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 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.

Step2

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.

Step3

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.

Step4

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.

Step5

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.

Step6:

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 : Sri Varshan P

Register Number: 212222240104

1. Smoothing Filters

Import necessary Libraries

import cv2
import numpy as np
import matplotlib.pyplot as plt

(i) Using Averaging Filter

image1 = cv2.imread('chad.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

image1 = cv2.imread('chad.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

image1 = cv2.imread('chad.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

image1 = cv2.imread('chad.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

image1 = cv2.imread('chad.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

image1 = cv2.imread('chad.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:

1. Smoothing Filters

(i) Using Averaging Filter

(ii) Using Weighted Averaging Filter

(iii) Using Gaussian Filter

(iv) Using Median Filter

2. Sharpening Filters

(i) Using Laplacian Kernal

(ii) Using Laplacian Operator

Result:

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

psrivarshan / implementation-of-filter Goto Github PK

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 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.

Step2

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.

Step3

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.

Step4

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.

Step5

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.

Step6:

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 : Sri Varshan P

Register Number: 212222240104

1. Smoothing Filters

Import necessary Libraries

(i) Using Averaging Filter

(ii) Using Weighted Averaging Filter

(iii) Using Gaussian Filter

(iv) Using Median Filter

2. Sharpening Filters

(i) Using Laplacian Kernal

(ii) Using Laplacian Operator

OUTPUT:

1. Smoothing Filters

(i) Using Averaging Filter

(ii) Using Weighted Averaging Filter

(iii) Using Gaussian Filter

(iv) Using Median Filter

2. Sharpening Filters

(i) Using Laplacian Kernal

(ii) Using Laplacian Operator

Result:

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

implementation-of-filter's People

Contributors

Recommend Projects

Recommend Topics

Recommend Org