python-opencv 中值滤波{cv2.medianBlur(src, ksize)}的用法

#用中值法
for y in xrange(1,myh-1):
    for x in xrange(1,myw-1):
        lbimg[y,x]=np.median(tmpimg[y-1:y+2,x-1:x+2]

# -*- coding: utf-8 -*-   
#code:myhaspl@myhaspl.com
#中值滤波
import cv2
import numpy as np
fn="test3.jpg"
myimg=cv2.imread(fn)
img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)
#加上椒盐噪声
#灰阶范围
w=img.shape[1]
h=img.shape[0]
newimg=np.array(img)
#噪声点数量
noisecount=50000
for k in xrange(0,noisecount):
    xi=int(np.random.unifORM(0,newimg.shape[1]))
    xj=int(np.random.uniform(0,newimg.shape[0]))
    newimg[xj,xi]=255
#滤波去噪
lbimg=cv2.medianBlur(newimg,3)
cv2.imshow('src',newimg)
cv2.imshow('dst',lbimg)
cv2.waiTKEy()
cv2.destroyAllwindows()       

>>> import random
>>> import numpy as np
>>> import scipy.signal as signal
>>> x=np.arange(0,100,10)
>>> random.shuffle(x)
>>> x
array([70, 80, 30, 20, 10, 90,  0, 60, 40, 50])
>>> signal.medfilt(x,3) #一维中值滤波
array([ 70.,  70.,  30.,  20.,  20.,  10.,  60.,  40.,  50.,  40.])

>>> x=np.random.randint(1,1000,(4,4))
>>> x
array([[ 31,  33, 745, 483],
       [331, 469, 804, 479],
       [235, 487, 244, 982],
       [857, 114, 167, 174]])
>>> signal.medfilt(x,(3,3)) #二维中值滤波
array([[   0.,   33.,  469.,    0.],
       [  33.,  331.,  483.,  479.],
       [ 235.,  331.,  469.,  174.],
       [   0.,  167.,  167.,    0.]])

import numpy as np
from PIL import Image
import scipy.signal as signal
im=Image.open('test.jpg') #读入图片并建立Image对象im
data=[] #存储图像中所有像素值的list(二维)
width,height=im.size #将图片尺寸记录下来
#读取图像像素的值
for h in range(height): #对每个行号h
    row=[] #记录每一行像素
    for w in range(width): #对每行的每个像素列位置w
        value=im.getpixel((h,w)) #用getpixel读取这一点像素值
        row.append(value)#把它加到这一行的list中去
    data.append(row) #把记录好的每一行加到data的子list中去，就建立了模拟的二维list
data=signal.medfilt(data,kernel_size=3) #二维中值滤波
data=np.int32(data) #转换为int类型，以使用快速二维滤波
#创建并保存结果
for h in range(height): #对每一行
    for w in range(width): #对该行的每一个列号
        im.putpixel((h,w),tuple(data[h][w])) #将data中该位置的值存进图像,要求参数为tuple
im.save('result.jpg')#存储

c++: void medianBlur(InputArray src, OutputArray dst, int ksize)

#include <iOStream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
using namespace std;
using namespace cv;
//定义全局变量
Mat g_mSrcImage;
Mat g_mDstImage;
const int g_nMedianBlurMaxValue = 5;
int g_nMedianBlurValue;
int g_nkernelSize;
//定义回调函数
void on_medianBlurTrackBar(int, void*);
int main()
{
    g_mSrcImage = imread("lena.jpg");
    //判断文件是否加载成功
    if(g_mSrcImage.empty())
    {
        cout << "图像加载失败!" << endl;
        return -1;
    }
    else
        cout << "图像加载成功!" << endl;
    //判断图像是否是CV_8U图像
    if(0 <= g_mSrcImage.depth() <= 255)
        cout << "加载图像符合处理要求!" << endl;
    else
    {
        cout << "图像深度不是CV_8U，程序即将退出..." << endl;
        return -1;
    }
    namedWindow("原图像", WINDOW_AUTOSIZE);
    imshow("原图像", g_mSrcImage);
    //输出图像窗口属性及轨迹条名称
    namedWindow("中值滤波图像", WINDOW_AUTOSIZE);
    char medianBlurName[20];
    sprintf(medianBlurName, "核函数尺寸 %d", g_nMedianBlurMaxValue);
    g_nMedianBlurValue = 1;
    //创建轨迹条
    createTrackbar(medianBlurName, "中值滤波图像", &g_nMedianBlurValue,
                    g_nMedianBlurMaxValue, on_medianBlurTrackBar);
    on_medianBlurTrackBar(g_nMedianBlurValue, 0);
    waitKey(0);
    return 0;
}
void on_medianBlurTrackBar(int, void*)
{
    //重新计算尺寸值，尺寸值应为大于1的奇数
    g_nkernelSize = g_nMedianBlurValue * 2 + 1;
    medianBlur(g_mSrcImage, g_mDstImage, g_nkernelSize);
    imshow("中值滤波图像", g_mDstImage);
}

    CV_8U - 8-bit unsigned integers ( 0..255 )
    CV_8S - 8-bit signed integers ( -128..127 )
    CV_16U - 16-bit unsigned integers ( 0..65535 )
    CV_16S - 16-bit signed integers ( -32768..32767 )
    CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
    CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
    CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )