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_preannotation_folder_upload_download_cli(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")
subprocess.run([
f"superannotate upload-preannotations --project '{name}' --folder '{from_folder}'"
],
check=True,
shell=True)
time.sleep(5)
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
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_preannotation_folder_upload_download_cli_vector_COCO(tmpdir):
project_type = "Vector"
name = "Example Project test vector2 preannotation cli upload coco vector"
description = "test"
from_folder = "./tests/converter_test/COCO/input/toSuperAnnotate/keypoint_detection"
task = "keypoint_detection"
dataset_name = "person_keypoints_test"
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")
subprocess.run(
f'superannotatecli upload-preannotations --project "{name}" --folder "{from_folder}" --format COCO --task {task} --dataset-name {dataset_name}',
check=True,
shell=True)
time.sleep(5)
count_in = 2
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
def test_preannotation_folder_upload_download_cli_pixel_object_COCO_folder(
tmpdir):
project_type = "Pixel"
name = "Example Project folder test pixel1 preannotation cli upload coco object pixel"
description = "test"
from_folder = "./tests/converter_test/COCO/input/toSuperAnnotate/panoptic_segmentation"
task = "panoptic_segmentation"
dataset_name = "panoptic_test"
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.create_folder(project, "folder1")
project_with_folder = project["name"] + "/folder1"
sa.upload_images_from_folder_to_project(project_with_folder,
from_folder,
annotation_status="InProgress")
subprocess.run(
f'superannotatecli upload-preannotations --project "{project_with_folder}" --folder "{from_folder}" --format COCO --task {task} --dataset-name {dataset_name}',
check=True,
shell=True)
time.sleep(5)
count_in = 3
images = sa.search_images(project_with_folder)
for image_name in images:
sa.download_image_preannotations(project_with_folder, image_name,
tmpdir)
count_out = len(list(Path(tmpdir).glob("*.json")))
assert count_in == count_out
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
def test_basic_images(project_type, name, description, from_folder, tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(name, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
projects_found = sa.search_projects(name, return_metadata=True)
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")
images = sa.search_images(project, "example_image_1")
assert len(images) == 1
image_name = images[0]
sa.download_image(project, image_name, tmpdir, True)
assert sa.get_image_preannotations(
project, image_name)["preannotation_json_filename"] is None
assert sa.get_image_annotations(
project, image_name)["annotation_json_filename"] is None
sa.download_image_annotations(project, image_name, tmpdir)
assert len(list(Path(tmpdir).glob("*"))) == 1
sa.download_image_preannotations(project, image_name, tmpdir)
assert len(list(Path(tmpdir).glob("*"))) == 1
assert (Path(tmpdir) / image_name).is_file()
sa.upload_annotations_from_json_to_image(
project, image_name,
sa.image_path_to_annotation_paths(from_folder / image_name,
project_type)[0],
None if project_type == "Vector" else
sa.image_path_to_annotation_paths(from_folder /
image_name, project_type)[1])
assert sa.get_image_annotations(
project, image_name)["annotation_json_filename"] is not None
sa.download_image_annotations(project, image_name, tmpdir)
annotation = list(Path(tmpdir).glob("*.json"))
assert len(annotation) == 1
annotation = json.load(open(annotation[0]))
sa.download_annotation_classes_json(project, tmpdir)
downloaded_classes = json.load(open(tmpdir / "classes.json"))
for a in annotation:
if "className" not in a:
continue
for c1 in downloaded_classes:
if a["className"] == c1["name"] or a[
"className"] == "Personal vehicle1": # "Personal vehicle1" is not existing class in annotations
break
else:
assert False
input_classes = json.load(open(from_folder / "classes" / "classes.json"))
assert len(downloaded_classes) == len(input_classes)
for c1 in downloaded_classes:
found = False
for c2 in input_classes:
if c1["name"] == c2["name"]:
found = True
break
assert found
sa.delete_project(project)
