I am attempting to embed data from a text file(which contains only numeric data) into LL subband of an image using a steganography. I am getting an error "Error using bitset ASSUMEDTYPE must be an integer type name" in the line of code:
L(ii,jj)=bitset(L(ii,jj),1,stego(ii,jj));
I have attempted to run in debugger but I am having no luck. I think it must be something to do with the data type of L?? I have tried changing image to binary,double etc but I still get this error! Please can someone give me some advice on where I am going wrong?I have a insert my code below
% loading cover image
img=imread('lena.bmp');
image=im2double(img);
% get DWT of image
[LL,LH,HL,HH] = dwt2(image,'haar');
A = importdata('minutiaTest.txt');
I = dec2bin(A,8);
L=LL;
% determine size of LL subband
Mc=size(L,1); %Height
Nc=size(L,2); %Width
% determine size of message object
Mm=size(I,1); %Height
Nm=size(I,2); %Width
for ii = 1:Mc
for jj = 1:Nc
stego(ii,jj)=I(mod(ii,Mm)+1,mod(jj,Nm)+1);
end
end
for ii = 1:Mc
for jj = 1:Nc
L(ii,jj)=bitset(L(ii,jj),1,stego(ii,jj));
end
end
stego_image = idwt2(LL,LH,HL,HH,'haar');
imwrite(uint8(stego_image),'stego.bmp');
figure; imshow(stego_image,title('Stego image'));
Related
I am trying to use DIPimage to get some measurements of each object in an image and I get this error:
Error using dip_measure
DIPlib Error in function dip_Measure.
DIPlib Error in function dip_ImageCheck: Data type not supported
Error in measure (line 209)
data = dip_measure(object_in,gray_in,measurementID,objectIDs,connectivity);
Error in Untitled (line 13)
msr = measure(b, [], ({'size', 'perimeter','podczeckShapes'}))
How can I solve it?
Code:
Image = rgb2gray(imread('pillsetc.png'));
BW = imbinarize(Image);
BW = imfill(BW,'holes');
imshow(BW);
[B,L] = bwboundaries(BW,'noholes');
k = 1;
b = B{k};
y = b(:,2);
x = b(:,1);
msr(k) = measure(BW, [], ({'size', 'perimeter','podczeckShapes'}))
sz = msr.size;
podczeckShapes = podczeckShapes;
One problem with your code is the call to imfill. Because the image has bright values all around the image, it is considered that there's a large object with a hole, and your actual objects are inside this hole. imfill fills the hole, leaving the whole image white.
Instead, I suggest the following code to remove the frame:
Image = rgb2gray(imread('https://i.stack.imgur.com/fmqAF.jpg'));
BW = imbinarize(Image);
BW = BW - bpropagation(false(size(BW)), BW);
Because we used a filter in DIPimage, the BW variable now contains a dip_image object, not a normal MATLAB array. dip_array(BW) extracts the normal MATLAB array that is inside. The dip_image object behaves differently from a MATLAB array. For example, you can display it to an interactive figure window by just typing its name:
BW
Next, we apply labeling so that we know which object ID in the measurement data corresponds to which object:
lab = label(BW);
dipshow(lab,'labels')
Now we can apply the measurement function. If we use BW as input, label will be called on it. Since we already have that result, let's use it directly:
msr = measure(lab, [], {'size', 'perimeter','podczeckShapes'});
Let's examine results for object ID 8, which is the large square:
sz = msr(8).size
square = msr(8).podczeckShapes(1)
triangle = msr(8).podczeckShapes(3)
There are other things you can do with the measurement structure, I suggest you read the documentation. For example, we can remove from it the measurement for the littlest objects, which to me look like noise:
msr = msr(msr.size>100); % remove measurement for noise
i am currently using bwconnomp to label each connected object and regionpropsto find area, majoraxis, minoraxis of each labeled object respectively. i am also displaying each labeled object its area,majoraxis and minoraxis. now i want to set some threshold for area,majoraxis and minoraxis and if the value of area,majoraxis and minoraxis is above specified threshold then that object has to be masked.how this can be done??
