test_dir,
target_dataset=None,
importer_args=None):
converter(source_dataset, test_dir)
if importer_args is None:
importer_args = {}
parsed_dataset = TfDetectionApiImporter()(test_dir, **importer_args) \
.make_dataset()
if target_dataset is None:
target_dataset = source_dataset
compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
def test_can_detect(self):
self.assertTrue(TfDetectionApiImporter.detect(DUMMY_DATASET_DIR))