Python-OpenCV 处理图像(四):图像直方图和反向投影



  • 直方图对比
  • 反向投影


0x01. 绘制直方图

12345678910111213141516171819202122232425262728293031323334353637383940 import 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(sizeintensity)),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

123456789101112131415161718192021222324252627 import 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)