高斯金字塔的思考

首先，关于理论的部分祭上两个博客，

http://blog.csdn.net/poem_qianmo/article/details/26157633

http://amitapba.blog.163.com/blog/static/203610207201281992239/

感觉说的已经比较详细了，在这里我就不搅舌跟子了。

直接上代码：

#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <boost/concept_check.hpp>using namespace std;using namespace cv;cv::Mat pyrDown(cv::Mat& src);cv::Mat pyrUp(cv::Mat& src);static const double gaussianTemplate[7][7] ={    {0.00000067, 0.00002292, 0.00019117, 0.00038771, 0.00019117, 0.00002292, 0.00000067},    {0.00002292, 0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633, 0.00002292},    {0.00019117, 0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965, 0.00019117},    {0.00038771, 0.01330373, 0.11098164, 0.22508352, 0.11098164, 0.01330373, 0.00038771},    {0.00019117, 0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965, 0.00019117},    {0.00002292, 0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633, 0.00002292},    {0.00000067, 0.00002292, 0.00019117, 0.00038771, 0.00019117, 0.00002292, 0.00000067}};int main(int argc, char **argv) {      Mat src = imread("../0.jpg",-1);      Mat dst = pyrDown(src);      Mat dst1 = pyrUp(dst);      imshow("111",src);      //cv::waitKey(0);      imshow("222",dst);      imshow("333",dst1);      cv::waitKey(0);      return 0;}//向下取样cv::Mat pyrDown(cv::Mat& src){      Mat dst;      dst.create(src.size(),src.type());      uchar* srcData = src.data;      uchar* dstData = dst.data;      //将原始图像进行高斯模糊，高斯卷积过程      for(int j = 0; j < src.cols-7; j++)      {  for(int i = 0; i < src.rows-7; i++)  {      double acc = 0;      double accb = 0, accg = 0, accr = 0;      for(int m = 0; m < 7; m++)      {for(int n = 0; n < 7; n++){      if(src.channels() == 1)  acc += *(srcData + src.step * (i+n) + src.channels() * (j+m)) * gaussianTemplate[m][n];      else      {  accb += *(srcData + src.step * (i+n) + src.channels() * (j+m) + 0) * gaussianTemplate[m][n];  accg += *(srcData + src.step * (i+n) + src.channels() * (j+m) + 1) * gaussianTemplate[m][n];  accr += *(srcData + src.step * (i+n) + src.channels() * (j+m) + 2) * gaussianTemplate[m][n];      }}      }      if(src.channels() == 1)  *(dstData + dst.step * (i+3) + dst.channels() * (j+3))=(int)acc;      else      {  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 0)=(int)accb;  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 1)=(int)accg;  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 2)=(int)accr;      }}    }    //将高斯卷积得到的结果取其奇数行和奇数列    cv::Mat dst1;    dst1.create(dst.rows/2,dst.cols/2,dst.type());    uchar* dst1Data = dst1.data;    //int m = 0,n = 0;    for(int j = 0,n=0; j < dst.rows-7; j = j + 2,n++)    {for(int i = 0,m=0; i < dst.cols-7; i = i + 2,m++){    if(dst.channels() == 1)*(dst1Data + dst1.step * n + dst1.channels() * m)=*(dstData + dst.step * j + dst.channels() * i);    else    {*(dst1Data + dst1.step * n + dst1.channels() * m + 0)=*(dstData + dst.step * j + dst.channels() * i + 0);*(dst1Data + dst1.step * n + dst1.channels() * m + 1)=*(dstData + dst.step * j + dst.channels() * i + 1);*(dst1Data + dst1.step * n + dst1.channels() * m + 2)=*(dstData + dst.step * j + dst.channels() * i + 2);    }    }    }    return dst1;    }cv::Mat pyrUp(cv::Mat& src){    cv::Mat dst1;    dst1.create(src.rows*2,src.cols*2,src.type());    uchar* dst1Data = dst1.data;    uchar* srcData = src.data;    //int m = 0,n = 0;    for(int n=0; n < src.rows; n++)    {for(int m=0; m < src.cols; m++){    if(src.channels() == 1)    {      *(dst1Data + dst1.step * n + dst1.channels() * m) = *(srcData + src.step * n + src.channels() * m);      *(dst1Data + dst1.step * n + dst1.channels() * (m+1)) = 0;      *(dst1Data + dst1.step * (n+1) + dst1.channels() * m) = 0;      *(dst1Data + dst1.step * (n+1) + dst1.channels() * (m+1)) = 0;    }    else    {*(dst1Data + dst1.step * 2 * n + dst1.channels()* 2 * m + 0) = *(srcData + src.step * n + src.channels() * m+ 0);*(dst1Data + dst1.step * 2 * n + dst1.channels() * 2 * (m+1) + 0) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * m + 0) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * (m+1) + 0) = 0;*(dst1Data + dst1.step * 2 * n + dst1.channels()* 2 * m + 1) = *(srcData + src.step * n + src.channels() * m+ 1);*(dst1Data + dst1.step * 2 * n + dst1.channels() * 2 * (m+1) + 1) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * m + 1) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * (m+1) + 1) = 0;*(dst1Data + dst1.step * 2 * n + dst1.channels()* 2 * m + 2) = *(srcData + src.step * n + src.channels() * m+ 2);*(dst1Data + dst1.step * 2 * n + dst1.channels() * 2 * (m+1) + 2) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * m + 2) = 0;*(dst1Data + dst1.step * 2 * (n+1) + dst1.channels() * 2 * (m+1) + 2) = 0;    }}    }    //cv::imshow("11",dst1);    //cv::waitKey(0);    Mat dst;    dst.create(dst1.size(),dst1.type());    uchar* dstData = dst.data;    for(int j = 0; j < dst1.cols - 7; j++)      {  for(int i = 0; i < dst1.rows - 7; i++)  {      double acc = 0;      double accb = 0, accg = 0, accr = 0;      for(int m = 0; m < 7; m++)      {for(int n = 0; n < 7; n++){      if(dst1.channels() == 1)  acc += *(dst1Data + dst1.step * (i+n) + dst1.channels() * (j+m)) * gaussianTemplate[m][n]*4;      else      {  accb += *(dst1Data + dst1.step * (i+n) + dst1.channels() * (j+m) + 0) * gaussianTemplate[m][n]*4;  accg += *(dst1Data + dst1.step * (i+n) + dst1.channels() * (j+m) + 1) * gaussianTemplate[m][n]*4;  accr += *(dst1Data + dst1.step * (i+n) + dst1.channels() * (j+m) + 2) * gaussianTemplate[m][n]*4;      }}      }      if(dst1.channels() == 1)  *(dstData + dst.step * (i+3) + dst.channels() * (j+3))=(int)acc;      else      {  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 0)=(int)accb;  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 1)=(int)accg;  *(dstData + dst.step * (i+3) + dst.channels() * (j+3) + 2)=(int)accr;      }}    }        return dst;}

这段代码主要是实现了高斯金字塔中向下取样算法和向上取样算法。