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import gradio as gr
import fitz # PyMuPDF
import cv2
from pdf2image import convert_from_path
import numpy as np
import os
from fpdf import FPDF
# Convert PDFs to images
def convert_pdf_to_images(pdf_path, dpi=300):
images = convert_from_path(pdf_path, dpi=dpi, poppler_path="/usr/bin")
return [cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) for image in images]
# Align images
def align_images(img1, img2):
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(gray1, None)
kp2, des2 = orb.detectAndCompute(gray2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
src_pts = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
matrix, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# Validate if alignment is good enough
if matrix is None or len(matches) < 10: # Check if sufficient matches exist
raise ValueError("Alignment failed. Insufficient matches between images.")
aligned_img = cv2.warpPerspective(img2, matrix, (img1.shape[1], img1.shape[0]))
return aligned_img
# Compare visual changes
def compare_visual_changes(orig_img, edit_img):
diff = cv2.absdiff(orig_img, edit_img)
gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur to reduce noise
blurred_diff = cv2.GaussianBlur(gray_diff, (5, 5), 0)
# Apply thresholding
_, thresh = cv2.threshold(blurred_diff, 70, 255, cv2.THRESH_BINARY)
# Morphological operations to clean noise
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
cleaned = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
contours, _ = cv2.findContours(cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
overlay = edit_img.copy()
for cnt in contours:
if cv2.contourArea(cnt) > 100: # Filter out small regions
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(overlay, (x, y), (x + w, y + h), (0, 0, 255), 2) # Red bounding box
return overlay
# Generate visual comparison report
def generate_visual_report(images, output_path):
pdf = FPDF()
for img in images:
temp_path = "temp_image.png"
cv2.imwrite(temp_path, img)
pdf.add_page()
pdf.image(temp_path, x=10, y=10, w=190)
os.remove(temp_path)
pdf.output(output_path)
return output_path
# Perform only visual comparison
def generate_visual_comparison(original_pdf, edited_pdf):
original_images = convert_pdf_to_images(original_pdf)
edited_images = convert_pdf_to_images(edited_pdf)
visual_combined_images = []
for orig_img, edit_img in zip(original_images, edited_images):
aligned_img = align_images(orig_img, edit_img)
highlighted_img = compare_visual_changes(orig_img, aligned_img)
visual_combined_images.append(np.hstack((orig_img, highlighted_img)))
# Generate visual changes report
visual_report_path = generate_visual_report(
visual_combined_images, "outputs/visual_changes.pdf"
)
return visual_report_path
# Gradio interface function
def pdf_visual_comparison(original_pdf, edited_pdf):
visual_path = generate_visual_comparison(original_pdf.name, edited_pdf.name)
return visual_path
# Gradio interface
interface = gr.Interface(
fn=pdf_visual_comparison,
inputs=[
gr.File(label="Upload Original PDF", file_types=[".pdf"]),
gr.File(label="Upload Edited PDF", file_types=[".pdf"])
],
outputs=[
gr.File(label="Download Visual Changes Report")
],
title="PDF Visual Comparison Tool",
description="Upload two PDFs: the original and the edited version. The tool generates a visual changes report."
)
if __name__ == "__main__":
interface.launch()
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