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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">
<title>AI Image Enhancer</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
<style>
/* Optional: Add custom styles or override Tailwind */
body {
font-family: sans-serif;
}
canvas {
max-width: 100%;
height: auto;
border: 1px solid #ccc;
}
.loader {
border: 5px solid #f3f3f3; /* Light grey */
border-top: 5px solid #3498db; /* Blue */
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
margin: 20px auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
</style>
</head>
<body class="bg-gray-100 p-8">
<div class="container mx-auto max-w-4xl bg-white p-6 rounded-lg shadow-lg">
<h1 class="text-3xl font-bold mb-6 text-center text-blue-600">AI Image Enhancer</h1>
<p class="text-center text-gray-600 mb-6">Increase resolution, retouch, denoise, and more using TensorFlow.js.</p>
<div class="mb-6 p-4 border rounded-md bg-gray-50">
<label for="imageUpload" class="block text-lg font-medium text-gray-700 mb-2">1. Upload Image:</label>
<input type="file" id="imageUpload" accept="image/*" class="block w-full text-sm text-gray-500
file:mr-4 file:py-2 file:px-4
file:rounded-full file:border-0
file:text-sm file:font-semibold
file:bg-blue-50 file:text-blue-700
hover:file:bg-blue-100
"/>
<p id="uploadError" class="text-red-500 text-sm mt-2"></p>
</div>
<div class="mb-6 p-4 border rounded-md bg-gray-50">
<label for="enhancementType" class="block text-lg font-medium text-gray-700 mb-2">2. Select Enhancement:</label>
<select id="enhancementType" class="block w-full p-2 border border-gray-300 rounded-md shadow-sm focus:ring-blue-500 focus:border-blue-500">
<option value="upscale">Increase Resolution (Upscale 2x)</option>
<option value="denoise">Denoise (Basic)</option>
<option value="retouch">Retouch (Simple Filter)</option>
</select>
</div>
<div class="text-center mb-6">
<button id="enhanceButton" class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-6 rounded-full text-lg disabled:opacity-50 disabled:cursor-not-allowed" disabled>
Enhance Image
</button>
</div>
<div id="status" class="text-center text-gray-600 mb-4 h-10"></div>
<div id="loader" class="loader hidden"></div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
<div>
<h2 class="text-xl font-semibold mb-2 text-center">Original Image</h2>
<canvas id="originalCanvas"></canvas>
</div>
<div>
<h2 class="text-xl font-semibold mb-2 text-center">Enhanced Image</h2>
<canvas id="enhancedCanvas"></canvas>
</div>
</div>
<div class="mt-8 text-center text-xs text-gray-500">
<p>Powered by TensorFlow.js</p>
<p>MIT License - [Your Name/Org] 2025</p>
</div>
</div>
<script>
// --- DOM Elements ---
const imageUpload = document.getElementById('imageUpload');
const enhanceButton = document.getElementById('enhanceButton');
const enhancementType = document.getElementById('enhancementType');
const originalCanvas = document.getElementById('originalCanvas');
const enhancedCanvas = document.getElementById('enhancedCanvas');
const statusDiv = document.getElementById('status');
const loader = document.getElementById('loader');
const uploadError = document.getElementById('uploadError');
const originalCtx = originalCanvas.getContext('2d');
const enhancedCtx = enhancedCanvas.getContext('2d');
let originalImage = null;
let model = null; // Placeholder for the loaded TFJS model
// --- Event Listeners ---
imageUpload.addEventListener('change', handleImageUpload);
enhanceButton.addEventListener('click', handleEnhancement);
// --- Functions ---
async function handleImageUpload(event) {
const file = event.target.files[0];
uploadError.textContent = '';
enhanceButton.disabled = true;
originalImage = null;
originalCtx.clearRect(0, 0, originalCanvas.width, originalCanvas.height); // Clear previous image
enhancedCtx.clearRect(0, 0, enhancedCanvas.width, enhancedCanvas.height); // Clear previous result
if (!file || !file.type.startsWith('image/')) {
uploadError.textContent = 'Please select a valid image file.';
return;
}
statusDiv.textContent = 'Loading image...';
try {
originalImage = await loadImageFromFile(file);
displayImageOnCanvas(originalImage, originalCanvas, originalCtx);
statusDiv.textContent = 'Image loaded. Ready to enhance.';
enhanceButton.disabled = false;
} catch (error) {
console.error("Error loading image:", error);
uploadError.textContent = 'Could not load the image.';
statusDiv.textContent = '';
}
}
function loadImageFromFile(file) {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = (e) => {
const img = new Image();
img.onload = () => resolve(img);
img.onerror = reject;
img.src = e.target.result;
};
reader.onerror = reject;
reader.readAsDataURL(file);
});
}
function displayImageOnCanvas(img, canvas, ctx) {
// Scale canvas to image size
canvas.width = img.naturalWidth;
canvas.height = img.naturalHeight;
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
console.log(`Displayed original image (${canvas.width}x${canvas.height})`);
}
async function handleEnhancement() {
if (!originalImage) {
statusDiv.textContent = 'Please upload an image first.';
return;
}
const selectedTask = enhancementType.value;
statusDiv.textContent = `Starting ${selectedTask}...`;
loader.classList.remove('hidden');
enhanceButton.disabled = true;
enhancedCtx.clearRect(0, 0, enhancedCanvas.width, enhancedCanvas.height); // Clear previous result
try {
// --- AI Processing ---
// This is where you'd load and run your specific TFJS model
await runAIEnhancement(selectedTask, originalCanvas, enhancedCanvas, enhancedCtx);
statusDiv.textContent = 'Enhancement complete!';
console.log("Enhancement successful.");
} catch (error) {
console.error(`Error during ${selectedTask}:`, error);
statusDiv.textContent = `Error during enhancement: ${error.message || error}`;
} finally {
loader.classList.add('hidden');
enhanceButton.disabled = false; // Re-enable button even on error
}
}
async function runAIEnhancement(task, sourceCanvas, targetCanvas, targetCtx) {
console.log(`Running task: ${task}`);
// Ensure TFJS backend is ready (optional, good practice)
await tf.ready();
console.log(`Using TFJS backend: ${tf.getBackend()}`);
// Get image data from the source canvas as a Tensor
// Using tf.browser.fromPixels() is efficient
const inputTensor = tf.browser.fromPixels(sourceCanvas);
console.log("Input tensor shape:", inputTensor.shape);
let outputTensor;
// ====== IMPORTANT: MODEL LOADING AND PREDICTION LOGIC GOES HERE ======
// You need to replace the following placeholder logic with actual
// model loading (e.g., tf.loadGraphModel(MODEL_URL)) and prediction.
