react-tfjs-models-movenet

react-tfjs-models

react-tfjs-models is a set of components and utilities to create machine learning applications using React. It's based on Google's tensorflow tfjs models, including image classification, pose detection, face detection, body segmentation and more.

Comparing to integrating the underlying library, this project provides various supports for non-machine learning experts to use these models in their Rect applications.

Credits

This project has been updated and forked from https://github.com/SeedV/react-tfjs-models

Usage example

import * as PoseDetector from '@tensorflow-models/pose-detection';
import {BlazePose, VideoPlayback} from "react-tfjs-models";
import {drawPose} from 'react-tfjs-models/src/utils/handpose';
import MoveNetLoader from "react-tfjs-models/src/models/MoveNetLoader";

export default function App() {
    const videoID = "videoElementId";
    const videoRef = React.useRef(null);
    const canvasRef = React.useRef(null);
    const videoSource = "climbing.mp4";
    const model = PoseDetector.SupportedModels.MoveNet;
    const keypointIndices = PoseDetector.util.getKeypointIndexBySide(model);
    const adjacentPairs = PoseDetector.util.getAdjacentPairs(model);
    const setCanvas = (canvas) => {
        canvasRef.current = canvas;
    };
    const onPoseEstimate = (pose) => {
        const ctx = canvasRef.current.getContext('2d');
        const canvas = canvasRef.current;
        ctx.clearRect(0, 0, canvas.width, canvas.height);
        drawPose(pose, keypointIndices, adjacentPairs, ctx);
    };
    const style = {
        position: 'absolute',
        top: 0,
        left: 0,
        right: 0,
        zIndex: 9,
    };
    return (
        <VideoPlayback style={style} videoSource={videoSource}
                       setCanvas={setCanvas}>
            <BlazePose
                backend='webgl'
                runtime='tfjs'
                type={PoseDetector.movenet.modelType.SINGLEPOSE_THUNDER}
                maxPoses={1}
                flipHorizontal={true}
                loader={MoveNetLoader}
                onPoseEstimate={onPoseEstimate}/>
        </VideoPlayback>
    );
}

React components hierarchy

react-tfjs-models has provided a more intuitive declarative syntax, rather than the traditional imperative approach. An application to use BlazePose model to analyze each frame from a webcam stream would look like:

<Camera ...>
  <BlazePose ...>
    <Animation />
  </BlazePose>
</Camera>

Generally speaking, a streaming based machine learning hierarchy would consist of an input layer, a model layer and an output layer, and each layer has swappable components developed in react-tfjs-models, and can also be implemented by application developers.

<Input ...>
  <Model ...>
    <Output />
  </Model>
</Input>

Input layer

The components in this layer generate a stream of images from input devices. It can be from a webcam or a video extractor. This layer wraps the heavy lifting in setting up the HTML structure of using <video> and <canvas> elements and convert the extracted frame into a Rect state.

react-tfjs-models provides the below components as input layer:

Component	Description
Camera	A webcam that provides video source to the model.
VideoPlayback	A video extractor that send video frames.

Model layer

The components in this layer are machine learning models provided by tfjs-models.

This layer will also support model acceleration on webgl and wasm backend, if the model supports.

react-tjfs-models provides the below components as models:

Component	Description
BlazePose	Pose estimator, the implementation can be chosen from BlazePose and MoveNet. (PoseNet isn't provided yet.)
HandPose	Mediapipe handpose, a 21-point 3D hand keypoints detector.
FaceMesh	Mediapipe facemesh, a 486-point 3D facial landmark detector.

Please refer to respective model details in https://github.com/tensorflow/tfjs-models.

Output layer

The components in this layer can be used to render the result. react-tfjs-models will provide some predefined overlay debug UI, e.g. rendering the skeleton on the web frames, to help developers to understand model performance and tweak the algorithms. It's also highly customizable to adopt to the real application need.

Demos

This project provides a list of demos to show case how the components work. Please check out the demos folder.

Demo	Description
RockPaperScissors	a HandPose estimation demo of the classic game.
CartoonMirror	a BlazePose demo to recognize the pose from webcam, and control a 3D character to mimic the pose.
FaceMeshDemo	a FaceMesh demo to recognize face landmarks (still in development).
VideoPlaybackDemo	a demo to use a video to test ML model (MoveNet).

Development

# Install dependencies
yarn install

# Start demo server on http.
yarn start