def test_image_quality_setting1(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME1, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME1, "test", "Vector")
sa.upload_videos_from_folder_to_project(
project, "./tests/sample_videos", target_fps=2
)
projects = sa.search_projects(PROJECT_NAME2, return_metadata=True)
for project in projects:
sa.delete_project(project)
sa.create_project(PROJECT_NAME2, "test", "Vector")
subprocess.run(
f'superannotate upload-videos --project "{PROJECT_NAME2}" --folder ./tests/sample_videos --target-fps 2',
check=True,
shell=True
)
time.sleep(5)
assert len(sa.search_images(PROJECT_NAME1)) == len(
sa.search_images(PROJECT_NAME2)
)
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_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_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_cli_image_upload_project_export(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
subprocess.run(
f'superannotate create-project --name "{PROJECT_NAME}" --description gg --type Vector',
check=True,
shell=True
)
project = PROJECT_NAME
sa.create_annotation_classes_from_classes_json(
PROJECT_NAME, "./tests/sample_recursive_test/classes/classes.json"
)
subprocess.run(
f'superannotate upload-images --project "{PROJECT_NAME}" --folder ./tests/sample_recursive_test --extensions=jpg --set-annotation-status QualityCheck'
,
check=True,
shell=True
)
time.sleep(1)
assert len(sa.search_images(project)) == 1
subprocess.run(
f'superannotate upload-images --project "{PROJECT_NAME}" --folder ./tests/sample_recursive_test --extensions=jpg --recursive'
,
check=True,
shell=True
)
time.sleep(1)
assert len(sa.search_images(project)) == 2
sa.upload_annotations_from_folder_to_project(
project, "./tests/sample_recursive_test"
)
subprocess.run(
f'superannotate export-project --project "{PROJECT_NAME}" --folder {tmpdir}'
,
check=True,
shell=True
)
assert len(list(tmpdir.glob("*.json"))) == 1
assert len(list(tmpdir.glob("*.jpg"))) == 0
assert len(list(tmpdir.glob("*.png"))) == 0
time.sleep(60)
subprocess.run(
f'superannotate export-project --project "{PROJECT_NAME}" --folder {tmpdir} --include-fuse'
,
check=True,
shell=True
)
assert len(list(tmpdir.glob("*.json"))) == 1
assert len(list(tmpdir.glob("*.jpg"))) == 1
assert len(list(tmpdir.glob("*.png"))) == 1
def test_run_prediction():
upload_project(
Path(PROJECT_PATH_VECTOR), PROJECT_NAME_VECTOR,
"Test for ml functionality", "Vector"
)
upload_project(
Path(PROJECT_PATH_PIXEL), PROJECT_NAME_PIXEL_PREDICTION,
"Test for ml functionality", "Pixel"
)
#Tests for the case when provided images do not exist in the project
with pytest.raises(SABaseException) as e:
sa.run_prediction(
PROJECT_NAME_VECTOR, ["NonExistantImage.jpg"], MODEL_NAME
)
assert str(e) == "No valid image names were provided"
#Tests that the argument 'project' is valid
with pytest.raises(SABaseException) as e:
sa.run_prediction(
[PROJECT_NAME_VECTOR, PROJECT_NAME_PIXEL_PREDICTION],
["DoesntMatter.jpg"], MODEL_NAME
)
assert str(
e
) == "smart prediction cannot be run on images from different projects simultaneously"
#Tests if prediction on all available images gets run
image_names_pixel = sa.search_images(PROJECT_NAME_PIXEL_PREDICTION)
image_names_vector = sa.search_images(PROJECT_NAME_VECTOR)
succeded_imgs, failed_imgs = sa.run_prediction(
PROJECT_NAME_VECTOR, image_names_vector[:4], MODEL_NAME
)
assert (len(succeded_imgs) + len(failed_imgs)) == 4
succeded_imgs, failed_imgs = sa.run_prediction(
PROJECT_NAME_PIXEL_PREDICTION, image_names_pixel[:4], MODEL_NAME
)
assert (len(succeded_imgs) + len(failed_imgs)) == 4
succeded_imgs, failed_imgs = sa.run_prediction(
PROJECT_NAME_PIXEL_PREDICTION, image_names_pixel[:4] + ["NA.jpg"],
MODEL_NAME
)
assert (len(succeded_imgs) + len(failed_imgs)) == 4
succeded_imgs, failed_imgs = sa.run_prediction(
PROJECT_NAME_VECTOR, image_names_vector[:4] + ["NA.jpg"], MODEL_NAME
)
assert (len(succeded_imgs) + len(failed_imgs)) == 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 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_direct_s3_upload_folder():
projects_found = sa.search_projects(TEST_PROJECT_NAME2,
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEST_PROJECT_NAME2, "a", "Vector")
print(project["id"])
csv = (Path.home() / ".aws" / "credentials").read_text().splitlines()
access_key_id = csv[1].split(" = ")[1]
access_secret = csv[2].split(" = ")[1]
sa.create_folder(project, "folder1")
project_folder = project["name"] + "/folder1"
sa.upload_images_from_s3_bucket_to_project(project_folder, access_key_id,
access_secret, S3_BUCKET,
S3_FOLDER)
s3_client = boto3.client('s3')
paginator = s3_client.get_paginator('list_objects_v2')
response_iterator = paginator.paginate(Bucket=S3_BUCKET, Prefix=S3_FOLDER)
on_s3 = []
for response in response_iterator:
if 'Contents' in response:
for object_data in response['Contents']:
key = object_data['Key']
if key[-4:] in [".jpg", ".png"]:
on_s3.append(key)
assert len(on_s3) == len(sa.search_images(project_folder))
def test_upload_images_from_public_to_project_with_image_name():
PROJECT_NAME = 'test_public_links_upload2'
test_img_list = [
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940',
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940',
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940',
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940',
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940'
]
img_name_list = ['img1.jpg', 'img2.jpg', 'img3.jpg', 'img4.jpg', 'img5.jpg']
if sa.search_projects(PROJECT_NAME) != []:
sa.delete_project(PROJECT_NAME)
proj_data = sa.create_project(PROJECT_NAME, "test", "Vector")
uploaded_urls, uploaded_filenames, duplicate_filenames, not_uploaded_urls = sa.upload_images_from_public_urls_to_project(
proj_data,
test_img_list,
img_name_list,
annotation_status='InProgress',
image_quality_in_editor="original"
)
images_in_project = sa.search_images(
proj_data, annotation_status='InProgress'
)
# check how many images were uploaded and how many were not
assert len(uploaded_urls) == 5
assert len(duplicate_filenames) == 0
assert len(uploaded_filenames) == 5
assert len(not_uploaded_urls) == 0
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_upload_images_from_public_urls_to_project():
PROJECT_NAME = 'test_public_links_upload1'
test_img_list = [
'https://images.pexels.com/photos/3702354/pexels-photo-3702354.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940',
'https://www.pexels.com/photo/5450829/download/',
'https://www.pexels.com/photo/3702354/download/',
'https://www.pexels.com/photo/3702354/download/',
'https://www.pexels.com/photo/3702354/dwnload/', '', 'test_non_url'
]
if sa.search_projects(PROJECT_NAME) != []:
sa.delete_project(PROJECT_NAME)
proj_data = sa.create_project(PROJECT_NAME, "test", "Vector")
uploaded_urls, uploaded_filenames, duplicate_filenames, not_uploaded_urls = sa.upload_images_from_public_urls_to_project(
proj_data,
test_img_list,
annotation_status='InProgress',
image_quality_in_editor="original"
)
images_in_project = sa.search_images(
proj_data, annotation_status='InProgress'
)
# check how many images were uploaded and how many were not
assert len(uploaded_urls) == 3
assert len(duplicate_filenames) == 1
assert len(uploaded_filenames) == 3
assert len(not_uploaded_urls) == 3
for image in images_in_project:
assert image in uploaded_filenames
def test_single_image_upload_bytesio(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_BYTES,
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_BYTES, "test", "Vector")
with open("./tests/sample_project_vector/example_image_1.jpg", "rb") as f:
img = io.BytesIO(f.read())
try:
sa.upload_image_to_project(project,
img,
annotation_status="InProgress")
except sa.SABaseException as e:
assert e.message == "Image name img_name should be set if img is not Pathlike"
else:
assert False
sa.upload_image_to_project(project,
img,
image_name="rr.jpg",
annotation_status="InProgress")
images = sa.search_images(project)
assert len(images) == 1
image = images[0]
assert sa.annotation_status_int_to_str(
sa.get_image_metadata(project,
image)["annotation_status"]) == "InProgress"
assert image == "rr.jpg"
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_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_image_move(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_MOVE,
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_MOVE, "test", "Vector")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_1.jpg",
annotation_status="InProgress")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_2.jpg",
annotation_status="InProgress")
images = sa.search_images(project)
assert len(images) == 2
image = images[0]
try:
sa.move_image(project, image, project)
except sa.SABaseException as e:
assert e.message == "Cannot move image if source_project == destination_project."
