def test_annotation_download_upload(project_type, name, description,
from_folder, tmpdir):
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")
sa.upload_annotations_from_folder_to_project(project, from_folder)
image = sa.search_images(project)[2]
sa.download_image_annotations(project, image, tmpdir)
anns_json_in_folder = list(Path(tmpdir).glob("*.json"))
anns_mask_in_folder = list(Path(tmpdir).glob("*.png"))
assert len(anns_json_in_folder) == 1
assert len(anns_mask_in_folder) == (1 if project_type == "Pixel" else 0)
input_annotation_paths = sa.image_path_to_annotation_paths(
from_folder / image, project_type)
json1 = json.load(open(input_annotation_paths[0]))
json2 = json.load(open(anns_json_in_folder[0]))
for i in json1:
i.pop("classId", None)
for i in json2:
i.pop("classId", None)
assert json1 == json2
if project_type == "Pixel":
assert filecmp.cmp(input_annotation_paths[1],
anns_mask_in_folder[0],
shallow=False)
def test_upload_from_s3(tmpdir):
projects_found = sa.search_projects(TEST_PROJECT1, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEST_PROJECT1, "hk_test", project_type="Pixel")
f = urlparse(f"s3://superannotate-python-sdk-test/{TEST_PROJECT_PIXEL}")
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) == 3
sa.upload_annotations_from_folder_to_project(project,
TEST_PROJECT_PIXEL,
from_s3_bucket=f.netloc)
for image in sa.search_images(project):
sa.download_image_annotations(project, image, tmpdir)
assert len(list(Path(tmpdir).glob("*.*"))) == 6
sa.delete_project(project)
def test_annotation_folder_upload_download_cli_vector_COCO(tmpdir):
project_type = "Vector"
name = "Example Project test vector2 annotation 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-annotations --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_annotations(project, image_name, tmpdir)
count_out = len(list(Path(tmpdir).glob("*.json")))
assert count_in == count_out
def test_annotation_download_upload(project_type, name, description,
from_folder, tmpdir):
# 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"
# )
# sa.upload_annotations_from_folder_to_project(project, from_folder)
project = upload_project(from_folder, name, description, project_type)
image = sa.search_images(project)[0]
paths = sa.download_image_annotations(project, image, tmpdir)
input_annotation_paths_after = sa.image_path_to_annotation_paths(
tmpdir / image, project_type)
assert paths[0] == str(input_annotation_paths_after[0])
if project_type == "Pixel":
assert paths[1] == str(input_annotation_paths_after[1])
else:
assert len(paths) == 1
anns_json_in_folder = list(Path(tmpdir).glob("*.json"))
anns_mask_in_folder = list(Path(tmpdir).glob("*.png"))
assert len(anns_json_in_folder) == 1
assert len(anns_mask_in_folder) == (1 if project_type == "Pixel" else 0)
input_annotation_paths = sa.image_path_to_annotation_paths(
from_folder / image, project_type)
json1 = json.load(open(input_annotation_paths[0]))
json2 = json.load(open(anns_json_in_folder[0]))
for i in json1["instances"]:
i.pop("classId", None)
for j in i["attributes"]:
j.pop("groupId", None)
j.pop("id", None)
for i in json2["instances"]:
i.pop("classId", None)
for j in i["attributes"]:
j.pop("groupId", None)
j.pop("id", None)
assert json1 == json2
if project_type == "Pixel":
assert filecmp.cmp(input_annotation_paths[1],
anns_mask_in_folder[0],
shallow=False)
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)