Untitled

dabblet.css

/**
 * The first commented line is your dabblet’s title
 */

background: #f06;
background: linear-gradient(45deg, #f06, yellow);
min-height: 100%;

dabblet.html

import cv2
import numpy as np
import fitz
from tkinter import filedialog
from tkinter import Tk
from skimage.metrics import structural_similarity as ssim

def pdf_to_image(pdf_file):
    doc = fitz.open(pdf_file)
    page = doc.load_page(0)
    pix = page.get_pixmap(matrix=fitz.Matrix(4, 4))
    img_buffer = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, pix.n)
    return cv2.cvtColor(img_buffer, cv2.COLOR_RGB2BGR)


def resize_images_to_same_size(img1, img2):
    height1, width1 = img1.shape[:2]
    height2, width2 = img2.shape[:2]

    if height1 != height2 or width1 != width2:
        height = min(height1, height2)
        width = min(width1, width2)

        img1 = cv2.resize(img1, (width, height))
        img2 = cv2.resize(img2, (width, height))

    return img1, img2


def process_plan(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    return blurred

def compare_plans_ssim(img1, img2):
    return ssim(img1, img2)

def compare_images_mse(img1, img2):
    err = np.sum((img1.astype("float") - img2.astype("float")) ** 2)
    err /= float(img1.shape[0] * img1.shape[1])

    return err

def compare_plans_fft(img1, img2):
    f1 = np.fft.fft2(img1)
    f1_shift = np.fft.fftshift(f1)

    f2 = np.fft.fft2(img2)
    f2_shift = np.fft.fftshift(f2)

    return np.mean(np.abs(f1_shift - f2_shift))

def highlight_changes(img1, img2):
    # Convert images to grayscale if they are not already
    if len(img1.shape) == 3:
        gray_img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    else:
        gray_img1 = img1

    if len(img2.shape) == 3:
        gray_img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    else:
        gray_img2 = img2

    # Calculate absolute difference
    abs_diff = cv2.absdiff(gray_img1, gray_img2)

    # Threshold the difference image
    _, thresh = cv2.threshold(abs_diff, 30, 255, cv2.THRESH_BINARY)

    # Dilate the thresholded image to highlight differences
    kernel = np.ones((3, 3), np.uint8)
    dilated = cv2.dilate(thresh, kernel, iterations=1)

    # Convert the dilated image to 3-channel
    color_diff = cv2.cvtColor(dilated, cv2.COLOR_GRAY2BGR)

    # Convert grayscale images to 3-channel images
    if len(gray_img1.shape) == 2:
        gray_img1 = cv2.cvtColor(gray_img1, cv2.COLOR_GRAY2BGR)
    if len(gray_img2.shape) == 2:
        gray_img2 = cv2.cvtColor(gray_img2, cv2.COLOR_GRAY2BGR)

    # Add the color_diff image to the original image
    result = cv2.addWeighted(gray_img2, 0.5, color_diff, 0.5, 0)

    return result

def calculate_weighted_score(ssim_value, mse_value, fft_value, ssim_weight=0.5, mse_weight=0.3, fft_weight=0.2):
    normalized_mse = 1 - (mse_value / (mse_value + 1))
    normalized_fft = 1 - (fft_value / (fft_value + 1))
    combined_score = ssim_weight * ssim_value + mse_weight * normalized_mse + fft_weight * normalized_fft
    
    return combined_score

def main():
    root = Tk()
    root.withdraw()

    file_path1 = filedialog.askopenfilename()
    file_path2 = filedialog.askopenfilename()

    if file_path1.endswith('.pdf') and file_path2.endswith('.pdf'):
        img1 = pdf_to_image(file_path1)
        img2 = pdf_to_image(file_path2)
    else:
        img1 = cv2.imread(file_path1)
        img2 = cv2.imread(file_path2)

    img1, img2 = resize_images_to_same_size(img1, img2)

    processed_img1 = process_plan(img1)
    processed_img2 = process_plan(img2)

    ssim_value = compare_plans_ssim(processed_img1, processed_img2)
    mse_value = compare_images_mse(processed_img1, processed_img2)
    fft_value = compare_plans_fft(processed_img1, processed_img2)

    print("SSIM: ", ssim_value)
    print("MSE: ", mse_value)
    print("FFT Difference: ", fft_value)

    combined_score = calculate_weighted_score(ssim_value, mse_value, fft_value)
    print("Combined Score: ", combined_score)

    result = highlight_changes(processed_img1, processed_img2)

    cv2.imwrite("result.png", result)
    print("Result image saved as result.png")

if __name__ == "__main__":
    main()

dabblet.js

// alert('Hello world!');

settings.json

{"view":"split","fontsize":"100","seethrough":"","prefixfree":"1","page":"all"}