def _export(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_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(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, 'tf_detection_api', 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)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
dm_env.converters.get('coco_instances').convert(
extractor, save_dir=temp_dir, 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_task(dst_file, task_data, anno_callback, save_images=False):
with TemporaryDirectory() as temp_dir:
with open(osp.join(temp_dir, 'annotations.xml'), 'wb') as f:
dump_task_anno(f, task_data, anno_callback)
if save_images:
dump_media_files(task_data, osp.join(temp_dir, 'images'))
make_zip_archive(temp_dir, dst_file)
def dump(file_object, annotations):
from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
from cvat.apps.dataset_manager.util import make_zip_archive
from tempfile import TemporaryDirectory
extractor = CvatAnnotationsExtractor('', annotations)
converter = CvatYoloConverter()
with TemporaryDirectory() as temp_dir:
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, file_object)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
converter = dm_env.make_converter('mot_seq_gt',
save_images=save_images)
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, dst_file)
def _export_segmentation(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_data, include_images=save_images), env=dm_env)
with TemporaryDirectory() as temp_dir:
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=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_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, 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)
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
from tempfile import TemporaryDirectory
extractor = CvatAnnotationsExtractor('', annotations)
converter = Environment().make_converter('yolo')
with TemporaryDirectory() as temp_dir:
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, file_object)
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,
'voc',
save_images=save_images,
label_map='source')
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, task_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
task_data, include_images=save_images),
env=dm_env)
dataset.transform(RotatedBoxesToPolygons)
dataset.transform('polygons_to_masks')
dataset.transform('merge_instance_segments')
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir, 'open_images', 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(RotatedBoxesToPolygons)
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(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 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 _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_project(dst_file: str,
project_data: ProjectData,
anno_callback: Callable,
save_images: bool = False):
with TemporaryDirectory() as temp_dir:
with open(osp.join(temp_dir, 'annotations.xml'), 'wb') as f:
dump_project_anno(f, project_data, anno_callback)
if save_images:
for task_data in project_data.task_data:
subset = get_defaulted_subset(task_data.db_task.subset,
project_data.subsets)
subset_dir = osp.join(temp_dir, 'images', subset)
os.makedirs(subset_dir, exist_ok=True)
dump_media_files(task_data, subset_dir, project_data)
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, 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')
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)
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, task_data, anno_callback, save_images=False):
with TemporaryDirectory() as temp_dir:
with open(osp.join(temp_dir, 'annotations.xml'), 'wb') as f:
anno_callback(f, task_data)
if save_images:
img_dir = osp.join(temp_dir, 'images')
frame_provider = FrameProvider(task_data.db_task.data)
frames = frame_provider.get_frames(
frame_provider.Quality.ORIGINAL,
frame_provider.Type.NUMPY_ARRAY)
for frame_id, (frame_data, _) in enumerate(frames):
frame_name = task_data.frame_info[frame_id]['path']
if '.' in frame_name:
save_image(osp.join(img_dir, frame_name),
frame_data, jpeg_quality=100, create_dir=True)
else:
save_image(osp.join(img_dir, frame_name + '.png'),
frame_data, create_dir=True)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, anno_callback, save_images=False):
with TemporaryDirectory() as temp_dir:
with open(osp.join(temp_dir, 'annotations.xml'), 'wb') as f:
anno_callback(f, task_data)
if save_images:
ext = ''
if task_data.meta['task']['mode'] == 'interpolation':
ext = FrameProvider.VIDEO_FRAME_EXT
img_dir = osp.join(temp_dir, 'images')
frame_provider = FrameProvider(task_data.db_task.data)
frames = frame_provider.get_frames(frame_provider.Quality.ORIGINAL,
frame_provider.Type.BUFFER)
for frame_id, (frame_data, _) in enumerate(frames):
frame_name = task_data.frame_info[frame_id]['path']
img_path = osp.join(img_dir, frame_name + ext)
os.makedirs(osp.dirname(img_path), exist_ok=True)
with open(img_path, 'wb') as f:
f.write(frame_data.getvalue())
make_zip_archive(temp_dir, dst_file)
def __call__(self, dst_file, task_data, save_images=False):
with TemporaryDirectory() as temp_dir:
self._export(task_data, save_dir=temp_dir, save_images=save_images)
make_zip_archive(temp_dir, dst_file)