Matlab 拥有丰富的功能，编程简单。不过，有些情况下，Matlab程序的执行速度比较慢。C/C++编译执行的程序速度比较快，编程难度上比Matlab要高一些。因此存在一种方案，就是使用Matlab实现我们的实验程序，用C/C++来实现Matlab程序中比较耗时的部分，从Matlab程序中调用C/C++的程序以实现加速。
Visual C++ 2015
1：配置环境
1.1：在vc++目录中
包含目录：(1)：生成的mydll.h所在目录。
(2)：matlab 内的include目录。
Include files
D:\Program Files\MATLAB\R2012a\extern\include
库目录：(1)：mydll.lib所在目录。
(2)：matlab的lib目录。
Library files
D:\New Project\手写体数字识别\QpPrj\distrib
D:\Program Files\MATLAB\R2012a\extern\lib\win64\microsoft
1.2：在连接器-》输入-》附加依赖项
mclmcrrt.lib
libmx.lib
libmat.lib
mclmcr.lib
QpPrj.lib
将编译好的dll复制到VC工程的Debug或者Release目录下，以使得dll可以被找到。还要把编译生成的QpPrj.h文件拷贝到VC工程里
代码实现：
#include <iostream>
#pragma comment(lib,"QpPrj.lib")
#include "QpPrj.h"
#include "mclmcr.h"
#include "matrix.h"
#include "mclcppclass.h"
using namespace std;
int main(int argc, char* argv[])
{
// 初始化
if (!QpPrjInitialize())
{
printf("Could not initialize !");
return -1;
}
// 1.调用MyAdd
double a = 6;
double b = 9;
double c;
// 为变量分配内存空间
mwArray mwA(1, 1, mxDOUBLE_CLASS); // 1，1表示矩阵的大小（所有maltab只有一种变量，就是矩阵，为了和Cpp变量接轨，设置成1*1的矩阵，mxDOUBLE_CLASS表示变量的精度）
mwArray mwB(1, 1, mxDOUBLE_CLASS);
mwArray mwC(1, 1, mxDOUBLE_CLASS);
// set data，调用类里面的SetData函数给类赋值
mwA.SetData(&a, 1);
mwB.SetData(&b, 1);
// using my add，掉我自己写的函数
// 调用示例: extern LIB_QpPrj_CPP_API void MW_CALL_CONV MyAdd(int nargout, mwArray& c, const mwArray& a, const mwArray& b);
MyAdd(1, mwC, mwA, mwB);
// get data，调用类里面的Get函数获取取函数返回值
c = mwC.Get(1, 1);
printf("c is %f\n", c);
// 2.调用TestChar
// extern LIB_QpPrj_CPP_API void MW_CALL_CONV TestChar(int nargout, mwArray& result, const mwArray& char0)
double d = 4;
double e;
// 为变量分配内存空间
mwArray mwInput(1, 1, mxDOUBLE_CLASS);
mwArray mwOutput(1, 1, mxDOUBLE_CLASS);
mwInput.SetData(&d, 1);
TestChar(1, mwOutput, mwInput);
e = mwOutput.Get(1, 1);
printf("e is %f\n", e);
char training_result_path[] = "D:\\New Project\\手写体数字识别";
char digital_img_path[] = "D:\\hh\\t_4.jpg";
char training_result_file[] = "training_result_200_trees";
double f;
cout << training_result_path << endl;
cout << digital_img_path << endl;
cout << training_result_file << endl;
// 为变量分配内存空间
mwArray recognition_result(1, 1, mxDOUBLE_CLASS);
// 调用示例:extern LIB_QpPrj_CPP_API void MW_CALL_CONV digital_recogn_9(int nargout, mwArray& recognition_result, const mwArray& training_result_path, const mwArray& digital_img_path, const mwArray& training_result_file);
digital_recogn_9(1, recognition_result, training_result_path, digital_img_path, training_result_file);
f = recognition_result.Get(1, 1);
// 终止调用的程序
QpPrjTerminate();
// terminate MCR
mclTerminateApplication();
return 0;
}
... MWMCR::EvaluateFunction error ...
