C++ 讀寫 CSV檔

在C++ 中讀寫 CSV的辦法

細節就看看 source code吧

	#include <iostream>
	#include <string>
	#include <fstream>
	#include <sstream>
	int main()
	{
	vector<double> matrix;
	//readfile
	fstream file;
	file.open("read.csv");
	string line
	while (getline( file, line,'\n')) //讀檔讀到跳行字元
	{
	istringstream templine(line); // string 轉換成 stream
	string data;
	while (getline( templine, data,',')) //讀檔讀到逗號
	{
	matrix.push_back(atof(data.c_str())); //string 轉換成數字
	}
	}
	file.close();
	//writefile
	file.open("write.csv");
	for (int i=0;i<matrix.size();i++)
	{
	file << matrix[i]<<",";
	}
	file.close();
	return 0;
	}

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Non local means filter

同樣都是去掉雜訊的方法

non local 和 local filter 最大的區別在於

Local 方法是只和要周圍的點 (Research window 內) 的相似度做比較而決定權重

而 Non Local 方法則是另外在每個點的周圍定義一個區塊 (Patch window)，

比較 Patch 與Patch 間的相似度而決定該patch 的中心點的權重

好處是比較能真實反映點與點間的相似度

數學結構與原理如下

Reference: http://www.ipol.im/pub/art/2011/bcm_nlm/?utm_source=doi

Source code (C++ with OPENCV API)

	Mat showDepth1(480, 640, CV_8U, Scalar(0));
	//non local filter
	//Mat showDepth1;
	//showDepth.copyTo(showDepth1);
	//fastNlMeansDenoising(showDepth, showDepth, 3, 3, 11 );
	int iheight=480;
	int iwidth=640;
	int patchwindow=1; // total window 2f+1
	int researchwindow=3; // total window 2r+1
	int sigma=5; // Noise parameter
	int fh=0.4*sigma; // filter parameter
	int sigma2=sigma*sigma;
	int Maxnoisedst=9*sigma2; //Max noise signal
	//tabulate the exp(-x)
	double *exptable =new double[Maxnoisedst];
	double fh2=fh*fh;
	for (int x=0;x<=Maxnoisedst;x++)
	exptable[x]=exp((-1*x)/(fh2));
	// for each pixel x,y
	for (int x=0; x < iwidth;x++)
	{
	for (int y=0; y < iheight;y++)
	{
	// Reduce the patch window if we meet the boundary
	int rPW=min(min(patchwindow,min(x,y)) ,min(iheight-1-y,iwidth-1-x));
	//Reduce the Research window if we meet the boundary
	int imax=min(x+researchwindow,iwidth-1-rPW);
	int imin=max(x-researchwindow,rPW);
	int jmax=min(y+researchwindow,iheight-1-rPW);
	int jmin=max(y-researchwindow,rPW);
	double totalweight=0;
	double maxweight=0;
	double depthdata=0;
	for (int i=imin;i<=imax ; i++) // sum all the point in the research window
	{
	for (int j=jmin; j<=jmax;j++)
	{
	if (i!=x || j!=y)
	{
	double weight=0;
	double dist=0;
	for (int ii=-rPW;ii<=rPW;ii++)// calculate the distance between the patch (x,y), (i,j)
	{
	for (int jj=-rPW;jj<=rPW;jj++)
	{
	int px=showDepth.at<uchar>(y+jj,x+ii);
	int qx=showDepth.at<uchar>(j+jj,i+ii);
	int dst=px-qx;
	dist+=dst*dst;
	}
	}
	// normalized distance
	dist/=(2*rPW+1)*(2*rPW+1);
	// max distance^2-2*sigma^2 of rPW , 0.0
	dist=max(dist -2*sigma2,0.0);
	if (dist>Maxnoisedst)
	weight=0;
	else
	{
	int temp=(int)floor((double)dist);
	double weight1=exptable[temp];
	double weight2=exptable[temp+1];
	weight=weight1 +(weight2-weight1)*(dist-temp);
	}
	if (weight> maxweight) maxweight=weight;
	totalweight+=weight;
	int temp1=showDepth.at<uchar>(j,i);
	depthdata+=temp1*weight;
	}
	}
	}
	int temp=showDepth.at<uchar>(y,x);
	depthdata+=temp*maxweight;
	totalweight+=maxweight;
	if (totalweight > 1e-10)
	showDepth1.at<uchar>(y,x)=(int) floor(depthdata/totalweight);
	}
	}

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Bicubic interpolation

wiki

https://en.wikipedia.org/wiki/Bicubic_interpolation

NR  3.6.3  page 160