def test_supervisely_convert_object(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'Supervisely' / 'input' / 'toSuperAnnotate' / 'vector'
out_dir = Path(tmpdir) / 'object_detection_desktop'
sa.import_annotation_format(input_dir, out_dir, 'Supervisely', '',
'Vector', 'object_detection', 'Desktop')
def test_coco_vector_object_instance(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_dir = Path(tmpdir) / "object_vector_instance_desktop"
sa.import_annotation_format(input_dir, out_dir, "COCO", "instances_test",
"Vector", "instance_segmentation", "Desktop")
def coco_desktop_object(tmpdir):
out_dir = tmpdir / "coco_from_desktop"
final_dir = tmpdir / "coco_to_Web"
sa.export_annotation_format(
"tests/converter_test/COCO/input/fromSuperAnnotate/cats_dogs_desktop",
str(out_dir), "COCO", "object_test", "Vector", "object_detection",
"Desktop")
image_list = glob(str(out_dir / 'train_set' / '*.jpg'))
for image in image_list:
shutil.copy(image, out_dir / Path(image).name)
shutil.rmtree(out_dir / 'train_set')
sa.import_annotation_format(str(out_dir), str(final_dir), "COCO",
"object_test_train", "Vector",
"object_detection", "Web")
project_name = "coco2sa_object_pipline"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, final_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, final_dir)
sa.upload_annotations_from_folder_to_project(project, final_dir)
return 0
def test_voc_vector_object(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "VOC" / "input" / "fromPascalVOCToSuperAnnotate" / "VOC2012"
out_dir = Path(tmpdir) / "object_vector"
sa.import_annotation_format(input_dir, out_dir, "VOC", "", "Vector",
"object_detection", "Desktop")
def test_sagemaker_object_detection(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'SageMaker' / 'input' / 'toSuperAnnotate' / 'object_detection'
out_dir = Path(tmpdir) / "object_detection"
sa.import_annotation_format(input_dir, out_dir, 'SageMaker',
'test-obj-detect', 'Vector',
'object_detection', 'Desktop')
def yolo_object_detection_desktop(tmpdir):
out_dir = tmpdir / "vector_annotation_desktop"
sa.import_annotation_format(
'tests/converter_test/YOLO/input/toSuperAnnotate', str(out_dir), 'YOLO',
'', 'Vector', 'object_detection', 'Desktop'
)
return 0
def test_googlecloud_convert_desktop(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "GoogleCloud" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / "output_desktop"
sa.import_annotation_format(
input_dir, out_dir, "GoogleCloud", "image_object_detection", "Vector",
"object_detection", "Desktop"
)
def test_dataloop_convert_object(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / 'output_object'
sa.import_annotation_format(
input_dir, out_dir, 'DataLoop', '', 'Vector', 'object_detection',
'Desktop'
)
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_format(
input_dir, out_path, "COCO", "person_keypoints_test", "Vector",
"keypoint_detection"
)
def test_vott_convert_instance(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "VoTT" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / "instance_segmentation"
sa.import_annotation_format(
input_dir, out_dir, "VoTT", "", "Vector", "instance_segmentation",
"Desktop"
)
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_format(
input_dir, out_path, "COCO", "panoptic_test", "Pixel",
"panoptic_segmentation"
)
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_format(
input_dir, out_path, "COCO", "instances_test", "Vector",
"instance_segmentation"
)
def preannotations_upload(args, annotations=False):
parser = argparse.ArgumentParser()
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)
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 = sa.project_type_int_to_str(
sa.get_project_metadata(args.project)["type"])
tempdir = tempfile.TemporaryDirectory()
tempdir_path = Path(tempdir.name)
sa.import_annotation_format(args.folder, tempdir_path, "COCO",
args.dataset_name, project_type, args.task)
args.folder = tempdir_path
sa.create_annotation_classes_from_classes_json(
args.project,
Path(args.folder) / "classes" / "classes.json")
if annotations:
sa.upload_annotations_from_folder_to_project(project=args.project,
folder_path=args.folder)
else:
sa.upload_preannotations_from_folder_to_project(
project=args.project, folder_path=args.folder)
def instance_segmentation_coco2sa(tmpdir):
out_path = tmpdir / "toSuperAnnotate/instances_test"
try:
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/instance_segmentation",
str(out_path), "COCO", "instances_test", "Vector",
"instance_segmentation")
except Exception as e:
return 1
return 0
def panoptic_segmentation_coco2sa(tmpdir):
out_path = tmpdir / "toSuperAnnotate/panoptic_test"
try:
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/panoptic_segmentation",
str(out_path), "COCO", "panoptic_test", "Pixel",
"panoptic_segmentation")
except Exception as e:
return 1
return 0
def keypoint_detection_coco2sa(tmpdir):
out_path = tmpdir / "toSuperAnnotate/keypoint_test"
try:
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/keypoint_detection",
str(out_path), "COCO", "person_keypoints_test", "Vector",
"keypoint_detection")
except Exception as e:
return 1
return 0
def test_coco_pixel_instance_desktop(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_dir = Path(tmpdir) / "instance_pixel_desktop"
with pytest.raises(sa.SABaseException) as e:
sa.import_annotation_format(input_dir, out_dir, "COCO",
"instances_test", "Pixel",
"instance_segmentation", "Desktop")
assert e.value.message == "Sorry, but Desktop Application doesn't support 'Pixel' projects."
