CFA.py

import gradio as gr
import numpy as np
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
from scipy.signal import convolve2d
import time
np.random.seed(123)

# --- Configuration ---
BOX_SIZE = 128

# --- Helper Functions (GetMap, getFourier) ---
# These computational functions remain the same as before.
def GetMap(image_channel):
    f = image_channel.astype(np.float64)
    alpha = np.random.rand(4)
    sigma = 0.005
    delta = 10
    count = 0
    max_iter = 30
    tolerance = 0.01
    while True:
        count += 1
        kernel = np.array([[0, alpha[0], 0], [alpha[1], 0, alpha[2]], [0, alpha[3], 0]])
        filtered = convolve2d(f, kernel, mode='same', boundary='symm')
        r = np.abs(f - filtered) / 255.0
        r = r[1:-1, 1:-1]
        e = np.exp(-r**2 / sigma)
        w = e / (e + 1 / delta)
        if np.sum(w) < 1e-6:
            print("Warning: Sum of weights is near zero. Exiting GetMap early.")
            return e.ravel()
        w_flat = w.ravel()
        W = np.diag(w_flat)
        value1 = f[:-2, 1:-1].ravel(); value2 = f[1:-1, :-2].ravel(); value3 = f[1:-1, 2:].ravel(); value4 = f[2:, 1:-1].ravel()
        Y = f[1:-1, 1:-1].ravel()
        X = np.column_stack((value1, value2, value3, value4))
        try:
            alpha_new = np.linalg.inv(X.T @ W @ X) @ (X.T @ W @ Y)
        except np.linalg.LinAlgError:
            print("Warning: Singular matrix encountered. Cannot compute inverse.")
            return e.ravel()
        if np.linalg.norm(alpha - alpha_new) < tolerance or count > max_iter: break
        alpha = alpha_new
        sigma = np.sum(w * (r**2)) / np.sum(w)
    return e.ravel()

def getFourier(prob):
    #imFft = np.fft.fftshift(np.fft.fft2(prob))
    #imFft = np.abs(imFft)
    #if np.max(imFft) > 0:
    #    imFft = (imFft / np.max(imFft) * 255)
    #imFft = imFft.astype(np.uint8)
    #imFft = (imFft > (0.5 * 255)).astype(np.uint8)
    # Compute the Fourier Transform and shift zero frequency to the center
    imFft = np.fft.fftshift(np.fft.fft2(prob))
    
    # Take the magnitude (absolute value)
    imFft = np.abs(imFft)
    
    # Convert to 8-bit unsigned integer (similar to uint8 in MATLAB)
    imFft = np.uint8(imFft)/255

    # Binarize the image with a threshold of 0.5
    imFft = (imFft > 0.5).astype(np.uint8)
    return imFft

# --- New Gradio Interaction Functions ---

def draw_box_on_image(image: np.ndarray, box_coords: tuple, color="red", width=3) -> np.ndarray:
    """Draws a bounding box on a NumPy image array."""
    pil_image = Image.fromarray(image)
    draw = ImageDraw.Draw(pil_image)
    x, y = box_coords
    rectangle = (x, y, x + BOX_SIZE, y + BOX_SIZE)
    draw.rectangle(rectangle, outline=color, width=width)
    return np.array(pil_image)

def on_upload_image(image: np.ndarray) -> tuple:
    """Called when an image is first uploaded. Stores the original image and draws the initial box."""
    initial_coords = (0, 0)
    image_with_box = draw_box_on_image(image, initial_coords)
    # Returns: (image_with_box for display, original_image for state, initial_coords for state)
    return image_with_box, image, initial_coords

def move_selection_box(evt: gr.SelectData, original_image: np.ndarray) -> tuple:
    """Called when the user clicks the image. It moves the box to the clicked location."""
    # Center the box on the user's click
    x = evt.index[0] - BOX_SIZE // 2
    y = evt.index[1] - BOX_SIZE // 2
    
