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 cvdef drawGraph(ar,im, size): #Draw the histogram on the imageminV, maxV, minloc, maxloc = cv.MinMaxLoc(ar) #Get the min and max valuehpt = 0.9 * histsizefor i in range(size):intensity = ar[i] * hpt / maxV #Calculate the intensity to make enter in the imagecv.Line(im, (i,size), (i,int(size-intensity)),cv.Scalar(255,255,255)) #Draw the linei += 1#---- Gray image orig = cv.LoadImage("img/lena.jpg", cv.CV_8U)histsize = 256 #Because we are working on grayscale pictures which values within 0-255hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)cv.CalcHist([orig], hist) #Calculate histogram for the given grayscale picturehistImg = 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 imagehistEq = 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

import cv2.cv as cvim = 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)

————————————————————————————————————————————————分割線————————————————————————————————————————————————

關于邊緣檢測的基礎來自于一個事實，即在邊緣部分，像素值出現”跳躍“或者較大的變化。如果在此邊緣部分求取一階導數，就會看到極值的出現。

而在一階導數為極值的地方，二階導數為0，基于這個原理，就可以進行邊緣檢測。

關于 Laplace 算法原理，可參考

• Laplace 算子

0x01. Laplace 算法

下面的代碼展示了分別對灰度化的圖像和原始彩色圖像中的邊緣進行檢測：

import cv2.cv as cvim=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_COLOR)# Laplace on a gray scale picture gray = cv.CreateImage(cv.GetSize(im), 8, 1) cv.CvtColor(im, gray, cv.CV_BGR2GRAY)aperture=3dst = cv.CreateImage(cv.GetSize(gray), cv.IPL_DEPTH_32F, 1) cv.Laplace(gray, dst,aperture)cv.Convert(dst,gray)thresholded = cv.CloneImage(im) cv.Threshold(im, thresholded, 50, 255, cv.CV_THRESH_BINARY_INV)cv.ShowImage('Laplaced grayscale',gray) #------------------------------------# Laplace on color planes = [cv.CreateImage(cv.GetSize(im), 8, 1) for i in range(3)] laplace = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) colorlaplace = cv.CreateImage(cv.GetSize(im), 8, 3)cv.Split(im, planes[0], planes[1], planes[2], None) #Split channels to apply laplace on each for plane in planes:cv.Laplace(plane, laplace, 3)cv.ConvertScaleAbs(laplace, plane, 1, 0)cv.Merge(planes[0], planes[1], planes[2], None, colorlaplace)cv.ShowImage('Laplace Color', colorlaplace) #-------------------------------------cv.WaitKey(0)

效果展示

原圖

灰度化圖片檢測

原始彩色圖片檢測

0x02. Sobel 算法

Sobel 也是很常用的一種輪廓識別的算法。

關于 Sobel 導數原理的介紹，可參考

• Sobel 導數

以下是使用 Sobel 算法進行輪廓檢測的代碼和效果

import cv2.cv as cvim=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_GRAYSCALE)sobx = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) cv.Sobel(im, sobx, 1, 0, 3) #Sobel with x-order=1soby = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) cv.Sobel(im, soby, 0, 1, 3) #Sobel withy-oder=1cv.Abs(sobx, sobx) cv.Abs(soby, soby)result = cv.CloneImage(im) cv.Add(sobx, soby, result) #Add the two results together.cv.Threshold(result, result, 100, 255, cv.CV_THRESH_BINARY_INV)cv.ShowImage('Image', im) cv.ShowImage('Result', result)cv.WaitKey(0)

處理之后效果圖（感覺比Laplace效果要好些）

0x03. cv.MorphologyEx

cv.MorphologyEx 是另外一種邊緣檢測的算法

import cv2.cv as cvimage=cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_GRAYSCALE)#Get edges morphed = cv.CloneImage(image) cv.MorphologyEx(image, morphed, None, None, cv.CV_MOP_GRADIENT) # Apply a dilate - Erodecv.Threshold(morphed, morphed, 30, 255, cv.CV_THRESH_BINARY_INV)cv.ShowImage("Image", image) cv.ShowImage("Morphed", morphed)cv.WaitKey(0)

