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_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 len(
sa.get_image_annotations(
project, image_name)["annotation_json"]["instances"]) == 0
sa.download_image_annotations(project, image_name, tmpdir)
assert len(list(Path(tmpdir).glob("*"))) == 2
# sa.download_image_preannotations(project, image_name, tmpdir)
# assert len(list(Path(tmpdir).glob("*"))) == 2
assert (Path(tmpdir) / image_name).is_file()
sa.upload_image_annotations(
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)
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) == len(annotations) + 8
export = sa.prepare_export(project, include_fuse=True)
sa.download_export(project, export, tmpdir)
df = sa.aggregate_annotations_as_df(tmpdir)
num = len(df[df["imageName"] == image_name]["instanceId"].dropna().unique())
assert num == len(
annotations
) - 6 + 7 # -6 for 3 comments and 3 invalid annotations, className or attributes
def test_basic_folders(tmpdir):
PROJECT_NAME = "test folder simple"
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')
project = project["name"]
sa.upload_images_from_folder_to_project(project,
FROM_FOLDER,
annotation_status="InProgress")
images = sa.search_images(project, "example_image_1")
assert len(images) == 1
folders = sa.search_folders(project)
assert len(folders) == 0
folder_metadata = sa.create_folder(project, "folder1")
assert folder_metadata["name"] == "folder1"
folders = sa.search_folders(project, return_metadata=True)
assert len(folders) == 1
assert folders[0]["name"] == "folder1"
folders = sa.search_folders(project)
assert len(folders) == 1
assert folders[0] == "folder1"
images = sa.search_images(project + "/folder1", "example_image_1")
assert len(images) == 0
images = sa.search_images_all_folders(project, "example_image_1")
assert len(images) == 1
folder = sa.get_folder_metadata(project, "folder1")
assert isinstance(folder, dict)
assert folder["name"] == "folder1"
with pytest.raises(SABaseException) as e:
folder = sa.get_folder_metadata(project, "folder2")
assert 'Folder not found' in str(e)
sa.upload_images_from_folder_to_project(project + "/folder1",
FROM_FOLDER,
annotation_status="InProgress")
images = sa.search_images(project + "/folder1", "example_image_1")
assert len(images) == 1
sa.upload_images_from_folder_to_project(project + "/folder1",
FROM_FOLDER,
annotation_status="InProgress")
images = sa.search_images(project + "/folder1")
assert len(images) == 4
with pytest.raises(SABaseException) as e:
sa.upload_images_from_folder_to_project(project + "/folder2",
FROM_FOLDER,
annotation_status="InProgress")
assert 'Folder not found' in str(e)
folder_metadata = sa.create_folder(project, "folder2")
assert folder_metadata["name"] == "folder2"
folders = sa.search_folders(project)
assert len(folders) == 2
folders = sa.search_folders(project, folder_name="folder")
assert len(folders) == 2
folders = sa.search_folders(project, folder_name="folder2")
assert len(folders) == 1
assert folders[0] == "folder2"
folders = sa.search_folders(project, folder_name="folder1")
assert len(folders) == 1
assert folders[0] == "folder1"
folders = sa.search_folders(project, folder_name="old")
assert len(folders) == 2
def test_create_like_project(tmpdir):
tmpdir = Path(tmpdir)
projects = sa.search_projects(PROJECT_NAME, return_metadata=True)
for project in projects:
sa.delete_project(project)
sa.create_project(PROJECT_NAME, "tt", "Vector")
sa.create_annotation_class(
PROJECT_NAME, "rrr", "#FFAAFF",
[{
"name": "tall",
"is_multiselect": 0,
"attributes": [{
"name": "yes"
}, {
"name": "no"
}]
}, {
"name": "age",
"is_multiselect": 0,
"attributes": [{
"name": "young"
}, {
"name": "old"
}]
}])
old_settings = sa.get_project_settings(PROJECT_NAME)
for setting in old_settings:
if "attribute" in setting and setting["attribute"] == "Brightness":
brightness_value = setting["value"]
sa.set_project_settings(PROJECT_NAME, [{
"attribute": "Brightness",
"value": brightness_value + 10
}])
sa.set_project_workflow(PROJECT_NAME, [{
"step":
1,
"className":
"rrr",
"tool":
3,
"attribute": [{
"attribute": {
"name": "young",
"attribute_group": {
"name": "age"
}
}
}, {
"attribute": {
"name": "yes",
"attribute_group": {
"name": "tall"
}
}
}]
}])
users = sa.search_team_contributors()
sa.share_project(PROJECT_NAME, users[1], "QA")
projects = sa.search_projects(PROJECT_NAME2, return_metadata=True)
for project in projects:
sa.delete_project(project)
new_project = sa.clone_project(PROJECT_NAME2,
PROJECT_NAME,
copy_contributors=True)
assert new_project["description"] == "tt"
assert new_project["type"] == "Vector"
time.sleep(1)
ann_classes = sa.search_annotation_classes(PROJECT_NAME2)
assert len(ann_classes) == 1
assert ann_classes[0]["name"] == "rrr"
assert ann_classes[0]["color"] == "#FFAAFF"
new_settings = sa.get_project_settings(PROJECT_NAME2)
for setting in new_settings:
if "attribute" in setting and setting["attribute"] == "Brightness":
new_brightness_value = setting["value"]
assert new_brightness_value == brightness_value + 10
new_workflow = sa.get_project_workflow(PROJECT_NAME2)
assert len(new_workflow) == 1
assert new_workflow[0]["className"] == "rrr"
assert new_workflow[0]["tool"] == 3
assert len(new_workflow[0]["attribute"]) == 2
assert new_workflow[0]["attribute"][0]["attribute"]["name"] == "young"
assert new_workflow[0]["attribute"][0]["attribute"]["attribute_group"][
"name"] == "age"
assert new_workflow[0]["attribute"][1]["attribute"]["name"] == "yes"
assert new_workflow[0]["attribute"][1]["attribute"]["attribute_group"][
"name"] == "tall"
new_project = sa.get_project_metadata(new_project["name"],
include_contributors=True)
assert len(new_project["contributors"]) == 1
assert new_project["contributors"][0]["user_id"] == users[1]
assert new_project["contributors"][0]["user_role"] == "QA"
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"] == sa.project_type_str_to_int(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,
return_metadata=True)
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