Implementation-of-filter

Aim:

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

Software Required:

Anaconda - Python 3.7

Algorithm:

Step 1:

Import necessary libraries: OpenCV, NumPy, and Matplotlib.Read an image, convert it to RGB format, define an 11x11 averaging kernel, and apply 2D convolution filtering.Display the original and filtered images side by side using Matplotlib.

Step 2:

Define a weighted averaging kernel (kernel2) and apply 2D convolution filtering to the RGB image (image2).Display the resulting filtered image (image4) titled 'Weighted Averaging Filtered' using Matplotlib's imshow function.

Step 3:

Apply Gaussian blur with a kernel size of 11x11 and standard deviation of 0 to the RGB image (image2).Display the resulting Gaussian-blurred image (gaussian_blur) titled 'Gaussian Blurring Filtered' using Matplotlib's imshow function.

Step 4:

Apply median blur with a kernel size of 11x11 to the RGB image (image2).Display the resulting median-blurred image (median) titled 'Median Blurring Filtered' using Matplotlib's imshow function.

Step 5 :

Define a Laplacian kernel (kernel3) and perform 2D convolution filtering on the RGB image (image2).Display the resulting filtered image (image5) titled 'Laplacian Kernel' using Matplotlib's imshow function.

Step 6 :

Apply the Laplacian operator to the RGB image (image2) using OpenCV's cv2.Laplacian function.Display the resulting image (new_image) titled 'Laplacian Operator' using Matplotlib's imshow function.

Program:

Developed By : Karthick P
Register Number: 212222100021

1) SMOOTHING FILTERS

i) Using Averaging Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1 = cv2.imread('dip 5.jpg')
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('dip 5.jpg')
image2 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

kernel2 = np.array([[1,2,1],[2,4,2],[1,2,1]])/16
image4 = cv2.filter2D(image2, -1, kernel2)
plt.imshow(image4)
plt.title('Weighted Averaging Filtered')

iii) Using Gaussian Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1 = cv2.imread('dip 5.jpg')
image2 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

gaussian_blur = cv2.GaussianBlur(src=image2, ksize=(11,11), sigmaX=0, sigmaY=0)
plt.imshow(gaussian_blur)
plt.title(' Gaussian Blurring Filtered')

iv) Using Median Filter

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1 = cv2.imread('dip 5.jpg')
image2 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

median=cv2.medianBlur (src=image2, ksize=11)
plt.imshow(median)
plt.title(' Median Blurring Filtered')

2) SHARPENING FILTERS

i) Using Laplacian Kernal

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1 = cv2.imread('dip 5.jpg')
image2 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

kernel3 = np.array([[0,1,0], [1, -4,1],[0,1,0]])
image5 =cv2.filter2D(image2, -1, kernel3)
plt.imshow(image5)
plt.title('Laplacian Kernel')

ii) Using Laplacian Operator

import cv2
import numpy as np
import matplotlib.pyplot as plt
image1 = cv2.imread('dip 5.jpg')
image2 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

new_image = cv2.Laplacian (image2, cv2.CV_64F)
plt.imshow(new_image)
plt.title('Laplacian Operator')

OUTPUT: