I am working on a project which is about pattern (male/female)classification with matlab.I have a problem, I need your help, please.
My program should find mean images of datasets. First dataset is women,second dataset is men. So first mean image has to look like a woman and second a man.I have different datasets which all have format of jpeg. I am trying different datasets for my program to check if it is working but when I use different datasets I can not see true mean images all the time, for ex:
They are mean images from a dataset:
But when I use another dataset my mean images are like this, they have no meanings, I mean they dont look like face:
What can be the reason for this? I should work with different datasets. Please help.
`
filenamesA = dir(fullfile(pathfora, '*.jpg'));
Train_NumberA = numel(filenamesA);
%%%%%%%%%%%%%%%%%%%% Finding Image Vectors for A
imagesA= [];
for k = 1 : Train_NumberA
str = int2str(k);
str= strcat(str);
str = strcat('\',str,'b','.jpg');
str = strcat(pathfora,str);
imgA = imread(str);
imgA = rgb2gray(imgA);
[irowA icolA] = size(imgA);
tempA = reshape(imgA',irowA*icolA,1); % Reshaping 2D images into 1D image vectors
imagesA = [imagesA tempA]; % 'imagesA' grows after each turn
imagesA=double(imagesA);
end`
`%%%%%%%%%%%%%%%%%%%%%%%% Calculate the MEAN IMAGE VECTOR for A
mean_vectorA= mean(imagesA,2); % Computing the average vector m = (1/P)*sum(Tj's) (j = 1 : P)
mean_imageA= reshape(mean_vectorA,irowA,icolA); % Average matrix of training set A
meanimgA=mat2gray(mean_imageA);
figure(1);
imshow(rot90(meanimgA,3));`
-------------------------------------And same for dataset B (male)
You could use a 3D matrix to store the images. I also cleaned up the code a bit. Not tested.
filenamesA = dir(fullfile(pathfora, '*.jpg'));
Train_NumberA = numel(filenamesA);
imagesA = [];
for k = 1:Train_NumberA
imgA = imread(strcat(pathfora, '\', int2str(k), 'b', '.jpg'));
imgA = rgb2gray(imgA);
imagesA = cat(3, imagesA, imgA);
end
double command moved out of loop.
imagesA = double(imagesA);
Calculate the mean over the 3rd dimension of the imagesA matrix to get the mean 2D image.
meanimage_A = mean(imagesA, 3);
Convert to grayscale image.
meanimgA = mat2gray(meanimage_A);
I think rot90 is not needed here...
figure(1);
imshow(meanimgA, 3);
Use a 3D array or cell array of images instead of reshaping 2D images into single rows of a matrix. The reshaping is unnecessary and can only add bugs.
If all your images are the same size, you can use a multidimensional array: Matlab documentation on multidimensional arrays
Otherwise, use a cell array: Matlab documentation on cell arrays
Related
In the implementation of downsampling by a factor of 2 to the image, the downsampled image is gray. What should I do in order to add all of the color components to the downsampling implementation so that it will be a color image?
I = imread('lena.gif','gif');
[j k] = size(I)
x_new = j./2;
y_new = k./2;
x_scale = j./x_new;
y_scale = k./y_new;
M = zeros(x_new,y_new);
for count1 = 1:x_new
for count2 = 1:y_new
M(count1,count2) = I(count1.*x_scale,count2.*y_scale);
end
end
figure,imshow(I);
title('Original Image');
M = uint8(M);
figure,imshow(M);
title('Downsample');
GIF images are what are known as indexed images. This means that what you read in with imread are values that are indices to a colour map. Each index generates a unique colour for you, and that's how GIF images are stored. They choose from a predefined set of colours, and each pixel in the GIF image comes from one of the colours in the colour map.
You first need to convert the image into RGB, and you do that with ind2rgb. However, you need to read in the colour map first with the two-output version of imread. You also will want to convert the images to uint8 as good practice with im2uint8:
[X,map] = imread('lena.gif');
I = im2uint8(ind2rgb(X,map));
What you need to do next is what #NKN suggested. You must apply the algorithm to all channels.
As such, simply make an output matrix that has three channels, and apply the algorithm to each plane independently. If I can make a suggestion, when accessing pixels this way after you downsample, make sure you floor or round the image coordinates so you're not inadvertently specifying locations that aren't defined - things like (13.8, 25.5) for example. Image pixel locations are integer, so you need to make sure the coordinates are integer too.
