SDK Quick Peek

folder_name = "<folder_name>"
folder_description = "A folder to store my files"
folder_metadata = {"my": "folder_metadata"}
storage_folder = user_client.create_storage_folder(folder_name, folder_description,client_metadata=folder_metadata)

storage_folder = user_client.create_storage_folder(
    name=folder_name,
    description=folder_description,
    client_metadata=folder_metadata,
    cloud_synced_folder_params=(integration_uuid=UUID(INTEGRATION_UUID), remote_url=REMOTE_URL,)
)

dataset.delete_data(
    [
        "<video1_data_hash\>",
        "<image_group1_data_hash>",
    ]
)

new_project_hash = project.copy_project(
    copy_datasets=True,
    copy_collaborators=True,
    copy_models=False,  # Not strictly needed
)

stage = project.workflow.get_stage(name="Annotate 1", type_=AnnotationStage)
for task in stage.get_tasks()

for task in stage.get_tasks():
    # The task is submitted as the user who is currently assigned to the task
    task.submit(retain_assignee=True, bundle=bundle)

for task in stage.get_tasks(data_hash=data_hashes):
    task.assign("chris@acme.com")
    task.reject(bundle=bundle)

for task in stage.get_tasks(data_hash=data_hashes):
    task.assign("chris@acme.com")
    task.approve(bundle=bundle)

added_users = project.add_users(
    ["example1@encord.com", "example2@encord.com"],
    ProjectUserRole.ANNOTATOR,
)

REVIEW_ISSUES = [
    {
        "data_title": "cherries-sequence",
        "file_issue": {"text": "Review file issue 1", "tags": ["incorrect object"]},
        "frame_issue": {"frame": 3, "text": "Review frame issue 1", "tags": ["label too large"]},
        "coordinate_issue": {"frame": 0, "x": 0.3, "y": 0.3, "text": "Review coordinate issue 1", "tags": ["incorrect object", "label too large"]},
    }
]

task.issues.add_file_issue(issue["file_issue"]["text"], issue["file_issue"]["tags"])
if "frame_issue" in issue:
    fi = issue["frame_issue"]
    task.issues.add_frame_issue(fi["frame"], fi["text"], fi["tags"])
    ci = issue["coordinate_issue"]
    task.issues.add_coordinate_issue(ci["frame"], ci["x"], ci["y"], ci["text"], ci["tags"])

# Get Time Entries
start_date = datetime(2025, 1, 1)
end_date = datetime(2025, 6, 8)

# Returns all data
time_entries = list(project.list_time_spent(start=start_date, end=end_date))

# Fetch label row
sample_label = project.get_label_row("sample_label_uid")

# Prepare interpolation
key_frames = sample_label["data_units"]["sample_data_hash"]["labels"]
objects_to_interpolate = ["sample_object_uid"]

# Run interpolation
interpolation_result = project.object_interpolation(key_frames, objects_to_interpolate)

box_object_instance.set_for_frames(
    coordinates=BoundingBoxCoordinates(
        height=<numerical-value>,
        width=<numerical-value>,
        top_left_x=<numerical-value>,
        top_left_y=<numerical-value>,
    ),
    # Add the bounding box to the frames specified
    frames=<frame-number>,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
 )

 rbb_object_instance.set_for_frames(
    coordinates=RotatableBoundingBoxCoordinates(
    height=<height-value>
    width=<width-value>,
    top_left_x=<top-left-x-value>,
    top_left_y=<top-left-y-value>,
    theta=<angle-of-rotation>
    ),
    # Add the rotatable bounding box to frame number specified
    frames=<frame-number>,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

polygon_object_instance.set_for_frames(
    coordinates=PolygonCoordinates(polygons=polygons),
    frames=0,
    manual_annotation=True,
    confidence=1.0,
    )

polyline_object_instance.set_for_frames(
    coordinates=PolylineCoordinates(
        [PointCoordinate(.x1,.y1), PointCoordinate(.x2,.y2), PointCoordinate(.x3,.y3), PointCoordinate(.13,.456)]
    ),
    # Add the polyline to the specified frame number
    frames=<frame-number>,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

keypoint_object_instance.set_for_frames(
    coordinates=PointCoordinate(
        x=<value-for-x-axis>
        y=<value-for-y-axis>
    ),
    # Add the keypoint to the specified frame number
    frames=<frame-number>,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

bitmask_object_instance.set_for_frames(
    # Create coordinates from provided numpy bitmask
    coordinates=BitmaskCoordinates(numpy_coordinates),
    # Add the bitmask to the first frame
    frames=0,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

skeleton_object_instance.set_for_frames(
    coordinates=skeleton_coordinates,
    # Add the polygon to the image
    frames=0,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

radio_classification_instance.set_for_frames(
    # Add the classification to the image
    frames=0,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

label_row.add_classification_instance(radio_classification_instance)

checklist_classification_instance = (
    checklist_ontology_classification.create_instance()
)

checklist_classification_instance.set_answer(
    [apple_option, kiwi_option]
)
checklist_classification_instance.set_for_frames(
    # Add the classification to the image
    frames=177,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

label_row.add_classification_instance(checklist_classification_instance)

for g in groups:
    group = folder.create_data_group(
        DataGroupGrid(
            name=g["name"],
            layout_contents=g["uuids"],
        )
    )

preset = user_client.create_preset(
    name="name-of-my-preset",
    description="A useful and meaningful description for the preset.",
    filter_preset_json={
        "global_filters": {
            "filters": [
                {
                    ... define your filter here ...
                }
            ]
        }
    },
)

preset.update_preset(
    name="updated-name-of-my-preset",
    description="Updated useful and meaningful description for the preset.",
    filter_preset_json={ ... }
)