here is my code
clc
clear all
close all
Index = 1;
scrsz = get(0,'ScreenSize');
%read an image
while Index ~= 0
% Open a dialog and select an image file
[FileName,FilePath,Index] = uigetfile('*.png', 'Open Imagefile ');
if Index == 0
disp('Procedure Done')
break;
end
inimage = imread([num2str(FilePath) FileName]);
D=inimage;
A=inimage;
subplot(2,3,1);
imshow(inimage);
title('original image');
%labeling algorithm
B=im2bw(inimage);
C=imfill(B,'holes');
label=bwlabel(C);
max(max(label))
CC = bwconncomp(B);
data = regionprops(CC,'all');
for j=1:max(max(label))
[row, col] = find(label==j);
len=max(row)-min(row)+2;
breadth=max(col)-min(col)+2;
target=uint8(zeros([len breadth] ));
sy=min(col)-1;
sx=min(row)-1;
for i=1:size(row,1)
x=row(i,1)-sx;
y=col(i,1)-sy;
target(x,y)=A(row(i,1),col(i,1));
end
mytitle=strcat('Object Number:' ,num2str(j),'area:', num2str(data(j).Area),'MajorAxis: ',num2str(data(j).MajorAxisLength),'MinorAxis: ',num2str(data(j).MinorAxisLength));
figure,imshow(target);title(mytitle);
a=size(target);
ax=a(1);
ay=a(2);
pos=[1,1,ay,ax];
rectangle('Position',pos,'EdgeColo','r')
end
next = input('next image? press Enter: ');
if next == 0
channelactivity = 0;
break
else
close all
disp('==================================')
pause(0.2)
continue
end
end
Here is a way to do it. The code is commented so easy to follow; the important line is the following:
AboveAreaIndices = find(vertcat(data.Area) > SomeValue)
In which you store the indices of the objects whose area is larger than SomeValue. In the example I color them red but you can do whatever you want with them or remove them altogether from the data structure.
You can also use logical operators to combine multiple conditions for example using the MinorAxis and MajorAxis properties. Note that I used AllArea as anew variable to store the concatenated areas to make things clearer, but you can keep them as vertcat(data.Area).
AboveIndices = find(vertcat(data.Area) > SomeValue & vertcat(data. MinorAxis) > SomeValue & Bla bla bla...);
Whole code:
clear
clc
close all
%// Read and clean up sample image
A = imread('rice.png');
A = im2bw(A,.5);
A = bwareaopen(A,50);
CC = bwconncomp(A);
%// Same as you.
data = regionprops(CC,'all');
%// Concatenate all the areas into an array.
AllArea = vertcat(data.Area);
%//========================================
%//==== Apply threshold on area here \\====
AboveAreaIndices = find(AllArea > 150);
%// If you wish to remove the entries from the data structure
% data(AllArea>150) = [];
%//========================================
%// Same for centroids...for display purposes
AllCentroids = vertcat(data.Centroid);
%// Display original and thresholded objects. Use the indices calculated
%// above to "mask" large areas if you want
imshow(A);
hold on
scatter(AllCentroids(:,1),AllCentroids(:,2),40,'b','filled')
scatter(AllCentroids(AboveAreaIndices,1),AllCentroids(AboveAreaIndices,2),40,'r','filled')
And sample output:
I am pretty new to Matlab and encountered a problem when working with images.
I want to get a pixel that is in a specific colour (blue) in the following image:
image
My current code looks something like this:
function p = mark(image)
%// display image I in figure
imshow(image);
%// first detect all blue values higher 60
high_blue = find(image(:,:,3)>60);
%cross elements is needed as an array later on, have to initialize it with 0
cross_elements = 0;
%// in this iteration the marked values are reduced to the ones
%where the statement R+G < B+70 applies
for i = 1:length(high_blue)
%// my image has the size 1024*768, so to access the red/green/blue values
%// i have to call the i-th, i+1024*768-th or i+1024*768*2-th position of the "array"
if ((image(high_blue(i))+image(high_blue(i)+768*1024))<...
image(high_blue(i)+2*768*1024)+70)
%add it to the array
cross_elements(end+1) = high_blue(i);
end
end
%// delete the zero element, it was only needed as a filler
cross_elements = cross_elements(cross_elements~=0);
high_vector = zeros(length(cross_elements),2);
for i = 1:length(cross_elements)
high_vector(i,1) = ceil(cross_elements(i)/768);
high_vector(i,2) = mod(cross_elements(i), 768);
end
black = zeros(768 ,1024);
for i = 1:length(high_vector)
black(high_vector(i,2), high_vector(i,1)) = 1;
end
cc = bwconncomp(black);
a = regionprops(cc, 'Centroid');
p = cat(1, a.Centroid);
%// considering the detection of the crosses:
%// RGB with B>100, R+G < 100 for B<150
%// consider detection in HSV?
%// close the figure
%// find(I(:,:,3)>150)
close;
end
but it is not optimized for Matlab, obviously.
So i was wondering if there was a way to search for pixels with specific values,
where the blue value is larger than 60 (not hard with the find command,
but at the same time the values in the red and green area not too high.
Is there a command I am missing?
