如何利用Python3和OpenCV对比两张图片的不同，提取差异性 |
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如何利用Python3和OpenCV对比两张图片的不同，提取差异性
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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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