AtoZ Virtual

atozvirtual

This HTML and JavaScript code creates a virtual background effect for a webcam feed on a web page. It utilizes the BodyPix library to segment the person from the background in real-time and replace the background with an image. Users can start and stop the virtual background effect with buttons. The code is designed for responsive design, and it allows users to choose between and segmentation models. It's a fun and interactive way to enhance video calls with a virtual background MobileNetV1 ResNet50 <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Virtual Background</title>
    <style>
        /* Add CSS styles for responsive design */
        body {
            margin: 0;
            padding: 0;
            display: flex;
            flex-direction: column;
            align-items: center;
            justify-content: center;
            height: 100vh;
            background-color: #f0f0f0;
        }

        #videoContainer {
            width: 100%;
            max-width: 600px; /* Adjust the maximum width as needed */
            position: relative;
        }

        video, canvas {
            width: 100%;
            height: auto;
            border-radius: 1rem;
        }

        img {
            max-width: 100%;
            height: auto;
        }

        button {
            margin: 10px;
            padding: 10px 20px;
            font-size: 18px;
            border-radius: 1rem;
            border: none;
            cursor: pointer;
            background-color: #e3e3e3;
        }

        button:hover {
            background-color: #1D2026;
            color: #FFF;
        }

        #errorText {
            width: 50%;
            color: red;
            font-weight: bold;
        }
    </style>

    
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix/dist/body-pix.min.js"></script>
</head>
<body>
    
    <p id="errorText"></p>

    
    <div id="videoContainer">
        
        <video id="videoElement" playsinline></video>

        
        <canvas id="backgroundCanvas" playsinline hidden></canvas>
    </div>

    
    <img id="yourBackgroundImage" src="https://i.postimg.cc/t9PJw5P7/forest.jpg" style="display: none;">

    
    <button id="startButton">Start Virtual Background</button>
    <button id="stopButton" style="display: none;">Stop Virtual Background</button>

    <script>
        // Initialize variables
        let isVirtual = false;

        // DOM elements
        const videoContainer = document.getElementById('videoContainer');
        const videoElement = document.getElementById('videoElement');
        const canvasElement = document.getElementById('backgroundCanvas');
        const backgroundImage = document.getElementById('yourBackgroundImage');
        const ctx = canvasElement.getContext('2d');
        const startButton = document.getElementById('startButton');
        const stopButton = document.getElementById('stopButton');
        const errorText = document.getElementById('errorText');

         // MobileNetV1 or ResNet50
        const BodyPixModel = 'MobileNetV1';

        async function startWebCamStream() {
            try {
                // Start the webcam stream
                const stream = await navigator.mediaDevices.getUserMedia({ video: true });
                videoElement.srcObject = stream;

                // Wait for the video to play
                await videoElement.play();
            } catch (error) {
                displayError(error)
            }
        }

        // Function to start the virtual background
        async function startVirtualBackground() {
            try {
                // Set canvas dimensions to match video dimensions
                canvasElement.width = videoElement.videoWidth;
                canvasElement.height = videoElement.videoHeight;

                // Load the BodyPix model: 
                let net = null;

                switch (BodyPixModel) {
                    case 'MobileNetV1':
                        /*
                            This is a lightweight architecture that is suitable for real-time applications and has lower computational requirements. 
                            It provides good performance for most use cases.
                        */
                        net = await bodyPix.load({
                            architecture: 'MobileNetV1',
                            outputStride: 16, // Output stride (16 or 32). 16 is faster, 32 is more accurate.
                            multiplier: 0.75, // The model's depth multiplier. Options: 0.50, 0.75, or 1.0.
                            quantBytes: 2, // The number of bytes to use for quantization (4 or 2).
                        });
                        break;
                    case 'ResNet50':
                        /*
                            This is a deeper and more accurate architecture compared to MobileNetV1. 
                            It may provide better segmentation accuracy, but it requires more computational resources and can be slower.
                        */
                        net = await bodyPix.load({
                            architecture: 'ResNet50',
                            outputStride: 16, // Output stride (16 or 32). 16 is faster, 32 is more accurate.
                            quantBytes: 4, // The number of bytes to use for quantization (4 or 2).
                        });
                        break;
                    default:
                        break;
                }

                // Start the virtual background loop
                isVirtual = true;
                videoElement.hidden = true;
                canvasElement.hidden = false;
                display(canvasElement, 'block');

                // Show the stop button and hide the start button
                startButton.style.display = 'none';
                stopButton.style.display = 'block';

                async function updateCanvas() {
                    if (isVirtual) {
                        // 1. Segmentation Calculation
                        const segmentation = await net.segmentPerson(videoElement, {
                            flipHorizontal: false, // Whether to flip the input video horizontally
                            internalResolution: 'medium', // The resolution for internal processing (options: 'low', 'medium', 'high')
                            segmentationThreshold: 0.7, // Segmentation confidence threshold (0.0 - 1.0)
                            maxDetections: 10, // Maximum number of detections to return
                            scoreThreshold: 0.2, // Confidence score threshold for detections (0.0 - 1.0)
                            nmsRadius: 20, // Non-Maximum Suppression (NMS) radius for de-duplication
                            minKeypointScore: 0.3, // Minimum keypoint detection score (0.0 - 1.0)
                            refineSteps: 10, // Number of refinement steps for segmentation
                        });

                        // 2. Creating a Background Mask
                        const background = { r: 0, g: 0, b: 0, a: 0 };
                        const mask = bodyPix.toMask(segmentation, background, { r: 0, g: 0, b: 0, a: 255 });

                        if (mask) {
                            ctx.putImageData(mask, 0, 0);
                            ctx.globalCompositeOperation = 'source-in';

                            // 3. Drawing the Background
                            if (backgroundImage.complete) {
                                ctx.drawImage(backgroundImage, 0, 0, canvasElement.width, canvasElement.height);
                            } else {
                                // If the image is not loaded yet, wait for it to load and then draw
                                backgroundImage.onload = () => {
                                    ctx.drawImage(backgroundImage, 0, 0, canvasElement.width, canvasElement.height);
                                };
                            }

                            // Draw the mask (segmentation)
                            ctx.globalCompositeOperation = 'destination-over';
                            ctx.drawImage(videoElement, 0, 0, canvasElement.width, canvasElement.height);
                            ctx.globalCompositeOperation = 'source-over';

                            // Add a delay to control the frame rate (adjust as needed) less CPU intensive
                            // await new Promise((resolve) => setTimeout(resolve, 100));

                            // Continue updating the canvas
                            requestAnimationFrame(updateCanvas);
                        }
                    }
                }
                // Start update canvas
                updateCanvas();
            } catch (error) {
                stopVirtualBackground();
                displayError(error);
            }
        }

        // Function to stop the virtual background
        function stopVirtualBackground() {
            isVirtual = false;
            videoElement.hidden = false;
            canvasElement.hidden = true;
            display(canvasElement, 'none');

            // Hide the stop button and show the start button
            startButton.style.display = 'block';
            stopButton.style.display = 'none';
        }

        // Helper function to set the display style of an element
        function display(element, style) {
            element.style.display = style;
        }

        // Helper function to display errors
        function displayError(error){
            console.error(error);
            // Display error message in the <p> element
            errorText.textContent = 'An error occurred: ' + error.message;
        }

        // Add click event listeners to the buttons
        startButton.addEventListener('click', startVirtualBackground);
        stopButton.addEventListener('click', stopVirtualBackground);

        // Start video stream
        startWebCamStream();
    </script>
</body>
</html>

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