小波变换 C++ opencv 实现

来源：互联网发布：mac os x sierra壁纸编辑：程序博客网时间：2024/05/01 20:35
小波简介： http://www.blogbus.com/shijuanfeng-logs/221293135.html

源码：
///  小波变换Mat WDT( const Mat &_src, const string _wname, const int _level )const{    int reValue = THID_ERR_NONE;    Mat src = Mat_<float>(_src);    Mat dst = Mat::zeros( src.rows, src.cols, src.type() );     int N = src.rows;    int D = src.cols;     /// 高通低通滤波器    Mat lowFilter;     Mat highFilter;    wavelet( _wname, lowFilter, highFilter );     /// 小波变换    int t=1;    int row = N;    int col = D;     while( t<=_level )    {        ///先进行行小波变换        for( int i=0; i<row; i++ )         {            /// 取出src中要处理的数据的一行            Mat oneRow = Mat::zeros( 1,col, src.type() );            for ( int j=0; j<col; j++ )            {                oneRow.at<float>(0,j) = src.at<float>(i,j);            }            oneRow = waveletDecompose( oneRow, lowFilter, highFilter );            /// 将src这一行置为oneRow中的数据            for ( int j=0; j<col; j++ )            {                dst.at<float>(i,j) = oneRow.at<float>(0,j);            }        } #if 0        //normalize( dst, dst, 0, 255, NORM_MINMAX );        IplImage dstImg1 = IplImage(dst);         cvSaveImage( "dst.jpg", &dstImg1 );#endif        /// 小波列变换        for ( int j=0; j<col; j++ )        {            /// 取出src数据的一行输入            Mat oneCol = Mat::zeros( row, 1, src.type() );            for ( int i=0; i<row; i++ )            {                oneCol.at<float>(i,0) = dst.at<float>(i,j);            }            oneCol = ( waveletDecompose( oneCol.t(), lowFilter, highFilter ) ).t();                    for ( int i=0; i<row; i++ )            {                dst.at<float>(i,j) = oneCol.at<float>(i,0);            }        } #if 0        //normalize( dst, dst, 0, 255, NORM_MINMAX );        IplImage dstImg2 = IplImage(dst);         cvSaveImage( "dst.jpg", &dstImg2 );#endif         /// 更新        row /= 2;        col /=2;        t++;        src = dst;    }     return dst;} ///  小波逆变换Mat IWDT( const Mat &_src, const string _wname, const int _level )const{    int reValue = THID_ERR_NONE;    Mat src = Mat_<float>(_src);    Mat dst = Mat::zeros( src.rows, src.cols, src.type() );     int N = src.rows;    int D = src.cols;     /// 高通低通滤波器    Mat lowFilter;     Mat highFilter;    wavelet( _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 )    {        /// 小波列逆变换        for ( int j=0; j<col; j++ )        {            /// 取出src数据的一行输入            Mat oneCol = Mat::zeros( row, 1, src.type() );            for ( int i=0; i<row; i++ )            {                oneCol.at<float>(i,0) = src.at<float>(i,j);            }            oneCol = ( waveletReconstruct( oneCol.t(), lowFilter, highFilter ) ).t();             for ( int i=0; i<row; i++ )            {                dst.at<float>(i,j) = oneCol.at<float>(i,0);            }        } #if 0        //normalize( dst, dst, 0, 255, NORM_MINMAX );        IplImage dstImg2 = IplImage(dst);         cvSaveImage( "dst.jpg", &dstImg2 );#endif        ///行小波逆变换        for( int i=0; i<row; i++ )         {            /// 取出src中要处理的数据的一行            Mat oneRow = Mat::zeros( 1,col, src.type() );            for ( int j=0; j<col; j++ )            {                oneRow.at<float>(0,j) = dst.at<float>(i,j);            }            oneRow = waveletReconstruct( oneRow, lowFilter, highFilter );            /// 将src这一行置为oneRow中的数据            for ( int j=0; j<col; j++ )            {                dst.at<float>(i,j) = oneRow.at<float>(0,j);            }        } #if 0        //normalize( dst, dst, 0, 255, NORM_MINMAX );        IplImage dstImg1 = IplImage(dst);         cvSaveImage( "dst.jpg", &dstImg1 );#endif         row *= 2;        col *= 2;        src = dst;    }     return dst;}  //////////////////////////////////////////////////////////////////////////////////////////// /// 调用函数 /// 生成不同类型的小波，现在只有haar，sym2void wavelet( const string _wname, Mat &_lowFilter, Mat &_highFilter )const{    if ( _wname=="haar" || _wname=="db1" )    {        int N = 2;        _lowFilter = Mat::zeros( 1, N, CV_32F );        _highFilter = Mat::zeros( 1, N, CV_32F );                _lowFilter.at<float>(0, 0) = 1/sqrtf(N);         _lowFilter.at<float>(0, 1) = 1/sqrtf(N);          _highFilter.at<float>(0, 0) = -1/sqrtf(N);         _highFilter.at<float>(0, 1) = 1/sqrtf(N);     }    if ( _wname =="sym2" )    {        int N = 4;        float h[] = {-0.483, 0.836, -0.224, -0.129 };        float l[] = {-0.129, 0.224,    0.837, 0.483 };         _lowFilter = Mat::zeros( 1, N, CV_32F );        _highFilter = Mat::zeros( 1, N, CV_32F );         for ( int i=0; i<N; i++ )        {            _lowFilter.at<float>(0, i) = l[i];             _highFilter.at<float>(0, i) = h[i];         }     }} /// 小波分解Mat waveletDecompose( const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter )const{    assert( _src.rows==1 && _lowFilter.rows==1 && _highFilter.rows==1 );    assert( _src.cols>=_lowFilter.cols && _src.cols>=_highFilter.cols );    Mat &src = Mat_<float>(_src);     int D = src.cols;        Mat &lowFilter = Mat_<float>(_lowFilter);    Mat &highFilter = Mat_<float>(_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    Mat downDst1 = Mat::zeros( 1, D/2, src.type() );    Mat downDst2 = Mat::zeros( 1, D/2, src.type() );     resize( dst1, downDst1, downDst1.size() );    resize( dst2, downDst2, downDst2.size() );      /// 数据拼接    for ( int i=0; i<D/2; i++ )    {        src.at<float>(0, i) = downDst1.at<float>( 0, i );        src.at<float>(0, i+D/2) = downDst2.at<float>( 0, i );    }     return src;} /// 小波重建Mat waveletReconstruct( const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter )const{    assert( _src.rows==1 && _lowFilter.rows==1 && _highFilter.rows==1 );    assert( _src.cols>=_lowFilter.cols && _src.cols>=_highFilter.cols );    Mat &src = Mat_<float>(_src);     int D = src.cols;     Mat &lowFilter = Mat_<float>(_lowFilter);    Mat &highFilter = Mat_<float>(_highFilter);     /// 插值;    Mat Up1 = Mat::zeros( 1, D, src.type() );    Mat Up2 = Mat::zeros( 1, D, src.type() );     /// 插值为0    //for ( int i=0, cnt=1; i<D/2; i++,cnt+=2 )    //{    //    Up1.at<float>( 0, cnt ) = src.at<float>( 0, i );     ///< 前一半    //    Up2.at<float>( 0, cnt ) = src.at<float>( 0, i+D/2 ); ///< 后一半    //}     /// 线性插值    Mat roi1( src, Rect(0, 0, D/2, 1) );    Mat roi2( src, Rect(D/2, 0, D/2, 1) );    resize( roi1, Up1, Up1.size(), 0, 0, INTER_CUBIC );    resize( roi2, Up2, Up2.size(), 0, 0, INTER_CUBIC );     /// 前一半低通，后一半高通    Mat dst1 = Mat::zeros( 1, D, src.type() );    Mat dst2= Mat::zeros( 1, D, src.type() );    filter2D( Up1, dst1, -1, lowFilter );    filter2D( Up2, dst2, -1, highFilter );     /// 结果相加    dst1 = dst1 + dst2;     return dst1; }
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