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)
