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_instance_segmentation_coco2sa(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_path = Path(tmpdir) / "toSuperAnnotate" / "instances_test"
sa.import_annotation(
input_dir, out_path, "COCO", "instances_test", "Vector",
"instance_segmentation"
)
def test_keypoint_detection_coco2sa(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "keypoint_detection"
out_path = Path(tmpdir) / "toSuperAnnotate" / "keypoint_test"
sa.import_annotation(
input_dir, out_path, "COCO", "person_keypoints_test", "Vector",
"keypoint_detection"
)
def test_panoptic_segmentation_coco2sa(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "panoptic_segmentation"
out_path = Path(tmpdir) / "toSuperAnnotate" / "panoptic_test"
sa.import_annotation(
input_dir, out_path, "COCO", "panoptic_test", "Pixel",
"panoptic_segmentation"
)
def test_vott_convert_object(tmpdir):
project_name = "vott_object"
input_dir = Path(
"tests") / "converter_test" / "VoTT" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "VoTT", "", "Vector",
"object_detection")
description = 'vott object detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_vott_convert_instance(tmpdir):
project_name = "vott_vector_instance"
input_dir = Path(
"tests") / "converter_test" / "VoTT" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "VoTT", "", "Vector",
"instance_segmentation")
description = 'vott instance segmentation'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_dataloop_convert_instance(tmpdir):
project_name = "dataloop2sa_vector_instance"
input_dir = Path(
'tests') / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, 'DataLoop', '', 'Vector',
'instance_segmentation')
description = 'dataloop instance segmentation'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_vgg_convert_vector(tmpdir):
project_name = "vgg_test_vector"
input_dir = Path(
"tests") / "converter_test" / "VGG" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "VGG", "vgg_test", "Vector",
"vector_annotation")
description = 'vgg vector annotation'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_yolo_object_detection_web(tmpdir):
project_name = "yolo_object_detection"
input_dir = Path(
'tests') / 'converter_test' / 'YOLO' / 'input' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, 'YOLO', '', 'Vector',
'object_detection')
description = 'yolo object detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_coco_vector_instance(tmpdir):
project_name = "coco2sa_vector_instance"
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "COCO", "instances_test",
"Vector", "instance_segmentation")
description = 'coco vector instance segmentation'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_coco_panoptic(tmpdir):
project_name = "coco2sa_panoptic"
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "panoptic_segmentation"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "COCO", "panoptic_test", "Pixel",
"panoptic_segmentation")
description = 'coco pixel panoptic segmentation'
ptype = 'Pixel'
upload_project(out_dir, project_name, description, ptype)
def test_coco_vector_keypoint(tmpdir):
project_name = "coco2sa_keypoint"
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "keypoint_detection/"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "COCO", "person_keypoints_test",
"Vector", "keypoint_detection")
description = 'coco vector keypoint detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_sagemaker_instance_segmentation(tmpdir):
project_name = "sagemaker_instance_pixel"
input_dir = Path(
'tests'
) / 'converter_test' / 'SageMaker' / 'input' / 'toSuperAnnotate' / 'instance_segmentation'
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, 'SageMaker', 'test-obj-detect',
'Pixel', 'instance_segmentation')
description = 'sagemaker vector instance segmentation'
ptype = 'Pixel'
upload_project(out_dir, project_name, description, ptype)
def test_sagemaker_object_detection(tmpdir):
project_name = "sagemaker_object_vector"
input_dir = Path(
'tests'
) / 'converter_test' / 'SageMaker' / 'input' / 'toSuperAnnotate' / 'object_detection'
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, 'SageMaker', 'test-obj-detect',
'Vector', 'object_detection')
description = 'sagemaker object detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_voc_pixel(tmpdir):
project_name = "voc2sa_pixel_instance"
input_dir = Path(
"tests"
) / "converter_test" / "VOC" / "input" / "fromPascalVOCToSuperAnnotate" / "VOC2012"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(
input_dir, out_dir, "VOC", "", "Pixel", "instance_segmentation"
)
description = 'voc pixel instance segmentation'
ptype = 'Pixel'
upload_project(out_dir, project_name, description, ptype)
def test_googlecloud_convert_web(tmpdir):
project_name = "googlcloud_object"
input_dir = Path(
"tests"
) / "converter_test" / "GoogleCloud" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(input_dir, out_dir, "GoogleCloud",
"image_object_detection", "Vector",
"object_detection")
description = 'googlecloud object detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_labelbox_convert_vector(tmpdir):
project_name = "labelbox_vector_annotation"
input_dir = Path(
'tests'
) / 'converter_test' / 'LabelBox' / 'vector_annotations' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / project_name
dataset_name = 'labelbox_example'
sa.import_annotation(input_dir, out_dir, 'LabelBox', dataset_name,
'Vector', 'vector_annotation')
description = 'labelbox vector vector annotation'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_labelbox_convert_instance_pixel(tmpdir):
project_name = "labelbox_pixel_instance"
input_dir = Path(
'tests'
) / 'converter_test' / 'LabelBox' / 'instance_segmentation' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / project_name
dataset_name = 'labelbox_example'
sa.import_annotation(input_dir, out_dir, 'LabelBox', dataset_name, 'Pixel',
'instance_segmentation')
description = 'labelbox pixel instance_segmentation'
ptype = 'Pixel'
upload_project(out_dir, project_name, description, ptype)
def test_voc_vector_object(tmpdir):
project_name = "voc2sa_vector_object"
input_dir = Path(
"tests"
) / "converter_test" / "VOC" / "input" / "fromPascalVOCToSuperAnnotate" / "VOC2012"
out_dir = Path(tmpdir) / project_name
sa.import_annotation(
input_dir, out_dir, "VOC", "", "Vector", "object_detection"
)
description = 'voc vector object detection'
ptype = 'Vector'
upload_project(out_dir, project_name, description, ptype)
def test_sa_to_coco_to_sa(tmpdir):
input_dir = Path("tests") / "sample_project_pixel"
output1 = Path(tmpdir) / 'to_coco'
output2 = Path(tmpdir) / 'to_sa'
sa.export_annotation(input_dir, output1, "COCO", "object_test", "Pixel",
"instance_segmentation")
sa.import_annotation(output1, output2, "COCO", "object_test", "Pixel",
"instance_segmentation", 'image_set')
project_name = 'coco_pipeline_new'
description = 'test_instane'
ptype = 'Pixel'
upload_project(output2, project_name, description, ptype)
