def test_folder_export(tmpdir):
PROJECT_NAME = "test folder export"
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME, 'test', 'Vector')
sa.create_annotation_classes_from_classes_json(
project, FROM_FOLDER / "classes" / "classes.json")
sa.upload_images_from_folder_to_project(project,
FROM_FOLDER,
annotation_status="InProgress")
sa.create_folder(project, "folder1")
project = PROJECT_NAME + "/folder1"
sa.upload_images_from_folder_to_project(project,
FROM_FOLDER,
annotation_status="InProgress")
sa.upload_annotations_from_folder_to_project(project, FROM_FOLDER)
num_images = sa.get_project_image_count(project)
assert num_images == 4
sa.create_folder(PROJECT_NAME, "folder2")
project2 = PROJECT_NAME + "/folder2"
num_images = sa.get_project_image_count(project2)
assert num_images == 0
sa.copy_images(project, ["example_image_2.jpg", "example_image_3.jpg"],
project2)
export = sa.prepare_export(PROJECT_NAME, ["folder1", "folder2"])
sa.download_export(project, export, tmpdir)
assert len(list((tmpdir / "classes").rglob("*"))) == 1
assert len(list((tmpdir / "folder1").rglob("*"))) == 4
assert len(list((tmpdir / "folder2").rglob("*"))) == 2
assert len(list((tmpdir).glob("*.*"))) == 0
export = sa.prepare_export(PROJECT_NAME)
sa.download_export(project, export, tmpdir)
assert len(list((tmpdir / "classes").rglob("*"))) == 1
assert len(list((tmpdir / "folder1").rglob("*"))) == 4
assert len(list((tmpdir / "folder2").rglob("*"))) == 2
assert len(list((tmpdir).glob("*.*"))) == 4
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) == 2
export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, export, tmpdir)
assert len(list(Path(tmpdir).rglob("*.*"))) == 4
def export_project(args):
parser = argparse.ArgumentParser()
parser.add_argument('--project',
required=True,
help='Project name to export')
parser.add_argument('--folder',
required=True,
help='Folder to which export')
parser.add_argument('--include-fuse',
default=False,
action='store_true',
help='Enables fuse image export')
parser.add_argument('--disable-extract-zip-contents',
default=False,
action='store_true',
help='Disables export zip extraction')
parser.add_argument(
'--annotation-statuses',
default=None,
type=_list_str,
help=
'List of image annotation statuses to include in export. Default is InProgress,QualityCheck,Returned,Completed'
)
args = parser.parse_args(args)
export = sa.prepare_export(args.project, args.annotation_statuses,
args.include_fuse)
sa.download_export(args.project, export, args.folder,
not args.disable_extract_zip_contents)
def test_recursive_annotations_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "1", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "1", "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=True
)
export = sa.prepare_export(project)
time.sleep(1)
sa.download_export(project, export, tmpdir)
assert len(list(tmpdir.glob("*.json"))) == 2
def test_basic_export(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME, "t", "Vector")
sa.upload_images_from_folder_to_project(
project, PROJECT_FOLDER, annotation_status="InProgress"
)
len_orig = len(sa.search_images(project))
sa.create_annotation_classes_from_classes_json(
project, PROJECT_FOLDER / "classes" / "classes.json"
)
sa.upload_annotations_from_folder_to_project(project, PROJECT_FOLDER)
export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, export, tmpdir)
projects_found = sa.search_projects(
PROJECT_NAME + " import", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project_new = sa.create_project(PROJECT_NAME + " import", "f", "Vector")
sa.upload_images_from_folder_to_project(
project_new, tmpdir, annotation_status="InProgress"
)
len_new = len(sa.search_images(project_new))
assert len_new == len_orig
def test_get_exports(tmpdir):
tmpdir = Path(tmpdir)
project = upload_project(PROJECT_FOLDER,
PROJECT_NAME1,
'gg',
'Vector',
annotation_status='QualityCheck')
