def __call__(self, extractor, save_dir):
from datumaro.components.project import Environment, Dataset
env = Environment()
id_from_image = env.transforms.get('id_from_image_name')
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc',
label_map='source',
save_images=self._save_images)
converter(extractor, save_dir=save_dir)
def dump(file_object, annotations):
from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
from cvat.apps.dataset_manager.util import make_zip_archive
from datumaro.components.project import Environment, Dataset
from tempfile import TemporaryDirectory
env = Environment()
id_from_image = env.transforms.get('id_from_image_name')
extractor = CvatAnnotationsExtractor('', annotations)
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc', label_map='source')
with TemporaryDirectory() as temp_dir:
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, file_object)
def __call__(self, extractor, save_dir):
from datumaro.components.project import Environment, Dataset
env = Environment()
polygons_to_masks = env.transforms.get('polygons_to_masks')
boxes_to_masks = env.transforms.get('boxes_to_masks')
merge_instance_segments = env.transforms.get('merge_instance_segments')
id_from_image = env.transforms.get('id_from_image_name')
extractor = extractor.transform(polygons_to_masks)
extractor = extractor.transform(boxes_to_masks)
extractor = extractor.transform(merge_instance_segments)
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc_segmentation',
apply_colormap=True,
label_map='source',
save_images=self._save_images)
converter(extractor, save_dir=save_dir)