Create bestscript.txt

bestscript.txt

@@ -0,0 +1,113 @@
+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();