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 test_can_skip_outside(self):
images = self._generate_task_images(3)
task = self._generate_task(images)
self._generate_annotations(task)
task_ann = TaskAnnotation(task["id"])
task_ann.init_from_db()
task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))
extractor = CvatTaskDataExtractor(task_data, include_outside=False)
dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)
self.assertEqual(4, len(dm_dataset.get("image_1").annotations))
extractor = CvatTaskDataExtractor(task_data, include_outside=True)
dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)
self.assertEqual(5, len(dm_dataset.get("image_1").annotations))
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_recognition(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_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):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_data, include_images=save_images),
env=dm_env)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir, 'wider_face', 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('mot_seq_gt').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):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_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(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)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
converter = dm_env.make_converter('tf_detection_api',
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, 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,
'coco_instances',
save_images=save_images,
merge_images=True)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_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(CvatTaskDataExtractor(
task_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(task_data))
make_zip_archive(temp_dir, dst_file)
def _export(self, task_data, save_dir, save_images=False):
dataset = CvatTaskDataExtractor(task_data, include_images=save_images)
converter = dm_env.make_converter('datumaro_project',
save_images=save_images,
config={ 'project_name': task_data.db_task.name, }
)
converter(dataset, save_dir=save_dir)
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 = 'task_%s_images' % task_data.db_task.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)),
task_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 Datumaro and CVAT CLI modules available to the user
shutil.copytree(DATUMARO_PATH, osp.join(save_dir, 'datumaro'),
ignore=lambda src, names: ['__pycache__'] + [
n for n in names
if sum([int(n.endswith(ext)) for ext in
['.pyx', '.pyo', '.pyd', '.pyc']])
])
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(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(self, task_data, save_dir, save_images=False):
dataset = CvatTaskDataExtractor(task_data, include_images=save_images)
dm_env.converters.get('datumaro_project').convert(
dataset,
save_dir=save_dir,
save_images=save_images,
project_config={
'project_name': task_data.db_task.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 = 'task_%s_images' % task_data.db_task.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)),
task_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'))
