VBA中如何定位程序代码行

VBA中代码注释的功能啊。。

就是在任意代码后面增加一个单引号'，在同行单引号后芹蠢敬面的所有内容即被视为注释。

给你一段代码看看：

Sub Macro1()

'VBA替换日期型数据

Dim cz As String, th As String

cz = "????-*-*" '查找内容

th = 41118 '替换内容

Dim c As Range '循环档团变量，代表选中区域内的每个单元格

'注意，先选择替换的区域，注意不要选择错了地方，再执行宏，否则后果比较严重

'下面的selection即表示被选择的区域

For Each c In Selection '循环选中区域的每一个单元嫌慎格

If c.Text Like cz Then '如果单元格的显示形式和????-*-*格式一样

c.Value = th '替换

End If

Next

End Sub

TS_EXISTS=0 % if 0 regenerate the training set and do SVD on it etc etc...

% establish the training set

directories = 35trainingSamplesPerDirectory = 10 % for directory-structured face database

trainingSamples = directories*trainingSamplesPerDirectoryfax = factor(trainingSamples) plotHt = prod(fax,2)/fax(length(fax))plotWd = fax(length(fax))

if (TS_EXISTS==0)

disp(['Loading the training set (',num2str(trainingSamples),' images)...'])

imageSize = 92

%exampleImage = double(imread('C:/Documents and Settings/aiw99r/My Documents/My Pictures/Brodatz Textures/D1.bmp','bmp'))

trainingSet = zeros(imageSize,imageSize,trainingSamples)

m=1

for dirNumber = 1:directories

for tspd=1:trainingSamplesPerDirectory

exampleImage = double(imread(strcat('C:\Documents and Settings\aiw99r\My Documents\Face Databases\ATandT_Cambridge\s',num2str(dirNumber),'\',num2str(tspd),'.bmp'升滑),'bmp'))

%exampleImage = double(imread(strcat('C:/Documents and Settings/aiw99r/My Documents/My Pictures/Faces/吵行腊face',num2str(im(m)),'.bmp'),'bmp'))

exIm = exampleImage(1:imageSize,1:imageSize)% crop to the desired training sample size

trainingSet(:,:,m) = exIm

m=m+1

end

end

if(1==0)

im_min = min(min(min(trainingSet)))im_max = max(max(max(trainingSet)))

figure

for m=1:trainingSamples

subplot(plotHt,plotWd,m)

imshow(trainingSet(:,:,m),[im_min im_max]),title(['带乎Original Image ',num2str(m)])

end

end

disp('Calculating mean image...')

% generate mean image

differenceImages = zeros(imageSize,imageSize,trainingSamples)

meanImage = mean(trainingSet,3)

figure,imagesc(meanImage),title('Mean Image'),colormap('gray')

disp('Calculating difference images...')

% subtract mean image to form the difference images

for m=1:trainingSamples

differenceImages(:,:,m) = trainingSet(:,:,m) - meanImage

end

%figure

%im_min = min(min(min(differenceImages)))im_max = max(max(max(differenceImages)))

%for m=1:trainingSamples

%subplot(plotHt,plotWd,m)

%imshow(differenceImages(:,:,m),[im_min im_max]),title(['Difference Image ',num2str(m)])

%end

disp('Vectorising difference images')

% form A -- which is a vector of the difference images

A = zeros(imageSize^2,trainingSamples)

for m=1:trainingSamples

A(:,m) = reshape(differenceImages(:,:,m),imageSize^2,1)

end

% form A'A and find e-vectors and e-values

%L = (A*A')

%[eVecsOfL,eVals] = eig(L) % V = full mx with columns being e-vectors, D = diagonal mx of e-values

%eVecs = zeros(imageSize^2,trainingSamples)

%eVecs = A*eVecsOfL % need to premultiply the e-vectors by A to get the e-vecs of A*A'

disp(['Extracting eigen-values and eigen-images from SVD (of ',num2str(size(A,1)*size(A,2)),'-element matrix)...'])

