MediaPipeHands

MediaPipeHands-MediaPipe wraps the MediaPipe JS Solution within the familiar TFJS API mediapipe.dev.

Two models are offered.

lite - our smallest model that is less accurate but smaller in model size and minimal memory footprint.
full - A middle ground between performance and accuracy.

Please try our our live demo.

Installation
Usage
Keypoint Diagram

Installation

To use MediaPipeHands:

Via script tags:

<!-- Require the peer dependencies of hand-pose-detection. -->
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>

<!-- You must explicitly require a TF.js backend if you're not using the TF.js union bundle. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>

<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/hand-pose-detection"></script>

Via npm:

yarn add @mediapipe/hands
yarn add @tensorflow/tfjs-core, @tensorflow/tfjs-backend-webgl
yarn add @tensorflow-models/hand-pose-detection

Usage

If you are using the hand-pose-detection API via npm, you need to import the libraries first.

Import the libraries

import * as handPoseDetection from '@tensorflow-models/hand-pose-detection';
import '@tensorflow/tfjs-core';
// Register WebGL backend.
import '@tensorflow/tfjs-backend-webgl';
import '@mediapipe/hands';

Create a detector

Pass in handPoseDetection.SupportedModels.MediaPipeHands from the handPoseDetection.SupportedModel enum list along with a detectorConfig to the createDetector method to load and initialize the model.

detectorConfig is an object that defines MediaPipeHands specific configurations for MediaPipeHandsMediaPipeModelConfig:

runtime: Must set to be ‘mediapipe’.
maxHands: Defaults to 2. The maximum number of hands that will be detected by the model. The number of returned hands can be less than the maximum (for example when no hands are present in the input). It is highly recommended to set this value to the expected max number of hands, otherwise the model will continue to search for the missing hands which can slow down the performance.
modelType: specify which variant to load from MediaPipeHandsModelType (i.e., ‘lite’, ‘full’). If unset, the default is ‘full’.
solutionPath: The path to where the wasm binary and model files are located.

const model = handPoseDetection.SupportedModels.MediaPipeHands;
const detectorConfig = {
  runtime: 'mediapipe',
  solutionPath: 'https://cdn.jsdelivr.net/npm/@mediapipe/hands'
                // or 'base/node_modules/@mediapipe/hands' in npm.
};
detector = await handPoseDetection.createDetector(model, detectorConfig);

Run inference

Now you can use the detector to detect hand poses. The estimateHands method accepts both image and video in many formats, including: HTMLVideoElement, HTMLImageElement, HTMLCanvasElement. If you want more options, you can pass in a second estimationConfig parameter.

estimationConfig is an object that defines MediaPipeHands specific configurations for MediaPipeHandsMediaPipeEstimationConfig:

flipHorizontal: Optional. Defaults to false. When image data comes from camera, the result has to flip horizontally.

The following code snippet demonstrates how to run the model inference:

const estimationConfig = {flipHorizontal: false};
const hands = await detector.estimateHands(image, estimationConfig);

The returned hands list contains a single element for each detected hand in the image (upto maxHands).

For each hand, the structure contains a prediction of the handedness (left or right) as well as a confidence score of this prediction. An array of keypoints is also returned. Each keypoint contains x, y, and name.

Example output:

[
  {
    score: 0.8,
    Handedness: ‘Right’,
    keypoints: [
      {x: 105, y: 107, name: "wrist"},
      {x: 108, y: 160, name: "pinky_tip"},
      ...
    ]
  }
]