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import cv2.cv as cv

image=cv.LoadImage('img/lena.jpg', cv.CV_LOAD_IMAGE_COLOR) #Load the image

cv.ShowImage("Original", image)

grey = cv.CreateImage((image.width ,image.height),8,1) #8depth, 1 channel so grayscale

cv.CvtColor(image, grey, cv.CV_RGBA2GRAY) #Convert to gray so act as a filter

cv.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.MEDIAN

cv.ShowImage("Smoothed", smoothed)

# 均衡处理

cv.EqualizeHist(grey, grey) #Work only on grayscaled pictures

cv.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_RECT

pos=3

element = 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 optionnal

cv.ShowImage("Dilated", grey)

cv.WaitKey(0)

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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 trackbar

cv.CreateTrackbar("Thresh", "Image", 100, 255, onChange) #Threshold value arbitrarily set to 100

cv.WaitKey(0)

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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)

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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 black

cv.ShowImage("Div", res)

# 与

cv.And(im, im2, res) #Bit and for every pixels

cv.ShowImage("And", res)

# 或

cv.Or(im, im2, res) # Bit or for every pixels

cv.ShowImage("Or", res)

# 非

cv.Not(im, res) # Bit not of an image

cv.ShowImage("Not", res)

# 异或

cv.Xor(im, im2, res) #Bit Xor

cv.ShowImage("Xor", res)

# 乘方

cv.Pow(im, res, 2) #Pow the each pixel with the given value

cv.ShowImage("Pow", res)

# 最大值

cv.Max(im, im2, res) #Maximum between two pixels

#Same form Min MinS

cv.ShowImage("Max",res)

cv.WaitKey(0)