body-measurement-ai v1.0.1

Body Measurement AI

body-measurement-ai is a lightweight package that leverages TensorFlow.js and PoseNet to estimate body measurements (shoulder width, hip width, and height) from a live video feed. This package can be easily integrated into React and Next.js applications to provide accurate body measurements for various applications like clothing size recommendations.

Features

• Pose Detection: Uses PoseNet to detect key points on the human body from a live video feed.
• Real-Time Measurements: Estimates shoulder width, hip width, and height in real-time.
• Simple Integration: Easily integrate with React or Next.js apps.

Installation

To install the package, use NPM or Yarn:

npm install body-measurement-ai

or

yarn add body-measurement-ai

Usage

Basic Setup in React/Next.js

Here is an example of how to use body-measurement-ai in a React or Next.js application:

import React, { useEffect, useRef, useState } from 'react';
import { setupCamera, loadPosenet, detectPose, PoseResult } from 'body-measurement-ai';

const BodyMeasurementComponent: React.FC = () => {
  const videoRef = useRef<HTMLVideoElement>(null); // Reference to the video element
  const [result, setResult] = useState<PoseResult | null>(null); // Store pose results or errors

  useEffect(() => {
    const setup = async () => {
      if (videoRef.current) {
        // Setup the camera
        const cameraResult = await setupCamera(videoRef.current);
        if ('success' in cameraResult && !cameraResult.success) {
          console.error(cameraResult.message); // Handle camera setup errors
          return;
        }

        // Load PoseNet model
        const netResult = await loadPosenet();
        if ('success' in netResult && !netResult.success) {
          console.error(netResult.message); // Handle PoseNet model load errors
          return;
        }

        // Start pose detection and updating measurements every second
        setInterval(async () => {
          const poseResult = await detectPose(videoRef.current!, netResult);
          if ('success' in poseResult && !poseResult.success) {
            console.error(poseResult.message); // Handle pose detection errors
          } else {
            setResult(poseResult); // Update the measurement result
          }
        }, 1000); // Run every 1 second
      }
    };

    setup(); // Initialize the camera and model when the component mounts
  }, []);

  return (
    <div>
      <h1>AI Body Measurement Tool</h1>
      <video ref={videoRef} width="640" height="480" autoPlay muted></video> {/* Video element for webcam */}
      {result && 'success' in result && !result.success ? (
        <p>Error: {result.message}</p> // Display error messages if any
      ) : result ? (
        <div>
          <p>Shoulder Width: {result.shoulderWidth}px</p> {/* Display shoulder width */}
          <p>Hip Width: {result.hipWidth}px</p> {/* Display hip width */}
          <p>Height: {result.height}px</p> {/* Display height */}
        </div>
      ) : null}
    </div>
  );
};

export default BodyMeasurementComponent;

Functions

1. setupCamera(video: HTMLVideoElement): Promise<HTMLVideoElement | ErrorResult>

   • Sets up the user's webcam and returns the video element with the live stream.
   • Handles potential errors, such as permission denial or device issues.

2. loadPosenet(): Promise<posenet.PoseNet | ErrorResult>

   • Loads the PoseNet model used for pose detection.
   • Returns either the loaded model or an error if something goes wrong.

3. detectPose(video: HTMLVideoElement, net: posenet.PoseNet): Promise<PoseResult>

   • Detects key points from the video stream and calculates shoulder width, hip width, and height.
   • Returns an object with the measurements or an error if detection fails.

Example Error Handling

All functions return either a success result or an error result. Here’s how to handle errors effectively:

const cameraResult = await setupCamera(videoRef.current);
if ('success' in cameraResult && !cameraResult.success) {
  console.error(cameraResult.message); // Log or display the error message
  return;
}

Types

• Measurements: Object containing the body measurements.

  interface Measurements {
    shoulderWidth: number;
    hipWidth: number;
    height: number;
  }

• ErrorResult: Object returned when an error occurs during camera setup, model loading, or pose detection.

  interface ErrorResult {
    success: false;
    message: string;
  }

• PoseResult: Union type that either contains Measurements or ErrorResult to ensure robust error handling.

  type PoseResult = Measurements | ErrorResult;

Example PoseResult Object

If the pose detection is successful:

{
  shoulderWidth: 300,
  hipWidth: 250,
  height: 600
}

If an error occurs:

{
  success: false,
  message: "Pose estimation failed. Could not detect key points."
}

Running Locally

You can run the project locally using a simple local server such as http-server.

1. Build the project:

   npm run build

2. Start a local server:

   npx http-server

3. Navigate to http://localhost:8080 in your browser to see the body measurement tool in action.

License

This project is licensed under the MIT License. See the LICENSE file for details.