import gradio as gr
import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim

def preprocess_image(image, blur_value):
    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0)
    return blurred

def compare_images(image1, image2, blur_value, technique, threshold_value):
    # Preprocess images
    gray1 = preprocess_image(image1, blur_value)
    gray2 = preprocess_image(image2, blur_value)
    
    # Compute SSIM between the two images
    score, diff = ssim(gray1, gray2, full=True)
    diff = (diff * 255).astype("uint8")
    
    if technique == "Adaptive Threshold":
        _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY_INV)
    elif technique == "Otsu's Threshold":
        _, thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
    else:  # Simple Binary
        _, thresh = cv2.threshold(diff, threshold_value, 255, cv2.THRESH_BINARY)
    
    # Find contours of differences
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    # Filter out small noise using contour area threshold
    filtered_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 500]
    
    # Create a mask to isolate only the significant added object
    mask = np.zeros_like(image1, dtype=np.uint8)
    cv2.drawContours(mask, filtered_contours, -1, (255, 255, 255), thickness=cv2.FILLED)
    
    # Apply the mask to highlight the object added in the second image
    highlighted = cv2.bitwise_and(image2, mask)
    
    # Create a magenta overlay where changes occurred
    diff_colored = np.zeros_like(image1, dtype=np.uint8)
    diff_colored[:, :, 0] = thresh  # Set blue channel to zero
    diff_colored[:, :, 1] = 0  # Set green channel to zero
    diff_colored[:, :, 2] = thresh  # Set red channel to highlight in magenta
    
    # Combine the original image with the magenta overlay
    overlayed = cv2.addWeighted(image1, 0.7, diff_colored, 0.3, 0)
    
    return highlighted, overlayed

def update_threshold_visibility(technique):
    return gr.update(visible=(technique == "Simple Binary"))

with gr.Blocks() as demo:
    gr.Markdown("# Object Difference Highlighter\nUpload two images: one without an object and one with an object. The app will highlight only the newly added object and show the real differences in magenta overlayed on the original image.")
    
    with gr.Row():
        img1 = gr.Image(type="numpy", label="Image Without Object")
        img2 = gr.Image(type="numpy", label="Image With Object")
    
    blur_slider = gr.Slider(minimum=1, maximum=15, step=2, value=5, label="Gaussian Blur")
    technique_dropdown = gr.Dropdown(["Adaptive Threshold", "Otsu's Threshold", "Simple Binary"], label="Thresholding Technique", value="Adaptive Threshold", interactive=True)
    threshold_slider = gr.Slider(minimum=0, maximum=255, step=1, value=50, label="Threshold Value", visible=False)
    
    technique_dropdown.change(update_threshold_visibility, inputs=[technique_dropdown], outputs=[threshold_slider])
    
    output1 = gr.Image(type="numpy", label="Highlighted Differences")
    output2 = gr.Image(type="numpy", label="Raw Difference Overlay (Magenta)")
    
    btn = gr.Button("Process")
    btn.click(compare_images, inputs=[img1, img2, blur_slider, technique_dropdown, threshold_slider], outputs=[output1, output2])

demo.launch()