Auto-annotation API

Overview

This layer provides functionality that allows you to automatically annotate a CVAT dataset by running a custom function on your local machine. A function, in this context, is a Python object that implements a particular protocol defined by this layer. To avoid confusion with Python functions, auto-annotation functions will be referred to as “AA functions” in the following text. A typical AA function will be based on a machine learning model and consist of the following basic elements:

• Code to load the ML model.

• A specification describing the annotations that the AA function can produce.

• Code to convert data from CVAT to a format the ML model can understand.

• Code to run the ML model.

• Code to convert resulting annotations to a format CVAT can understand.

The layer can be divided into several parts:

• The interface, containing the protocol that an AA function must implement.

• The driver, containing functionality to annotate a CVAT dataset using an AA function.

• The predefined AA function based on Ultralytics YOLOv8n.

The auto-annotate CLI command provides a way to use an AA function from the command line rather than from a Python program. See the CLI documentation for details.

Example

from typing import List
import PIL.Image

import torchvision.models

from cvat_sdk import make_client
import cvat_sdk.models as models
import cvat_sdk.auto_annotation as cvataa

class TorchvisionDetectionFunction:
    def __init__(self, model_name: str, weights_name: str, **kwargs) -> None:
        # load the ML model
        weights_enum = torchvision.models.get_model_weights(model_name)
        self._weights = weights_enum[weights_name]
        self._transforms = self._weights.transforms()
        self._model = torchvision.models.get_model(model_name, weights=self._weights, **kwargs)
        self._model.eval()

    @property
    def spec(self) -> cvataa.DetectionFunctionSpec:
        # describe the annotations
        return cvataa.DetectionFunctionSpec(
            labels=[
                cvataa.label_spec(cat, i)
                for i, cat in enumerate(self._weights.meta['categories'])
            ]
        )

    def detect(self, context, image: PIL.Image.Image) -> List[models.LabeledShapeRequest]:
        # convert the input into a form the model can understand
        transformed_image = [self._transforms(image)]

        # run the ML model
        results = self._model(transformed_image)

        # convert the results into a form CVAT can understand
        return [
            cvataa.rectangle(label.item(), [x.item() for x in box])
            for result in results
            for box, label in zip(result['boxes'], result['labels'])
        ]

# log into the CVAT server
with make_client(host="localhost", credentials=("user", "password")) as client:
    # annotate task 12345 using Faster R-CNN
    cvataa.annotate_task(client, 41617,
        TorchvisionDetectionFunction("fasterrcnn_resnet50_fpn_v2", "DEFAULT", box_score_thresh=0.5),
    )

Auto-annotation interface

Currently, the only type of AA function supported by this layer is the detection function. Therefore, all of the following information will pertain to detection functions.

A detection function accepts an image and returns a list of shapes found in that image. When it is applied to a dataset, the AA function is run for every image, and the resulting lists of shapes are combined and uploaded to CVAT.

A detection function must have two attributes, spec and detect.

spec must contain the AA function’s specification, which is an instance of DetectionFunctionSpec.

DetectionFunctionSpec must be initialized with a sequence of PatchedLabelRequest objects that represent the labels that the AA function knows about. See the docstring of DetectionFunctionSpec for more information on the constraints that these objects must follow.

detect must be a function/method accepting two parameters:

• context (DetectionFunctionContext). Contains information about the current image. Currently DetectionFunctionContext only contains a single field, frame_name, which contains the file name of the frame on the CVAT server.

• image (PIL.Image.Image). Contains image data.

detect must return a list of LabeledShapeRequest objects, representing shapes found in the image. See the docstring of DetectionFunctionSpec for more information on the constraints that these objects must follow.

The same AA function may be used with any dataset that contain labels with the same name as the AA function’s specification. The way it works is that the driver matches labels between the spec and the dataset, and replaces the label IDs in the shape objects with those defined in the dataset.

For example, suppose the AA function’s spec defines the following labels:

And the dataset defines the following labels:

Then suppose detect returns a shape with label_id equal to 1. The driver will see that it refers to the rat label, and replace it with 102, since that’s the ID this label has in the dataset.

The same logic is used for sub-label IDs.

Helper factory functions

The CVAT API model types used in the AA function protocol are somewhat unwieldy to work with, so it’s recommented to use the helper factory functions provided by this layer. These helpers instantiate an object of their corresponding model type, passing their arguments to the model constructor and sometimes setting some attributes to fixed values.

The following helpers are available for building specifications:

The following helpers are available for use in detect:

Auto-annotation driver

The annotate_task function uses an AA function to annotate a CVAT task. It must be called as follows:

annotate_task(<client>, <task ID>, <AA function>, <optional arguments...>)

The supplied client will be used to make all API calls.

By default, new annotations will be appended to the old ones. Use clear_existing=True to remove old annotations instead.

If a detection function declares a label that has no matching label in the task, then by default, BadFunctionError is raised, and auto-annotation is aborted. If you use allow_unmatched_label=True, then such labels will be ignored, and any shapes referring to them will be dropped. Same logic applies to sub-label IDs.

annotate_task will raise a BadFunctionError exception if it detects that the function violated the AA function protocol.

Predefined AA functions

This layer includes several predefined AA functions. You can use them as-is, or as a base on which to build your own.

Each function is implemented as a module to allow usage via the CLI auto-annotate command. Therefore, in order to use it from the SDK, you’ll need to import the corresponding module.

cvat_sdk.auto_annotation.functions.torchvision_detection

This AA function uses object detection models from the torchvision library. It produces rectangle annotations.

To use it, install CVAT SDK with the pytorch extra:

$ pip install "cvat-sdk[pytorch]"

Usage from Python:

from cvat_sdk.auto_annotation.functions.torchvision_detection import create as create_torchvision
annotate_task(<client>, <task ID>, create_torchvision(<model name>, ...))

Usage from the CLI:

cvat-cli auto-annotate "<task ID>" --function-module cvat_sdk.auto_annotation.functions.torchvision_detection \
      -p model_name=str:"<model name>" ...

The create function accepts the following parameters:

• model_name (str) - the name of the model, such as fasterrcnn_resnet50_fpn_v2. This parameter is required.
• weights_name (str) - the name of a weights enum value for the model, such as COCO_V1. Defaults to DEFAULT.

It also accepts arbitrary additional parameters, which are passed directly to the model constructor.

cvat_sdk.auto_annotation.functions.torchvision_keypoint_detection

This AA function is analogous to torchvision_detection, except it uses torchvision’s keypoint detection models and produces skeleton annotations. Keypoints which the model marks as invisible will be marked as occluded in CVAT.

Refer to the previous section for usage instructions and parameter information.