HandSignPrediction/index.html at main · tron01/HandSignPrediction · GitHub

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<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
    <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands/hands.js"></script>
    <title>Hand Sign Recognition</title>
</head>

<body class="container  mt-4">
    <h2 class="text-center">Hand Sign Recognition</h2>

    <div class="d-flex justify-content-center">
        <div class="justify-content-start p-4 bg-danger ">
            <ol class="text-white mt-4">
                <li>Enter the HandSign Name.</li>
                <li>Click Train Now.</li>
                <li>Click Start Training to start the sample capture.</li>
                <li>To stop sample, click Stop Training.</li>
                <li>After adding all desired hand signs, click Predict.</li>
            </ol>

        </div>
        <video id="video" class="border d-none" width="640" height="480" autoplay></video>
        <canvas id="canvas" class="border" width="640" height="480"></canvas>
    </div>
    <div class="mt-3 text-center">
        <input type="text" id="handSign" class="form-control d-inline w-auto" placeholder="Enter Hand Sign Name">
        <button id="trainNow" class="btn btn-primary">Train Now</button>
        <button id="startTrain" class="btn btn-success">Start Training</button>
        <button id="stopTrain" class="btn btn-danger">Stop Training</button>
        <button id="predict" class="btn btn-warning">Predict</button>
    </div>

    <h4 class="mt-4 text-center">Trained Signs</h4>
    <div id="trainedSigns" class="d-flex flex-wrap justify-content-center"></div>

    <script type="module">
        const videoElement = document.getElementById('video');
        const canvasElement = document.getElementById('canvas');
        const canvasCtx = canvasElement.getContext('2d');

        const trainButton = document.getElementById('trainNow');
        const startTrainButton = document.getElementById('startTrain');
        const stopTrainButton = document.getElementById('stopTrain');
        const predictButton = document.getElementById('predict');
        const trainedSignsDiv = document.getElementById('trainedSigns');

        let training = false;
        let currentSign = "";
        let trainedSigns = {};
        let isPredicting = false;
        let predictedText = "";

        function onResults(results) {
            canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);

            if (results.image) {
                canvasCtx.drawImage(results.image, 0, 0, canvasElement.width, canvasElement.height);
            }

            if (results.multiHandLandmarks) {
                for (const landmarks of results.multiHandLandmarks) {
                    drawConnectors(canvasCtx, landmarks, HAND_CONNECTIONS, { color: '#00FF00', lineWidth: 5 });
                    drawLandmarks(canvasCtx, landmarks, { color: '#FF0000', lineWidth: 2 });

                    if (training && currentSign) {
                        captureHandSign(landmarks, currentSign);
                    }
                    if (isPredicting) {
                        predictHandSign(landmarks);
                    }
                }
            }

            canvasCtx.font = "24px Arial";
            canvasCtx.fillStyle = "#FF0000";
            canvasCtx.fillText(predictedText, 10, 50);
        }

        function captureHandSign(landmarks, label) {
            const keypoints = landmarks.map(point => [point.x, point.y, point.z]);
            if (!trainedSigns[label]) {
                trainedSigns[label] = [];
            }
            trainedSigns[label].push(keypoints);
            displayTrainedSigns();
        }

        function displayTrainedSigns() {
            trainedSignsDiv.innerHTML = "";
            Object.keys(trainedSigns).forEach(label => {
                const div = document.createElement('div');
                div.classList.add("m-2", "text-center");
                div.innerHTML = `<p>${label} (${trainedSigns[label].length})</p>`;
                trainedSignsDiv.appendChild(div);
            });
        }

        function predictHandSign(landmarks) {
            let bestMatch = null;
            let minDistance = Infinity;

            const currentKeypoints = landmarks.map(point => [point.x, point.y, point.z]);

            Object.keys(trainedSigns).forEach(label => {
                trainedSigns[label].forEach(trainedKeypoints => {
                    const distance = euclideanDistance(currentKeypoints, trainedKeypoints);
                    if (distance < minDistance) {
                        minDistance = distance;
                        bestMatch = label;
                    }
                });
            });

            if (bestMatch) {
                predictedText = `Predicted: ${bestMatch}`;
            } else {
                predictedText = "No match found";
            }
        }

        function euclideanDistance(arr1, arr2) {
            if (arr1.length !== arr2.length) return Infinity;
            return Math.sqrt(arr1.reduce((sum, point, index) =>
                sum + Math.pow(point[0] - arr2[index][0], 2) +
                Math.pow(point[1] - arr2[index][1], 2) +
                Math.pow(point[2] - arr2[index][2], 2), 0));
        }

        trainButton.addEventListener('click', () => {
            currentSign = document.getElementById('handSign').value;
            if (!currentSign) {
                alert("Please enter a hand sign name.");
                return;
            }
        });

        startTrainButton.addEventListener('click', () => { training = true; });
        stopTrainButton.addEventListener('click', () => { training = false; });
        predictButton.addEventListener('click', () => { isPredicting = !isPredicting; });

        const hands = new Hands({ locateFile: file => `https://cdn.jsdelivr.net/npm/@mediapipe/hands/${file}` });
        hands.setOptions({ maxNumHands: 2, modelComplexity: 1, minDetectionConfidence: 0.5, minTrackingConfidence: 0.5 });
        hands.onResults(onResults);

        const camera = new Camera(videoElement, {
            onFrame: async () => { await hands.send({ image: videoElement }); },
            width: 640,
            height: 480
        });
        camera.start();
    </script>
</body>

</html>