opencv 小波变换与逆变换C++
在此记录一下
兴幸在网上看到关于小波变换的代码，但是在逆变换的时候结果跟matlab的有很大差别，因此对照一下matlab的具体代码，对已有的代码进行一点改动。
说明：
1.配置好opencv，就可以运行（附Demo）
2.小波包的选择，代码中只包括了haar，db1，sym2，如需要更多种类的小波包，可以在matlab里查看wfilters函数，对应写进代码中。
代码如下：
1. WaveTransform.h文件
#include
#include
using namespace cv;
class WaveTransform
{
public:
WaveTransform(void);
~WaveTransform(void);
Mat WDT(const Mat &_src,const string _wname,const int _level);//小波分解
Mat IWDT(const Mat &_src,const string _wname,const int _level);//小波重构
void wavelet_D( const string _wname, Mat &_lowFilter, Mat &_highFilter );//分解包
void wavelet_R( const string _wname, Mat &_lowFilter, Mat &_highFilter );//重构包
Mat waveletDecompose( const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter );
Mat waveletReconstruct(const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter);
}; 2. .WaveTransform.cpp文件
#include "WaveTransform.h"
WaveTransform::WaveTransform(void)
{
}
WaveTransform::~WaveTransform(void)
{
}
Mat WaveTransform::WDT(const Mat &_src,const string _wname,const int _level)
{
//int reValue=THID_ERR_NONE;
Mat_ src=Mat_(_src);
Mat dst=Mat::zeros(src.rows,src.cols,src.type());
int row=src.rows;
int col=src.cols;
//高通低通滤波器
Mat lowFilter;
Mat highFilter;
wavelet_D(_wname,lowFilter,highFilter);
//小波变换
int t=1;
while (t<=_level)
{
//先进行 行小波变换
//#pragma omp parallel for
for (int i=0;i(0,j)=src.at(i,j);
}
oneRow=waveletDecompose(oneRow,lowFilter,highFilter);
for (int j=0;j(i,j)=oneRow.at(0,j);
}
}
#if 0
// normalize(dst,dst,0,255,NORM_MINMAX);
IplImage dstImg1=IplImage(dst);
cvSaveImage("dst1.jpg",&dstImg1);
#endif
//小波列变换
//#pragma omp parallel for
for (int j=0;j(i,0)=dst.at(i,j);//dst,not src
}
oneCol=(waveletDecompose(oneCol.t(),lowFilter,highFilter)).t();
for (int i=0;i(i,j)=oneCol.at(i,0);
}
}
#if 0
// normalize(dst,dst,0,255,NORM_MINMAX);
IplImage dstImg2=IplImage(dst);
cvSaveImage("dst2.jpg",&dstImg2);
#endif
/*
char s[10];
itoa(t,s,10);
imshow(s,Mat_(dst));
waitKey(1);
*/
//归化各子图范围0~255
/*
int r_len=row/2,c_len=col/2;
for(int i=0;i<2;i++)
{
for(int j=0;j<2;j++)
{
Point p1=Point(i*c_len,j*r_len);
Point p2=Point((i+1)*c_len,(j+1)*r_len);
Mat ROI=dst(Rect(p1,p2));
//ROI=cv::abs(ROI);
normalize(ROI,ROI,0,255,CV_MINMAX);
//imshow("a",Mat_(ROI));
//waitKey(0);
}
}
*/
//
//更新
row/=2;
col/=2;
t++;
src=dst;
}
return dst;
}
Mat WaveTransform::IWDT(const Mat &_src,const string _wname,const int _level)
{
//int reValue=THID_ERR_NONE;
Mat src=Mat_(_src);
Mat dst;//=Mat::zeros(src.rows,src.cols,src.type());
src.copyTo(dst);
int N=src.rows;
int D= src.cols;
//高低通滤波器
Mat lowFilter;
Mat highFilter;
wavelet_R(_wname,lowFilter,highFilter);
//小波变换
int t=1;
int row=N/std::pow(2.,_level-1);
int col=D/std::pow(2.,_level-1);
while(row<=N && col<=D)
//while(t<=_level)
{
//列逆变换
for(int j=0;j(i,0)=src.at(i,j);
}
oneCol=(waveletReconstruct(oneCol.t(),lowFilter,highFilter)).t();
for(int i=0;i(i,j)=oneCol.at(i,0);
}
}
#if 0
IplImage dstImg2=IplImage(dst);
cvSaveImage("dst.jpg",&dstImg2);
#endif
//行逆变换
for(int i=0;i(0,j)=dst.at(i,j);
}
oneRow=waveletReconstruct(oneRow,lowFilter,highFilter);
for(int j=0;j(i,j)=oneRow.at(0,j);
}
}
#if 0
IplImage dstImg1=IplImage(dst);
cvSaveImage("dst.jpg",&dstImg1);
#endif
char s[10];
itoa(t,s,10);
//Rect rrr=Rect(Point(col-1,row-1),Point(src.cols-1,src.rows-1));
//Rect rrr=Rect(Point(0,0),Point(col-1,row-1));
/*
Mat showImg;//=//dst;//(rrr);
dst.copyTo(showImg);
Mat showImg1;
showImg.copyTo(showImg1);
normalize(showImg1,showImg1,0,255,CV_MINMAX);
