Understanding Gaussian Blur using OpenCV in Python
In this tutorial, we will see:
- What is Gaussian blur?
- How can we apply gaussian blur to our images in Python using OpenCV?
Gaussian Blur is a smoothening technique which is used to reduce noise in an image. Noise in digital images is a random variation of brightness or colour information. This degradation is caused by external sources.
In Gaussian Blur, a gaussian filter is used instead of a box filter. In Python, we can use GaussianBlur() function of the open cv library for this purpose.
To work with open cv, import open cv using:
import cv2
Syntax of GaussianBlur() function in OpenCV – Python
cv2.GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]])
where,
src: Source image
dst: Output image of same size and type of source image
ksize: Size of Gaussian kernel. It should be odd and positive
sigmaX: Gaussian kernel standard deviation in x direction
sigmaY: Gaussian kernel standard deviation in y direction. If sigmaY=0, it is set equal to sigmaX
borderType: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_REFLECT_101, cv2.BORDER_TRANSPARENT, cv2.BORDER_REFLECT101, cv2.BORDER_DEFAULT, cv2.BORDER_ISOLATED
Example 1
#Gaussian Blur using opencv import cv2 #loading source image img=cv2.imread("unnamed.jpg") #showing source image cv2.imshow("SOURCE IMAGE",img) #applying gaussian blur gaus=cv2.GaussianBlur(img,(5,5),0) #image after gaussian blur cv2.imshow("AFTER GAUSSIAN BLUR", gaus) cv2.waitKey(0) cv2.destroyAllWindows()
OUTPUT
Example 2
#Gaussian Blur using open cv import cv2 #loading source image img=cv2.imread("unnamed.jpg") #showing source image cv2.imshow("SOURCE IMAGE",img) #applying gaussian blur gaus=cv2.GaussianBlur(img,(21,21),0) #image after gaussian blur cv2.imshow("AFTER GAUSSIAN BLUR", gaus) cv2.waitKey(0) cv2.destroyAllWindows()
OUTPUT
Hope you liked this tutorial!
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