def test_image_quality_setting2(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME2, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME2, "test", "Vector")
sa.set_project_default_image_quality_in_editor(project, "original")
sa.upload_images_from_folder_to_project(project,
"./tests/sample_project_vector")
sa.download_image(project, "example_image_1.jpg", tmpdir, variant="lores")
assert filecmp.cmp(tmpdir / "example_image_1.jpg___lores.jpg",
"./tests/sample_project_vector/example_image_1.jpg",
shallow=False)
def test_supervisely_convert_keypoint(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'Supervisely' / 'input' / 'toSuperAnnotate' / 'keypoints'
out_dir = Path(tmpdir) / 'keypoint_detection'
sa.import_annotation_format(input_dir, 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)
def test_supervisely_convert_instance_pixel(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'Supervisely' / 'input' / 'toSuperAnnotate' / 'instance'
out_dir = Path(tmpdir) / 'instance_segmentation_pixel'
sa.import_annotation_format(input_dir, out_dir, 'Supervisely', '', 'Pixel',
'instance_segmentation', 'Web')
project_name = "supervisely_test_instance_pixel"
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 test_coco_pixel_instance(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
out_dir = Path(tmpdir) / "instance_pixel"
sa.import_annotation_format(input_dir, 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)
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 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_single_image_upload_s3(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_S3, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_S3, "test", "Vector")
sa.upload_image_to_project(project,
"sample_project_vector/example_image_1.jpg",
annotation_status="InProgress",
from_s3_bucket="superannotate-python-sdk-test")
images = sa.search_images(project)
assert len(images) == 1
image = images[0]
assert sa.get_image_metadata(project,
image)["annotation_status"] == "InProgress"
def test_voc_vector_instance(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "VOC" / "input" / "fromPascalVOCToSuperAnnotate" / "VOC2012"
out_dir = Path(tmpdir) / "instance_vector"
sa.import_annotation_format(input_dir, out_dir, "VOC", "", "Vector",
"instance_segmentation", "Web")
project_name = "voc2sa_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 test_export_s3(tmpdir):
paginator = s3_client.get_paginator('list_objects_v2')
response_iterator = paginator.paginate(Bucket=S3_BUCKET, Prefix=S3_PREFIX2)
for response in response_iterator:
if 'Contents' in response:
for object_data in response['Contents']:
key = object_data['Key']
s3_client.delete_object(Bucket=S3_BUCKET, Key=key)
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME_EXPORT, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME_EXPORT, "test", "Vector")
sa.upload_images_from_folder_to_project(
project,
Path("./tests/sample_project_vector"),
annotation_status="InProgress")
sa.create_annotation_classes_from_classes_json(
project, Path("./tests/sample_project_vector/classes/classes.json"))
sa.upload_annotations_from_folder_to_project(
project, Path("./tests/sample_project_vector"))
images = sa.search_images(project)
for img in images:
sa.set_image_annotation_status(project, img, 'QualityCheck')
new_export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, new_export, S3_PREFIX2, to_s3_bucket=S3_BUCKET)
files = []
response_iterator = paginator.paginate(Bucket=S3_BUCKET, Prefix=S3_PREFIX2)
for response in response_iterator:
if 'Contents' in response:
for object_data in response['Contents']:
key = object_data['Key']
files.append(key)
output_path = tmpdir / S3_PREFIX2
output_path.mkdir()
sa.download_export(project, new_export, output_path)
local_files = list(output_path.rglob("*.*"))
assert len(local_files) == len(files)
def supervisely_convert_instance_pixel(tmpdir):
out_dir = tmpdir / 'instance_segmentation_pixel'
sa.import_annotation_format(
'tests/converter_test/Supervisely/input/toSuperAnnotate/instance',
str(out_dir), 'Supervisely', '', 'Pixel', 'instance_segmentation',
'Web')
project_name = "supervisely_test_instance_pixel"
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
def test_attach_image_urls():
projects = sa.search_projects(PROJECT_NAME_VECTOR, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME_VECTOR, "test", "Vector")
uploaded, could_not_upload, existing_images = sa.attach_image_urls_to_project(
project, PATH_TO_URLS)
assert len(uploaded) == 7
assert len(could_not_upload) == 0
assert len(existing_images) == 1
uploaded, could_not_upload, existing_images = sa.attach_image_urls_to_project(