    # Clamp coordinates to ensure the box stays within the image boundaries
    img_h, img_w, _ = original_image.shape
    x = max(0, min(x, img_w - BOX_SIZE))
    y = max(0, min(y, img_h - BOX_SIZE))
    
    new_coords = (int(x), int(y))
    image_with_box = draw_box_on_image(original_image, new_coords)
    # Returns: (image_with_box for display, new_coords for state)
    return image_with_box, new_coords

def analyze_region(original_image: np.ndarray, box_coords: tuple):
    """The main analysis function, triggered by the 'Analyze' button."""
    if original_image is None:
        gr.Warning("Please upload an image first!")
        return None
        
    print(f"\n--- Analysis Started for region at {box_coords} ---")
    start_time = time.time()
    
    x, y = box_coords
    patch = original_image[y:y + BOX_SIZE, x:x + BOX_SIZE]
    print(f"1. Patch extracted with shape: {patch.shape}")

    if len(patch.shape) == 3: analysis_channel = patch[:, :, 1] # Green channel
    else: analysis_channel = patch # Grayscale
    
    print("2. Computing probability map...")
    prob_flat = GetMap(analysis_channel)
    prob_map_shape = (analysis_channel.shape[0] - 2, analysis_channel.shape[1] - 2)
    prob_map = prob_flat.reshape(prob_map_shape)
    
    print("3. Computing Fourier transform...")
    fft_result = getFourier(prob_map)

    # Plotting
    fig, axs = plt.subplots(1, 3, figsize=(12, 4))
    axs[0].imshow(patch); axs[0].set_title("Selected 128x128 Patch"); axs[0].axis("off")
    axs[1].imshow(prob_map, cmap='gray'); axs[1].set_title("Probability Map"); axs[1].axis("off")
    axs[2].imshow(np.abs(fft_result), cmap='gray'); axs[2].set_title("Fourier Transform"); axs[2].axis("off")
    plt.tight_layout()
    
    print(f"4. Analysis complete in {time.time() - start_time:.2f} seconds.")
    return fig

def build_demo():
    # --- Build the Gradio Interface using Blocks ---
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        # State variables store data (like the original image) between user interactions
        original_image_state = gr.State()
        box_coords_state = gr.State(value=(0, 0))

        gr.Markdown("# 🎨 Color Filter Array Analysis")
        gr.Markdown(
            "Analyzes artifacts introduced during the camera's raw image processing. Inconsistencies in the **Color Filter Array (CFA)** interpolation pattern can reveal areas that have been spliced from another image or copy-pasted within the same image (copy-move).\n"
            "\n"
            "## Instructions:\n"
            "1. **Upload** an image.\n"
            "2. **Click** anywhere on the image to move the 128x128 selection box.\n"
            "3. Press the **Analyze Region** button to start processing."
        )

        with gr.Row():
            image_display = gr.Image(type="numpy", label="Selection Canvas", interactive=True)
            output_plot = gr.Plot(label="Analysis Results")

        analyze_button = gr.Button("Analyze Region", variant="primary")

        # --- Wire up the event listeners ---

        # 1. When a new image is uploaded, call on_upload_image
        image_display.upload(
            fn=on_upload_image,
            inputs=[image_display],
            outputs=[image_display, original_image_state, box_coords_state]
        )

        # 2. When the user clicks the image, call move_selection_box
        image_display.select(
            fn=move_selection_box,
            inputs=[original_image_state],
            outputs=[image_display, box_coords_state]
        )
        
        # 3. When the user clicks the analyze button, call analyze_region
        analyze_button.click(
            fn=analyze_region,
            inputs=[original_image_state, box_coords_state],
            outputs=[output_plot],
            # Show a progress bar during analysis
            show_progress="full" 
        )
    return demo

if __name__ == "__main__":
    app = build_demo()
    app.launch()