# projects_found = sa.search_projects(PROJECT_NAME1, return_metadata=True)
# for pr in projects_found:
# sa.delete_project(pr)
# project = sa.create_project(PROJECT_NAME1, "gg", "Vector")
# sa.upload_images_from_folder_to_project(
# project,
# "./tests/sample_project_vector/",
# annotation_status="QualityCheck"
# )
# sa.create_annotation_classes_from_classes_json(
# project,
# "./tests/sample_project_vector/classes/classes.json",
# )
# sa.upload_annotations_from_folder_to_project(
# project,
# "./tests/sample_project_vector/",
# )
exports_old = sa.get_exports(project)
export = sa.prepare_export(project)
sa.download_export(project, export["name"], tmpdir)
js = list(tmpdir.glob("*.json"))
assert len(js) == 4
exports_new = sa.get_exports(project)
assert len(exports_new) == len(exports_old) + 1
def test_download_stress(tmpdir):
project = sa.search_projects(name_prefix="test_test_15")[0]
export = sa.prepare_export(project)
sa.download_export(project, export, tmpdir)
count_in_project = sa.get_project_image_count(project)
count_in_folder = len(list(Path(tmpdir).glob("*.jpg")))
assert count_in_project == count_in_folder
def sa_download_files(
project_name: str,
destination: str = "./downloads",
conf_path: object = "./config.json",
):
sa.init(conf_path)
export = sa.prepare_export(project_name, include_fuse=True)
sa.download_export(project=project_name,
export=export,
folder_path=destination)
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)
project = upload_project(Path("./tests/sample_project_vector"),
PROJECT_NAME_EXPORT,
'test',
'Vector',
annotation_status='InProgress')
# 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 test_fuse_image_create_pixel(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME_PIXEL, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME_PIXEL, "test", "Pixel")
sa.upload_image_to_project(
project,
"./tests/sample_project_pixel/example_image_1.jpg",
annotation_status="QualityCheck"
)
sa.create_annotation_classes_from_classes_json(
project, "./tests/sample_project_pixel/classes/classes.json"
)
sa.upload_image_annotations(
PROJECT_NAME_PIXEL, "example_image_1.jpg",
"./tests/sample_project_pixel/example_image_1.jpg___pixel.json",
"./tests/sample_project_pixel/example_image_1.jpg___save.png"
)
export = sa.prepare_export(project, include_fuse=True)
(tmpdir / "export").mkdir()
sa.download_export(project, export, (tmpdir / "export"))
# sa.create_fuse_image(
# "./tests/sample_project_vector/example_image_1.jpg",
# "./tests/sample_project_vector/classes/classes.json", "Vector"
# )
paths = sa.download_image(
project,
"example_image_1.jpg",
tmpdir,
include_annotations=True,
include_fuse=True
)
print(paths, paths[2])
im1 = Image.open(tmpdir / "export" / "example_image_1.jpg___fuse.png")
im1_array = np.array(im1)
im2 = Image.open(paths[2][0])
im2_array = np.array(im2)
assert im1_array.shape == im2_array.shape
assert im1_array.dtype == im2_array.dtype
assert np.array_equal(im1_array, im2_array)
def test_annotations_nonrecursive_s3_folder(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "5", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "5", "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=False,
from_s3_bucket="superannotate-python-sdk-test"
)
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 export_project(command_name, args):
parser = argparse.ArgumentParser(prog=_CLI_COMMAND + " " + command_name)
parser.add_argument('--project',
required=True,
help='Project name to export')
parser.add_argument('--folder',
required=True,
help='Folder to which export')
parser.add_argument('--include-fuse',
default=False,
action='store_true',
help='Enables fuse image export')
parser.add_argument('--disable-extract-zip-contents',
default=False,
action='store_true',
help='Disables export zip extraction')
parser.add_argument(
'--annotation-statuses',
default=None,