// Pre-processing (resizing, normalizing) and post-processing (denormalizing)
// depend heavily on the specific model you use.
statusDiv.textContent = 'Loading AI model... (Placeholder)'; // Update status
// Example: Simulating model load delay
await new Promise(resolve => setTimeout(resolve, 500));
statusDiv.textContent = 'Processing image... (Placeholder)'; // Update status
if (task === 'upscale') {
// --- Placeholder for Upscaling ---
// A *real* upscaling model would output a larger tensor.
// Here, we'll just draw the original image slightly larger as a visual cue.
console.log("Simulating upscale...");
const scale = 1.5; // Simulate 1.5x upscale for demo
targetCanvas.width = Math.round(sourceCanvas.width * scale);
targetCanvas.height = Math.round(sourceCanvas.height * scale);
targetCtx.drawImage(sourceCanvas, 0, 0, targetCanvas.width, targetCanvas.height);
// No tensor output in this simple simulation
} else if (task === 'denoise') {
// --- Placeholder for Denoising ---
// A real denoising model takes the noisy tensor and outputs a cleaner one.
// Simulate by applying a slight blur using canvas filter
console.log("Simulating denoise...");
targetCanvas.width = sourceCanvas.width;
targetCanvas.height = sourceCanvas.height;
targetCtx.filter = 'blur(1px)'; // Basic canvas blur
targetCtx.drawImage(sourceCanvas, 0, 0);
targetCtx.filter = 'none'; // Reset filter
// No tensor output in this simple simulation
} else if (task === 'retouch') {
// --- Placeholder for Retouching ---
// Simulate a simple filter like sepia using canvas filter
console.log("Simulating retouch (sepia filter)...");
targetCanvas.width = sourceCanvas.width;
targetCanvas.height = sourceCanvas.height;
targetCtx.filter = 'sepia(60%)';
targetCtx.drawImage(sourceCanvas, 0, 0);
targetCtx.filter = 'none'; // Reset filter
// No tensor output in this simple simulation
} else {
console.warn("Unknown enhancement task:", task);
// Draw original if task is unknown
targetCanvas.width = sourceCanvas.width;
targetCanvas.height = sourceCanvas.height;
targetCtx.drawImage(sourceCanvas, 0, 0);
outputTensor = inputTensor.clone(); // Just copy input
}
// --- IF YOU HAD A REAL MODEL, you would do something like: ---
/*
if (!model) { // Load model if not already loaded
statusDiv.textContent = 'Loading AI model...';
const modelUrl = 'URL_TO_YOUR_TFJS_MODEL/model.json'; // <-- Replace with your model URL
model = await tf.loadGraphModel(modelUrl);
console.log("Model loaded successfully");
}
statusDiv.textContent = 'Preprocessing image...';
// 1. Preprocess the inputTensor (resize, normalize based on model needs)
// Example: Normalize to [0, 1]
const processedInput = tf.tidy(() => {
// Assuming model expects float input normalized to [0, 1]
let tensor = inputTensor.toFloat().div(tf.scalar(255));
// Add batch dimension if needed: tensor = tensor.expandDims(0);
// Resize if needed: tensor = tf.image.resizeBilinear(tensor, [targetH, targetW]);
return tensor;
});
inputTensor.dispose(); // Dispose original tensor
statusDiv.textContent = 'Running AI inference...';
// 2. Run prediction
const prediction = await model.predict(processedInput); // Use executeAsync for models with control flow ops
processedInput.dispose(); // Dispose preprocessed tensor
statusDiv.textContent = 'Postprocessing result...';
// 3. Postprocess the prediction (denormalize, resize, remove batch dim)
// Example: Assuming output is also [0, 1]
outputTensor = tf.tidy(() => {
let tensor = prediction;
// Remove batch dim if added: tensor = tensor.squeeze([0]);
// Clamp and convert back to integer range [0, 255]
tensor = tensor.mul(tf.scalar(255)).clipByValue(0, 255).toInt();
return tensor;
});
prediction.dispose(); // Dispose prediction tensor
// 4. Draw the outputTensor to the target canvas
await tf.browser.toPixels(outputTensor, targetCanvas);
console.log("Drew tensor to canvas. Output shape:", outputTensor.shape);
outputTensor.dispose(); // Dispose final output tensor
*/
// --- End of Placeholder/Real Model Section ---
// Clean up the input tensor if it wasn't used by a real model or simulation
if (inputTensor && !inputTensor.isDisposed) {
inputTensor.dispose();
console.log("Disposed input tensor.");
}
}
// --- Initial Setup ---
statusDiv.textContent = 'Ready. Please upload an image.';
</script>
</body>
</html>
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