def preannotations_upload(command_name, args):
parser = argparse.ArgumentParser(prog=_CLI_COMMAND + " " + command_name)
parser.add_argument('--project',
required=True,
help='Project name to upload')
parser.add_argument(
'--folder',
required=True,
help=
'Folder (SuperAnnotate format) or JSON path (COCO format) from which to upload'
)
parser.add_argument('--format',
required=False,
default="SuperAnnotate",
help='Input preannotations format.')
parser.add_argument(
'--dataset-name',
required=False,
help='Input annotations dataset name for COCO projects')
parser.add_argument(
'--task',
required=False,
help=
'Task type for COCO projects can be panoptic_segmentation (Pixel), instance_segmentation (Pixel), instance_segmentation (Vector), keypoint_detection (Vector)'
)
args = parser.parse_args(args)
project_metadata, folder_metadata = sa.get_project_and_folder_metadata(
args.project)
if args.format != "SuperAnnotate":
if args.format != "COCO":
raise sa.SABaseException(
0, "Not supported annotations format " + args.format)
if args.dataset_name is None:
raise sa.SABaseException(
0, "Dataset name should be present for COCO format upload.")
if args.task is None:
raise sa.SABaseException(
0, "Task name should be present for COCO format upload.")
logger.info("Annotations in format %s.", args.format)
project_type = project_metadata["type"]
tempdir = tempfile.TemporaryDirectory()
tempdir_path = Path(tempdir.name)
sa.import_annotation(args.folder, tempdir_path, "COCO",
args.dataset_name, project_type, args.task)
args.folder = tempdir_path
sa.create_annotation_classes_from_classes_json(
project_metadata,
Path(args.folder) / "classes" / "classes.json")
if "pre" not in command_name:
sa.upload_annotations_from_folder_to_project(
(project_metadata, folder_metadata), folder_path=args.folder)
else:
sa.upload_preannotations_from_folder_to_project(
(project_metadata, folder_metadata), folder_path=args.folder)
def test_recursive_preannotations_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "2", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "2", "test", "Vector")
sa.upload_images_from_folder_to_project(
project,
"./tests/sample_recursive_test",
annotation_status="QualityCheck",
recursive_subfolders=True
)
assert len(sa.search_images(project)) == 2
sa.create_annotation_classes_from_classes_json(
project, "./tests/sample_recursive_test/classes/classes.json"
)
sa.upload_preannotations_from_folder_to_project(
project, "./tests/sample_recursive_test", recursive_subfolders=True
)
for image in sa.search_images(project):
sa.download_image_preannotations(project, image, tmpdir)
assert len(list(tmpdir.glob("*.json"))) == 2
def test_preannotations_nonrecursive_s3_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "7", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "7", "test", "Vector")
sa.upload_images_from_folder_to_project(
project,
"sample_recursive_test",
from_s3_bucket="superannotate-python-sdk-test",
recursive_subfolders=True
)
assert len(sa.search_images(project)) == 2
sa.create_annotation_classes_from_classes_json(
project,
"sample_recursive_test/classes/classes.json",
from_s3_bucket="superannotate-python-sdk-test"
)
sa.upload_preannotations_from_folder_to_project(
project,
"sample_recursive_test",
recursive_subfolders=False,
from_s3_bucket="superannotate-python-sdk-test"
)
for image in sa.search_images(project):
sa.download_image_preannotations(project, image, tmpdir)
def test_vector_preannotation_upload_from_s3(tmpdir):
projects_found = sa.search_projects(TEST_PROJECT3, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEST_PROJECT3,
"hk_test",
project_type="Vector")
f = urlparse(f"s3://superannotate-python-sdk-test/{TEST_PROJECT_VECTOR}")
sa.upload_images_from_folder_to_project(project,
f.path[1:],
annotation_status="NotStarted",
from_s3_bucket=f.netloc)
sa.create_annotation_classes_from_classes_json(project,
f.path[1:] +
'/classes/classes.json',
from_s3_bucket=f.netloc)
assert sa.get_project_image_count(project) == 4
sa.upload_preannotations_from_folder_to_project(project,
TEST_PROJECT_VECTOR,
from_s3_bucket=f.netloc)
for image in sa.search_images(project):
sa.download_image_preannotations(project, image, tmpdir)
assert len(list(Path(tmpdir).glob("*.*"))) == 4
sa.delete_project(project)
def test_missing_preannotation_upload(tmpdir):
name = "Example Project test vector missing preannotation upload"
project_type = "Vector"
description = "test vector"
from_folder = Path("./tests/sample_project_vector_for_checks")
projects = sa.search_projects(name, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(name, description, project_type)
sa.upload_images_from_folder_to_project(project,
from_folder,
annotation_status="NotStarted")
sa.create_annotation_classes_from_classes_json(
project, from_folder / "classes" / "classes.json")
uploaded, couldnt_upload, missing_images = sa.upload_preannotations_from_folder_to_project(
project, from_folder)
print(uploaded, couldnt_upload)
assert len(uploaded) == 1
assert len(couldnt_upload) == 2
assert len(missing_images) == 1
assert "tests/sample_project_vector_for_checks/example_image_1.jpg___objects.json" in uploaded
assert "tests/sample_project_vector_for_checks/example_image_2.jpg___objects.json" in couldnt_upload
assert "tests/sample_project_vector_for_checks/example_image_4.jpg___objects.json" in couldnt_upload
assert "tests/sample_project_vector_for_checks/example_image_5.jpg___objects.json" in missing_images
def test_preannotation_folder_upload_download(project_type, name, description,
from_folder, tmpdir):
projects_found = sa.search_projects(name, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(name, description, project_type)
sa.upload_images_from_folder_to_project(project,
from_folder,
annotation_status="InProgress")
sa.create_annotation_classes_from_classes_json(
project, from_folder / "classes" / "classes.json")
sa.upload_preannotations_from_folder_to_project(project, from_folder)
count_in = len(list(from_folder.glob("*.json")))
images = sa.search_images(project)
for image_name in images:
sa.download_image_preannotations(project, image_name, tmpdir)
count_out = len(list(Path(tmpdir).glob("*.json")))
assert count_in == count_out
