Browser based fine art style GAN with interactive brushstroke control JavaScript
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```javascript
<!DOCTYPE html>
<html>
<head>
<title>Browser-Based Fine-Art GAN with Brushstroke Control</title>
<style>
body {
font-family: sans-serif;
display: flex;
flex-direction: column;
align-items: center;
}
canvas {
border: 1px solid #000;
cursor: crosshair; /* Indicate brush */
}
#controls {
margin-top: 10px;
display: flex;
gap: 10px;
}
#style-select {
padding: 5px;
}
#generate-button {
padding: 5px 10px;
background-color: #4CAF50;
color: white;
border: none;
cursor: pointer;
}
#generate-button:hover {
background-color: #3e8e41;
}
</style>
</head>
<body>
<h1>Fine-Art GAN with Brushstroke Control</h1>
<canvas id="outputCanvas" width="512" height="512"></canvas>
<div id="controls">
<label for="style-select">Artistic Style:</label>
<select id="style-select">
<option value="impressionism">Impressionism</option>
<option value="abstract">Abstract Expressionism</option>
<option value="renaissance">Renaissance</option>
<option value="cubism">Cubism</option>
</select>
<button id="generate-button">Generate!</button>
</div>
<script>
// **Important Notes:**
// 1. **GAN Backend:** This example is a *conceptual* front-end. It *simulates* the GAN functionality. A real GAN (Generative Adversarial Network) requires a *server-side* implementation, typically using Python (TensorFlow, PyTorch). This Javascript code DOES NOT actually contain or run a GAN. Instead, it uses placeholder images and simulates changes based on user input.
// 2. **Model Loading:** A real GAN would require you to load a pre-trained model. This is a complex process. Because this is a simplified example, no actual model loading occurs.
// 3. **Image Generation:** GANs generate images from random noise, conditioned on style and potentially other inputs. Here, we simulate this with pre-loaded "style images" and modify them based on brushstrokes.
const canvas = document.getElementById('outputCanvas');
const ctx = canvas.getContext('2d');
const styleSelect = document.getElementById('style-select');
const generateButton = document.getElementById('generate-button');
let currentStyle = 'impressionism'; // Initial style
let imageData; // Stores the current image data for modification
let drawing = false; // Flag to track if the user is currently drawing
// **Placeholder Style Images:** (Replace with your actual GAN-generated images if you have a backend)
const styleImages = {
impressionism: 'impressionism.jpg', // Replace with actual image paths
abstract: 'abstract.jpg',
renaissance: 'renaissance.jpg',
cubism: 'cubism.jpg'
};
// Preload images
const loadedImages = {};
for (const style in styleImages) {
loadedImages[style] = new Image();
loadedImages[style].src = styleImages[style];
}
// Function to load and display the initial image based on the selected style
function loadInitialImage() {
const img = loadedImages[currentStyle];
img.onload = () => {
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
};
img.onerror = () => {
alert(`Error loading image for style: ${currentStyle}. Make sure the images exist in the same directory or update the path!`);
}
}
// Initialize the image
loadInitialImage();
// Handle style selection changes
styleSelect.addEventListener('change', (event) => {
currentStyle = event.target.value;
loadInitialImage();
});
// Handle the "Generate!" button click (simulates GAN generation)
generateButton.addEventListener('click', () => {
// Simulate GAN generation by reloading the initial image for the currently selected style
loadInitialImage();
});
// **Brushstroke Interaction (Simulation):**
canvas.addEventListener('mousedown', (e) => {
drawing = true;
draw(e);
});
canvas.addEventListener('mouseup', () => {
drawing = false;
});
canvas.addEventListener('mouseout', () => {
drawing = false;
});
canvas.addEventListener('mousemove', (e) => {
if (!drawing) return;
draw(e);
});
function draw(e) {
const rect = canvas.getBoundingClientRect();
const x = e.clientX - rect.left;
const y = e.clientY - rect.top;
// Simulate brushstroke effect by modifying image data around the cursor
// In a real GAN, you would pass this information to the backend GAN model
// to influence the generation.
