柔性模板匹配"/>
BBS柔性模板匹配
BBS是柔性模板匹配算法
背景介绍：
模板匹配的目的是为了检测目标物体的位置，大多数模板匹配都是基于像素值或者轮廓地图，且假设原图和目标图片存在一定的参数变换，但这些只适用于刚性物体的情况。
BBS就是模板图片和待匹配图片相似度的度量方式，其采用的搜索方式还是传统的滑窗方式。
在待检测图片划出跟template同样大小的块，BBS实际上是两个点集相似度的衡量，如果两个点集中有一对点是彼此的最近邻，那么它俩就是一个BBP(Best-Buddies Points)。
其度量标准就是对应的patch的值相减（3*3patch的话就是9个值）+a(常数系数）*距离
针对滑窗过程中的每一个位置，记录配对成功的BBP的个数，个数最多的位置为成功匹配的位置
以下是代码部分（注意依赖opencv以及eigen 3.2）
#include<opencv2/opencv.hpp>
//#include<valarray>
//#include<numeric>
#include<Eigen/Dense>
#include <opencv2/core/eigen.hpp>
#include<vector>
#include <Eigen/Sparse>
#include<valarray>
#include<numeric>
#include<unsupported/Eigen/CXX11/Tensor>
#if (_MSC_VER >= 1915)
#define no_init_all deprecated
#endif
using namespace Eigen;
int grid_number=3;
double gamma = 2.0;
const int template_patch_number = 15 * 11;
const int image_patch_number = 75 * 75;
const int S_patch_X = 75;// image_cols / grid_number;
const int S_patch_Y = 75;// image_rows / grid_number;
const int T_patch_X = 15;// template_cols / grid_number;
const int T_patch_Y = 11;// template_rows / grid_number;
Eigen::Matrix <double, template_patch_number, template_patch_number>D;
int main()
{int grid_number_square = grid_number * grid_number;cv::Mat template_ = cv::imread("D:\\11月份相关资料\\star_test.jpg");cv::Mat image_ = cv::imread("D:\\11月份相关资料\\test_NCC_SSD.jpg");int template_cols = template_.cols;int template_rows = template_.rows;Eigen::Matrix <int, Dynamic, Dynamic,RowMajor>template_grid_index= VectorXi::LinSpaced(T_patch_X*T_patch_Y, 0, T_patch_X*T_patch_Y);//索引以及建立好template_grid_index.resize(T_patch_X, T_patch_Y);template_grid_index.transposeInPlace();int image_cols = image_.cols;int image_rows = image_.rows;Eigen::Matrix <int, Dynamic, Dynamic, RowMajor>image_grid_index = VectorXi::LinSpaced(S_patch_X*S_patch_Y, 0, S_patch_X*S_patch_Y);//索引以及建立好image_grid_index.resize(S_patch_X, S_patch_Y);image_grid_index.transposeInPlace();//std::cout << image_grid_index;//先造template和image,注意是三通道的//在eigen中,matrix是二维的，tensor是多维的，std::vector<cv::Mat> template_channels(3), image_channels(3);cv::split(template_, template_channels);cv::split(image_, image_channels);std::vector<Eigen::Matrix<double, Dynamic, Dynamic>> template_eigen_channels(3),image_eigen_channels(3);for (int i = 0; i < template_channels.size(); i++){cv2eigen(template_channels[i], template_eigen_channels[i]);template_eigen_channels[i]=(template_eigen_channels[i].array())/ 255;cv2eigen(image_channels[i], image_eigen_channels[i]);image_eigen_channels[i] = image_eigen_channels[i].array()/ 255;}std::vector<Eigen::Matrix<double, 9, template_patch_number>>template_feature_map_channel(3);std::vector<Eigen::Matrix<double, 9, image_patch_number>>image_feature_map_channel(3);for (int i = 0; i < template_eigen_channels.size(); i++){int count_T = 0;for (int m = 0; m < template_eigen_channels[i].cols() - grid_number + 1; m = m + grid_number)for (int n = 0; n < template_eigen_channels[i].rows()- grid_number+1; n = n + grid_number){Eigen::Matrix<double, Dynamic, Dynamic>temp = template_eigen_channels[i].block(n,m, grid_number, grid_number);temp.resize(grid_number_square, 1);template_feature_map_channel[i].col(count_T)=temp.col(0);count_T++;}}for (int i = 0; i < image_eigen_channels.size(); i++){int count_T = 0;for (int m = 0; m < image_eigen_channels[i].cols() - grid_number + 1; m = m + grid_number)for (int n = 0; n < image_eigen_channels[i].rows() - grid_number + 1; n = n + grid_number){Eigen::Matrix<double, Dynamic, Dynamic>temp = image_eigen_channels[i].block(n, m, grid_number, grid_number);temp.resize(grid_number_square, 1);image_feature_map_channel[i].col(count_T) = temp.col(0);count_T++;}}//总算把两个feature_Map都建立好了//下面建立高斯核cv::Mat