Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+
+def load_image(image):
+    """ Convert uploaded image to grayscale. """
+    return image.convert('L')
+
+def compute_gradients(image):
+    """ Compute horizontal and vertical gradients of the image. """
+    image_array = np.asarray(image, dtype=float)
+    x_gradient = np.gradient(image_array, axis=1)
+    y_gradient = np.gradient(image_array, axis=0)
+    return x_gradient, y_gradient
+
+def create_normal_map(image):
+    """ Generate a normal map from an image. """
+    image = load_image(image)
+    x_grad, y_grad = compute_gradients(image)
+    
+    # Normalize gradients
+    max_grad = max(np.max(np.abs(x_grad)), np.max(np.abs(y_grad)))
+    x_grad /= max_grad
+    y_grad /= max_grad
+
+    # Calculate z component of the normal (assumed perpendicular to the surface)
+    z = np.sqrt(1 - (x_grad ** 2) - (y_grad ** 2))
+
+    # Normalize to 0-255 and format as uint8
+    normal_map = np.dstack(((x_grad * 0.5 + 0.5) * 255,
+                            (y_grad * 0.5 + 0.5) * 255,
+                            (z * 1.0) * 255)).astype(np.uint8)
+
+    return Image.fromarray(normal_map, 'RGB')
+
+def convert_to_grayscale(image):
+    """ Convert the normal map to a grayscale image. """
+    grayscale_image = image.convert('L')
+    return grayscale_image
+
+def interface(image):
+    normal_map = create_normal_map(image)
+    grayscale_image = convert_to_grayscale(normal_map)
+    return normal_map, grayscale_image
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=interface,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[gr.Image(type="pil", label="Normal Map"), gr.Image(type="pil", label="Grayscale Image")],
+    title="Normal Map and Grayscale Generator",
+    description="Upload an image to generate its normal map and a grayscale version of the normal map. Both images retain the original dimensions."
+)
+
+iface.launch()