OpenCV and Image Processing - Blur Image (Gaussian Blur)

Now we add the Gaussian blur function to the ImageProcessing class.

1 def blur(self, ksize=(1,1), image=None):

2 if image is None:

3 image = self.image

4 if ksize[0] % 2 == 0:

5 ksize = (ksize[0] + 1, ksize[1])

6 if ksize[1] % 2 == 0:

7 ksize = (ksize[0], ksize[1] + 1)

8 result = cv2.GaussianBlur(image,ksize, cv2.BORDER_DEFAULT)

9 return result 

Explanations:

Line 1 Define the blur() function, pass ksize as parameter.

Line 4 – 7 ksize must be odd numbers, check ksize if not odd then change it to odd.

Line 8 Invoke cv2.GaussianBlur() function.

BlurImage.py file has the source codes to perform the Gaussian blur, a trackbar is added to change the kernel size, by changing the kernel size we can observe how the blurring effects are different. In this example a square kernel is used, meaning the width and height are equal. Feel free to modify the codes to use a rectangle kernel and observe the blurring effects.

1 import cv2

2 import common.ImageProcessing as ip

3

4 def change_ksize(value):

5 global ksize

6 if value % 2 == 0:

7 ksize = (value+1, value+1)

8 else:

9 ksize = (value, value)

10

11 if __name__ == “__main__”:

12 global ksize

13 ksize = (5,5)

14 iproc = ip.ImageProcessing(“Original”,

“../res/flower003.jpg”)

15 iproc.show()

16

17 cv2.namedWindow(“Gaussian Blur”)

18 cv2.createTrackbar(“K-Size”, “Blur”, 5, 21,

change_ksize)

19

20 while True:

21 blur = iproc.blur(ksize)

22 iproc.show(“Blur”, blur)

23

24 ch = cv2.waitKey(10)

25 # Press ‘ESC’ to exit

if (ch & 0xFF) == 27:

26 break

27 # Press ‘s’ to save

elif ch == ord(‘s’):

28 filepath = “C:/temp/blend_image.png”

29 cv2.imwrite(filepath, blur)

30 print(“File saved to “ + filepath)

31 cv2.destroyAllWindows()

Explanations:

Line 4 – 9 Trackbar callback function, get kernel size from trackbar, and change it to odd number if it’s not.

Line 14 – 15 Instantiate the ImageProcessing class with an image and show it as original image.

Line 17 – 18 Create a trackbar in another window called “Gaussian Blur”.

Line 21 Call blur() function defined above in ImageProcessing class, pass the kernel size as a parameter.

Line 22 Show the blurred image in the “Gaussian Blur” window.

Figure 4 is the result, as the K-Size trackbar is changing, the degree of blurring is also changing in real-time.