标签：兴趣 blur mile 处理 images 自己实现 cto The https
今天闲着无聊，实现了下《数字图像处理（第三版）》P209页的，自适应中值滤波器。
原理书上都有，我自己实现的代码可能有点复杂。对图片的边缘处理有些粗糙。有兴趣可以自己改进下。
看下实验效果吧！
左边第一幅图片是原始图片
中间的是对全图 加上了0.25 比例的椒盐噪声，可以看出来，几乎已经看不出来原始模样了。
右边的是经过自适应中值滤波处理后的图片。
逐像素的处理的额，速度有点慢。边缘简单的处理了，可以改进。
这段代码地址：https://github.com/cyssmile/openCV_learning_notes/blob/master/opencv_test/opencv_023/opencv_023.cpp
#include<opencv2/opencv.hpp>
#include<iostream>
#include<vector>
#include<cmath>
using namespace std;
using namespace cv;
void addSoaltAndPepperNoise(Mat &images, int numberOfNoise);
void adaptiveMedianBlur(Mat &images);
int getBordValue(int Length, int step, int kernelSize);
void autoCopyMakeBorder(Mat &images, int borderType, int step, int kernelSize);
void getMinMaxSplitChannel(Mat &images, double &min_val, double &max_val);
void getMedianSplitChannel(Mat &images, double &median_val);
double process_B(double Z_xy, double min_val, double max_val, double median_val);
double dealSplitSubImages(Mat &split_images,int &S_now);
void dealMainSplitImages(Mat &split_images, Mat &split_images_clone);
void dealDstEdges(Mat &src, int edges);
int main(int argc, char** argv)
{
Mat src = imread("D:/images/test.jpg", -1);
if (src.empty())
{
cout << " can`t open this ph" << endl;
}
imshow("src_win", src);
Mat images_src = src.clone();
/*
* 添加全图0.25比例的椒盐噪声
*/
int row = images_src.rows;
int col = images_src.cols;
addSoaltAndPepperNoise(images_src, row*col*0.25);
adaptiveMedianBlur(images_src);
waitKey(0);
destroyAllWindows();
return 0;
}
/*
* add salt and pepper noise into source images
* cyssmile
* 2020/3/15
*/
void addSoaltAndPepperNoise(Mat &images, int numberOfNoise)
{
RNG rng(12345);
int row = images.rows;
int col = images.cols;
for (int i = 0; i < numberOfNoise; i++)
{
int x = rng.uniform(0, row);
int y = rng.uniform(0, col);
if (i % 2 == 0)
{
images.at<Vec3b>(x, y) = Vec3b(255, 255, 255);
}
else
{
images.at<Vec3b>(x, y) = Vec3b(0, 0, 0);
}
}
imshow("salt and pepper noise", images);
}
/*
* get the value of copyMakeBorder
* cyssmile
* 2020/3/19
*/
int getBordValue(int Length, int step, int kernelSize)
{
return (Length*(step - 1) - step + kernelSize) / 2;
}
/*
* auto fill picture
* cyssmile
* 2020/3/19
*/
void autoCopyMakeBorder(Mat &images, int borderType, int step, int kernelSize)
{
int row = images.rows;
int col = images.cols;
int hightValue = getBordValue(row, step, kernelSize);
int widthValue = getBordValue(col, step, kernelSize);
copyMakeBorder(images, images, hightValue, hightValue, widthValue, widthValue, borderType);
}
/*
* adaptive median filtering
* Anchor Point at top left corner
* S_Min =3,S_Max=5
* cyssmile
* 2020/3/19
*/
void adaptiveMedianBlur(Mat &images)
{
int S_Max = 5;
int S_Min = 3;
int row = images.rows;
int col = images.cols;
Mat images_clone = images.clone();
autoCopyMakeBorder(images, BORDER_DEFAULT, 1, S_Min);
vector<Mat> sub_images;
split(images,sub_images);
vector<Mat> sub_images_clone;
split(images_clone, sub_images_clone);
for (int i=0;i<images.channels();i++)
{
dealMainSplitImages(sub_images[i], sub_images_clone[i]);
}
Mat dst;