tic[U,S,V] = svd(A,0)toc

Eimage = zeros(imageSize,imageSize,trainingSamples)

for i = 1:trainingSamples

Eimage(:,:,i) = reshape(U(:,i),imageSize,imageSize) % the columns of U are automatically the e-vectors of A*A' (i.e., the covariance matrix)

Eval(i) = S(i,i)^2 % S is a diagonal matrix of singular values, we need to square them to get eigen-values

end

if(1==0)

figure

im_min = min(min(min(Eimage)))im_max = max(max(max(Eimage)))

for m=1:trainingSamples

subplot(plotHt,plotWd,m)

%imshow(Eimage(:,:,m),[im_min im_max]),title(['Eigen-Image ',num2str(m),' with SVD'])

imagesc(Eimage(:,:,m)),title(['Eigen-Image ',num2str(m),' with SVD']),colormap('gray')

end

end

figure,plot(Eval),title('Eigen-values')

% ---------------- END OF ASIDE ----------------------

%eVecs = U(1:exImages,1:exImages)

% perform linear combinations to get the eigen-images

%eigenImage = zeros(imageSize,imageSize,trainingSamples)

%for l=1:trainingSamples

%for k=1:trainingSamples

%eigenImage(:,:,l) = eVecs(k,l)*differenceImages(:,:,k)

%end

%end

%im_min = min(min(min(eigenImage)))im_max = max(max(max(eigenImage)))

%figure

%for l=1:trainingSamples

%subplot(plotHt,plotWd,l)

%imshow(eigenImage(:,:,l),[im_min im_max]),title(['E-Image ',num2str(l)])

% imagesc(eigenImage(:,:,l)),title(['E-Image ',num2str(l)]),colormap('gray')

% disp(['Eigenvalue ',num2str(l),' : ',num2str(Eval(l))])

%end

end

% ---------------------------------------- RECOGNITION TEST -------------------------------------------------

disp('Performing recognition...')

figure

% present a new face and attempt to recognise it

%recogniseImage = double(imread('C:/Documents and Settings/aiw99r/My Documents/My Pictures/Faces/face3.bmp','bmp'))

recogniseImage = double(imread(strcat('C:\Documents and Settings\aiw99r\My Documents\Face Databases\ATandT_Cambridge\s37\10.bmp'),'bmp'))

%recogniseImageTemp(1:121,1:160,1) = recogniseImage(1:121,2:161,1)recogniseImageTemp(1:121,161,1) = recogniseImage(1:121,1,1)recogniseImage=recogniseImageTemp% implement a 1-pixel shift to investigate how well-posed the weights are

%recogniseImage = double(imread('C:/Documents and Settings/aiw99r/My Documents/My Pictures/Faces/lady1.bmp','bmp'))

recIm = recogniseImage(1:imageSize,1:imageSize)% crop to the desired training sample size

subplot(1,3,1),imagesc(recIm),title('Image to recognise')

% transform into eigenimage space

recDiffIm = recIm-meanImage % firstly, subtract the mean from it to get the difference image

subplot(1,3,2),imagesc(recDiffIm),title('Mean Difference Image'),colormap('gray')

weight = zeros(trainingSamples,1)

for m=1:trainingSamples

weight(m) = (reshape(Eimage(:,:,m),imageSize^2,1))'*reshape(recDiffIm,imageSize^2,1) % do inner product of each eigenimage with the difference image to get a weight vector

end

% use the weights to remake the image

reconstructionImage = zeros(imageSize,imageSize)

for m=1:trainingSamples

reconstructionImage = reconstructionImage+(weight(m)*Eimage(:,:,m)) % should we incorporate in here (i.e., multiply weight(m) by eVals(m,m)????)

end

subplot(1,3,3),imagesc(reconstructionImage+meanImage),title('Eigen-image basis reconstruction'),colormap('gray')

figure,plot(weight),title('Weight vector for image to recognise')

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