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小编给大家分享一下Python怎么实现中值滤波、均值滤波,相信大部分人都还不怎么了解,因此分享这篇文章给大家参考一下,希望大家阅读完这篇文章后大有收获,下面让我们一起去了解一下吧!
python常用的库:1.requesuts;2.scrapy;3.pillow;4.twisted;5.numpy;6.matplotlib;7.pygama;8.ipyhton等。
红包:
Lena椒盐噪声图片:
# -*- coding: utf-8 -*- """ Created on Sat Oct 14 22:16:47 2017 @author: Don """ from tkinter import * from skimage import io import numpy as np im=io.imread('lena_sp.jpg', as_grey=True) im_copy_med = io.imread('lena_sp.jpg', as_grey=True) im_copy_mea = io.imread('lena_sp.jpg', as_grey=True) #io.imshow(im) for i in range(0,im.shape[0]): for j in range(0,im.shape[1]): im_copy_med[i][j]=im[i][j] im_copy_mea[i][j]=im[i][j] #ui root = Tk() root.title("lena") root.geometry('300x200') medL = Label(root, text="中值滤波:") medL.pack() med_text = StringVar() med = Entry(root, textvariable = med_text) med_text.set("") med.pack() meaL = Label(root, text="均值滤波:") meaL.pack() mea_text = StringVar() mea = Entry(root, textvariable = mea_text) mea_text.set("") mea.pack() def m_filter(x, y, step): sum_s=[] for k in range(-int(step/2),int(step/2)+1): for m in range(-int(step/2),int(step/2)+1): sum_s.append(im[x+k][y+m]) sum_s.sort() return sum_s[(int(step*step/2)+1)] def mean_filter(x, y, step): sum_s = 0 for k in range(-int(step/2),int(step/2)+1): for m in range(-int(step/2),int(step/2)+1): sum_s += im[x+k][y+m] / (step*step) return sum_s def on_click(): if(med_text): medStep = int(med_text.get()) for i in range(int(medStep/2),im.shape[0]-int(medStep/2)): for j in range(int(medStep/2),im.shape[1]-int(medStep/2)): im_copy_med[i][j] = m_filter(i, j, medStep) if(mea_text): meaStep = int(mea_text.get()) for i in range(int(meaStep/2),im.shape[0]-int(meaStep/2)): for j in range(int(meaStep/2),im.shape[1]-int(meaStep/2)): im_copy_mea[i][j] = mean_filter(i, j, meaStep) io.imshow(im_copy_med) io.imsave(str(medStep) + 'med.jpg', im_copy_med) io.imshow(im_copy_mea) io.imsave(str(meaStep) + 'mea.jpg', im_copy_mea) Button(root, text="filterGo", command = on_click).pack() root.mainloop()
运行结果截图:
以上是“Python怎么实现中值滤波、均值滤波”这篇文章的所有内容,感谢各位的阅读!相信大家都有了一定的了解,希望分享的内容对大家有所帮助,如果还想学习更多知识,欢迎关注亿速云行业资讯频道!
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