merge(sub_images_clone,dst);
dealDstEdges(dst, S_Max);
imshow("dst_output", dst);
}
/*
* get the min and max value in images
* cyssmile
* 2020/03/19
*/
void getMinMaxSplitChannel(Mat &images,double &min_val,double &max_val)
{
Point minloc, maxloc;
minMaxLoc(images, &min_val, &max_val, &minloc, &maxloc);
}
/*
* get the median value in images(roi)
* cyssmile
* 2020/03/19
*/
void getMedianSplitChannel(Mat &images,double &median_val)
{
vector<double> images_data;
for (int i =0;i<images.rows;i++)
{
for (int j = 0; j < images.cols; j++)
{
images_data.push_back(images.at<uchar>(i, j));
}
}
sort(images_data.begin(),images_data.end());
median_val = images_data[images.rows*images.cols/2];
}
/*
* process_B
* cyssmile
* 2020/03/19
*/
double process_B(double Z_xy,double min_val,double max_val,double median_val)
{
if (Z_xy-min_val>0 && Z_xy-max_val<0)
{
return Z_xy;
}
else
{
return median_val;
}
}
/*
* give a roi images and then return in (x,y) its (maybe) value
* cyssmile
* 20/03/19
*/
double dealSplitSubImages(Mat &split_images,int &S_now)
{
double min = split_images.at<uchar>(0, 0);
double &min_val = min;
double max = split_images.at<uchar>(0, 0);
double &max_val = max;
getMinMaxSplitChannel(split_images, min_val, max_val);
double median = split_images.at<uchar>(0, 0);
double &median_val = median;
getMedianSplitChannel(split_images, median_val);
double result_piexl= split_images.at<uchar>(0, 0);
if (median - min_val > 0 && median - max_val < 0)
{ // turn process B
result_piexl = process_B(split_images.at<uchar>(0, 0), min_val, max_val, median_val);
}else {
S_now = S_now + 2;
}
return result_piexl;
}
/*
* deal a splited channel images
* cyssmile
* 20/03/19
*/
void dealMainSplitImages(Mat &split_images,Mat &split_images_clone)
{
int S_Min = 3, S_Max = 7;
for (int i = 0; i < split_images_clone.rows; i++)
{
for (int j = 0; j < split_images_clone.cols; j++)
{
int S_now = S_Min;
double median;
double result_piexl = split_images_clone.at<uchar>(i, j);
if ((i + S_Max < split_images.rows )&& (j + S_Max < split_images.cols))
{
while (S_now <= S_Max)
{ //重复A处理过程
Rect rec;
rec.x = j;
rec.y = i;
rec.width = S_now;
rec.height = S_now;
if ((rec.x + S_now >= split_images.rows) && (rec.y + S_now >= split_images.cols))
{
break;
}
Mat sub = split_images(rec);
int S_old = S_now;
result_piexl = dealSplitSubImages(sub, S_now);
if (S_old == S_now)
{
break;
}
else
{
getMedianSplitChannel(sub, median);
result_piexl = median;
}
}
}
split_images_clone.at<uchar>(i, j) = result_piexl;
}
}
}
/*
* deal edges int dst images
* cyssmile
* 20/03/19
*/
void dealDstEdges(Mat &src,int edges)
{
Rect rec;
rec.x = src.cols - edges;
rec.y = 0 ;
rec.width = edges;
rec.height = src.rows;
Mat NeedDealMat = src(rec);
medianBlur(NeedDealMat, NeedDealMat, 3);
Rect rec_bottom;
rec_bottom.x = 0;
rec_bottom.y = src.rows - edges;
rec_bottom.width = src.cols;
rec_bottom.height = edges;
Mat NeedDealMat_bottom = src(rec_bottom);
medianBlur(NeedDealMat_bottom, NeedDealMat_bottom, 3);
}标签：兴趣 blur mile 处理 images 自己实现 cto The https
原文地址：https://www.cnblogs.com/cyssmile/p/12527109.html
