I am working on image processing. I want to add the difference of pixels of two images.
Suppose I have two images A, and B. I pick the first pixel of both images and store the difference value. I want to add this difference value to next pixel-difference. I try using this code, but it is not working. How can I do it?
A = imread('sub2.jpg');
B = imread('sub1.jpg');
tic
[rows cols] = size(A);
diff1 = 0;
for x = 1:rows
for y = 1:cols
diff = A(x,y)-B(x,y);
diff1 = diff1+diff;
end
end
disp(diff1);
toc
You can do it in one line as follows:
sum(sum(imsubtract(A-B)))
imsubtract subtracts two images and saves the difference in a matrix with the same size as A. Then, sum takes the sum of the result.
If you need absolute differences, you may use imabsdiff instead of imsubtract.
Note that the values of the differences are in 0 and 255. If you want negative instances, then you should directly subtract the matrices as A-B.
Looks okay though, but you're better off with:
diff1 = sum(sum(A-B));
or if B is larger than A:
diff1 = sum(sum(A-B(1:size(A,1),1:size(A,2))));
This gives just one value (as your code does), I'm not sure if that really is what you want..
Related
My problem is one related to image registration. I have a number of images in a .tif-file, all the same size. I read those into MATLAB as a 3D-array of matrices and try to optimize the overlay of features in those images solely by rotation. I tried using imabsdiff, but resorted to just doing it like shown below.
In short, I input a vector containing as many angles as I have images in my stack. I rotate each image in the stack by each corresponding angle. Then, I calculate the absolute differences ([image1-image2] + [image2-image1]) which is what imabsdiff does, but faster. For this, I use two loop variables and compare each individual image to the whole stack, while leaving out comparison between identical images. Cost is the sum of differences between all images.
for oo = 1:slidecount
centered_stack(:,:,oo) = imrotate(centered_stack(:,:,oo),
angle_in(oo), 'bilinear', 'crop');
end
for pp = 1:slidecount
image1 = centered_stack(:,:,pp);
for qq = 1:slidecount
if qq ~= pp % only do this if comparing different images
image2 = centered_stack(:,:,qq);
cost_temp(qq) = sum(sum(abs(image1 - image2))) +
sum(sum(abs(image2 - image1)));
else
cost_temp(qq) = 0;
end
cost_temp = sum(cost_temp);
end
cost(pp) = cost_temp;
end
cost = sum(cost);
This then serves as a cost value for an optimization procedure. Can someone please tell me if there is a faster, maybe vectorized way to do this or something conceptually completely different? This approach gets very time consuming with many images. FFT based registration maybe? Thanks!
In your code you compare each pair of images twice:
image_1 to image_2 (pp == 1, qq == 2)
image_2 to image_1 (pp == 2, qq == 1)
Is this intended? If you make second loop look like this:
for qq = (pp+1):slidecount
you will reduce the computation by the factor of 2. Also your condition for checking qq ~= pp will not be needed anymore.
I have the following problem: given a 3D irregular geometry A with
(i,j,k)-coordinates, which are the centroids of connected voxels, create a table with the (i_out,j_out,k_out)-coordinates of the cells that represent the complementary set B of the bounding box of A, which we may call C. That is to say, I need the voxel coordinates of the set B = C - A.
To get this done, I am using the Matlab code below, but it is taking too much time to complete when C is fairly large. Then, I would like to speed up the code. To make it clear: cvc is the matrix of voxel coordinates of A; allcvc should produce C and B results from outcvc after setdiff.
Someone has a clue regarding the code performance, or even to improve my strategy?
Problem: the for-loop seems to be the villain.
My attempts: I have tried to follow some hints of Yair Altman's book by doing some tic,toc analyses, using pre-allocation and int8 since I do not need double values. deal yet gave me a slight improvement with min,max. I have also checked this discussion here, but, parallelism, for instance, is a limitation that I have for now.
% A bounding box limits
m = min(cvc,[],1);
M = max(cvc,[],1);
[im,jm,km,iM,jM,kM] = deal(m(1),m(2),m(3),M(1),M(2),M(3));
% (i,j,k) indices of regular grid
I = im:iM;
J = jm:jM;
K = km:kM;
% (i,j,k) table
m = length(I);
n = length(J);
p = length(K);
num = m*n*p;
allcvc = zeros(num,3,'int8');
for N = 1:num
for i = 1:m
for j = 1:n
for k = 1:p
aux = [I(i),J(j),K(k)];
allcvc(N,:) = aux;
end
end
end
end
% operation of exclusion: out = all - in
[outcvc,~] = setdiff(allcvc,cvc,'rows');
To avoid all for-loops in the present code you can use ndgrid or meshgrid functions. For example
[I,J,K] = ndgrid(im:iM, jm:jM, km:kM);
allcvc = [I(:),J(:),K(:)];
instead of your code between % (i,j,k) indices of regular grid and % operation of exclusion: out =.
I have two geotiff images (saying "A" and "B") imported in Matlab as matrices with Geotiffread. One has different values, while the second has only 0 and 255s.