imshow(s,Mat_(showImg1));
waitKey(1);
*/
row*=2;
col*=2;
t++;
src=dst;
}
return dst;
}
void WaveTransform::wavelet_D( const string _wname, Mat &_lowFilter, Mat &_highFilter )
{
if (_wname=="haar"||_wname=="db1")
{
int N=2;
_lowFilter=Mat::zeros(1,N,CV_32F);
_highFilter=Mat::zeros(1,N,CV_32F);
_lowFilter.at(0,0)=1/sqrtf(N);
_lowFilter.at(0,1)=1/sqrtf(N);
_highFilter.at(0,0)=-1/sqrtf(N);
_highFilter.at(0,1)=1/sqrtf(N);
}
else if (_wname=="sym2")
{
int N=4;
float h[]={-0.4830, 0.8365, -0.2241, -0.1294};
float l[]={-0.1294, 0.2241, 0.8365, 0.4830};
_lowFilter=Mat::zeros(1,N,CV_32F);
_highFilter=Mat::zeros(1,N,CV_32F);
for (int i=0;i(0,i)=l[i];
_highFilter.at(0,i)=h[i];
}
}
}
void WaveTransform::wavelet_R( const string _wname, Mat &_lowFilter, Mat &_highFilter )
{
if (_wname=="haar"||_wname=="db1")
{
int N=2;
_lowFilter=Mat::zeros(1,N,CV_32F);
_highFilter=Mat::zeros(1,N,CV_32F);
_lowFilter.at(0,0)=1/sqrtf(N);
_lowFilter.at(0,1)=1/sqrtf(N);
_highFilter.at(0,0)=1/sqrtf(N);
_highFilter.at(0,1)=-1/sqrtf(N);
}
else if (_wname=="sym2")
{
int N=4;
float h[]={-0.1294,-0.2241,0.8365,-0.4830};
float l[]={0.4830, 0.8365, 0.2241, -0.1294};
_lowFilter=Mat::zeros(1,N,CV_32F);
_highFilter=Mat::zeros(1,N,CV_32F);
for (int i=0;i(0,i)=l[i];
_highFilter.at(0,i)=h[i];
}
}
}
Mat WaveTransform::waveletDecompose( const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter )
{
assert(_src.rows==1 && _lowFilter.rows==1 && _highFilter.rows ==1);
assert(_src.cols>=_lowFilter.cols && _src.cols>=_highFilter.cols );
Mat &src=Mat_(_src);
int D=src.cols;
Mat &lowFilter=Mat_(_lowFilter);
Mat &highFilter=Mat_(_highFilter);
//频域滤波或时域卷积；ifft( fft(x) * fft(filter)) = cov(x,filter)
Mat dst1=Mat::zeros(1,D,src.type());
Mat dst2=Mat::zeros(1,D,src.type());
filter2D(src,dst1,-1,lowFilter);
filter2D(src,dst2,-1,highFilter);
//下采样
//数据拼接
for (int i=0,j=1;i(0,i)=dst1.at(0,j);//lowFilter
src.at(0,i+D/2)=dst2.at(0,j);//highFilter
}
return src;
}
Mat WaveTransform::waveletReconstruct(const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter)
{
assert(_src.rows == 1 && _lowFilter.rows == 1 && _highFilter.rows == 1);
assert(_src.cols >= _lowFilter.cols && _src.cols >= _highFilter.cols);
Mat &src = Mat_(_src);
int D = src.cols;
Mat &lowFilter = Mat_(_lowFilter);
Mat &highFilter = Mat_(_highFilter);
/// 插值;
Mat Up1 = Mat::zeros(1, D, src.type());
Mat Up2 = Mat::zeros(1, D, src.type());
//Mat roi1(src, Rect(0, 0, D / 2, 1));
//Mat roi2(src, Rect(D / 2, 0, D / 2, 1));
/// 插值为0
for ( int i=0, cnt=0; i( 0, cnt ) = src.at( 0, i ); ///< 前一半
Up2.at( 0, cnt ) = src.at( 0, i+D/2 ); ///< 后一半
}
//std::cout< 3. Demo
#include "WaveTransform.h"
#include
#include
using namespace cv;
using namespace std;
int main()
{
char*filename="1.jpg";
Mat src=imread(filename);
int level=3;//分解阶次
double dishu=2;
int result=(int)pow(dishu,level);
WaveTransform m_waveTransform;
//double a=clock();
//resize(src,src,Size((512/result)*result,(512/result)*result));
Mat img;
cvtColor(src,img,CV_RGB2GRAY);
normalize(img,img,0,255,CV_MINMAX);
imshow("img",img);
Mat float_src;
img.convertTo(float_src,CV_32F);
Mat imgWave=m_waveTransform.WDT(float_src,"sym2",level); //haar,sym2
imgWave.convertTo(float_src,CV_32F);
Mat imgIWave=m_waveTransform.IWDT(float_src,"sym2",level);
imshow("imgWave",Mat_(imgWave));
normalize(imgIWave,imgIWave,0,255,CV_MINMAX);
imshow("IWDT",Mat_(imgIWave));
waitKey(0)
return 0;
} 4. 效果图
原图：
小波分解图：（为了显示效果，对每个子图灰度值进行了normalization，WDT程序中注释掉部分）
小波重构图：
完！