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28 lines
1.4 KiB
Python
28 lines
1.4 KiB
Python
import cv2 as cv
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img = cv.imread('Photos/cats.jpg') # apply a blurring method to smooth out the image or reduce some of the noise
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cv.imshow('Cats', img)
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# kernel (or window) = a "window" you draw over an image
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# the size of the window is called "kernel size" (# of rows and # of columns)
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# blur is applied to the middle pixel as a result of the pixels around it (surrounding pixels)
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# Averaging (the window will compute the pixel intesity of the middle pixel of the true center as the average of the surrounding pixel intensities)
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average = cv.blur(img, (3,3)) #increase kernel size to increase blur
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cv.imshow('Average Blur', average)
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# Gaussian Blur (each surrounding pixel is given a weight, and the average of the products of those weights give you the value for the true center)
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# less blur, but more natural than averaging
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gauss = cv.GaussianBlur(img, (3,3), 0)
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cv.imshow('Gaussian Blur', gauss)
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# Median Blur (same as averaging, except it finds the median of the surrounding pixels instead of the average)
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# more effective in reducing noise in an image compared to Averaging and Gaussian Blur
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median = cv.medianBlur(img, 3) # openCV automatically assumes that this kernel size will be a 3 by 3 just by the integer
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cv.imshow('Median Blur', median) # not meant for high kernel sizes like 7
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# Bilateral Blurring (applies blurring but retains the edges in the image)
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bilateral = cv.bilateralFilter(img, 10, 35, 25)
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cv.imshow('Bilateral', bilateral)
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cv.waitKey(0) |