LBP OPENCV 实现

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opencv LBP 算法实现
#include "stdafx.h"#include <opencv2\opencv.hpp>#include <iterator>using namespace cv;using namespace std;//opencv 2.0以后版本的LBP算法template <typename _Tp> static  void olbp_(InputArray _src, OutputArray _dst) {      // get matrices      Mat src = _src.getMat();      // allocate memory for result      _dst.create(src.rows-2, src.cols-2, CV_8UC1);      Mat dst = _dst.getMat();      // zero the result matrix      dst.setTo(0);      // calculate patterns      for(int i=1;i<src.rows-1;i++) {          cout<<endl;          for(int j=1;j<src.cols-1;j++) {                            _Tp center = src.at<_Tp>(i,j);              //cout<<"center"<<(int)center<<"  ";              unsigned char code = 0;              code |= (src.at<_Tp>(i-1,j-1) >= center) << 7;              code |= (src.at<_Tp>(i-1,j  ) >= center) << 6;              code |= (src.at<_Tp>(i-1,j+1) >= center) << 5;              code |= (src.at<_Tp>(i  ,j+1) >= center) << 4;              code |= (src.at<_Tp>(i+1,j+1) >= center) << 3;              code |= (src.at<_Tp>(i+1,j  ) >= center) << 2;              code |= (src.at<_Tp>(i+1,j-1) >= center) << 1;              code |= (src.at<_Tp>(i  ,j-1) >= center) << 0;                dst.at<unsigned char>(i-1,j-1) = code;          }      }  }    //基于旧版本的opencv的LBP算法opencv1.0  void LBP (IplImage *src,IplImage *dst)  {      int tmp[8]={0};      CvScalar s;        IplImage * temp = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U,1);      uchar *data=(uchar*)src->imageData;      int step=src->widthStep;        cout<<"step"<<step<<endl;        for (int i=1;i<src->height-1;i++)        for(int j=1;j<src->width-1;j++)        {            int sum=0;            if(data[(i-1)*step+j-1]>=data[i*step+j])              tmp[0]=1;            else              tmp[0]=0;            if(data[i*step+(j-1)]>=data[i*step+j])              tmp[1]=1;            else              tmp[1]=0;            if(data[(i+1)*step+(j-1)]>=data[i*step+j])              tmp[2]=1;            else              tmp[2]=0;            if (data[(i+1)*step+j]>=data[i*step+j])              tmp[3]=1;        else              tmp[3]=0;            if (data[(i+1)*step+(j+1)]>=data[i*step+j])              tmp[4]=1;            else              tmp[4]=0;            if(data[i*step+(j+1)]>=data[i*step+j])              tmp[5]=1;            else              tmp[5]=0;            if(data[(i-1)*step+(j+1)]>=data[i*step+j])              tmp[6]=1;            else              tmp[6]=0;            if(data[(i-1)*step+j]>=data[i*step+j])              tmp[7]=1;            else              tmp[7]=0;               //计算LBP编码              s.val[0]=(tmp[0]*1+tmp[1]*2+tmp[2]*4+tmp[3]*8+tmp[4]*16+tmp[5]*32+tmp[6]*64+tmp[7]*128);                    cvSet2D(dst,i,j,s);//写入LBP图像        }  }  IplImage* face = cvLoadImage("D://1.jpg",CV_LOAD_IMAGE_ANYDEPTH | CV_LOAD_IMAGE_ANYCOLOR);           IplImage* Gray_face = cvCreateImage( cvSize( face->width,face->height ), face->depth, 1);//先分配图像空间      cvCvtColor(face, Gray_face ,CV_BGR2GRAY);//把载入图像转换为灰度图      IplImage* lbp_face =   cvCreateImage(cvGetSize(Gray_face), IPL_DEPTH_8U,1);//先分配图像空间        cvNamedWindow("Gray Image",1);      cvShowImage("Gray Image",Gray_face);           Mat face2 = imread("D://1.jpg",0);              Mat lbp_face2 = Mat::zeros(face2.size(),face2.type()) ;             //显示原始的输入图像      cvNamedWindow("Src Image",CV_WINDOW_AUTOSIZE);      cvShowImage("Src Image",face);      //imshow("Src Image",face);        //计算输入图像的LBP纹理特征      LBP(Gray_face,lbp_face);      //olbp_<uchar>((Mat)face,(Mat)lbp_face);//有问题的调用      olbp_<uchar>(face2,lbp_face2);              //显示第一幅图像的LBP纹理特征图      cvNamedWindow("LBP Image",CV_WINDOW_AUTOSIZE);      cvShowImage("LBP Image",lbp_face);      //显示第二幅图 的LBP纹理特征图-一张yaleface人脸库中的人脸LBP特征图      namedWindow("LBP Image2",1);      imshow("LBP Image2",lbp_face2);      waitKey();        //cvReleaseImage(&face);      cvDestroyWindow("Src Image");
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