What I'd like to do is replacing all the 255s with the values inside the other image (or matrix), according to their positions.
A and B differs in size, but they have the same projections.
I tried this:
A (A== 255)= B;
the output is the error:
??? In an assignment A(:) = B, the number of elements in A and B must be the same.
Else, I also tried with the logical approach:
if A== 255
A= B;
end
and nothing happens.
Is there a way to replace the values of A with values of B according to a specific value and the position in the referenced space?
As darthbith put in his comment, you need to make sure that the number of entries you want to replace is the same as the number values you are putting in.
By doing A(A==255)=B you are trying to put the entire matrix B into the subset of A that equals 255.
However, if, as you said, the projections are the same, you can simply do A(A==255) = B(A==255), under the assumption that B is larger or the same size as A.
Some sample code to provide a proof of concept.
A = randi([0,10],10,10);
B = randi([0,4],15,15);
C = A % copy original A matrix for comparison later
A(A==5) = B(A==5); % replace values
C==A % compare original and new
This example code creates two matrices, A is a 10x10 and B is a 15x15 and replaces all values that equal 5 in A with the corresponding values in B. This is shown to be true by doing C==A which shows where the new matrix and the old matrix vary, proving replacement did happen.
It seems to me that you are trying to mask an image with a binary mask. You can do this:
BW = im2bw(B,0.5);
A=A.*BW;
hope it helps
Try A(A==255) = B(A==255). The error is telling you that when you try to assign values to the elements of an array, you cannot give it any more or fewer values than you are trying to assign.
Also, regarding the if statement: if A==255 means the same as if all(A==255), as in, if any elements of A are not 255, false is returned. You can check this at the command line.
If you're really desperate, you can use a pair of nested for loops to achieve this (assuming A and B are the same size and shape):
[a,b] = size(A);
for ii = 1:a
for jj = 1:b
if A(ii,jj) == 255
A(ii,jj) = B(ii,jj);
end
end
end
I am a new student learning to use Matlab.
Could anyone please tell me is there a faster way possibly without loops:
to assign for each row only two values 1, -1 into different positions of a big sparse matrix.
My code to build a bimatrix or bibimatrix for the MILP problem of condition :
f^k_{ij} <= y_{ij} for every arc (i,j) and all k ~=r; in a multi-commodity flow model.
Naive approach:
bimatrix=[];
% create each row and then add to bimatrix
newrow4= zeros(1,n*(n+1)^2);
for k=1:n
for i=0:n
for j=1: n
if j~=i
%change value of some positions to -1 and 1
newrow4(i*n^2+(j-1)*n+k)=1;
newrow4((n+1)*n^2+i*n+j)=-1;
% add to bimatrix
bimatrix=[bimatrix; newrow4];
% change newrow4 back to zeros row.
newrow4(i*n^2+(j-1)*n+k)=0;
newrow4((n+1)*n^2+i*n+j)=0;
end
end
end
end
OR:
% Generate the big sparse matrix first.
bibimatrix=zeros(n^3 ,n*(n+1)^2);
t=1;
for k=1:n
for i=0:n
for j=1: n
if j~=i
%Change 2 positions in each row to -1 and 1 in each row.
bibimatrix(t,i*n^2+(j-1)*n+k)=1;
bibimatrix(t,(n+1)*n^2+i*n+j)=-1;
t=t+1
end
end
end
end
With these above code in Matlab, the time to generate this matrix, with n~12, is more than 3s. I need to generate a larger matrix in less time.
Thank you.
Suggestion: Use sparse matrices.
You should be able to create two vectors containing the column number where you want your +1 and -1 in each row. Let's call these two vectors vec_1 and vec_2. You should be able to do this without loops (if not, I still think the procedure below will be faster).
Let the size of your matrix be (max_row X max_col). Then you can create your matrix like this:
bibimatrix = sparse(1:max_row,vec_1,1,max_row,max_col);
bibimatrix = bibimatrix + sparse(1:max_row, vec_2,-1,max_row,max_col)
If you want to see the entire matrix (which you don't, since it's huge) you can write: full(bibimatrix).
EDIT:
You may also do it this way:
col_vec = [vec_1, vec_2];
row_vec = [1:max_row, 1:max_row];
s = [ones(1,max_row), -1*ones(1,max_row)];
bibimatrix = sparse(row_vec, col_vec, s, max_row, max_col)
Disclaimer: I don't have MATLAB available, so it might not be error-free.
Background
I have a large set of vectors (orientation data in an axis-angle representation... the axis is the vector). I want to apply a clustering algorithm to. I tried kmeans but the computational time was too long (never finished). So instead I am trying to implement KFCG algorithm which is faster (Kirke 2010):
Initially we have one cluster with the entire training vectors and the codevector C1 which is centroid. In the first iteration of the algorithm, the clusters are formed by comparing first element of training vector Xi with first element of code vector C1. The vector Xi is grouped into the cluster 1 if xi1< c11 otherwise vector Xi is grouped into cluster2 as shown in Figure 2(a) where codevector dimension space is 2. In second iteration, the cluster 1 is split into two by comparing second element Xi2 of vector Xi belonging to cluster 1 with that of the second element of the codevector. Cluster 2 is split into two by comparing the second element Xi2 of vector Xi belonging to cluster 2 with that of the second element of the codevector as shown in Figure 2(b). This procedure is repeated till the codebook size is reached to the size specified by user.