type=_list_str,
help=
'List of image annotation statuses to include in export. Default is InProgress,QualityCheck,Returned,Completed'
)
args = parser.parse_args(args)
parts = args.project.split('/')
if len(parts) == 1:
project, project_folder = parts[0], None
elif len(parts) == 2:
project, project_folder = parts
else:
raise SABaseException(
0, "Project should be in format <project>[/<folder>]")
export = sa.prepare_export(
project,
None if project_folder is None else [project_folder],
annotation_statuses=args.annotation_statuses,
include_fuse=args.include_fuse)
sa.download_export(args.project, export, args.folder,
not args.disable_extract_zip_contents)
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_meta_init(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="InProgress")
for image in from_folder.glob("*.jpg"):
size = cv2.imread(str(image)).shape
annot = sa.get_image_annotations(project,
image.name)["annotation_json"]
print(annot)
assert annot["metadata"]["width"] == size[1]
assert annot["metadata"]["height"] == size[0]
assert annot["metadata"]["name"] == image.name
assert len(annot["metadata"]) == 3
sa.download_export(project, sa.prepare_export(project), tmpdir)
def test_get_exports(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME, "gg", "Vector")
sa.upload_images_from_folder_to_project(project,
"./tests/sample_project_vector/",
annotation_status="QualityCheck")
sa.create_annotation_classes_from_classes_json(
project,
"./tests/sample_project_vector/classes/classes.json",
)
sa.upload_annotations_from_folder_to_project(
project,
"./tests/sample_project_vector/",
)
export = sa.prepare_export(project)
sa.download_export(project, export, tmpdir)
js = list(tmpdir.glob("*.json"))
assert len(js) == 4
def test_fuse_image_create_vector(tmpdir):
tmpdir = Path(tmpdir)
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")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_1.jpg",
annotation_status="QualityCheck")
sa.create_annotation_classes_from_classes_json(
project, "./tests/sample_project_vector/classes/classes.json")
sa.add_annotation_bbox_to_image(project, "example_image_1.jpg",
[20, 20, 40, 40], "Human")
sa.add_annotation_polygon_to_image(project, "example_image_1.jpg",
[60, 60, 100, 100, 80, 100],
"Personal vehicle")
sa.add_annotation_polyline_to_image(project, "example_image_1.jpg",
[200, 200, 300, 200, 350, 300],
"Personal vehicle")
sa.add_annotation_point_to_image(project, "example_image_1.jpg",
[400, 400], "Personal vehicle")
sa.add_annotation_ellipse_to_image(project, "example_image_1.jpg",
[600, 600, 50, 100, 20],
"Personal vehicle")
sa.add_annotation_template_to_image(
project, "example_image_1.jpg",
[600, 300, 600, 350, 550, 250, 650, 250, 550, 400, 650, 400],
[1, 2, 3, 1, 4, 1, 5, 2, 6, 2], "Human")
sa.add_annotation_cuboid_to_image(project, "example_image_1.jpg",
[60, 300, 200, 350, 120, 325, 250, 500],
"Human")
export = sa.prepare_export(project, include_fuse=True)
(tmpdir / "export").mkdir()
sa.download_export(project, export, (tmpdir / "export"))
# sa.create_fuse_image(
# "./tests/sample_project_vector/example_image_1.jpg",
# "./tests/sample_project_vector/classes/classes.json", "Vector"
# )
paths = sa.download_image(project,
"example_image_1.jpg",
tmpdir,
include_annotations=True,
include_fuse=True,
include_overlay=True)
im1 = Image.open(tmpdir / "export" / "example_image_1.jpg___fuse.png")
im1_array = np.array(im1)
im2 = Image.open(paths[2][0])
im2_array = np.array(im2)
assert im1_array.shape == im2_array.shape
assert im1_array.dtype == im2_array.dtype
def test_add_bbox(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
for pr in projects_found:
if pr["name"] == PROJECT_NAME:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME, PROJECT_DESCRIPTION, "Vector")
sa.upload_images_from_folder_to_project(PROJECT_NAME,
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",
[{