Since English isn't my native language, it might even help if you gave me some suitable keywords for googling ;)
Thanks in advance
Based on your question at the end of the code, you could get what you want in a single line:
NewImage = OldImage(:,:,1) < SomeValue & OldImage(:,:,2) < SomeValue & OldImage(:,:,3) > 60;
imshow(NewImage);
for example, where as you see you provide a restriction for each channel using logical operators, that you can customize of course (eg. using | as logical OR). Is this what you are looking for? According to your code you seem to be looking for specific regions in the image like crosses or coins is that the case? Please provide more details if the code I gave you is completely off the track :)
Simple example:
A = imread('peppers.png');
B = A(:,:,3)>60 & A(:,:,2)<150 & A(:,:,1) < 100;
figure;
subplot(1,2,1);
imshow(A);
subplot(1,2,2)
imshow(B);
Giving this:
My code works in the following manner:
1.First, it obtains several images from the training set
2.After loading these images, we find the normalized faces,mean face and perform several calculation.
3.Next, we ask for the name of an image we want to recognize
4.We then project the input image into the eigenspace, and based on the difference from the eigenfaces we make a decision.
5.Depending on eigen weight vector for each input image we make clusters using kmeans command.
Source code i tried:
clear all
close all
clc
% number of images on your training set.
M=1200;
%Chosen std and mean.
%It can be any number that it is close to the std and mean of most of the images.
um=60;
ustd=32;
%read and show images(bmp);
S=[]; %img matrix
for i=1:M
str=strcat(int2str(i),'.jpg'); %concatenates two strings that form the name of the image
eval('img=imread(str);');
[irow icol d]=size(img); % get the number of rows (N1) and columns (N2)
temp=reshape(permute(img,[2,1,3]),[irow*icol,d]); %creates a (N1*N2)x1 matrix
S=[S temp]; %X is a N1*N2xM matrix after finishing the sequence
%this is our S
end
%Here we change the mean and std of all images. We normalize all images.
%This is done to reduce the error due to lighting conditions.
for i=1:size(S,2)
temp=double(S(:,i));
m=mean(temp);
st=std(temp);
S(:,i)=(temp-m)*ustd/st+um;
end
%show normalized images
for i=1:M
str=strcat(int2str(i),'.jpg');
img=reshape(S(:,i),icol,irow);
img=img';
end
%mean image;
m=mean(S,2); %obtains the mean of each row instead of each column
tmimg=uint8(m); %converts to unsigned 8-bit integer. Values range from 0 to 255
img=reshape(tmimg,icol,irow); %takes the N1*N2x1 vector and creates a N2xN1 matrix
img=img'; %creates a N1xN2 matrix by transposing the image.
% Change image for manipulation
dbx=[]; % A matrix
for i=1:M
temp=double(S(:,i));
dbx=[dbx temp];
end
%Covariance matrix C=A'A, L=AA'
A=dbx';
L=A*A';
% vv are the eigenvector for L
% dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx';
[vv dd]=eig(L);
% Sort and eliminate those whose eigenvalue is zero
v=[];
d=[];
for i=1:size(vv,2)
if(dd(i,i)>1e-4)
v=[v vv(:,i)];
d=[d dd(i,i)];
end
end
%sort, will return an ascending sequence
[B index]=sort(d);
ind=zeros(size(index));
dtemp=zeros(size(index));
vtemp=zeros(size(v));
len=length(index);
for i=1:len
dtemp(i)=B(len+1-i);
ind(i)=len+1-index(i);
vtemp(:,ind(i))=v(:,i);
end
d=dtemp;
v=vtemp;
%Normalization of eigenvectors
for i=1:size(v,2) %access each column
kk=v(:,i);
temp=sqrt(sum(kk.^2));
v(:,i)=v(:,i)./temp;
end
%Eigenvectors of C matrix
u=[];
for i=1:size(v,2)
temp=sqrt(d(i));
u=[u (dbx*v(:,i))./temp];
end
%Normalization of eigenvectors
for i=1:size(u,2)
kk=u(:,i);
temp=sqrt(sum(kk.^2));
u(:,i)=u(:,i)./temp;
end
% show eigenfaces;
for i=1:size(u,2)
img=reshape(u(:,i),icol,irow);
img=img';
img=histeq(img,255);
end
% Find the weight of each face in the training set.
omega = [];
for h=1:size(dbx,2)
WW=[];
for i=1:size(u,2)
t = u(:,i)';
WeightOfImage = dot(t,dbx(:,h)');
WW = [WW; WeightOfImage];
end
omega = [omega WW];
end
% Acquire new image
% Note: the input image must have a bmp or jpg extension.
