Mixed programming of C/C + + matlab

Matlab has a wealth of functions, simple programming. However, in some cases, the MATLAB program is slow to execute. C + + compiler executes the program faster, more difficult to program than MATLAB. Therefore, there is a solution, is to use MATLAB to implement our experimental program, in C + + to achieve a more time-consuming part of the MATLAB program, from the MATLAB program to call C + + program to achieve acceleration.

Visual C + + 2015

1 : Configuring the Environment

1.1: In the VC + + directory

Include directory: (1): The directory where the generated mydll.h is located.

(2): the Include directory within MATLAB.

Include files

D:\Program Files\matlab\r2012a\extern\include

Library directory: (1): directory where the Mydll.lib is located.

(2): The lib directory of Matlab.

Library files

D:\New project\ handwritten numeral recognition \qpprj\distrib

D:\Program Files\matlab\r2012a\extern\lib\win64\microsoft

1.2: Additional Dependencies in connector-"input-"

Mclmcrrt.lib

Libmx.lib

Libmat.lib

Mclmcr.lib

QpPrj.lib

put the compiled DLL Copy to VC Debug of the project or Release directory to make the DLL can be found. and copy the compiled QpPrj.h file into the VC project.

Code implementation:

#include <iostream> #pragma comment (lib, "QpPrj.lib") #include "QpPrj.h" #include "mclmcr.h" #include "matrix.h" #i Nclude "Mclcppclass.h" using namespace Std;int main (int argc, char* argv[]) {//Initialize if (! Qpprjinitialize ()) {printf ("Could not initialize!"); return-1;} 1. Call Myadddouble A = 6;double B = 9;double c;//Allocate memory space for the variable Mwarray mwA (1, 1, mxdouble_class); The size of the matrix (all Maltab only one variable, that is, the matrix, in order to be in line with the CPP variable, set to 1*1 Matrix, Mxdouble_class represents the variable precision) Mwarray MwB (1, 1, mxdouble_class); Mwarray MwC (1, 1, mxdouble_class);//Set data, call the SetData function inside the class to assign a value Mwa.setdata (&a, 1); Mwb.setdata (&b, 1);//Usin  G My Add, drop my own Write function//Call example: 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, call the Get function inside the class get function return value c = Mwc.get (1, 1);p rintf ("C is%f\n", c);//2. Call testchar//Exter n lib_qpprj_cpp_api void Mw_call_conv testchar (int nargout, mwarray& result, const mwarray& CHAR0) Double d = 4;dou Ble e;//allocates memory space for variables 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);p rintf ("E is%f\n", e); char training_result_path[] = "D:\\new projec t\\ handwritten numeral recognition "; 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 << Trai Ning_result_file << endl;//allocates memory space for variables Mwarray recognition_result (1, 1, mxdouble_class); Invocation example: 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);//The program terminating the call Qpprjterminate ();//Terminate MCRMCLTERMINATEAPPLIcation (); return 0;} 

　　

... Mwmcr::evaluatefunction error ...

Error using Predict (line 85)

Systems of UInt32 class cannot is used with the "predict" command. Convert the system to a identified model first, such as by using the "IDSS" command.

\

Matlab codeDIGITAL_RECOGN_9.M

function [Recognition_result]

= Digital_recogn_9 (Training_result_path, Digital_img_path, Training_result_file)

1 Training_result_path

Training Library path ' D:\New project\ handwritten digit recognition '

2 Digital_img_path

Incoming picture path ' D:\hh\t_4.jpg '

3 Training_result_file

Training library filename ' training_result_200_trees '

Invocation Example:

Digital_recogn_9 (' D:\New project\ handwritten numeral recognition ', ' 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; percent 16*16=256 trimming_img = Imresize (trimming_img, [Edge_length, Edge_length]); %% comment out temporarily %%imshow (TRIMMING_IMG); Flatten_img = Reshape (trimming_img, 1, 256); %%%%%%%%%%%%%%%%%%%%% CD (Training_result_path) %% add semicolon, training library filename ' 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); Percent Recognition_result = ypred (n); Recognition_result = ypred (a); 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

Mixed programming of C/C + + matlab