def labelbox_convert_instance(tmpdir):
out_dir = tmpdir / "output_insance"
dataset_name = 'labelbox_example'
sa.import_annotation_format(
'tests/converter_test/LabelBox/input/toSuperAnnotate', str(out_dir),
'LabelBox', dataset_name, 'Vector', 'instance_segmentation', 'Desktop')
all_files = os.listdir(out_dir)
json_files = set([
file.replace('___objects.json', '') for file in all_files
if os.path.splitext(file) == '.json'
])
image_files = set(
[file for file in all_files if os.path.splitext(file) == '.jpg'])
if json_files != image_files:
return 1
return 0
def test_vgg_convert_object(tmpdir):
input_dir = Path(
"tests") / "converter_test" / "VGG" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / "object_detection"
sa.import_annotation_format(input_dir, out_dir, "VGG", "vgg_test",
"Vector", "object_detection", "Web")
project_name = "vgg_test_object"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
def test_supervisely_convert_instance(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'Supervisely' / 'input' / 'toSuperAnnotate' / 'vector'
out_dir = Path(tmpdir) / 'instance_segmentation'
sa.import_annotation_format(input_dir, out_dir, 'Supervisely', '',
'Vector', 'instance_segmentation', 'Web')
project_name = "supervisely_test_vector_convert_instance"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
def test_yolo_object_detection_web(tmpdir):
input_dir = Path(
'tests') / 'converter_test' / 'YOLO' / 'input' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / "vector_annotation_web"
sa.import_annotation_format(input_dir, out_dir, 'YOLO', '', 'Vector',
'object_detection', 'Web')
project_name = "yolo_object_detection"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
def dataloop_convert_vector(tmpdir):
out_dir = tmpdir / 'output_vector'
sa.import_annotation_format(
'tests/converter_test/DataLoop/input/toSuperAnnotate', str(out_dir),
'DataLoop', '', 'Vector', 'vector_annotation', 'Web')
# project_name = "dataloop_test"
# projects = sa.search_projects(project_name, True)
# if projects:
# sa.delete_project(projects[0])
# project = sa.create_project(project_name, "converter vector", "Vector")
# sa.create_annotation_classes_from_classes_json(
# project, out_dir + "/classes/classes.json"
# )
# sa.upload_images_from_folder_to_project(project, out_dir)
# sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def supervisely_convert_keypoint(tmpdir):
out_dir = tmpdir / 'keypoint_detection'
sa.import_annotation_format(
'tests/converter_test/Supervisely/input/toSuperAnnotate/keypoints',
str(out_dir), 'Supervisely', '', 'Vector', 'keypoint_detection', 'Web')
project_name = "supervisely_test_keypoint"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def test_coco_vector_instance(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_dir = Path(tmpdir) / "instance_vector"
sa.import_annotation_format(input_dir, out_dir, "COCO", "instances_test",
"Vector", "instance_segmentation", "Web")
project_name = "coco2sa_vector_instance"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
def vott_convert_vector(tmpdir):
out_dir = tmpdir / "vector_annotation"
sa.import_annotation_format(
"tests/converter_test/VoTT/input/toSuperAnnotate", str(out_dir),
"VoTT", "", "Vector", "vector_annotation", "Web")
project_name = "vott_vector"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def test_sagemaker_instance_segmentation(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'SageMaker' / 'input' / 'toSuperAnnotate' / 'instance_segmentation'
out_dir = Path(tmpdir) / "instance_segmentation"
sa.import_annotation_format(input_dir, out_dir, 'SageMaker',
'test-obj-detect', 'Pixel',
'instance_segmentation', 'Web')
project_name = "sagemaker_instance_segmentation"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Pixel")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
def sagemaker_object_detection(tmpdir):
out_dir = tmpdir / "object_detection"
sa.import_annotation_format(
'tests/converter_test/SageMaker/input/toSuperAnnotate/object_detection',
str(out_dir), 'SageMaker', 'test-obj-detect', 'Vector',
'object_detection', 'Web')
project_name = "sagemaker_object_detection"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir + "/classes/classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def coco_vector_object(tmpdir):
out_dir = tmpdir / "object_vector"
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/instance_segmentation/",
str(out_dir), "COCO", "instances_test", "Vector", "object_detection",
"Desktop")
# project_name = "coco2sa_vector_object"
# projects = sa.search_projects(project_name, True)
# if projects:
# sa.delete_project(projects[0])
# project = sa.create_project(project_name, "converter vector", "Vector")
# sa.create_annotation_classes_from_classes_json(
# project, out_dir / "classes" / "classes.json"
# )
# sa.upload_images_from_folder_to_project(project, out_dir)
# sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def coco_vector_keypoint(tmpdir):
out_dir = tmpdir / "vector_keypoint"
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/keypoint_detection/",
str(out_dir), "COCO", "person_keypoints_test", "Vector",
"keypoint_detection", "Web")
project_name = "coco2sa_keypoint"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Vector")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0
def coco_pixel_instance(tmpdir):
out_dir = tmpdir / "instance_pixel"
sa.import_annotation_format(
"tests/converter_test/COCO/input/toSuperAnnotate/instance_segmentation/",
str(out_dir), "COCO", "instances_test", "Pixel",
"instance_segmentation", "Web")
project_name = "coco2sa_pixel_instance"
projects = sa.search_projects(project_name, True)
if projects:
sa.delete_project(projects[0])
project = sa.create_project(project_name, "converter vector", "Pixel")
sa.create_annotation_classes_from_classes_json(
project, out_dir / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project, out_dir)
sa.upload_annotations_from_folder_to_project(project, out_dir)
return 0