(转载) Python-OpenCV 处理图像（四）：图像直方图和反向投影

原文出处： Tairy   

系列文章目录

当我们想比较两张图片相似度的时候，可以使用这一节提到的技术

• 直方图对比
• 反向投影

关于这两种技术的原理可以参考我上面贴的链接，下面是示例的代码：

0x01. 绘制直方图

Python

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 image orig = 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 values drawGraph(hist.bins, histImg, histsize) cv.ShowImage("Original Image", orig) cv.ShowImage("Original Histogram", histImg) #--------------------- #---- Equalized image imEq = 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 picture eqImg = cv.CreateMat(histsize, histsize, cv.CV_8U) #Image that will contain the graph of the repartition of values drawGraph(histEq.bins, eqImg, histsize) cv.ShowImage("Image Equalized", imEq) cv.ShowImage("Equalized HIstogram", eqImg) #-------------------------------- cv.WaitKey(0)

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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 image orig = 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 values drawGraph ( hist . bins , histImg , histsize )   cv . ShowImage ( "Original Image" , orig ) cv . ShowImage ( "Original Histogram" , histImg ) #---------------------   #---- Equalized image imEq = 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 picture eqImg = cv . CreateMat ( histsize , histsize , cv . CV_8U ) #Image that will contain the graph of the repartition of values drawGraph ( histEq . bins , eqImg , histsize )   cv . ShowImage ( "Image Equalized" , imEq ) cv . ShowImage ( "Equalized HIstogram" , eqImg ) #--------------------------------   cv . WaitKey ( 0 )

0x02. 反向投影

Python

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 pictures hist = 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 = 1.0 cv.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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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 pictures hist = 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 = 1.0 cv . 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 )