How can I deal with overlapping rectangles? [close

Question

I am comparing two images and find the difference using compare_ssim, in that case I got the contours of differences, which i need to highlight by drawing rectangle around it, but I am facing the issue that some of the rectangles overlapping each other I want to remove those overlapping. Given is my code and the image.

import os
import csv
from datetime import datetime
from datetime import date
from datetime import timedelta
import tldextract
import time
import requests
import json
from urllib.parse import urlparse
import tldextract
import os
from PIL import Image
from PIL import ImageChops
from PIL import ImageDraw
from skimage.measure import compare_ssim
import numpy as np
import argparse
import imutils
import cv2
import urllib.request as req
import math


def is_contour_bad(c):
    # approximate the contour
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    # the contour is 'bad' if it is not a rectangle
    return not len(approx) == 4

initial_view = "first_image.jpg"
secondary_view = "seconda_image.jpg"
initial = cv2.imread(initial_view)
secondary = cv2.imread(secondary_view)
size_of_initial_image =  heighta, widtha = initial.shape[:2]
size_of_secondary_image = heightb, widthb = secondary.shape[:2]
if size_of_initial_image == size_of_secondary_image:
    grayA = cv2.cvtColor(initial, cv2.COLOR_BGR2GRAY)
    grayB = cv2.cvtColor(secondary, cv2.COLOR_BGR2GRAY)
    (score, diff) = compare_ssim(grayA, grayB, full=True)
    diff = (diff * 255).astype("uint8")
    if score == 1.0:
        print('images are identical')
    else:

        thresh = cv2.threshold(diff, 0, 255,
            cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
            cv2.CHAIN_APPROX_SIMPLE)
        cnts = cnts[0] if imutils.is_cv2() else cnts[1]

        # output = secondary.copy()
        # alpha = 0.3

        threshold_area = 1000
        for c in cnts:
            if is_contour_bad(c):
                pass
            area = cv2.contourArea(c)
            if area > threshold_area:
                (x, y, w, h) = cv2.boundingRect(c)
                cv2.rectangle(secondary, (x, y), (x + w , y + h), (0,255,255), 2)

            else:
                (x, y, w, h) = cv2.boundingRect(c)
                if h >= 7 and w >= 7:
                    changed_w = w + 100
                    changed_h = h + 20
                    changed_x = x - 20
                    cv2.rectangle(secondary, (changed_x, y), (changed_x + changed_w , y + changed_h), (0,255,255), 2)
        complete_path = "result_image.jpg"
        cv2.imwrite( complete_path, secondary );


else:
  continue

Answer 1

This sounds like a problem for Non Maximum Suppression. Pyimagesearch has a pretty good article on it which I highly recommend reading. You can use the result of compare_ssim similar to how the article uses the result of the matching algorithm.