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This article introduces the relevant knowledge of "how to use OpenCV and Python to achieve picture Gaussian blur". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Gaussian blur

Gaussian blur (English: Gaussian Blur) is usually used to reduce image noise and level of detail. The visual effect of the image generated by this blur technology is like observing the image through a translucent screen, which is obviously different from the out-of-focus imaging effect of the lens and the effect in the shadow of ordinary lighting. Gaussian blur is also used in the pre-processing stage of computer vision algorithms to enhance the image effect at different scale sizes (see scale space representation and scale space implementation). From a mathematical point of view, the Gaussian blur process of an image is the convolution of the image with the normal distribution. Because the normal distribution is also called Gaussian distribution, this technique is called Gaussian blur. Convolution of the image with a circular box blur will result in a more accurate out-of-focus imaging effect. Because the Fourier transform of Gaussian function is another Gaussian function, Gaussian blur is a low-pass filter for the image.

Gaussian blur principle: "blur" is the process of resetting the value of each pixel in an image by setting each pixel to the average of the surrounding pixels.

# Gaussian blur # Operation # cv2.GaussianBlur (image, (135,135), 0) # (5,5) indicates that the length and width of the Gaussian matrix (Gaussian kernel) are both 5 (must be odd), and the standard deviation is 0 import cv2import numpy as np def clamp (pv): # ensure that the value of the RGB trichromatic value does not exceed 255if pv > 255: return 255if pv

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