matlab runing problem(libmwlapack.dll missing)

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reema
reema 2014-5-31
评论： reema 2014-5-31
i have a problem to run matlab software matlab r2013a, when me open this its give the error+ "libmwlapack.dll" files missing in your systemm...now i can resolve this issuee?

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采纳的回答

Markus
Markus 2014-5-31
Someone I know had this problem today. He is a user of "avast" anti virus which (very probably wrongfully) flagged this file as containing a virus and therefore contained it.
Restoring the file through avast's interface (and telling avast to behave) solved the problem.
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reema
reema 2014-5-31
i have another problem can you tell me about it
my project is "feature based face detection and recognition" me complete the detection part (detect the face from an image on the bases of skin color).now i want to applyy code for recognition using PCA of that detected face me have code
handles = guidata(hObject);
handles.output = hObject;
dir1 = handles.d ;
filenames1=dir(fullfile(dir1,'*.tif'));
no_subs=numel(filenames1);
no_images=no_subs;
% Array of Images
t_array=[];
% Creating array of images by converting them to vector
for n=1:no_subs
sub=fullfile(dir1,filenames1(n).name);
img=imread(sub);
img=im2double(img);
img=imresize(img,[92 112]);
t_array(:,n)=img(:);
end
% Code for creating the Projected Images:
% Mean face image vector
mean_face=mean(t_array,2);
m=mean_face ;
img=reshape(m,[92 112]);
varargout{1} = handles.output;
axes(handles.axes5);
imshow(img);
title('Eigen Image','fontsize',10)
% Shifted images of all the images
shifted_images_array=t_array-repmat(mean_face,1,no_images);
% Matrix with reduced eigen vectors
new_matrix=shifted_images_array'*shifted_images_array;
% Eigen vectors of the new matrix taking 6 out of 10
no_eigen_vectrs=5;
[eigen_vect,eigen_val]=eigs(new_matrix,no_eigen_vectrs);
% Eigen vector of the covariance matrix(matirx array formed by image set "t_array")
eigen_vect_covmat=shifted_images_array*eigen_vect;
% Array of Weights of the projected images of every image
omega_array=eigen_vect_covmat'*shifted_images_array;
% Code for creating the test input:
TestImage = handles.im;
input_img=imread(TestImage);
input_img=im2double(input_img);
input_img=imresize(input_img,[92 112]);
input_vect=input_img(:);
input_shift=input_vect-mean_face;
input_omega=eigen_vect_covmat'*input_shift;
% % Code for Calculating the Euclidean Distance :
% % Find the similarity score
ss=arrayfun(@(n) norm(omega_array(:,n)-input_omega),1:no_images);
% Find image with highest similarity or min value of ss
[match_score,match_idx]=min(ss);
ab = reshape(t_array(:,match_idx),92,112);
% Display Image and Statistics
varargout{1} = handles.output;
axes(handles.axes6);
imshow(ab);
title( 'Match image with database', 'fontsize',10);
handles.b = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Bags');
handles.f = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Face');
handles.m = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Flow');
a = string('Bags');
b = string('Face');
c = string('Flower');
if handles.dr == handles.b
set (handles.edit1, 'String' ,a);
elseif handles.dr == handles.f
set (handles.edit1, 'String' ,b);
elseif handles.dr == handles.m
set (handles.edit1, 'String' ,c);
end
guidata(hObject, handles);
% --- Executes during object creation, after setting all properties.
function axes2_CreateFcn(hObject, eventdata, handles)
% hObject handle to axes2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
b=imread('B.tif');
imshow(b);
% Hint: place code in OpeningFcn to populate axes2
% --- Executes during object creation, after setting all properties.
function axes3_CreateFcn(hObject, eventdata, handles)