[X,map] = imread('lena.gif');
I = im2uint8(ind2rgb(X,map));
j = size(I,1); %// Change
k = size(I,2);
x_new = j./2;
y_new = k./2;
x_scale = j./x_new;
y_scale = k./y_new;
M = zeros(x_new,y_new,size(I,3)); %// Change
for jj = 1 : size(I,3) %// Change
for count1 = 1:x_new
for count2 = 1:y_new
M(count1,count2,jj) = I(floor(count1.*x_scale),floor(count2.*y_scale),jj); %// Change
end
end
end
figure,imshow(I);
title('Original Image');
M = uint8(M);
figure,imshow(M);
title('Downsample');
To test this, I'm using the mandrill dataset that's part of MATLAB. It is an indexed image with an associated colour map. These are coincidentally stored in X and map respectfully:
load mandrill;
I = im2uint8(ind2rgb(X,map));
Running the modified code, I get these two figures:
When you read the original image it contains 3 layers, R-G-B (as suggested by #rayryeng:
[X,map] = imread('lena.gif');
I = ind2rgb(X,map);
size(I)
ans =
768 1024 3
You should perform the down-sampling process on all the layers:
The code you provided does not down-sample. A simple downsampling example is as follows:
imshow(I(1:2:end,1:2:end,:))
I calculated the distance between an image A1 and different images like image1,image2,image3 and image4 based on its hierarchicalCentroid. dist_1_1{ii} contains 4 values. I want to find the minimum value present in the dist_1_1{ii}.But I shows the value 1 and also i want to show the image which gives minimum value. Please help me. Thanks in advance
%% demo
clc,clear all,close all
plotFlag = 1;
depth = 6;
alef1 = im2bw(imread('C1.bmp')); %% Binary image
vec1 = hierarchicalCentroid(alef1,depth,plotFlag);
% subplot(1,3,1);
A=[];
vec2=[];
dist_1_1=[];
for ii=1:4
A{ii} = imread(['image' num2str(ii) '.bmp']);
% subplot(1,3,2);
vec2{ii} = hierarchicalCentroid(A{ii},depth,plotFlag);
%subplot(1,3,3);
%vec3 = hierarchicalCentroid(tav,depth,plotFlag);
% vec4=hierarchicalCentroid(A,depth,plotFlag);
% vec5=hierarchicalCentroid(A,depth,plotFlag);
dist_1_1{ii} = sum((vec1 - vec2{ii}) .^ 2);
[~,I] = min(dist_1_1{ii});
figure;
subplot(1,2,1);imshow(alef1);
subplot(1,2,2);imshow(A{I});
end
Considering that your images are named such as image1.png, image2.png,...
first, read and store the images in a cell
for ii=1:n
A{ii} = imread(['image' num2str(ii) '.png']);
end
Then compute the similarity between the image A1 and other images:
ind = computeSimilarity(A1,A); % here you compute the similarity and
(of course you would need a for-loop.)
After you have stored the values in the ind vector:
ind = [0.76,1.96,2.96];
Then find the index of a minimum value and choose the image accordingly
[~,I] = min(ind);
figure;
subplot(1,2,1);imshow(A1);
subplot(1,2,2);imshow(A{I});
What should be corrected in your code:
First of all, avoid using cell when it is not necessary and define it correctly when you using it. You cannot define a cell array like A=[]. You should do it like this: A=cell(2,3). For instance, for storing the vector of the descriptors you do not need a cell, just store them as a matrix, as I did.
Second of all when posting your code here, remove the unnecessary parts such as commented plots and commands.
And then, try to modify your code as follows, I might made some mistake about the dimensions, but you can get the main idea.
and also remember that you do not need to check each distance inside the loop. Calculate the vectors first and then find the distances in one step, as I did.
depth = 6;
alef1 = im2bw(imread('C1.bmp'));
vec1 = hierarchicalCentroid(alef1,depth,0);
A=cell(1,4);
vMatrix=zeros(4,length(vec1));
for ii=1:4
A{1,ii} = imread(['image' num2str(ii) '.bmp']);
vecMatrix(ii,:) = hierarchicalCentroid(A{1,ii},depth,0);
end
dist = sum((repmat(vec1,4,1) - vMatrix) .^ 2,2);
[~,I] = min(dist);
i want to draw image, do not know how to portray the image output kmeans
my code:
close all; clc; clear;
img = imread('pic7.png');
figure(), imshow(img);
impixelregion;
% nastavenie noveho obrazka
[y x z] = size(img)
for i=1:1:y
for j=1:1:x
imgNew(i, j, :) = 0;
end
end
[X_no_dither, map]= rgb2ind(img,8,'nodither');
figure, imshow(X_no_dither, map);
impixelregion;
m = im2double(X_no_dither)
idx = kmeans(m,4,'emptyaction','singleton');
how i draw image ?
Thanks.
In kmeans, the rows are the things we want to cluster (i.e. pixels by colour), the columns are the variables. Therefore, we cannot just pass in a image straight in, it has to be reshaped first. For example if we look at this MATLAB example., the image is first converted into lab colorspace (and is in lab_he).
