Created
December 12, 2020 04:37
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| import cv2 | |
| import numpy as np | |
| import pandas as pd | |
| from matplotlib import pyplot as plt | |
| from math import log10, sqrt | |
| img = cv2.imread('original_boat_img.gif') | |
| def PSNR(original, compressed): | |
| mse = np.mean((original - compressed) ** 2) | |
| if(mse == 0): # MSE is zero means no noise is present in the signal . | |
| # Therefore PSNR have no importance. | |
| return 100 | |
| max_pixel = 255.0 | |
| psnr = 20 * log10(max_pixel / sqrt(mse)) | |
| return psnr | |
| def sp_noise(image,prob): | |
| ''' | |
| Add salt and pepper noise to image | |
| prob: Probability of the noise | |
| ''' | |
| output = np.zeros(image.shape,np.uint8) | |
| thres = 1 - prob | |
| for i in range(image.shape[0]): | |
| for j in range(image.shape[1]): | |
| rdn = random.random() | |
| if rdn < prob: | |
| output[i][j] = 0 | |
| elif rdn > thres: | |
| output[i][j] = 255 | |
| else: | |
| output[i][j] = image[i][j] | |
| return output | |
| image = sp_noise(img,0.20) | |
| # apply the 3x3 mean filter on the image | |
| kernel = np.ones((3,3),np.float32)/9 | |
| processed_image = cv2.filter2D(image,-1,kernel) | |
| Y = np.square(np.subtract(image,processed_image)).mean() | |
| print("MSE:", Y) | |
| value = PSNR(image, processed_image) | |
| print("PSNR: " ,value) | |
| # display image | |
| cv2.imshow('Mean Filter Processing', processed_image) | |
| # save image to disk | |
| cv2.imwrite('processed_image.png', processed_image) | |
| # pause the execution of the script until a key on the keyboard is pressed | |
| cv2.waitKey(0) |
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