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implementation-of-filter's Introduction

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:

Developed By : Divakar R

Register Number: 212222240026

1. Smoothing Filters

i) Using Averaging Filter

import cv2
import matplotlib.pyplot as plt
import numpy as np
image1=cv2.imread("cad.jpeg")
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()


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.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("Weighted Average Filter Image")
plt.axis("off")
plt.show()

iii) Using Gaussian Filter

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

iv) Using Median Filter

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

2. Sharpening Filters

i) Using Laplacian Kernal

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

ii) Using Laplacian Operator

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

OUTPUT:

1. Smoothing Filters

i) Using Averaging Filter 313991337-f458aacd-377e-44f7-80a6-595102f3ef02

ii) Using Weighted Averaging Filter 313991363-98df351b-196a-49ce-9db8-55b48b694181

iii) Using Gaussian Filter 313991408-4f15808c-ec5f-4e8e-b36a-7ff37d373029

iv) Using Median Filter 313991454-22fd1fec-1e5c-4701-86e0-f4d1c516857d

2. Sharpening Filters

i) Using Laplacian Kernal

313991498-757802db-014a-47a0-bfc3-660d422f22c8

ii) Using Laplacian Operator

313991522-21b6646d-215c-4abe-9401-8574e4506a20

Result:

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

implementation-of-filter's People

Contributors

swedha333 avatar divakar618 avatar

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