bestscript.txt

let currentStream = null;
let currentFacingMode = 'user'; 

async function loadTFLiteModel() {
    try {
        // Set TensorFlow.js to use CPU backend
        await tf.setBackend('cpu');
        await tf.ready(); // Ensure TensorFlow is ready to use CPU backend
        
        const modelUrl = 'midas.tflite'; 
        const wasmPath = await tflite.setWasmPath('https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/wasm/');
        const model = await tflite.loadTFLiteModel(modelUrl);
        console.log('Model loaded successfully');
        return model;
    } catch (error) {
        console.error('Error loading TFLite model:', error);
    }
}

function preprocessImage(imageElement) {
    try {
        const tensor = tf.browser.fromPixels(imageElement).toFloat();
        const resized = tf.image.resizeBilinear(tensor, [256, 256]); // Resize to 256x256 as required by the model
        const normalized = resized.div(255.0).expandDims(0); // Normalize to [0,1] and add batch dimension
        return normalized;
    } catch (error) {
        console.error('Error during image preprocessing:', error);
    }
}

async function predictDepth(model, preprocessedImage) {
    try {
        const depthMap = model.predict(preprocessedImage);
        const squeezed = depthMap.squeeze(); // Remove batch dimension
        const normalizedDepthMap = squeezed.div(squeezed.max()).mul(255).toInt(); // Normalize the depth map to [0,255]
        return normalizedDepthMap;
    } catch (error) {
        console.error('Error during depth prediction:', error);
    }
}

function renderDepthMap(depthMap, canvasElement) {
    try {
        const [width, height] = [depthMap.shape[1], depthMap.shape[0]];
        const imageData = new ImageData(width, height);

        const data = depthMap.dataSync();

        for (let i = 0; i < data.length; i++) {
            const value = data[i];
            imageData.data[4 * i] = value; // R
            imageData.data[4 * i + 1] = value; // G
            imageData.data[4 * i + 2] = value; // B
            imageData.data[4 * i + 3] = 255; // A
        }

        const ctx = canvasElement.getContext('2d');
        canvasElement.width = width;
        canvasElement.height = height;
        ctx.putImageData(imageData, 0, 0);
    } catch (error) {
        console.error('Error rendering depth map:', error);
    }
}

function analyzeDepth(depthMap) {
    try {
        const depthArray = depthMap.dataSync();
        const averageDepth = depthArray.reduce((a, b) => a + b, 0) / depthArray.length;
        console.log('Average Depth:', averageDepth);
        document.getElementById('averageDepth').innerText = `Average Depth: ${averageDepth.toFixed(2)}`;
    } catch (error) {
        console.error('Error analyzing depth:', error);
    }
}

async function startVideo(facingMode = 'user') {
    const video = document.getElementById('video');

    if (currentStream) {
        currentStream.getTracks().forEach(track => track.stop());
    }

    try {
        const stream = await navigator.mediaDevices.getUserMedia({ video: { facingMode } });
        currentStream = stream;
        video.srcObject = stream;

        video.addEventListener('loadeddata', async () => {
            const model = await loadTFLiteModel();

            if (model) {
                setInterval(async () => {
                    const preprocessedImage = preprocessImage(video);
                    const depthMap = await predictDepth(model, preprocessedImage);

                    const canvas = document.getElementById('depthCanvas');
                    renderDepthMap(depthMap, canvas);
                    analyzeDepth(depthMap);
                }, 500); // Run depth prediction every 500ms
            }
        });
    } catch (error) {
        console.error('Error accessing the camera:', error);
    }
}

document.getElementById('swapButton').addEventListener('click', () => {
    currentFacingMode = currentFacingMode === 'user' ? 'environment' : 'user';
    startVideo(currentFacingMode);
});

startVideo();