app.py

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()