project, PATH_TO_URLS)
assert len(uploaded) == 2
assert len(could_not_upload) == 0
assert len(existing_images) == 6
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 test_coco_vector_keypoint(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "keypoint_detection/"
out_dir = Path(tmpdir) / "vector_keypoint"
sa.import_annotation_format(input_dir, 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)
def test_dataloop_convert_vector(tmpdir):
input_dir = Path(
'tests'
) / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
out_dir = Path(tmpdir) / 'output_vector'
sa.import_annotation_format(
input_dir, out_dir, 'DataLoop', '', 'Vector', 'vector_annotation', 'Web'
)
project_name = "dataloop_test_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)
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
def test_upload_stress(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME, "hk", "Vector")
sa.create_annotation_classes_from_classes_json(
project, "tests/sample_project_vector/classes/classes.json"
)
sa.upload_images_from_folder_to_project(
project,
"/media/disc_drive/datasets/COCO/test2017",
annotation_status="QualityCheck"
)
time.sleep(60)
count = sa.get_project_image_count(project)
assert count == 40670
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 googlecloud_convert_web(tmpdir):
out_dir = tmpdir / "output_web"
sa.import_annotation_format(
"tests/converter_test/GoogleCloud/input/toSuperAnnotate", str(out_dir),
"GoogleCloud", "image_object_detection", "Vector", "object_detection",
"Web")
project_name = "googlcloud_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 yolo_object_detection_web(tmpdir):
out_dir = tmpdir / "vector_annotation_web"
sa.import_annotation_format(
'tests/converter_test/YOLO/input/toSuperAnnotate', str(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)
return 0
def test_vott_convert_object(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "VoTT" / "input" / "toSuperAnnotate"
out_dir = Path(tmpdir) / "object_detection"
sa.import_annotation_format(
input_dir, out_dir, "VoTT", "", "Vector", "object_detection", "Web"
)
project_name = "vott_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_single_image_upload(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
print(projects_found)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME, "test", "Vector")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_1.jpg",
annotation_status="InProgress")
images = sa.search_images(project)
assert len(images) == 1
image = images[0]
assert sa.get_image_metadata(project,
image)["annotation_status"] == "InProgress"
assert image == "example_image_1.jpg"
def test_upload_images_from_azure_storage_to_project():
folder_path_with_test_imgs = 'cat_pics_sdk_test'
folder_path_nested = 'cat_pics_nested_test'
folder_path_non_existent = 'nonex'
test_folders = [(folder_path_with_test_imgs, [6, 0, 6, 0]),
(folder_path_nested, [0, 6, 0, 0]),
(folder_path_non_existent, [0, 0, 0, 0])]
if sa.search_projects(PROJECT_NAME) != []:
sa.delete_project(PROJECT_NAME)
proj_data = sa.create_project(PROJECT_NAME, "test", "Vector")
for folder_path, true_res in test_folders:
uploaded_urls, uploaded_filenames, duplicate_filenames, not_uploaded_urls = sa.upload_images_from_azure_blob_to_project(
proj_data,
CONTAINER_NAME,
folder_path,
annotation_status='InProgress',
image_quality_in_editor="original")
assert len(uploaded_urls) == true_res[0]
assert len(duplicate_filenames) == true_res[1]
assert len(uploaded_filenames) == true_res[2]
assert len(not_uploaded_urls) == true_res[3]
def voc_vector_instance(tmpdir):
out_dir = tmpdir / "instance_vector"
sa.import_annotation_format(
"tests/converter_test/VOC/input/fromPascalVOCToSuperAnnotate/VOC2012/",
str(out_dir), "VOC", "", "Vector", "instance_segmentation", "Web"
)
project_name = "voc2sa_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)
return 0
def test_nonrecursive_annotations_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "0", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "0", "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_annotations_from_folder_to_project(
project, "./tests/sample_recursive_test", recursive_subfolders=False
)
export = sa.prepare_export(project)
time.sleep(1)
sa.download_export(project, export, tmpdir)
non_empty_annotations = 0
json_files = tmpdir.glob("*.json")
for json_file in json_files:
json_ann = json.load(open(json_file))
if "instances" in json_ann and len(json_ann["instances"]) > 0:
non_empty_annotations += 1
assert non_empty_annotations == 1
def test_add_bbox_noinit(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_NOINIT,