% It should have the same size as the ones in your training set.
% It should be placed on your desktop
ed_min=[];
srcFiles = dir('G:\newdatabase\*.jpg'); % the folder in which ur images exists
for b = 1 : length(srcFiles)
filename = strcat('G:\newdatabase\',srcFiles(b).name);
Imgdata = imread(filename);
InputImage=Imgdata;
InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]);
temp=InImage;
me=mean(temp);
st=std(temp);
temp=(temp-me)*ustd/st+um;
NormImage = temp;
Difference = temp-m;
p = [];
aa=size(u,2);
for i = 1:aa
pare = dot(NormImage,u(:,i));
p = [p; pare];
end
InImWeight = [];
for i=1:size(u,2)
t = u(:,i)';
WeightOfInputImage = dot(t,Difference');
InImWeight = [InImWeight; WeightOfInputImage];
end
noe=numel(InImWeight);
% Find Euclidean distance
e=[];
for i=1:size(omega,2)
q = omega(:,i);
DiffWeight = InImWeight-q;
mag = norm(DiffWeight);
e = [e mag];
end
ed_min=[ed_min MinimumValue];
theta=6.0e+03;
%disp(e)
z(b,:)=InImWeight;
end
IDX = kmeans(z,5);
clustercount=accumarray(IDX, ones(size(IDX)));
disp(clustercount);
Running time for 100 images:Elapsed time is 103.947573 seconds.
QUESTIONS:
1.It is working fine for M=50(i.e Training set contains 50 images) but not for M=1200(i.e Training set contains 1200 images).It is not showing any error.There is no output.I waited for 10 min still there is no output.What is the problem?Where i was wrong?
To answer your second question, you can simply 'save' any generated variable as a .mat file in your working directory (Current folder) which can be accessed later. So in your code, if the 'training eigenfaces' is given by the variable 'u', you can use the following:
save('eigenface.mat','u')
This creates a .mat file with the name eigenface.mat which contains the variable 'u', the eigenfaces. Note that this variable is saved in your Current Folder.
In a later instance when you are trying out with your test data, you can simply 'load' this variable:
load('eigenface.mat')
This automatically loads 'u' into your workspace.
You can also save additional variables in the same .mat file if necessary
save('eigenface.mat','u','v'...)
The answer to the first question is that the code is simply not done running yet. Vectorizing the code (instead of using a for loop), as suggested in the comment section above can improve the speed significantly.
[EDIT]
Since the images are not very big, the code is not significantly slowed down by the first for loop. You can improve the performance of the rest of the code by vectorizing the code. Vectorized operations are faster than for-loops. This link might be useful in understanding vectorization:
http://www.mathworks.com/help/matlab/matlab_prog/vectorization.html
For example, the second for-loop can be replaced by the following vectorized form, as suggested in the comments
tempS = double(S);
meanS = mean(S,1);
stdS = std(S,0,1);
S = (tempS - meanS) ./ stdS;
Use MATLAB's timer functions tic and toc for finding how long the first for-loop alone is taking to execute. Add tic before the for loop and toc after it. If the time taken for 50 images is about 104 seconds, then it would be significantly more for 1200 images.
I'm new to Matlab.
I'm trying to apply PCA function(URL listed below)into my palm print recognition program to generate the eigenpalms. My palm print grey scale images dimension are 450*400.
Before using it, I was trying to study these codes and add some codes to save the eigenvector as .mat file. Some of the %comments added by me for my self understanding.
After a few days of studying, I still unable to get the answers.
I decided to ask for helps.I have a few questions to ask regarding this PCA.m.
PCA.m
What is the input of the "options" should be? of "PCA(data,details,options)"
(is it an integer for reduced dimension? I was trying to figure out where is the "options" value passing, but still unable to get the ans. The msgbox of "h & h2", is to check the codes run until where. I was trying to use integer of 10, but the PCA.m processed dimension are 400*400.)
The "eigvector" that I save as ".mat" file is ready to perform Euclidean distance classifier with other eigenvector? (I'm thinking that eigvector is equal to eigenpalm, like in face recognition, the eigen faces. I was trying to convert the eigenvector matrix back to image, but the image after PCA process is in Black and many dots on it)
mySVD.m
In this function, there are two values that can be changed, which are MAX_MATRIX_SIZE set by 1600 and EIGVECTOR_RATIO set by 0.1%. May I know these values will affect the results? ( I was trying to play around with the values, but I cant see the different. My palm print image dimension is set by 450*400, so the Max_matrix_size should set at 180,000?)