"name": "height",
"attributes": [{
"name": "tall"
}, {
"name": "short"
}]
}])
sa.upload_annotations_from_folder_to_project(project,
PATH_TO_SAMPLE_PROJECT)
images = sa.search_images(project, "example_image_1")
image_name = images[0]
annotations = sa.get_image_annotations(project,
image_name)["annotation_json"]
sa.add_annotation_bbox_to_image(project, image_name, [10, 10, 500, 100],
"test_add")
sa.add_annotation_polyline_to_image(project, image_name,
[110, 110, 510, 510, 600, 510],
"test_add")
sa.add_annotation_polygon_to_image(project, image_name,
[100, 100, 500, 500, 200, 300],
"test_add", [{
"name": "tall",
"groupName": "height"
}])
sa.add_annotation_point_to_image(project, image_name, [250, 250],
"test_add")
sa.add_annotation_ellipse_to_image(project, image_name,
[405, 405, 20, 70, 15], "test_add")
sa.add_annotation_template_to_image(project, image_name,
[600, 30, 630, 30, 615, 60],
[1, 3, 2, 3], "test_add")
sa.add_annotation_cuboid_to_image(project, image_name,
[800, 500, 900, 600, 850, 450, 950, 700],
"test_add")
sa.add_annotation_comment_to_image(project, image_name, "hey", [100, 100],
"*****@*****.**", True)
annotations_new = sa.get_image_annotations(project,
image_name)["annotation_json"]
json.dump(annotations_new, open(tmpdir / "new_anns.json", "w"))
assert len(annotations_new["instances"]) + len(
annotations_new["comments"]) == len(annotations["instances"]) + len(
annotations["comments"]) + 8
export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, export, tmpdir)
df = sa.aggregate_annotations_as_df(tmpdir)
print(df)
print(image_name)
num = len(
df[df["imageName"] == image_name]["instanceId"].dropna().unique())
assert num == len(
annotations["instances"]
) - 3 + 7 # -6 for 3 comments and 3 invalid annotations, className or attributes
def test_basic_project(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)
assert len(projects_found) == 0
project = sa.create_project(name, description, project_type)
assert project["name"] == name
assert project["description"] == description
assert project["type"] == project_type
projects_found = sa.search_projects(name)
assert len(projects_found) == 1
assert projects_found[0] == name
sa.upload_images_from_folder_to_project(project,
from_folder,
annotation_status="InProgress")
count_in_folder = len(list(from_folder.glob("*.jpg"))) + len(
list(from_folder.glob("*.png")))
count_in_folder -= len(list(from_folder.glob("*___fuse.png")))
if project_type == "Pixel":
count_in_folder -= len(list(from_folder.glob("*___save.png")))
images = sa.search_images(project)
assert count_in_folder == len(images)
sa.create_annotation_classes_from_classes_json(
project, from_folder / "classes" / "classes.json")
classes_in_file = json.load(open(from_folder / "classes" / "classes.json"))
classes_in_project = sa.search_annotation_classes(project)
json.dump(classes_in_project, open(Path(tmpdir) / "tmp_c.json", 'w'))
assert len(classes_in_file) == len(classes_in_project)
for cl_f in classes_in_file:
found = False
for cl_c in classes_in_project:
if cl_f["name"] == cl_c["name"]:
found = True
break
assert found
sa.upload_annotations_from_folder_to_project(project, from_folder)
export = sa.prepare_export(project)
sa.download_export(project, export, tmpdir)
for image in from_folder.glob("*.[jpg|png]"):
found = False
for image_in_project in tmpdir.glob("*.jpg"):
if image.name == image_in_project.name:
found = True
break
assert found, image
for json_in_folder in from_folder.glob("*.json"):
found = False
for json_in_project in tmpdir.glob("*.json"):
if json_in_folder.name == json_in_project.name:
found = True
break
assert found, json_in_folder
if project_type == "Pixel":
for mask_in_folder in from_folder.glob("*___save.png"):
found = False
for mask_in_project in tmpdir.glob("*___save.png"):
if mask_in_folder.name == mask_in_project.name:
found = True
break
assert found, mask_in_folder
