【无标题】

import cv2import numpy as npfrom PIL import Imageimport matplotlib.pyplot as plt def medianfliter(a, windowsize): output = a if windowsize == 3 : output1 = np.zeros(a.shape, np.uint8) for i in range(1, output.shape[0]-1): # 求齐周围9个方格与模版进行冒泡排序 for j in range(1, output.shape[1]-1): value1 = [output[i-1][j-1], output[i-1][j], output[i-1][j+1], output[i][j-1], output[i][j], output[i][j+1], output[i+1][j-1], output[i+1][j], +output[i+1][j+1]] np.sort(value1) # 对这九个数进行排序 value = value1[4] # 中值为排序后中间这个数的正中间 output1[i-1][j-1] = value return output1def Gaussian_noise(img, mean=0, std=0.05): image = np.array(img/255, dtype=float) noise = np.random.normal(mean, std ** 0.5, image.shape) out = image + noise if out.min() < 0: low_clip = -1. else: low_clip = 0. out = np.clip(out, low_clip, 1.0) out = np.uint8(out*255) return outdef gaussian(im): im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) b = np.array([[2, 4, 5, 2, 2], [4, 9, 12, 9, 4], [5, 12, 15, 12, 5], [4, 9, 12, 9, 4], [2, 4, 5, 4, 2]]) / 156 kernel = np.zeros(im.shape) kernel[:b.shape[0], :b.shape[1]] = b fim = np.fft.fft2(im) fkernel = np.fft.fft2(kernel) fil_im = np.fft.ifft2(fim * fkernel) return abs(fil_im).astype(int)img = Image.open(r’C:\Users\Administrator\Desktop\a.jpg’)img = np.array(img)img_noise = Gaussian_noise(img)cv2.imshow(‘noise’,img_noise)cv2.waitKey(0)noise = medianfliter(img,3)cv2.imshow(‘noise’,noise)cv2.waitKey(0)noise1 = gaussian(noise)cv2.imshow(‘noise1’,noise1)cv2.waitKey(0)

import cv2
import numpy as np
def random_noise(image,noise_num):
    # 参数image：，noise_num：
    img = cv2.imread(image)
    img_noise = img
    # cv2.imshow(“src”, img)
    rows, cols, chn = img_noise.shape
    # 加噪声
    for i in range(noise_num):
        x = np.random.randint(0, rows)#随机生成指定范围的整数
        y = np.random.randint(0, cols)
        img_noise[x, y, :] = 255
    return img_noise
img_noise = random_noise(r’C:\Users\Administrator\Desktop\a.jpg’,3000)
cv2.imshow(‘noise’,img_noise)
cv2.waitKey(0)