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Beginning with OpenCV in Python

· Web Dev Zone

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OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision [Ref]. In this post we will see how to use some of the basic functions of OpenCV in Python.

The following code opens an image from the disk, prints some image properties on the console and shows a window that contains the image.
# load and show an image in gray scale
image = cv.LoadImage('ariellek.jpg',cv.CV_LOAD_IMAGE_GRAYSCALE)

# print some image properties
print 'Depth:',image.depth,'# Channels:',image.nChannels
print 'Size:',image.width,image.height
print 'Pixel values average',cv.Avg(image)

# create the window
cv.NamedWindow('my window', cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage('my window', image) # show the image
cv.WaitKey() # the window will be closed with a (any)key press
This is the image I used for this example.
And this is what the script showed on the console:
Depth: 8 # Channels: 1
Size: 366 550
Pixel values average (80.46735717834079, 0.0, 0.0, 0.0)
Now we can resize the image loaded above:
# resize the image
dst = cv.CreateImage((150,150), 8, 1)
cv.ShowImage('my window', dst)
cv.SaveImage('image2.jpg', dst) # save the image
And this is the result.
A Sobel operator can be applied as follow:
# Sobel operator
dstSobel = cv.CreateMat(image.height, image.width, cv.CV_32FC1)
cv.ShowImage('my window', dstSobel)
cv.SaveImage('imageSobel.jpg', dstSobel)
And this is the result on the picture that I'm using:
The final example below uses two operation, a smoothing filter and a subtraction. It applies a Gaussian Blur to the original image and subtracts the result of the filtering from the original image.
# image smoothing and subtraction
imageBlur = cv.CreateImage(cv.GetSize(image), image.depth, image.nChannels)
# filering the original image
cv.Smooth(image, imageBlur, cv.CV_BLUR, 15, 15)
diff = cv.CreateImage(cv.GetSize(image), image.depth, image.nChannels)
# subtraction (original - filtered)
cv.ShowImage('my window', diff)
cv.SaveImage('imageDiff.jpg', diff)
The final output is:

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