EX.No: 6

Implementation-of-Filters

Aim:

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

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1

Import the necessary modules.

Step2

For performing smoothing operation on a image.

Average filter

avg_kernel=np.ones((13,13),np.float32)/169
average_filter_image=cv2.filter2D(image,-1,avg_kernel)

Weighted average filter

wt_avg_kernel=np.array([[1,2,1],[2,4,2],[1,2,1]])/16
wt_average_filter_image=cv2.filter2D(image,-1,wt_avg_kernel)

Gaussian Blur

gaussian_blur=cv2.GaussianBlur(image,(31,31),0,0)
Median filter
median_blur=cv2.medianBlur(image,11)

Step3

For performing sharpening on a image.

Laplacian Kernel

lap_kernel=np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]])
lap_image=cv2.filter2D(image,-1,lap_kernel)

Laplacian Operator

Lap_sharp=cv2.Laplacian(image,cv2.CV_64F)

Step4

Display all the images with their respective filters.

Program:

 Developed By   :DurgaDevi.P
 Register Number:212220230015

### 1. Smoothing Filters

i) Using Averaging Filter

average_kernel=np.ones((13,13),np.float32)/169
average_filter_image=cv2.filter2D(image,-1,avg_kernel)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image)
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Average Filter image')
plt.imshow(average_filter_image)
plt.show()

ii) Using Weighted Averaging Filter

weight_average_kernel=np.array([[1,2,1],[2,4,2],[1,2,1]])/16
weight_average_filter_image=cv2.filter2D(image,-1,weight_average_kernel)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image[30:200,50:200])
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Weighted average Filter image')
plt.imshow(weight_average_filter_image[30:200,50:200])
plt.show()

iii) Using Gaussian Filter

gaussian_blur=cv2.GaussianBlur(image,(31,31),0,0)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image)
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Gaussian Filter image')
plt.imshow(gaussian_blur)
plt.show()

iv) Using Median Filter

median_blur=cv2.medianBlur(image,11)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image)
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Median Filter image')
plt.imshow(median_blur)
plt.show()

2. Sharpening Filters

i) Using Laplacian Kernal

laplacian_kernel=np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]])
laplacian_image=cv2.filter2D(image,-1,laplacian_kernel)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image)
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Laplacian Kernel Filter image')
plt.imshow(laplacian_image)
plt.show()

ii) Using Laplacian Operator

Laplacian_sharp=cv2.Laplacian(image,cv2.CV_64F)
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.axis("off")
plt.title('Original image')
plt.imshow(image)
plt.subplot(1,2,2)
plt.axis("off")
plt.title('Laplacian Operator Filter image')
plt.imshow(Laplacian_sharp)
plt.show()