本文實例為大家分享了Python OpenCV圖像直方圖和反向投影的具體代碼，供大家參考，具體內容如下

當我們想比較兩張圖片相似度的時候，可以使用這一節提到的技術

直方圖對比

反向投影

關于這兩種技術的原理可以參考我上面貼的鏈接，下面是示例的代碼：

0x01. 繪制直方圖

import cv2.cv as cv def drawGraph(ar,im, size): #Draw the histogram on the image  minV, maxV, minloc, maxloc = cv.MinMaxLoc(ar) #Get the min and max value  hpt = 0.9 * histsize  for i in range(size):    intensity = ar[i] * hpt / maxV #Calculate the intensity to make enter in the image    cv.Line(im, (i,size), (i,int(size-intensity)),cv.Scalar(255,255,255)) #Draw the line    i += 1 #---- Gray imageorig = cv.LoadImage("img/lena.jpg", cv.CV_8U) histsize = 256 #Because we are working on grayscale pictures which values within 0-255 hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1) cv.CalcHist([orig], hist) #Calculate histogram for the given grayscale picture histImg = cv.CreateMat(histsize, histsize, cv.CV_8U) #Image that will contain the graph of the repartition of valuesdrawGraph(hist.bins, histImg, histsize) cv.ShowImage("Original Image", orig)cv.ShowImage("Original Histogram", histImg)#--------------------- #---- Equalized imageimEq = cv.CloneImage(orig)cv.EqualizeHist(imEq, imEq) #Equlize the original image histEq = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)cv.CalcHist([imEq], histEq) #Calculate histogram for the given grayscale pictureeqImg = cv.CreateMat(histsize, histsize, cv.CV_8U) #Image that will contain the graph of the repartition of valuesdrawGraph(histEq.bins, eqImg, histsize) cv.ShowImage("Image Equalized", imEq)cv.ShowImage("Equalized HIstogram", eqImg)#-------------------------------- cv.WaitKey(0)

0x02. 反向投影

import cv2.cv as cv im = cv.LoadImage("img/lena.jpg", cv.CV_8U) cv.SetImageROI(im, (1, 1,30,30)) histsize = 256 #Because we are working on grayscale pictureshist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)cv.CalcHist([im], hist)  cv.NormalizeHist(hist,1) # The factor rescale values by multiplying values by the factor_,max_value,_,_ = cv.GetMinMaxHistValue(hist) if max_value == 0:  max_value = 1.0cv.NormalizeHist(hist,256/max_value) cv.ResetImageROI(im) res = cv.CreateMat(im.height, im.width, cv.CV_8U)cv.CalcBackProject([im], res, hist) cv.Rectangle(im, (1,1), (30,30), (0,0,255), 2, cv.CV_FILLED)cv.ShowImage("Original Image", im)cv.ShowImage("BackProjected", res)cv.WaitKey(0)

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