Implementation-of-filter

Aim:

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

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1

Import the required libraries.

Step2

Convert the image from BGR to RGB.

Step3

Apply the required filters for the image separately.

Step4

Plot the original and filtered image by using matplotlib.pyplot.

Step5

End the program.

Program:

1. Smoothing Filters

i) Using Averaging Filter

import cv2
import matplotlib.pyplot as plt
import numpy as np
image1=cv2.imread("taj.jpg")
image2=cv2.cvtColor(image1,cv2.COLOR_BGR2RGB)
kernel=np.ones((11,11),np.float32)/169
image3=cv2.filter2D(image2,-1,kernel)
plt.figure(figsize=(9,9))
plt.subplot(1,2,1)
plt.imshow(image2)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title("Average Filter Image")
plt.axis("off")
plt.show()

Output

ii) Using Weighted Averaging Filter

kernel1=np.array([[1,2,1],[2,4,2],[1,2,1]])/16
image3=cv2.filter2D(image2,-1,kernel1)
plt.imshow(image3)
plt.title("Weighted Average Filter Image")
plt.axis("off")
plt.show()

Output

iii) Using Gaussian Filter

gaussian_blur=cv2.GaussianBlur(image2,(33,33),0,0)
plt.imshow(gaussian_blur)
plt.title("Gaussian Blur")
plt.axis("off")
plt.show()

Output

iv)Using Median Filter

median=cv2.medianBlur(image2,13)
plt.title("Median Blur")
plt.axis("off")
plt.show()

Output

2. Sharpening Filters

i) Using Laplacian Linear Kernal

kernel2=np.array([[-1,-1,-1],[2,-2,1],[2,1,-1]])
image3=cv2.filter2D(image2,-1,kernel2)
plt.imshow(image3)
plt.title("Laplacian Kernel")
plt.axis("off")
plt.show()

Output

ii) Using Laplacian Operator

laplacian=cv2.Laplacian(image2,cv2.CV_64F)
plt.imshow(laplacian)
plt.title("Laplacian Operator")
plt.axis("off")
plt.show()

Output

Result:

Thus the filters are designed for smoothing and sharpening the images in the spatial domain.