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)