def _export(dst_file, task_data, save_images=False): extractor = CvatTaskDataExtractor(task_data, include_images=save_images) envt = dm_env.transforms extractor = extractor.transform(KeepTracks) # can only export tracks extractor = extractor.transform(envt.get('polygons_to_masks')) extractor = extractor.transform(envt.get('boxes_to_masks')) extractor = extractor.transform(envt.get('merge_instance_segments')) extractor = Dataset.from_extractors(extractor) # apply lazy transforms with TemporaryDirectory() as temp_dir: dm_env.converters.get('mots_png').convert(extractor, save_dir=temp_dir, save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False): extractor = CvatTaskDataExtractor(task_data, include_images=save_images) envt = dm_env.transforms extractor = extractor.transform(envt.get('polygons_to_masks')) extractor = extractor.transform(envt.get('boxes_to_masks')) extractor = extractor.transform(envt.get('merge_instance_segments')) extractor = Dataset.from_extractors(extractor) # apply lazy transforms with TemporaryDirectory() as temp_dir: converter = dm_env.make_converter('voc_segmentation', apply_colormap=True, label_map=make_colormap(task_data), save_images=save_images) converter(extractor, save_dir=temp_dir) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False): extractor = CvatTaskDataExtractor(task_data, include_images=save_images) envt = dm_env.transforms extractor = extractor.transform(envt.get('polygons_to_masks')) extractor = extractor.transform(envt.get('boxes_to_masks')) extractor = extractor.transform(envt.get('merge_instance_segments')) extractor = Dataset.from_extractors(extractor) # apply lazy transforms label_map = make_colormap(task_data) with TemporaryDirectory() as temp_dir: dm_env.converters.get('camvid').convert( extractor, save_dir=temp_dir, save_images=save_images, apply_colormap=True, label_map={label: label_map[label][0] for label in label_map}) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False): extractor = CvatTaskDataExtractor(task_data, include_images=save_images) envt = dm_env.transforms extractor = extractor.transform(envt.get('id_from_image_name')) extractor = Dataset.from_extractors(extractor) # apply lazy transforms with TemporaryDirectory() as temp_dir: converter = dm_env.make_converter('label_me', save_images=save_images) converter(extractor, save_dir=temp_dir) make_zip_archive(temp_dir, dst_file)