IMPLEMENTATION-OF-FILTERSS

Aim:

To implement filters for smoothing and sharpening the images in the spatial domain.

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1

Read and show the image

Step2

Apply the filtering technique that we want to perform

Step3

Show the filtered image

Program:

Developed By : Aakash S

Register Number: 212221240001

1. Smoothing Filters

i) Using Averaging Filter

import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel1 = np.ones((11,11),np.float32)/121
box_filter = cv2.filter2D(original_image,-1,kernel1)
cv2.imshow('box_filter',box_filter)
cv2.waitKey(0)
cv2.destroyAllWindows()

ii) Using Weighted Averaging Filter

#ii) Using Weighted Averaging Filter
import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel2 = np.array([[1,2,1],[2,4,2],[1,2,1]])/16
weighted_filter = cv2.filter2D(original_image,-1,kernel2)
cv2.imshow('weighted_filter',weighted_filter)
cv2.waitKey(0)
cv2.destroyAllWindows()

iii) Using Gaussian Filter

#iii) Using Gaussian Filter
import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
gaussian_blur = cv2.GaussianBlur(src = original_image, ksize = (11,11), sigmaX=0,
sigmaY=0)
cv2.imshow('gaussian_filter',gaussian_blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

iv) Using Median Filter

#iv) Using Median Filter
import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
median = cv2.medianBlur(src=original_image,ksize = 11)
cv2.imshow('median_filter',median)
cv2.waitKey(0)
cv2.destroyAllWindows()

2. Sharpening Filters

i) Using Laplacian Kernal

#i) Using Laplacian Kernal
import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel3 = np.array([[0,1,0],[1,-4,1],[0,1,0]])
laplacian_kernel = cv2.filter2D(original_image,-1,kernel3)
cv2.imshow('laplacian_kernel',laplacian_kernel)
cv2.waitKey(0)
cv2.destroyAllWindows()

ii) Using Laplacian Operator

#ii) Using Laplacian Operator
import cv2
import numpy as np
image = cv2.imread("cat.jpg")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow('original',original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
laplacian_operator = cv2.Laplacian(original_image,cv2.CV_64F)
cv2.imshow('laplacian_operator',laplacian_operator)
cv2.waitKey(0)
cv2.destroyAllWindows()