else:
assert False
projects_found = sa.search_projects(PROJECT_NAME_MOVE + "dif",
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
dest_project = sa.create_project(PROJECT_NAME_MOVE + "dif", "test",
"Vector")
sa.move_image(project, image, dest_project)
di = sa.search_images(dest_project, image)
assert len(di) == 1
assert di[0] == image
si = sa.search_images(project, image)
assert len(si) == 0
si = sa.search_images(project)
assert len(si) == 1
def test_image_copy_mult(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_CPY_MULT,
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_CPY_MULT, "test", "Vector")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_1.jpg",
annotation_status="InProgress")
sa.create_annotation_classes_from_classes_json(
project, "./tests/sample_project_vector/classes/classes.json")
sa.upload_image_annotations(
project, "example_image_1.jpg",
"./tests/sample_project_vector/example_image_1.jpg___objects.json")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_2.jpg",
annotation_status="InProgress")
sa.pin_image(project, "example_image_1.jpg")
images = sa.search_images(project)
assert len(images) == 2
image = images[0]
for _ in range(3):
sa.copy_image(project,
image,
project,
include_annotations=True,
copy_annotation_status=True,
copy_pin=True)
assert len(sa.search_images(project)) == 5
images = sa.search_images(project)
for i in range(3):
assert f"example_image_1_({i+1}).jpg" in images
anns = sa.get_image_annotations(project, f"example_image_1_({i+1}).jpg")
assert anns["annotation_json"] is not None
metadata = sa.get_image_metadata(project, f"example_image_1_({i+1}).jpg")
assert metadata["is_pinned"] == 1
def test_video(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME1, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME1, "test", "Vector")
time.sleep(1)
sa.create_annotation_class(project, "fr", "#FFAAAA")
time.sleep(1)
sa.create_annotation_class(project, "fr2", "#FFAACC")
sa.upload_videos_from_folder_to_project(
project, "./tests/sample_videos", target_fps=2
)
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")
subprocess.run(
f'superannotatecli upload-videos --project "{PROJECT_NAME2}" --folder ./tests/sample_videos --target-fps 2',
check=True,
shell=True
)
time.sleep(5)
sa.create_annotation_class(project, "fr2", "#FFAACC")
assert len(sa.search_images(PROJECT_NAME1)) == len(
sa.search_images(PROJECT_NAME2)
)
sa.create_folder(project, "new folder")
sa.upload_videos_from_folder_to_project(
PROJECT_NAME2 + "/new folder", "./tests/sample_videos", target_fps=2
)
assert len(sa.search_images(PROJECT_NAME2 + "/new folder")) == len(
sa.search_images(PROJECT_NAME2)
)
def test_image_copy(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(PROJECT_NAME_CPY, return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(PROJECT_NAME_CPY, "test", "Vector")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_1.jpg",
annotation_status="InProgress")
sa.upload_image_to_project(
project,
"./tests/sample_project_vector/example_image_2.jpg",
annotation_status="InProgress")
images = sa.search_images(project)
assert len(images) == 2
image = images[0]
sa.copy_image(project, image, project)
images = sa.search_images(project)
assert len(images) == 3
image = "example_image_1_(1).jpg"
