58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
# 阈值邻域平滑滤波和均值滤波
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import matplotlib.pyplot as plt
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import cv2
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import numpy as np
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import random
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# from ImageAddNoise import *
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plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
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plt.rcParams['axes.unicode_minus']=False #用来正常显示负
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# 添加高斯噪声
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def addGaussianNoise(src,mu,sigma):
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NoiseImg=src.copy()
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NoiseImg=NoiseImg/NoiseImg.max()
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rows,cols=NoiseImg.shape[:2]
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for i in range(rows):
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for j in range(cols):
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#python里使用random.gauss函数加高斯噪声
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NoiseImg[i,j]=NoiseImg[i,j]+random.gauss(mu,sigma)
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# NoiseImg[i,j]=NoiseImg[i,j]+np.random.normal(mu,sigma)
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if NoiseImg[i,j]< 0:
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NoiseImg[i,j]=0
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elif NoiseImg[i,j]>1:
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NoiseImg[i,j]=1
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NoiseImg=np.uint8(NoiseImg*255)
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return NoiseImg
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img = cv2.imread(r"..\img\train1.jpg",0)
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row,col=img.shape
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ImgGuassNoise = addGaussianNoise(img,0,0.1) #添加0均值，0.2方差的高斯分布噪声
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imgAver=cv2.blur(ImgGuassNoise,(5,5))
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imgThresh=np.zeros((row,col))
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T=20
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for i in range(row):
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for j in range(col):
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if np.abs(ImgGuassNoise[i,j]-imgAver[i,j])>T:
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imgThresh[i,j]=imgAver[i,j]
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else:
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imgThresh[i,j]=ImgGuassNoise[i,j]
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plt.figure(figsize=(10,6))
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plt.subplot(221)
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plt.imshow(img,cmap='gray')
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plt.title("原图")
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plt.axis('off') #不显示坐标轴
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plt.subplot(222)
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plt.imshow(ImgGuassNoise,cmap='gray')
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plt.title("加高斯噪声图像")
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plt.axis('off') #不显示坐标轴
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plt.subplot(223)
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plt.imshow(imgAver,cmap='gray')
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plt.title("7x7均值滤波")
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plt.axis('off') #不显示坐标轴
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plt.subplot(224)
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plt.imshow(imgThresh,cmap='gray')
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plt.title("阈值邻域平滑滤波")
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plt.axis('off') #不显示坐标轴
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plt.show()
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plt.savefig("ch03-29-thresh.jpg") |