cv_guass_x=cv::getGaussianKernel(grid_number,0.6);cv::Mat cv_guass_y = cv::getGaussianKernel(grid_number, 0.6);cv::Mat cv_guass = cv_guass_x * cv_guass_y.t();cv_guass=cv_guass.reshape(0, grid_number_square);//打成跟Template patch个数大小相同的矩阵，方便后面直接相乘cv_guass=cv::repeat(cv_guass,1,template_patch_number);Eigen::MatrixXd FMat;cv2eigen(cv_guass,FMat);//高斯核造好了以后开始造距离矩阵//TMat 大小的矩阵之间的距离Eigen::Matrix <int, Dynamic, Dynamic> distance_index_= VectorXi::LinSpaced(image_patch_number, 0, image_patch_number);distance_index_.resize(S_patch_Y,S_patch_X);Eigen::Matrix <double, Dynamic, Dynamic> xx= VectorXd::LinSpaced(T_patch_X, 0, T_patch_X-1)*0.0039*grid_number;xx.transposeInPlace();Eigen::Matrix <double, Dynamic, Dynamic >xx_=xx.replicate(T_patch_Y, 1);Eigen::Matrix <double, Dynamic, Dynamic> yy = VectorXd::LinSpaced(T_patch_Y, 0, T_patch_Y-1)*0.0039*grid_number;Eigen::Matrix <double, Dynamic, Dynamic>yy_ = yy.replicate(1, T_patch_X);Eigen::MatrixXd X = xx_;X.resize(template_patch_number, 1);Eigen::MatrixXd Y = yy_;Y.resize(template_patch_number, 1);Eigen::Matrix <double, template_patch_number, template_patch_number>Dxy;for (int i = 0; i < template_patch_number; i++){Dxy.col(i) = (X.col(0).array() - X(i,0)).square() + (Y.col(0).array() - Y(i,0)).square();}//下面以image为目标，一个patch，一个patch移动，计算每个位置每个通道互为最近邻的patch的个数//以怎样的数据格式建立呢Eigen::Matrix<double, template_patch_number, template_patch_number>Drgb;std::vector<Eigen::Matrix<double, template_patch_number, template_patch_number>>Drgb_buffer(image_cols - template_cols);cv::Mat Drgb_test;//逐行逐列遍历image上的每个patchEigen::Matrix<int,S_patch_Y, S_patch_X>BBS;BBS.setZero();for(int colI=0; colI <S_patch_X-T_patch_X+1; colI++)for (int rowI = 0; rowI < S_patch_Y-T_patch_Y+1; rowI++){ if (rowI == 0 && colI == 0){//首先用block函数把符合template相同大小的一堆patch的index取出来 //ind = IndMat(rowI:rowI + szT(1) / pz - 1, rowI : rowI + szT(2) / pz - 1);Eigen::Matrix<int,Dynamic,Dynamic> ind=image_grid_index.block(rowI,colI,T_patch_Y,T_patch_X);ind.resize(template_patch_number,1);std::vector<Eigen::Matrix<double,9,template_patch_number>>PMat(3);for (int C = 0; C < 3; C++){ for (int i = 0; i < template_patch_number; i++)PMat[C].col(i)=image_feature_map_channel[C].col(ind(i,0));}std::vector<Eigen::Matrix<double, 9, template_patch_number>>temp(3);for (int j = 0; j < template_patch_number; j++){for (int C = 0; C < 3; C++){// PMat.replicate(1, 2);Eigen::VectorXd PMat_sub_v = PMat[C].col(j);Eigen::MatrixXd PMat_sub = PMat_sub_v.replicate(1, template_patch_number);temp[C] = (PMat_sub - template_feature_map_channel[C]).array()*(FMat.array()); //*FMat;temp[C] = temp[C].array()*temp[C].array();}//三个通道相加Eigen::Matrix<double, 9, template_patch_number>temp_sum = temp[0] + temp[1] + temp[2];//每个patch的值相加Drgb.col(j) = temp_sum.colwise().sum();}}else if (rowI > 0 && colI == 0){Drgb.block(0, 0, template_patch_number, template_patch_number - 1) =Drgb.block(0,1,template_patch_number, template_patch_number - 1);Eigen::Matrix<int, Dynamic, Dynamic> ind = image_grid_index.block(rowI+T_patch_Y-1, colI, 1, T_patch_X);ind.resize(T_patch_X, 1);std::vector<Eigen::Matrix<double, 9, T_patch_X>>R(3);for (int C = 0; C < 3; C++){for (int i = 0; i < T_patch_X; i++)R[C].col(i) = image_feature_map_channel[C].col(ind(i, 0));}std::vector<Eigen::Matrix<double, 9, template_patch_number>>temp(3);int idxP = T_patch_Y - 1;for (int j = 0; j < T_patch_X; j++){for (int C = 0; C < 3; C++){Eigen::VectorXd RMat_sub_v = R[C].col(j);Eigen::MatrixXd RMat_sub = RMat_sub_v.replicate(1, template_patch_number);temp[C] = (RMat_sub - template_feature_map_channel[C]).array()*(FMat.array()); //*FMat;temp[C] = temp[C].array()*temp[C].array();}//三个通道相加Eigen::Matrix<double, 9, template_patch_number>temp_sum = temp[0] + temp[1] + temp[2];//每个patch的值相加Drgb.col(idxP) = temp_sum.colwise().sum();idxP = idxP + T_patch_Y;}}else if (rowI == 0 && colI > 0){Eigen::Matrix<double, template_patch_number, template_patch_number>Drgb_prev = Drgb_buffer[0];Drgb.block(0, 0, template_patch_number, template_patch_number - T_patch_Y) = Drgb_prev.block(0, T_patch_Y, template_patch_number, template_patch_number - T_patch_Y);Eigen::Matrix<int, Dynamic, Dynamic> ind = image_grid_index.block(rowI, colI+T_patch_X-1, T_patch_Y, 1);ind.resize(T_patch_Y, 1);std::vector<Eigen::Matrix<double, 9, T_patch_Y>>R(3);for (int C = 0; C < 3; C++){for (int i = 0; i < T_patch_Y; i++)R[C].col(i) = image_feature_map_channel[C].col(ind(i, 0));}std::vector<Eigen::Matrix<double, 9, template_patch_number>>temp(3);int idxP = (T_patch_X - 1)*T_patch_Y;for (int j = 0; j < T_patch_Y; j++){for (int C = 0; C < 3; C++){Eigen::VectorXd RMat_sub_v = R[C].col(j);Eigen::MatrixXd RMat_sub = RMat_sub_v.replicate(1, template_patch_number);temp[C] = (RMat_sub - template_feature_map_channel[C]).array()*(FMat.array()); //*FMat;temp[C] = temp[C].array()*temp[C].array();}//三个通道相加Eigen::Matrix<double, 9, template_patch_number>temp_sum = temp[0] + temp[1] + temp[2];//每个patch的值相加Drgb.col(idxP) = temp_sum.colwise().sum();idxP = idxP + 1;}}else{Drgb.block(0, 0, template_patch_number, template_patch_number - 1) = Drgb.block(0, 1, template_patch_number, template_patch_number - 1);Eigen::Matrix<double, template_patch_number, template_patch_number>Drgb_prev = Drgb_buffer[rowI];for (int k = T_patch_Y - 1; k < template_patch_number - 1; k = k + T_patch_Y){Drgb.col(k)=Drgb_prev.col(k+T_patch_Y);}std::vector<Eigen::Matrix<double, 9, 1>>R(3);for (int C = 0; C < 3; C++){R[C].col(0) = image_feature_map_channel[C].col(image_grid_index(rowI+T_patch_Y-1,colI+T_patch_X-1));}std::vector<Eigen::Matrix<double, 9, template_patch_number>>temp(3);for (int C = 0; C < 3; C++){Eigen::VectorXd RMat_sub_v = R[C].col(0);Eigen::MatrixXd RMat_sub = RMat_sub_v.replicate(1, template_patch_number);temp[C] = (RMat_sub - template_feature_map_channel[C]).array()*(FMat.array()); //*FMat;temp[C] = temp[C].array()*temp[C].array();}//三个通道相加Eigen::Matrix<double, 9, template_patch_number>temp_sum = temp[0] + temp[1] + temp[2];//每个patch的值相加Drgb.col(template_patch_number-1) = temp_sum.colwise().sum();}Drgb_buffer[rowI]=Drgb; D = gamma * Dxy.array() + Drgb.array();D= (D.array()> 1e-4).cast<double>() *D.array();//每个patch有9个像素位，一个165个patch，两个patch彼此两两相减得到Drgb,加上位置距离得到D//找到彼此的最近邻需要//按行取最小值，A patch到B patch最近的，每行可以得到一个（i,j)//按列取最小值，B中patch到A中patch的最小值，每列可以得到(m,n)//如果有(i,j)==(m,n)，则凑成一对MatrixXd::Index minRow[template_patch_number], minCol[template_patch_number];for (int i = 0; i < template_patch_number; i++){D.col(i).minCoeff(&minCol[i]);D.row(i).minCoeff(&minRow[i]);}int nearest_patch_number = 0;for (int i = 0; i < template_patch_number; i++){if (minRow[minCol[i]] == i)nearest_patch_number++;}BBS(rowI, colI) = nearest_patch_number;}Eigen::MatrixXd::Index colMax, rowMax;BBS.maxCoeff(&rowMax, &colMax);//线性拉伸BBScv::Mat BBS_mat;cv::eigen2cv(BBS,BBS_mat);double inMaxVal, inMinVal;cv::minMaxLoc(BBS_mat, &inMinVal, &inMaxVal, NULL, NULL);// 输出图像的最大最小值double outMaxVal = 255, outMinVal = 0;// 计算 alpha 和 bdouble alpha = (outMaxVal - outMinVal) / (inMaxVal - inMinVal);double b = outMinVal - alpha * inMinVal;cv::convertScaleAbs(BBS_mat, BBS_mat, alpha, b);int rowMax_ = rowMax;int colMax_ = colMax;cv::rectangle(image_, cv::Point(colMax*grid_number, rowMax_*grid_number), cv::Point(colMax*grid_number+template_cols, rowMax_*grid_number + template_rows), cv::Scalar(255, 255, 0));return 1;
}
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