assert len(sa.search_images(project, image)) == 1
sa.copy_image(project, image, project)
image = "example_image_1_(2).jpg"
assert len(sa.search_images(project, image)) == 1
projects_found = sa.search_projects(PROJECT_NAME_CPY + "dif",
return_metadata=True)
for pr in projects_found:
sa.delete_project(pr)
dest_project = sa.create_project(PROJECT_NAME_CPY + "dif", "test",
"Vector")
sa.copy_image(project, image, dest_project)
di = sa.search_images(dest_project, image)
assert len(di) == 1
assert di[0] == image
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_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_from_s3_upload():
projects = sa.search_projects(PROJECT_NAME_UPLOAD, return_metadata=True)
for project in projects:
sa.delete_project(project)
project = sa.create_project(PROJECT_NAME_UPLOAD, "hk", "Vector")
sa.create_annotation_classes_from_classes_json(
project, "frex9/classes/classes.json", S3_BUCKET)
sa.upload_images_from_folder_to_project(project,
S3_PREFIX, ["jpg"],
annotation_status="QualityCheck",
from_s3_bucket=S3_BUCKET)
assert len(sa.search_images(project)) == 4
def test_images_nonrecursive(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "9", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "9", "test", "Vector")
sa.upload_images_from_folder_to_project(
project, "./tests/sample_recursive_test", recursive_subfolders=False
)
assert len(sa.search_images(project)) == 1
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 test_images_nonrecursive_s3(tmpdir):
tmpdir = Path(tmpdir)
projects_found = sa.search_projects(
TEMP_PROJECT_NAME + "8", return_metadata=True
)
for pr in projects_found:
sa.delete_project(pr)
project = sa.create_project(TEMP_PROJECT_NAME + "8", "test", "Vector")
sa.upload_images_from_folder_to_project(
project,
"sample_recursive_test",
from_s3_bucket="superannotate-python-sdk-test",
recursive_subfolders=False
)
assert len(sa.search_images(project)) == 1
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_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_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_run_segmentation():
model_auto = 'autonomous'
model_generic = 'generic'
upload_project(
Path(PROJECT_PATH_PIXEL), PROJECT_NAME_PIXEL_SEGMENTATION,
"Test for ml functionality", "Pixel"
)
image_names_pixel = sa.search_images(PROJECT_NAME_PIXEL_SEGMENTATION)
with pytest.raises(SABaseException) as e:
sa.run_segmentation(PROJECT_NAME_VECTOR, image_names_pixel, model_auto)
assert str(e) == "Operation not supported for given project type"
with pytest.raises(SABaseException) as e:
sa.run_segmentation(
PROJECT_NAME_PIXEL_SEGMENTATION, image_names_pixel[:2],
"NonExistantModel"
)
assert str(e) == "Model Does not exist"
with pytest.raises(SABaseException) as e:
sa.run_segmentation(
PROJECT_NAME_PIXEL_SEGMENTATION, ["NonExistantImage.jpg"],
MODEL_NAME
)
assert str(e) == "No valid image names were provided"
succeded_imgs, failed_imgs = sa.run_segmentation(
PROJECT_NAME_PIXEL_SEGMENTATION, image_names_pixel[:4] + ["NA.jpg"],
model_generic
)
assert (len(succeded_imgs) + len(failed_imgs)) == 4
succeded_imgs, failed_imgs = sa.run_segmentation(
PROJECT_NAME_PIXEL_SEGMENTATION, image_names_pixel[:4] + ["NA.jpg"],
model_auto
)
assert (len(succeded_imgs) + len(failed_imgs)) == 4