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_NOINIT, PROJECT_DESCRIPTION,
"Vector")
sa.upload_images_from_folder_to_project(project,
PATH_TO_SAMPLE_PROJECT,
annotation_status="InProgress")
sa.create_annotation_classes_from_classes_json(
project, PATH_TO_SAMPLE_PROJECT / "classes" / "classes.json")
sa.create_annotation_class(project, "test_add", "#FF0000")
images = sa.search_images(project, "example_image_1")
image_name = images[0]
sa.add_annotation_bbox_to_image(project, image_name, [10, 10, 500, 100],
"test_add")
sa.add_annotation_polygon_to_image(project, image_name,
[100, 100, 500, 500, 200, 300],
"test_add")
annotations_new = sa.get_image_annotations(project,
image_name)["annotation_json"]
assert len(annotations_new["instances"]) == 2
export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, export, tmpdir)
non_empty_annotations = 0
json_files = tmpdir.glob("*.json")
for json_file in json_files:
json_ann = json.load(open(json_file))
if "instances" in json_ann and len(json_ann["instances"]) > 0:
non_empty_annotations += 1
assert len(json_ann["instances"]) == 2
assert non_empty_annotations == 1
def test_pin_image_in_folder(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME2, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME2, "test", "Vector")
sa.create_folder(project, FOLDER2)
project_folder = project["name"] + "/" + FOLDER2
sa.upload_images_from_folder_to_project(project_folder,
"./tests/sample_project_vector",
annotation_status="QualityCheck")
img_metadata0 = sa.get_image_metadata(project_folder,
"example_image_1.jpg")
assert img_metadata0["is_pinned"] == 0
sa.pin_image(project_folder, "example_image_1.jpg")
time.sleep(1)
img_metadata = sa.get_image_metadata(project_folder, "example_image_1.jpg")
assert img_metadata["is_pinned"] == 1
sa.pin_image(project_folder, "example_image_1.jpg", True)
time.sleep(1)
img_metadata = sa.get_image_metadata(project_folder, "example_image_1.jpg")
assert img_metadata["is_pinned"] == 1
sa.pin_image(project_folder, "example_image_1.jpg", False)
time.sleep(1)
img_metadata = sa.get_image_metadata(project_folder, "example_image_1.jpg")
assert img_metadata["is_pinned"] == 0
del img_metadata["updatedAt"]
del img_metadata0["updatedAt"]
assert img_metadata == img_metadata0
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_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_format(input_dir, output1, "COCO", "object_test",
"Pixel", "instance_segmentation", "Web")
sa.import_annotation_format(output1, output2, "COCO", "object_test",
"Pixel", "instance_segmentation", "Web",
'image_set')
project_name = 'coco_pipeline_new'
project = sa.search_projects(project_name, return_metadata=True)
for pr in project:
sa.delete_project(pr)
project = sa.create_project(project_name, 'test_instane', 'Pixel')
sa.upload_images_from_folder_to_project(project, output2)
sa.create_annotation_classes_from_classes_json(
project, output2 / "classes" / "classes.json")
sa.upload_annotations_from_folder_to_project(project, output2)
def test_conversion(tmpdir):
input_dir = Path(
"tests"
) / "converter_test" / "COCO" / "input" / "fromSuperAnnotate" / "cats_dogs_vector_instance_segm"
temp_dir = Path(tmpdir) / 'output_Desktop'
final_dir = Path(tmpdir) / 'output_Web'
sa.convert_platform(input_dir, temp_dir, "Web")
sa.convert_platform(temp_dir, final_dir, "Desktop")
init_gen = input_dir.glob('*.json')
init_jsons = [file.name for file in init_gen]
final_gen = final_dir.glob('*.json')
final_jsons = [file.name for file in final_gen]
assert set(init_jsons) == set(final_jsons)
init_file_names = set(
[file.replace('___objects.json', '') for file in init_jsons])
temp_file_names = set(
json.load(open(temp_dir / 'annotations.json')).keys())
final_file_names = set(
[file.replace('___objects.json', '') for file in final_jsons])
assert init_file_names == temp_file_names
assert init_file_names == final_file_names
project_name = "platform_conversion"
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, input_dir)
sa.upload_annotations_from_folder_to_project(project, final_dir)
def test_annotations_recursive_s3_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "4", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "4", "test", "Vector")
sa.upload_images_from_folder_to_project(
project,
"sample_recursive_test",
annotation_status="QualityCheck",
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_annotations_from_folder_to_project(
project,
"sample_recursive_test",
recursive_subfolders=True,
from_s3_bucket="superannotate-python-sdk-test"
)
export = sa.prepare_export(project)
time.sleep(1)
sa.download_export(project, export, tmpdir)
assert len(list(tmpdir.glob("*.json"))) == 2