Error using predict (line 85)
Systems of uint32 class cannot be used with the "predict" command. Convert the system to an identified model first, such as by using the "idss" command.
\
matlab代码
digital_recogn_9.m
function [recognition_result]
= digital_recogn_9( training_result_path , digital_img_path , training_result_file )
1 training_result_path
训练库路径 ' D:\New Project\手写体数字识别'
2 digital_img_path
传入的图片路径 ' D:\hh\t_4.jpg '
3 training_result_file
训练库文件名 ' training_result_200_trees '
调用示例：
digital_recogn_9('D:\New Project\手写体数字识别','D:\hh\t_4.jpg','training_result_200_trees')
| function [recognition_result] = digital_recogn_9( training_result_path , digital_img_path , training_result_file )
input_number = imread(digital_img_path) ;
%%%%%%%%%%%% image trsformation input_number = 255 - rgb2gray(input_number) ; threshold_noise = 35 ;
for ii = 1:size(input_number,1) for jj = 1:size(input_number,2) if input_number( ii , jj ) < threshold_noise input_number( ii , jj ) = 0; end end end
%%%%%%%%%%%%%%%%%%%%%%%% projecting_width = sum(input_number, 1); projecting_height = sum(input_number, 2);
%%%%%%% cut off the boundary boundary_width = zeros( size( projecting_width ) ); boundary_height = zeros( size( projecting_height ) ); offset_width = 3; offset_height = 3;
aa = size( boundary_width , 2) - offset_width + 1 ; boundary_width ( 1 , offset_width : aa ) = 1 ;
bb = size( boundary_height , 1) - offset_height + 1 ; boundary_height ( offset_height : bb , 1 ) = 1 ;
projecting_width = projecting_width .* boundary_width ; projecting_height = projecting_height .* boundary_height ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% threshold_width = max ( projecting_width ) / size(projecting_width , 2 ) ; threshold_height = max ( projecting_height ) / size(projecting_height , 1 ) ;
s1 = ( projecting_width > threshold_width ) ; new_width = sum(s1) ;
s2 = ( projecting_height > threshold_height ) ; new_height = sum(s2) ;
trimming_img = zeros( new_height , new_width ); trimming_img = uint8(trimming_img) ; counter_height =1 ; for ii=1 : (size( input_number , 1 ) - 1) %%%%%%% select qualified rows if s2( ii , 1 )==true counter_height = counter_height +1 ; end
%%%%%%% select qualified columns counter_width =1 ; for jj=1 : (size( input_number , 2 ) - 1) if s1( 1 , jj )==true counter_width = counter_width +1 ; end
%%%%% copy pixels to new image if s2( ii , 1 ) == true || s1( 1 , jj ) == true s3 = input_number( ii , jj ); trimming_img( counter_height , counter_width ) = s3; end end end
%%%%%%%%%% flatten the image edge_length =16 ; %% 16*16=256 trimming_img = imresize( trimming_img , [edge_length , edge_length] ) ; %% 暂时注释 掉 %%imshow( trimming_img ) ;
flatten_img = reshape( trimming_img , 1 , 256 ) ;
%%%%%%%%%%%%%%%%%%%%% cd( training_result_path ) %% 加上分号,训练库文件名 ' training_result_200_trees ' training_result = load( training_result_file ); Xtest = training_result.Xtest ; mdl = training_result.mdl ;
%%%%%%%%%%%%%%%%%%%%% image_set = zeros( size(Xtest) ) ; image_set = uint8( image_set );
for ii=1:size(Xtest, 1) image_set( ii , : ) = flatten_img(: , :); end
%%%% Train and Predict Using a Single Classification Tree
Xtest = double( image_set ); %%ypred = predict(mdl, Xtest); ypred = predict(mdl, Xtest);
% % Confmat_bag = confusionmat(Ytest,ypred);
%% recognition_result = ypred(1,1);
recognition_result = ypred(1,1);
end |
MyAdd.m
| function [c] = MyAdd(a, b) %UNTITLED Summary of this function goes here % Detailed explanation goes here c = a + b;
end
|
MyChar.m
| function [result] = MyChar(str) %UNTITLED2 Summary of this function goes here % Detailed explanation goes here result = str;
end |