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 cv2, matplotlib.py libraries and read the saved images using cv2.imread().

Step2

Convert the saved BGR image to RGB using cvtColor().

Step3

By using the following filters for image smoothing:filter2D(src, ddepth, kernel), Box filter,Weighted Average filter,GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]), medianBlur(src, ksize),and for image sharpening:Laplacian Kernel,Laplacian Operator.

Step4

Apply the filters using cv2.filter2D() for each respective filters.

Step5

Plot the images of the original one and the filtered one using plt.figure() and cv2.imshow().

Program:

Developed By : K.M.Swetha
Register Number: 212221240055

import cv2
import numpy as np
import matplotlib.pyplot as plt
image = cv2.imread("kore.png")
original_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

## 1. Smoothing Filters
# i) Using Averaging Filter
kernel1 = np.ones((11,11),np.float32)/121
avg_filter = cv2.filter2D(original_image,-1,kernel1)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(avg_filter)
plt.title("Filtered")
plt.axis("off")

# ii) Using Weighted Averaging Filter
kernel2 = np.array([[1,2,1],[2,4,2],[1,2,1]])/16
weighted_filter = cv2.filter2D(original_image,-1,kernel2)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(weighted_filter)
plt.title("Filtered")
plt.axis("off")

# iii) Using Gaussian Filter
gaussian_blur = cv2.GaussianBlur(src = original_image, ksize = (11,11), sigmaX=0, sigmaY=0)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(gaussian_blur)
plt.title("Filtered")
plt.axis("off")

# iv) Using Median Filter
median = cv2.medianBlur(src=original_image,ksize = 11)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(median)
plt.title("Filtered")
plt.axis("off")

## 2. Sharpening Filters
# i) Using Laplacian Kernel
kernel3 = np.array([[0,1,0],[1,-4,1],[0,1,0]])
laplacian_kernel = cv2.filter2D(original_image,-1,kernel3)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(laplacian_kernel)
plt.title("Filtered")
plt.axis("off")

# ii) Using Laplacian Operator
laplacian_operator = cv2.Laplacian(original_image,cv2.CV_64F)
plt.figure(figsize = (9,9))
plt.subplot(1,2,1)
plt.imshow(original_image)
plt.title("Original")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(laplacian_operator)
plt.title("Filtered")
plt.axis("off")

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.

swethamohanraj / implementation-of-filter Goto Github PK