I'm unsure what ratio is appropriate for the codebook, but it shouldn't matter for the code optimization. Also note mine is 3-D so the same process is done for the 3rd dimension.
My code attempts
I've tried implementing the above algorithm into Matlab 2013 (Student Version). Here's some different structures I've tried - BUT take way too long (have never seen it completed):
%training vectors:
Atgood = Nx4 vector (see test data below if want to test);
vecA = Atgood(:,1:3);
roA = size(vecA,1);
%Codebook size, Nsel, is ratio of data
remainFrac2=0.5;
Nseltemp = remainFrac2*roA; %codebook size
%Ensure selected size after nearest power of 2 is NOT greater than roA
if 2^round(log2(Nseltemp)) < roA
NselIter = round(log2(Nseltemp));
else
NselIter = ceil(log2(Nseltemp)-1);
end
Nsel = 2^NselIter; %power of 2 - for LGB and other algorithms
MAIN BLOCK TO OPTIMIZE:
%KFCG:
%%cluster = cell(1,Nsel); %Unsure #rows - Don't know how to initialize if need mean...
codevec(1,1:3) = mean(vecA,1);
count1=1;
count2=1;
ind=1;
for kk = 1:NselIter
hh2 = 1:2:size(codevec,1)*2;
for hh1 = 1:length(hh2)
hh=hh2(hh1);
% for ii = 1:roA
% if vecA(ii,ind) < codevec(hh1,ind)
% cluster{1,hh}(count1,1:4) = Atgood(ii,:); %want all 4 elements
% count1=count1+1;
% else
% cluster{1,hh+1}(count2,1:4) = Atgood(ii,:); %want all 4
% count2=count2+1;
% end
% end
%EDIT: My ATTEMPT at optimizing above for loop:
repcv=repmat(codevec(hh1,ind),[size(vecA,1),1]);
splitind = vecA(:,ind)>=repcv;
splitind2 = vecA(:,ind)<repcv;
cluster{1,hh}=vecA(splitind,:);
cluster{1,hh+1}=vecA(splitind2,:);
end
clear codevec
%Only mean the 1x3 vector portion of the cluster - for centroid
codevec = cell2mat((cellfun(#(x) mean(x(:,1:3),1),cluster,'UniformOutput',false))');
if ind < 3
ind = ind+1;
else
ind=1;
end
end
if length(codevec) ~= Nsel
warning('codevec ~= Nsel');
end
Alternatively, instead of cells I thought 3D Matrices would be faster? I tried but it was slower using my method of appending the next row each iteration (temp=[]; for...temp=[temp;new];)
Also, I wasn't sure what was best to loop with, for or while:
%If initialize cell to full length
while length(find(~cellfun('isempty',cluster))) < Nsel
Well, anyways, the first method was fastest for me.
Questions
Is the logic standard? Not in the sense that it matches with the algorithm described, but from a coding perspective, any weird methods I employed (especially with those multiple inner loops) that slows it down? Where can I speed up (you can just point me to resources or previous questions)?
My array size, Atgood, is 1,000,000x4 making NselIter=19; - do I just need to find a way to decrease this size or can the code be optimized?
Should this be asked on CodeReview? If so, I'll move it.
Testing Data
Here's some random vectors you can use to test:
for ii=1:1000 %My size is ~ 1,000,000
omega = 2*rand(3,1)-1;
omega = (omega/norm(omega))';
Atgood(ii,1:4) = [omega,57];
end
Your biggest issue is re-iterating through all of vecA FOR EACH CODEVECTOR, rather than just the ones that are part of the corresponding cluster. You're supposed to split each cluster on it's codevector. As it is, your cluster structure grows and grows, and each iteration is processing more and more samples.
Your second issue is the loop around the comparisons, and the appending of samples to build up the clusters. Both of those can be solved by vectorizing the comparison operation. Oh, I just saw your edit, where this was optimized. Much better. But codevec(hh1,ind) is just a scalar, so you don't even need the repmat.
Try this version:
% (preallocs added in edit)
cluster = cell(1,Nsel);
codevec = zeros(Nsel, 3);
codevec(1,:) = mean(Atgood(:,1:3),1);
cluster{1} = Atgood;
nClusters = 1;
ind = 1;
while nClusters < Nsel
for c = 1:nClusters
lower_cluster_logical = cluster{c}(:,ind) < codevec(c,ind);
cluster{nClusters+c} = cluster{c}(~lower_cluster_logical,:);
cluster{c} = cluster{c}(lower_cluster_logical,:);
codevec(c,:) = mean(cluster{c}(:,1:3), 1);
codevec(nClusters+c,:) = mean(cluster{nClusters+c}(:,1:3), 1);
end
ind = rem(ind,3) + 1;
nClusters = nClusters*2;
end