如何利用Python3和OpenCV对比两张图片的不同，提取差异性

导言：通过机器视觉来计算两个图片之间的差异性，可以快速有效辨别文件、图片是否被篡改，也能帮助用户轻松识别钓鱼网站，确保财产安全。

一、所需模块

pip install --upgrade scikit-image

pip install --upgrade imutils

二、Python实现

# import the necessary packages

from skimage.measure import compare_ssim

import argparse

import imutils

import cv2

# construct the argument parse and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-f", "--first", required=True,

help="first input image")

ap.add_argument("-s", "--second", required=True,

help="second")

args = vars(ap.parse_args())

# load the two input images

imageA = cv2.imread(args["first"])

imageB = cv2.imread(args["second"])

# convert the images to grayscale

grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)

grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)

# compute the Structural Similarity Index (SSIM) between the two

# images, ensuring that the difference image is returned

(score, diff) = compare_ssim(grayA, grayB, full=True)

diff = (diff * 255).astype("uint8")

print("SSIM: {}".format(score))

# threshold the difference image, followed by finding contours to

# obtain the regions of the two input images that differ

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 = imutils.grab_contours(cnts)

# loop over the contours

for c in cnts:

# compute the bounding box of the contour and then draw the

# bounding box on both input images to represent where the two

# images differ

(x, y, w, h) = cv2.boundingRect(c)

cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2)

cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)

# show the output images

cv2.imshow("Original", imageA)

cv2.imshow("Modified", imageB)

cv2.imshow("Diff", diff)

cv2.imshow("Thresh", thresh)

cv2.waitKey(0)

三、运行代码

如下图所示，cmd打开电脑终端，输入运行参数(即图片存储路径)，Enter键运行。具体方法见上一篇博文：如何使用Argparse模块

四、运行结果

后记：需要对比的两张图片必须尺寸大小一致，即像素矩阵一致，否则无法对比提取差异性。有问题欢迎留言！

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