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)