Real-time Animal Sketch Classification using TensorFlow.js: Designing Interactive Website Components

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To build an interactive website where users can draw sketches of animals and use TensorFlow.js to classify the sketches into different animal categories in real-time, you would need to design the HTML, JavaScript, and TensorFlow.js components accordingly. Here's a high-level overview of how you could achieve this functionality:

HTML Structure:

Create a canvas element where users can draw their sketches.
Add a button or a trigger element to initiate the classification process.
Display the classification results on the webpage.

JavaScript Implementation:

Set up event listeners to capture user interactions with the canvas element, such as mouse or touch events.
Store the drawn sketches as a collection of data points (e.g., x and y coordinates) while the user is drawing.
When the user clicks the classification button, pass the collected data points to TensorFlow.js for real-time classification.

TensorFlow.js Integration:

Import the TensorFlow.js library into your JavaScript file.
Load a pre-trained model capable of classifying animal sketches. You can use an existing model like "MobileNet" or train your own model using TensorFlow.js.
Preprocess the user's drawn sketch data to match the format expected by the model. Resize the sketch to a consistent size and convert it to the appropriate data format (e.g., a tensor or an image data array).
Pass the preprocessed sketch data to the loaded model for classification.
Receive the classification results, which will likely include a probability distribution over the different animal categories.
Process the results and display them on the webpage, showing the predicted animal category and the corresponding confidence score.

Answered by: CopyMan

I think this will fulfill your desire.

HTML (index.html)

<!DOCTYPE html>
<html>
<head>
    <title>Sketch Classification</title>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.6.0/dist/tf.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@2.0.4/dist/mobilenet.min.js"></script>
</head>
<body>
    <h1>Sketch Classification</h1>
    <canvas id="canvas" width="400" height="400"></canvas>
    <button id="classifyBtn">Classify</button>
    <div id="result"></div>

    <script src="script.js"></script>
</body>
</html>

JavaScript (script.js)

let model;

// Load the MobileNet model
async function loadModel() {
    model = await mobilenet.load();
}

// Classify the sketch
async function classifySketch() {
    const canvas = document.getElementById('canvas');
    const image = tf.browser.fromPixels(canvas);
    const resizedImage = tf.image.resizeBilinear(image, [224, 224]).toFloat();
    const normalizedImage = resizedImage.div(tf.scalar(255));
    const batchedImage = normalizedImage.expandDims(0);

    const predictions = await model.classify(batchedImage);

    const resultDiv = document.getElementById('result');
    resultDiv.innerHTML = `Prediction: ${predictions[0].className}
Probability: ${predictions[0].probability.toFixed(4)}`;

    image.dispose();
    resizedImage.dispose();
    normalizedImage.dispose();
    batchedImage.dispose();
}

// Initialize the canvas and event listeners
function initializeCanvas() {
    const canvas = document.getElementById('canvas');
    const ctx = canvas.getContext('2d');
    let isDrawing = false;

    canvas.addEventListener('mousedown', startDrawing);
    canvas.addEventListener('mousemove', draw);
    canvas.addEventListener('mouseup', stopDrawing);
    canvas.addEventListener('mouseout', stopDrawing);

    function startDrawing(event) {
        isDrawing = true;
        const x = event.offsetX;
        const y = event.offsetY;
        ctx.beginPath();
        ctx.moveTo(x, y);
    }

    function draw(event) {
        if (!isDrawing) return;
        const x = event.offsetX;
        const y = event.offsetY;
        ctx.lineTo(x, y);
        ctx.stroke();
    }

    function stopDrawing() {
        isDrawing = false;
    }
}

// Load the model and initialize the canvas
async function initializeApp() {
    await loadModel();
    initializeCanvas();

    const classifyBtn = document.getElementById('classifyBtn');
    classifyBtn.addEventListener('click', classifySketch);
}

// Start the application
initializeApp();

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Real-time Animal Sketch Classification using TensorFlow.js: Designing Interactive Website Components

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