0x04. Canny 邊緣檢測

Canny 算法可以對直線邊界做出很好的檢測；

關于 Canny 算法原理的描述，可參考：

• Canny 邊緣檢測

import cv2.cv as cv import mathim=cv.LoadImage('img/road.png', cv.CV_LOAD_IMAGE_GRAYSCALE)pi = math.pi #Pi valuedst = cv.CreateImage(cv.GetSize(im), 8, 1)cv.Canny(im, dst, 200, 200) cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY)#---- Standard ---- color_dst_standard = cv.CreateImage(cv.GetSize(im), 8, 3) cv.CvtColor(im, color_dst_standard, cv.CV_GRAY2BGR)#Create output image in RGB to put red lineslines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD, 1, pi / 180, 100, 0, 0) for (rho, theta) in lines[:100]:a = math.cos(theta) #Calculate orientation in order to print themb = math.sin(theta)x0 = a * rhoy0 = b * rhopt1 = (cv.Round(x0 + 1000*(-b)), cv.Round(y0 + 1000*(a)))pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a)))cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2, 4) #Draw the line#---- Probabilistic ---- color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3) cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idemrho=1 theta=pi/180 thresh = 50 minLength= 120 # Values can be changed approximately to fit your image edges maxGap= 20lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_PROBABILISTIC, rho, theta, thresh, minLength, maxGap) for line in lines:cv.Line(color_dst_proba, line[0], line[1], cv.CV_RGB(255, 0, 0), 2, 8)cv.ShowImage('Image',im) cv.ShowImage("Cannied", dst) cv.ShowImage("Hough Standard", color_dst_standard) cv.ShowImage("Hough Probabilistic", color_dst_proba) cv.WaitKey(0)

原圖

使用 Canny 算法處理之后

標記出標準的直線

標記出所有可能的直線

0x05. 輪廓檢測

OpenCV 提供一個 FindContours 函數可以用來檢測出圖像中對象的輪廓：

import cv2.cv as cvorig = cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_COLOR) im = cv.CreateImage(cv.GetSize(orig), 8, 1) cv.CvtColor(orig, im, cv.CV_BGR2GRAY) #Keep the original in colour to draw contours in the endcv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY) cv.ShowImage("Threshold 1", im)element = cv.CreateStructuringElementEx(5*2+1, 5*2+1, 5, 5, cv.CV_SHAPE_RECT)cv.MorphologyEx(im, im, None, element, cv.CV_MOP_OPEN) #Open and close to make appear contours cv.MorphologyEx(im, im, None, element, cv.CV_MOP_CLOSE) cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage("After MorphologyEx", im) # --------------------------------vals = cv.CloneImage(im) #Make a clone because FindContours can modify the image contours=cv.FindContours(vals, cv.CreateMemStorage(0), cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, (0,0))_red = (0, 0, 255); #Red for external contours _green = (0, 255, 0);# Gren internal contours levels=2 #1 contours drawn, 2 internal contours as well, 3 ... cv.DrawContours (orig, contours, _red, _green, levels, 2, cv.CV_FILLED) #Draw contours on the colour imagecv.ShowImage("Image", orig) cv.WaitKey(0)

效果圖：

原圖

識別結果

0x06. 邊界檢測

全選<button href="javascript:void(0);" _xhe_href="javascript:void(0);" class="copyCode btn btn-xs" data-clipboard-text="" import="" cv2.cv="" as="" cv"="" data-toggle="tooltip" data-placement="bottom" title="" style="color: rgb(255, 255, 255); font-style: inherit; font-variant: inherit; font-stretch: inherit; font-size: 12px; line-height: 1.5; font-family: inherit; margin: 0px 0px 0px 5px; overflow: visible; cursor: pointer; vertical-align: middle; border: 1px solid transparent; white-space: nowrap; padding-right: 5px; padding-left: 5px; border-radius: 3px; -webkit-user-select: none; box-shadow: rgba(0, 0, 0, 0.0980392) 0px 1px 2px; background-image: none; background-color: rgba(0, 0, 0, 0.74902);">復制放進筆記import cv2.cv as cvim = cv.LoadImage("img/build.png", cv.CV_LOAD_IMAGE_GRAYSCALE)dst_32f = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_32F, 1)neighbourhood = 3 aperture = 3 k = 0.01 maxStrength = 0.0 threshold = 0.01 nonMaxSize = 3cv.CornerHarris(im, dst_32f, neighbourhood, aperture, k)minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)dilated = cv.CloneImage(dst_32f) cv.Dilate(dst_32f, dilated) # By this way we are sure that pixel with local max value will not be changed, and all the others willlocalMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U) cv.Cmp(dst_32f, dilated, localMax, cv.CV_CMP_EQ) #compare allow to keep only non modified pixel which are local maximum values which are corners.threshold = 0.01 * maxv cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)cornerMap = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U) cv.Convert(dst_32f, cornerMap) #Convert to make the and cv.And(cornerMap, localMax, cornerMap) #Delete all modified pixelsradius = 3 thickness = 2l = [] for x in range(cornerMap.height): #Create the list of point take all pixel that are not 0 (so not black)for y in range(cornerMap.width):if cornerMap[x,y]:l.append((y,x))for center in l:cv.Circle(im, center, radius, (255,255,255), thickness)cv.ShowImage("Image", im) cv.ShowImage("CornerHarris Result", dst_32f) cv.ShowImage("Unique Points after Dilatation/CMP/And", cornerMap)cv.WaitKey(0)

from: https://segmentfault.com/a/1190000003742455

https://segmentfault.com/a/1190000003742461

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