First, they take only the a and b elements:
ab = double(lab_he(:,:,2:3));
Then, reshape so that ab is of size n x m where n = total number of pixels and m = 2 - this is because each pixel is being clustered based on both a and b values:
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
Now, sort those pixels into three colors:
nColors = 3;
% repeat the clustering 3 times to avoid local minima
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
'Replicates',3);
The result then has to be reshape'd back into an image to be displayed:
pixel_labels = reshape(cluster_idx,nrows,ncols);
imshow(pixel_labels,[]), title('image labeled by cluster index');
In your case, as you're using a indexed image, try something like this:
idx = kmeans(m(:),4,'emptyaction','singleton');
idx = reshape(idx,size(m));
imshow(idx, []);
m(:) just arranges the pixels as a single column. kmeans then sorts them into four clusters. Afterwards, we reshape the indexes back using the size of our input image, and then display with imshow as normal.
You should read the linked example closely and make sure you understand what they are doing and why.
I want to compute the morlet wavelet of each row of 480X480 image. I have to save the output of the transform of each row which is a 2d array(matrix).
Then i will be taking the average all 480 2d matrices i have to get one final plot of the average.
clc;
close all;
clear all;
I=imread('lena.jpg');
J=rgb2gray(I);
%K=J(1:480)
%coefs = cwt(K,1:128,'morl','plot');
coefs = cell(480,1);
for i = 1:480
K=J(i,:);
coefs(i) = cwt(K,1:128,'morl');
end
Here i want to take the avg of the 480 coeff matrices. Here am getting the error
Conversion to cell from double is not possible.
Error in soilwave (line 12) coefs(i) = cwt(K,1:128,'morl');
Could anyone suggest a better method or tweaks to this.
Cell arrays are practical if you need to store elements that have inconsistent format or dimensions, but for what you are trying to do, a 3D array is easier to work with. Here is what I would do:
Preassign a 3D array:
coefs = zeros(128, size(J, 2), size(J,1));
then compute and populate the stack:
for ii = 1:size(J, 1)
K=J(ii,:);
coefs(:,:,ii) = cwt(K,1:128,'morl');
end
Finally, compute the mean along the third dimension:
MeanCoeff=mean(coefs, 3);
I've been performing a 2D mode filter on an RGB image by running medfilt2 independently on the R,G and B channels. However, splitting the RGB channels like this gives artifacts in the colouring. Is there a way to perform the 2D median filter while keeping RGB values 'together'?
Or, I could explain this more abstractly: Imagine I had a 2D matrix, where each value contained a pair of index coordinates (i.e. a cell matrix of 2X1 vectors). How would I go about performing a median filter on this?
Here's how I can do an independent mode filter (giving the artifacts):
r = colfilt(r0,[5 5],'sliding',#mode);
g = colfilt(g0,[5 5],'sliding',#mode);
b = colfilt(b0,[5 5],'sliding',#mode);
However colfilt won't work on a cell matrix.
Another approach could be to somehow combine my RGB channels into a single number and thus create a standard 2D matrix. Not sure how to implement this, though...
Any ideas?
Thanks for your help.
Cheers,
Hugh
EDIT:
OK, so problem solved. Here's how I did it.
I adapted my question so that I'm no longer dealing with (RGB) vectors, but (UV) vectors. Still essentially the same problem, except that my vectors are 2D not 3D.
So firstly I load the individual U and V channels, arrange them each into a 1D list, then combine them, so I essentially have a list of vectors. Then I reduce it to just those which are unique. Then, I assign each pixel in my matrix the value of the index of that unique vector. After this I can do the mode filter. Then I basically do the reverse, in that I go through the filtered image pixelwise, and read the value at each pixel (i.e. an index in my list), and find the unique vector associated with that index and insert it at that pixel.
% Create index list
img_u = img_iuv(:,:,2);
img_v = img_iuv(:,:,3);
coordlist = unique(cat(2,img_u(:),img_v(:)),'rows');
% Create a 2D matrix of indices
img_idx = zeros(size(img_iuv,1),size(img_iuv,2),2);
for y = 1:length(Y)
for x = 1:length(X)
coords = squeeze(img_iuv(x,y,2:3))';
[~,idx] = ismember(coords,coordlist,'rows');
img_idx(x,y) = idx;
end
end
% Apply the mode filter
img_idx = colfilt(img_idx,[n,n],'sliding',#mode);
% Re-construct the original image using the filtered data
for y = 1:length(Y)
for x = 1:length(X)
idx = img_idx(x,y);
try
coords = coordlist(idx,:);
end
img_iuv(x,y,2:3) = coords(:);
end
end
Not pretty but it gets the job done. I suppose this approach would also work for RGB images, or other similar situations.
Cheers,
Hugh
I don't see how you can define the median of a vector variable. You probably need to reduce the R,G,B components to a single value and then compunte the median on that value. Why not use the intensity level as that single value? You could do it easily with rgb2gray.