** I hope you guys able to understand what I'm asking, please help, Thanks guys (=
Original Version : http://www.cad.zju.edu.cn/home/dengcai/Data/code/PCA.m
mySVD: http://www.cad.zju.edu.cn/home/dengcai/Data/code/mySVD.m
% Edited Version by me
function [eigvector, eigvalue] = PCA(data,details,options)
%PCA Principal Component Analysis
%
% Usage:
% [eigvector, eigvalue] = PCA(data, options)
% [eigvector, eigvalue] = PCA(data)
%
% Input:
% data - Data matrix. Each row vector of fea is a data point.
% fea = finite element analysis ?????
% options.ReducedDim - The dimensionality of the reduced subspace. If 0,
% all the dimensions will be kept.
% Default is 0.
%
% Output:
% eigvector - Each column is an embedding function, for a new
% data point (row vector) x, y = x*eigvector
% will be the embedding result of x.
% eigvalue - The sorted eigvalue of PCA eigen-problem.
%
% Examples:
% fea = rand(7,10);
% options=[]; %store an empty matrix in options
% options.ReducedDim=4;
% [eigvector,eigvalue] = PCA(fea,4);
% Y = fea*eigvector;
%
% version 3.0 --Dec/2011
% version 2.2 --Feb/2009
% version 2.1 --June/2007
% version 2.0 --May/2007
% version 1.1 --Feb/2006
% version 1.0 --April/2004
%
% Written by Deng Cai (dengcai AT gmail.com)
%
if (~exist('options','var'))
%A = exist('name','kind')
% var = Checks only for variables.
%http://www.mathworks.com/help/matlab/matlab_prog/symbol-reference.html#bsv2dx9-1
%The tilde "~" character is used in comparing arrays for unequal values,
%finding the logical NOT of an array,
%and as a placeholder for an input or output argument you want to omit from a function call.
options = [];
end
h2 = msgbox('not yet');
ReducedDim = 0;
if isfield(options,'ReducedDim')
%tf = isfield(S, 'fieldname')
h2 = msgbox('checked');
ReducedDim = options.ReducedDim;
end
[nSmp,nFea] = size(data);
if (ReducedDim > nFea) || (ReducedDim <=0)
ReducedDim = nFea;
end
if issparse(data)
data = full(data);
end
sampleMean = mean(data,1);
data = (data - repmat(sampleMean,nSmp,1));
[eigvector, eigvalue] = mySVD(data',ReducedDim);
eigvalue = full(diag(eigvalue)).^2;
if isfield(options,'PCARatio')
sumEig = sum(eigvalue);
sumEig = sumEig*options.PCARatio;
sumNow = 0;
for idx = 1:length(eigvalue)
sumNow = sumNow + eigvalue(idx);
if sumNow >= sumEig
break;
end
end
eigvector = eigvector(:,1:idx);
end
%dt get from C# program, user ID and name
evFolder = 'ev\';
userIDName = details; %get ID and Name
userIDNameWE = strcat(userIDName,'\');%get ID and Name with extension
filePath = fullfile('C:\Users\***\Desktop\Data Collection\');
userIDNameFolder = strcat(filePath,userIDNameWE); %ID and Name folder
userIDNameEVFolder = strcat(userIDNameFolder,evFolder);%EV folder in ID and Name Folder
userIDNameEVFile = strcat(userIDNameEVFolder,userIDName); % EV file with ID and Name
if ~exist(userIDNameEVFolder, 'dir')
mkdir(userIDNameEVFolder);
end
newFile = strcat(userIDNameEVFile,'_1');
searchMat = strcat(newFile,'.mat');
if exist(searchMat, 'file')
filePattern = strcat(userIDNameEVFile,'_');
D = dir([userIDNameEVFolder, '*.mat']);
Num = length(D(not([D.isdir])))
Num=Num+1;
fileName = [filePattern,num2str(Num)];
save(fileName,'eigvector');
else
newFile = strcat(userIDNameEVFile,'_1');
save(newFile,'eigvector');
end
You pass options in a structure, for instance:
options.ReducedDim = 2;
or
options.PCARatio =0.4;
The option ReducedDim selects the number of dimensions you want to use to represent the final projection of the original matrix. For instance if you pick option.ReducedDim = 2 you use only the two eigenvectors with largest eigenvalues (the two principal components) to represent your data (in effect the PCA will return the two eigenvectors with largest eigenvalues).
PCARatio instead allows you to pick the number of dimensions as the first eigenvectors with largest eigenvalues that account for fraction PCARatio of the total sum of eigenvalues.
In mySVD.m, I would not increase the default values unless you expect more than 1600 eigenvectors to be necessary to describe your dataset. I think you can safely leave the default values.