% hObject handle to axes3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
c=imread('F.tif');
imshow(c);
% Hint: place code in OpeningFcn to populate axes3
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
dir1 = handles.d ;
handles = guidata(hObject);
handles.output = hObject;
% prompt = {'Enter test image name (a number between 1 to 40):'};
% dlg_title = 'Input ';
% num_lines= 1;
% def = {'1'};
%
% TestImage = inputdlg(prompt,dlg_title,num_lines,def);
% TestImage = strcat(dir1,'\',char(TestImage),'.tif');
% im = imread(TestImage);
% ime=imresize(im,[92 112]);
% axes(handles.axes4);
% imshow(ime);
% title('input image of database','fontsize',10);
filenames1=dir(fullfile(dir1,'*.tif'));
no_subs=numel(filenames1);
no_images=no_subs;
% Array of Images
t_array=[];
% Creating array of images by converting them to vector
for n=1:no_subs
sub=fullfile(dir1,filenames1(n).name);
img=imread(sub);
img=im2double(img);
img=imresize(img,[92 112]);
t_array(:,n)=img(:);
end
% Code for creating the Projected Images:
% Mean face image vector
fimg=mean(t_array,2);
m=fimg ;
img=reshape(m,[92 112]);
varargout{1} = handles.output;
axes(handles.axes5);
imshow(img);
title('Fisher Image','fontsize',10)
% Shifted images of all the images
L=t_array-repmat(fimg,1,no_images);
% Matrix with reduced eigen vectors L = A'*A;
SW=L'*L;
% Calculate the within class variance (SW)
invsw=inv(SW);
sb=no_images*(L)'*(L);
SB=sb;
v=(invsw)*SB;
% find eigne values and eigen vectors of the (v)
[evec,eval]=eig(v);
no_eigen_vectrs=5;
[eigen_vect,eigen_val]=eigs(SW,no_eigen_vectrs);
% Eigen vector of the covariance matrix(matirx array formed by image set "t_array")
eigen_vect_covmat=L*eigen_vect;
% Array of Weights of the projected images of every image
omega_array=eigen_vect_covmat'*L;
% Code for creating the test input:
TestImage = handles.im;
input_img=imread(TestImage);
input_img=im2double(input_img);
input_img=imresize(input_img,[92 112]);
input_vect=input_img(:);
input_shift=input_vect-fimg;
input_omega=eigen_vect_covmat'*input_shift;
% % Code for Calculating the Euclidean Distance :
% % Find the similarity score
ss=arrayfun(@(n) norm(omega_array(:,n)-input_omega),1:no_images);
% Find image with highest similarity or min value of ss
[match_score,match_idx]=min(ss);
ab = reshape(t_array(:,match_idx),92,112);
% Display Image and Statistics
varargout{1} = handles.output;
axes(handles.axes6);
imshow(ab);
title( 'Match image with database', 'fontsize',10);
handles.b = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Bags');
handles.f = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Face');
handles.m = ('C:\Users\Student\Documents\MATLAB\GUI\Data\Test Database\Flow');
a = string('Bags');
b = string('Face');
c = string('Flower');
if handles.dr == handles.b
set (handles.edit1, 'String' ,a);
elseif handles.dr == handles.f
set (handles.edit1, 'String' ,b);
elseif handles.dr == handles.m
set (handles.edit1, 'String' ,c);
end
guidata(hObject, handles);
is it right?or me can use for my project. me need the information about that person which recognizeed.. how i can embed it with my coding?enter image description here
this is detected phase which me compelete now tell mee how i can compelte recognition phase using PCA?

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更多回答（1 个）

Nils
Nils 2014-5-31
Same here with R2013b, allready tried to reinstall but it didn't solve the problem.
Yesterday everything was just working fine ???
  2 个评论
reema
reema 2014-5-31
Someone I know had this problem today. He is a user of "avast" anti virus which (very probably wrongfully) flagged this file as containing a virus and therefore contained it.
Restoring the file through avast's interface (and telling avast to behave) solved the problem..(that is answer of Question)

请先登录，再进行评论。

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