如何处理重叠的矩形？

Question

我正在比较两个图像并使用 compare_ssim 找到差异，在这种情况下，我得到了差异的轮廓，我需要通过在其周围绘制矩形来突出显示，但我面临的问题是某些矩形相互重叠我想要删除那些重叠。给定的是我的代码和图像。

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)

Answer 1

这听起来像是非最大抑制的问题。 Pyimagesearch有一篇很好的文章，我强烈推荐阅读。您可以使用类似于文章如何使用匹配算法的结果的 compare_ssim 的结果。