Python-OpenCV 处理图像（二）：滤镜和图像运算_程序人生

系列文章目录

0x01. 滤镜

喜欢自拍的人肯定都知道滤镜了，下面代码尝试使用一些简单的滤镜，包括图片的平滑处理、灰度化、二值化等：

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import cv2.cv as cv image=cv.LoadImage(\’img/lena.jpg\’, cv.CV_LOAD_IMAGE_COLOR) #Load the imagecv.ShowImage(\”Original\”, image) grey = cv.CreateImage((image.width ,image.height),8,1) #8depth, 1 channel so grayscalecv.CvtColor(image, grey, cv.CV_RGBA2GRAY) #Convert to gray so act as a filtercv.ShowImage(\’Greyed\’, grey) # 平滑变换smoothed = cv.CloneImage(image)cv.Smooth(image,smoothed,cv.CV_MEDIAN) #Apply a smooth alogrithm with the specified algorithm cv.MEDIANcv.ShowImage(\”Smoothed\”, smoothed) # 均衡处理cv.EqualizeHist(grey, grey) #Work only on grayscaled picturescv.ShowImage(\’Equalized\’, grey) # 二值化处理threshold1 = cv.CloneImage(grey)cv.Threshold(threshold1,threshold1, 100, 255, cv.CV_THRESH_BINARY)cv.ShowImage(\”Threshold\”, threshold1) threshold2 = cv.CloneImage(grey)cv.Threshold(threshold2,threshold2, 100, 255, cv.CV_THRESH_OTSU)cv.ShowImage(\”Threshold 2\”, threshold2) element_shape = cv.CV_SHAPE_RECTpos=3element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)cv.Dilate(grey,grey,element,2) #Replace a pixel value with the maximum value of neighboors#There is others like Erode which replace take the lowest value of the neighborhood#Note: The Structuring element is optionnalcv.ShowImage(\”Dilated\”, grey) cv.WaitKey(0)

0x02. HighGUI

OpenCV 内建了一套简单的 GUI 工具，方便我们在处理界面上编写一些控件，动态的改变输出：

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import cv2.cv as cv im = cv.LoadImage(\”img/lena.jpg\”, cv.CV_LOAD_IMAGE_GRAYSCALE)thresholded = cv.CreateImage(cv.GetSize(im), 8, 1) def onChange(val): cv.Threshold(im, thresholded, val, 255, cv.CV_THRESH_BINARY) cv.ShowImage(\”Image\”, thresholded) # 创建一个滑动条控件onChange(100) #Call here otherwise at startup. Show nothing until we move the trackbarcv.CreateTrackbar(\”Thresh\”, \”Image\”, 100, 255, onChange) #Threshold value arbitrarily set to 100 cv.WaitKey(0)

0x03. 选区操作

有事希望对图像中某一块区域进行变换等操作，就可以使用如下方式：

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import cv2.cv as cv im = cv.LoadImage(\”img/lena.jpg\”,3) # 选择一块区域cv.SetImageROI(im, (50,50,150,150)) #Give the rectangle coordinate of the selected area # 变换操作cv.Zero(im)#cv.Set(im, cv.RGB(100, 100, 100)) put the image to a given value # 解除选区cv.ResetImageROI(im) # Reset the ROI cv.ShowImage(\”Image\”,im) cv.WaitKey(0)

0x04. 运算

对于多张图片，我们可以进行一些运算操作(包括算数运算和逻辑运算)，下面的代码将演示一些基本的运算操作：

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import cv2.cv as cv#or simply import cv im = cv.LoadImage(\”img/lena.jpg\”)im2 = cv.LoadImage(\”img/fruits-larger.jpg\”)cv.ShowImage(\”Image1\”, im)cv.ShowImage(\”Image2\”, im2) res = cv.CreateImage(cv.GetSize(im2), 8, 3) # 加cv.Add(im, im2, res) #Add every pixels together (black is 0 so low change and white overload anyway)cv.ShowImage(\”Add\”, res) # 减cv.AbsDiff(im, im2, res) # Like minus for each pixel im(i) – im2(i)cv.ShowImage(\”AbsDiff\”, res) # 乘cv.Mul(im, im2, res) #Multiplie each pixels (almost white)cv.ShowImage(\”Mult\”, res) # 除cv.Div(im, im2, res) #Values will be low so the image will likely to be almost blackcv.ShowImage(\”Div\”, res) # 与cv.And(im, im2, res) #Bit and for every pixelscv.ShowImage(\”And\”, res) # 或cv.Or(im, im2, res) # Bit or for every pixelscv.ShowImage(\”Or\”, res) # 非cv.Not(im, res) # Bit not of an imagecv.ShowImage(\”Not\”, res) # 异或cv.Xor(im, im2, res) #Bit Xorcv.ShowImage(\”Xor\”, res) # 乘方cv.Pow(im, res, 2) #Pow the each pixel with the given valuecv.ShowImage(\”Pow\”, res) # 最大值cv.Max(im, im2, res) #Maximum between two pixels#Same form Min MinScv.ShowImage(\”Max\”,res) cv.WaitKey(0)