Implementation-of-Filters

Aim:

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

Software Required:

Anaconda - Python 3.7

Algorithm:

Step 1:

Import All The Necessary Modules.

Step 2:

Using Averaging Filter Smoothen the given image.

Step 3:

Using the Weighted Averaging Filter Smoothen the given Image.

Step 4:

Using the Gaussian Filter Smoothen the given Image.

Step 5:

Using the Median Filter Smoothen the given Image.

Step 6:

Using the Laplacian Kernel Filter Sharpen the given Image.

Step 7:

Using the Laplacian Operator Filter Sharpen the given Image.

Program:

Developed By : Jeswanth S

Register Number: 212221230042

1. Smoothing Filters

i) Using Averaging Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
kernel = np.ones ((11,11), np.float32)/121
image3=cv2.filter2D(image2,-1, kernel)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

ii) Using Weighted Averaging Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
kernal2 = np.array([[1,2,1],[2,4,2],[1,2,1]])/16 
image3 = cv2.filter2D(image2,-1,kernal2)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

iii) Using Gaussian Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
gaussian_blur=cv2.GaussianBlur(src=image2,ksize=(11,11),sigmaX=0,sigmaY=0)
image3 = cv2.filter2D(image2,-1,kernal2)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

iv) Using Median Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
median=cv2.medianBlur(src=image2, ksize=11)
image3 = cv2.filter2D(image2,-1,kernal2)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

2. Sharpening Filters

i) Using Laplacian Kernel

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
kernel3=np.array([[0,1,0],[1,-4,1],[0,1,0]])
image3=cv2.filter2D(image2,-1, kernel3)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

ii) Using Laplacian Operator

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1=cv2.imread('amaze.jgp')
image2=cv2.cvtColor (image1,cv2.COLOR_BGR2RGB) 
new_image = cv2.Laplacian(image2, cv2.CV_64F)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1) 
plt.imshow(image2)
plt.title('Orignal') 
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(image3)
plt.title('Filtered')
plt.axis('off')

OUTPUT: