我如何將MATLAB代碼轉換爲C＃

Question

我有一個MATLAB代碼，通過首先將圖像轉換爲灰度圖像並將圖像分配給一維數組，將圖像進行面部識別 我需要將此代碼轉換爲C＃。

該函數讀取圖片：

function T = CreateDatabase(TrainDatabasePath) 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% File management 
 
TrainFiles = dir(TrainDatabasePath); 
 
Train_Number = 0; 
 

 
for i = 1:size(TrainFiles,1) 
 
    if not(strcmp(TrainFiles(i).name,'.')|strcmp(TrainFiles(i).name,'..')|strcmp(TrainFiles(i).name,'Thumbs.db')) 
 
     Train_Number = Train_Number + 1; % Number of all images in the training database 
 
    end 
 
end 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Construction of 2D matrix from 1D image vectors 
 
T = []; 
 
for i = 1 : Train_Number 
 
    
 
    % I have chosen the name of each image in databases as a corresponding 
 
    % number. However, it is not mandatory! 
 
    str = int2str(i); 
 
    str = strcat('\',str,'.jpg'); 
 
    str = strcat(TrainDatabasePath,str); 
 
    
 
    img = imread(str); 
 
    img = rgb2gray(img); 
 
    
 
    [irow icol] = size(img); 
 
    
 
    temp = reshape(img',irow*icol,1); % Reshaping 2D images into 1D image vectors 
 
    T = [T temp]; % 'T' grows after each turn      
 
end

獲取的特徵值

function [m, A, Eigenfaces] = EigenfaceCore(T) 
 
       
 
    
 
%%%%%%%%%%%%%%%%%%%%%%%% Calculating the mean image 
 
m = mean(T,2); % Computing the average face image m = (1/P)*sum(Tj's) (j = 1 : P) 
 
Train_Number = size(T,2); 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Calculating the deviation of each image from mean image 
 
A = []; 
 
for i = 1 : Train_Number 
 
    temp = double(T(:,i)) - m; % Computing the difference image for each image in the training set Ai = Ti - m 
 
    A = [A temp]; % Merging all centered images 
 
end 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Snapshot method of Eigenface methos 
 
% We know from linear algebra theory that for a PxQ matrix, the maximum 
 
% number of non-zero eigenvalues that the matrix can have is min(P-1,Q-1). 
 
% Since the number of training images (P) is usually less than the number 
 
% of pixels (M*N), the most non-zero eigenvalues that can be found are equal 
 
% to P-1. So we can calculate eigenvalues of A'*A (a PxP matrix) instead of 
 
% A*A' (a M*NxM*N matrix). It is clear that the dimensions of A*A' is much 
 
% larger that A'*A. So the dimensionality will decrease. 
 

 
L = A'*A; % L is the surrogate of covariance matrix C=A*A'. 
 
[V D] = eig(L); % Diagonal elements of D are the eigenvalues for both L=A'*A and C=A*A'. 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Sorting and eliminating eigenvalues 
 
% All eigenvalues of matrix L are sorted and those who are less than a 
 
% specified threshold, are eliminated. So the number of non-zero 
 
% eigenvectors may be less than (P-1). 
 

 
L_eig_vec = []; 
 
for i = 1 : size(V,2) 
 
    if(D(i,i)>1) 
 
     L_eig_vec = [L_eig_vec V(:,i)]; 
 
    end 
 
end 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Calculating the eigenvectors of covariance matrix 'C' 
 
% Eigenvectors of covariance matrix C (or so-called "Eigenfaces") 
 
% can be recovered from L's eiegnvectors. 
 
Eigenfaces = A * L_eig_vec; % A: centered image vectors

識別

function OutputName = Recognition(TestImage, m, A, Eigenfaces) 
 

 

 
       
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Projecting centered image vectors into facespace 
 
% All centered images are projected into facespace by multiplying in 
 
% Eigenface basis's. Projected vector of each face will be its corresponding 
 
% feature vector. 
 

 
ProjectedImages = []; 
 
Train_Number = size(Eigenfaces,2); 
 
for i = 1 : Train_Number 
 
    temp = Eigenfaces'*A(:,i); % Projection of centered images into facespace 
 
    ProjectedImages = [ProjectedImages temp]; 
 
end 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Extracting the PCA features from test image 
 
InputImage = imread(TestImage); 
 
temp = InputImage(:,:,1); 
 

 
[irow icol] = size(temp); 
 
InImage = reshape(temp',irow*icol,1); 
 
Difference = double(InImage)-m; % Centered test image 
 
ProjectedTestImage = Eigenfaces'*Difference; % Test image feature vector 
 

 
%%%%%%%%%%%%%%%%%%%%%%%% Calculating Euclidean distances 
 
Euc_dist = []; 
 
for i = 1 : Train_Number 
 
    q = ProjectedImages(:,i); 
 
    temp = (norm(ProjectedTestImage - q))^2; 
 
    Euc_dist = [Euc_dist temp]; 
 
end 
 
[Euc_dist_min , Recognized_index] = min(Euc_dist); 
 
OutputName = strcat(int2str(Recognized_index),'.jpg'); 
 

 

 
%%%%%%%%%%%%%%%%%%%%%%%% Calculating Mahanalobis %distances 
 
%maha_dist = []; 
 
%X = []; 
 
%Y = []; 
 
%for i = 1 : Train_Number 
 
    % S = cov(X); 
 
% mu = mean(X); 
 
%temp = (Y(i,:)-mu)*inv(S)*(Y(i,:)-mu)'; 
 

 
%maha_dist = [maha_dist temp]; 
 
%end 
 

 
%[maha_dist_min , Recognized_index] = min(maha_dist); 
 
%OutputName = strcat(int2str(Recognized_index),'.jpg');

Answer 1

您可以call a matlab function from C# client.

創建MATLAB功能，MYFUNC，在文件夾C：\ TEMP \例子。

function [x,y] = myfunc(a,b,c) 
x = a + b; 
y = sprintf('Hello %s',c); 

在Microsoft®VisualStudio®的，參考到C＃項目添加到 MATLAB的COM對象。從項目菜單中選擇添加引用。

在添加引用對話框中選擇COM選項卡。

選擇MATLAB應用程序。

using System; 
using System.Collections.Generic; 
using System.Text; 

namespace ConsoleApplication2 
{ 
    class Program 
    { 
     static void Main(string[] args) 
     { 
      // Create the MATLAB instance 
      MLApp.MLApp matlab = new MLApp.MLApp(); 

      // Change to the directory where the function is located 
      matlab.Execute(@"cd c:\temp\example"); 

      // Define the output 
      object result = null; 

      // Call the MATLAB function myfunc 
      matlab.Feval("myfunc", 2, out result, 3.14, 42.0, "world"); 

      // Display result 
      object[] res = result as object[]; 

      Console.WriteLine(res[0]); 
      Console.WriteLine(res[1]); 
      Console.ReadLine(); 
     } 
    } 
}