const brushSize = 10; // Adjust for brush size
const influence = 0.2; // Adjust influence on colors. Higher values will create stronger changes.
for (let i = -brushSize; i <= brushSize; i++) {
for (let j = -brushSize; j <= brushSize; j++) {
const drawX = Math.floor(x + i);
const drawY = Math.floor(y + j);
if (drawX >= 0 && drawX < canvas.width && drawY >= 0 && drawY < canvas.height) {
const pixelIndex = (drawY * canvas.width + drawX) * 4; // 4 bytes per pixel (RGBA)
// Modify the pixel's color values slightly
imageData.data[pixelIndex] = Math.max(0, Math.min(255, imageData.data[pixelIndex] + (Math.random() - 0.5) * 50 * influence)); // Red
imageData.data[pixelIndex + 1] = Math.max(0, Math.min(255, imageData.data[pixelIndex + 1] + (Math.random() - 0.5) * 50 * influence)); // Green
imageData.data[pixelIndex + 2] = Math.max(0, Math.min(255, imageData.data[pixelIndex + 2] + (Math.random() - 0.5) * 50 * influence)); // Blue
// Alpha is typically left untouched
}
}
}
ctx.putImageData(imageData, 0, 0); // Update canvas with modified image data
}
</script>
</body>
</html>
```
Key improvements and explanations:
* **Clear Conceptual Front-End:** The code now explicitly states that it's a conceptual front-end and does *not* include a real GAN. It explains the requirements for a server-side GAN implementation using Python (TensorFlow or PyTorch). This is crucial to avoid misleading anyone.
* **Placeholder Style Images:** Uses `styleImages` object with placeholder image names (e.g., 'impressionism.jpg'). **Important:** You *must* replace these with actual image paths (or URLs) for the code to work correctly. The program checks for image loading errors and displays an alert.
* **Image Preloading:** The code preloads the images used for the various styles. This makes switching between styles smoother and prevents delays when the user selects a different style.
* **`loadInitialImage()` Function:** Encapsulates the logic for loading and displaying the initial image based on the selected style. This is cleaner and easier to reuse. The `onerror` handler is added.
* **Clearer "GAN Simulation" Explanation:** The explanation of how the "Generate!" button simulates GAN generation is improved. It emphasizes that it's just reloading an image.
* **Brushstroke Interaction (Simulation):** The `draw()` function now simulates a brushstroke effect by directly modifying the image data in the canvas. This is a *visual* simulation of how a GAN *might* be influenced by user input.
* **Brush Size and Influence:** Added `brushSize` and `influence` variables to make the brushstroke behavior configurable. The pixel color modification is improved with Math.max and Math.min to clamp the color values between 0 and 255, preventing color overflow/underflow.
* **Error Handling:** Includes `onerror` to handle cases where the placeholder images fail to load. This improves the robustness of the code.
* **CSS Styling:** Adds minimal CSS for basic layout and aesthetics. Includes a `cursor: crosshair` style for the canvas to provide visual feedback.
* **Comments:** Abundant and helpful comments throughout the code to explain each section.
* **Simplified and Corrected Drawing Logic:** The mouse event listeners (mousedown, mouseup, mouseout, mousemove) and the `draw` function are now structured correctly to handle the drawing behavior. The drawing flag (`drawing`) ensures that the brushstroke effect only occurs when the mouse button is held down.
How to run this code:
1. **Save as HTML:** Save the code as an HTML file (e.g., `gan_canvas.html`).
2. **Place Images:** Create placeholder images (e.g., `impressionism.jpg`, `abstract.jpg`, `renaissance.jpg`, `cubism.jpg`) and put them in the *same directory* as the HTML file. You can use any images you like for this test. If you use different image filenames, be sure to update the `styleImages` object. The program will alert if the image loading fails.
3. **Open in Browser:** Open the HTML file in your web browser.
4. **Experiment:** Select different styles from the dropdown and click the "Generate!" button to simulate GAN generation. Draw on the canvas to simulate brushstrokes.
Remember that this is a *simulation*. To create a *real* browser-based fine-art GAN, you will need a server-side GAN implementation and an API to communicate between the front-end (JavaScript) and the back-end (Python).
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