相思资源网 Design By www.200059.com
使用OpenCV和Python查找图片差异
flyfish
方法1 均方误差的算法(Mean Squared Error , MSE)
下面的一些表达与《TensorFlow - 协方差矩阵》式子表达式一样的
拟合 误差平方和( sum of squared errors)
residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (SSE),
also known as 就我们所说的
RSS, SSR ,SSE表达的是一个意思
def mse(imageA, imageB): # the 'Mean Squared Error' between the two images is the # sum of the squared difference between the two images; # NOTE: the two images must have the same dimension err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2) err /= float(imageA.shape[0] * imageA.shape[1]) # return the MSE, the lower the error, the more "similar" # the two images are return err
方法2 SSIM
"_blank" href="https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/#comment-429138">Image Difference with OpenCV and Python
原理
根据参数读取两张图片并转换为灰度:
使用SSIM计算两个图像之间的差异,这种方法已经在scikit-image 库中实现
在两个图像之间的不同部分绘制矩形边界框。
代码如下 已编译通过
from skimage.measure import compare_ssim #~ import skimage as 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"]) ''' imageA = cv2.imread("E:\\1.png") imageB = cv2.imread("E:\\2.png") ''' # 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 #"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 = cnts[0] if imutils.is_cv2() else cnts[1] # 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)
使用方法
python image_diff.py –first original.png –second images/modified.png
如果不想使用参数将参数代码部分直接变成
imageA = cv2.imread(“E:\1.png”) imageB = cv2.imread(“E:\2.png”)
以上这篇利用OpenCV和Python实现查找图片差异就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
相思资源网 Design By www.200059.com
广告合作:本站广告合作请联系QQ:858582 申请时备注:广告合作(否则不回)
免责声明:本站文章均来自网站采集或用户投稿,网站不提供任何软件下载或自行开发的软件! 如有用户或公司发现本站内容信息存在侵权行为,请邮件告知! 858582#qq.com
免责声明:本站文章均来自网站采集或用户投稿,网站不提供任何软件下载或自行开发的软件! 如有用户或公司发现本站内容信息存在侵权行为,请邮件告知! 858582#qq.com
相思资源网 Design By www.200059.com
暂无利用OpenCV和Python实现查找图片差异的评论...