def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'yolo', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export_recognition(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) dataset.transform(LabelToCaption) with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'icdar_word_recognition', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) with TemporaryDirectory() as temp_dir: dataset.transform(LabelAttrToAttr, 'market-1501') dataset.export(temp_dir, 'market1501', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export_images(dst_file, task_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( task_data, include_images=save_images, format_type='sly_pointcloud', dimension=DimensionType.DIM_3D), env=dm_env) with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'sly_pointcloud', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data=instance_data, include_images=save_images), env=dm_env) if not save_images: dataset.transform(DeleteImagePath) with TemporaryDirectory() as tmp_dir: dataset.export(tmp_dir, 'datumaro', save_images=save_images) make_zip_archive(tmp_dir, dst_file)
def _export_segmentation(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) with TemporaryDirectory() as temp_dir: dataset.transform(RotatedBoxesToPolygons) dataset.transform('polygons_to_masks') dataset.transform('boxes_to_masks') dataset.transform('merge_instance_segments') dataset.export(temp_dir, 'icdar_text_segmentation', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) dataset.transform(KeepTracks) # can only export tracks dataset.transform('polygons_to_masks') dataset.transform('boxes_to_masks') dataset.transform('merge_instance_segments') with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'mots_png', save_images=save_images) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) dataset.transform('polygons_to_masks') dataset.transform('boxes_to_masks') dataset.transform('merge_instance_segments') with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'voc_segmentation', save_images=save_images, apply_colormap=True, label_map=make_colormap(instance_data)) make_zip_archive(temp_dir, dst_file)
def _export(self, instance_data, save_dir, save_images=False): dataset = GetCVATDataExtractor(instance_data, include_images=save_images) db_instance = instance_data.db_project if isinstance( instance_data, ProjectData) else instance_data.db_task dm_env.converters.get('datumaro_project').convert( dataset, save_dir=save_dir, save_images=save_images, project_config={ 'project_name': db_instance.name, }) project = Project.load(save_dir) target_dir = project.config.project_dir os.makedirs(target_dir, exist_ok=True) shutil.copyfile(osp.join(self._TEMPLATES_DIR, 'README.md'), osp.join(target_dir, 'README.md')) if not save_images: # add remote links to images source_name = '{}_{}_images'.format( 'project' if isinstance(instance_data, ProjectData) else 'task', db_instance.id, ) project.add_source(source_name, { 'format': self._REMOTE_IMAGES_EXTRACTOR, }) self._save_image_info( osp.join(save_dir, project.local_source_dir(source_name)), instance_data) project.save() templates_dir = osp.join(self._TEMPLATES_DIR, 'plugins') target_dir = osp.join(project.config.project_dir, project.config.env_dir, project.config.plugins_dir) os.makedirs(target_dir, exist_ok=True) shutil.copyfile( osp.join(templates_dir, self._REMOTE_IMAGES_EXTRACTOR + '.py'), osp.join(target_dir, self._REMOTE_IMAGES_EXTRACTOR + '.py')) # Make CVAT CLI module available to the user cvat_utils_dst_dir = osp.join(save_dir, 'cvat', 'utils') os.makedirs(cvat_utils_dst_dir) shutil.copytree(osp.join(BASE_DIR, 'utils', 'cli'), osp.join(cvat_utils_dst_dir, 'cli'))
def _export_images(dst_file, task_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( task_data, include_images=save_images, format_type="kitti_raw", dimension=DimensionType.DIM_3D), env=dm_env) with TemporaryDirectory() as temp_dir: dataset.export(temp_dir, 'kitti_raw', save_images=save_images, reindex=True) make_zip_archive(temp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) with TemporaryDirectory() as tmp_dir: dataset.transform(RotatedBoxesToPolygons) dataset.transform('polygons_to_masks') dataset.transform('merge_instance_segments') dataset.export(tmp_dir, format='kitti', label_map={ k: v[0] for k, v in make_colormap(instance_data).items() }, apply_colormap=True, save_images=save_images) make_zip_archive(tmp_dir, dst_file)
def _export(dst_file, instance_data, save_images=False): dataset = Dataset.from_extractors(GetCVATDataExtractor( instance_data, include_images=save_images), env=dm_env) dataset.transform(RotatedBoxesToPolygons) dataset.transform('polygons_to_masks') dataset.transform('boxes_to_masks') dataset.transform('merge_instance_segments') label_map = make_colormap(instance_data) with TemporaryDirectory() as temp_dir: dataset.export( temp_dir, 'camvid', 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)