I am trying to write a function that implements imfilter function. but getting this error.
??? Subscript indices must either be real positive integers or logicals
at this point
s= size(img);
Find below the code snippet
s = size(img);
Ix = zeros(s);
Iy = zeros(s);
for i = 1:s
for j = 1:s
temp = img(i-1:i+1,j-1:j+1) .* Gx;
Ix(i,j) = sum(temp(:));
end
end
Please is there anything Im doing wrong?
EDITED CODE
s = size(img);
Ix = zeros(s);
Iy = zeros(s);
for i = 2:s(1)-1
for j = 2:s(2)-1
temp = img(i-1:i+1,j-1:j+1) .* Gx;
Ix(i,j) = sum(temp(:));
end
end
If it genuinely happens at the point of calling s= size(img);, then you probably have a variable size in your workspace which is shadowing the size function.
In addition, there are a couple of possible issues with your loop. First, you can't use zero as in index in MATLAB. Hence, when you have i = 1, j = 1 at the start of your loop, you would expect the temp line to return the Subscript indices error.
the output of size, presuming img is a greyscale image, is going to be two numbers. When you do i = 1:s, it will ignore the second one. This is fine so long as your image is square but will not do what you expect if it isn't.
Finally, have a look at conv2 for cases like this rather than creating a loop.
Related
I wrote this matlab code in order to concatenate the results of the integration of all the columns of a matrix extracted form a multi matrix array.
"datimf" is a matrix composed by 100 matrices, each of 224*640, vertically concatenated.
In the first loop i select every single matrix.
In the second loop i integrate every single column of the selected matrix
obtaining a row of 640 elements.
The third loop must concatenate vertically all the lines previously calculated.
Anyway i got always a problem with the third loop. Where is the error?
singleframe = zeros(224,640);
int_frame_all = zeros(1,640);
conc = zeros(100,640);
for i=0:224:(22400-224)
for j = 1:640
for k = 1:100
singleframe(:,:) = datimf([i+1:(i+223)+1],:);
int_frame_all(:,j) = trapz(singleframe(:,j));
conc(:,k) = vertcat(int_frame_all);
end
end
end
An alternate way to do this without using any explicit loops (edited in response to rayryeng's comment below. It's also worth noting that using cellfun may not be more efficient than explicitly looping.):
nmats = 100;
nrows = 224;
ncols = 640;
datimf = rand(nmats*nrows, ncols);
% convert to an nmats x 1 cell array containing each matrix
cellOfMats = mat2cell(datimf, ones(1, nmats)*nrows, ncols);
% Apply trapz to the contents of each cell
cellOfIntegrals = cellfun(#trapz, cellOfMats, 'UniformOutput', false);
% concatenate the results
conc = cat(1, cellOfIntegrals{:});
Taking inspiration from user2305193's answer, here's an even better "loop-free" solution, based on reshaping the matrix and applying trapz along the appropriate dimension:
datReshaped = reshape(datimf, nrows, nmats, ncols);
solution = squeeze(trapz(datReshaped, 1));
% verify solutions are equivalent:
all(solution(:) == conc(:)) % ans = true
I think I understand what you want. The third loop is unnecessary as both the inner and outer loops are 100 elements long. Also the way you have it you are assigning singleframe lots more times than necessary since it does not depend on the inner loops j or k. You were also trying to add int_frame_all to conc before int_frame_all was finished being populated.
On top of that the j loop isn't required either since trapz can operate on the entire matrix at once anyway.
I think this is closer to what you intended:
datimf = rand(224*100,640);
singleframe = zeros(224,640);
int_frame_all = zeros(1,640);
conc = zeros(100,640);
for i=1:100
idx = (i-1)*224+1;
singleframe(:,:) = datimf(idx:idx+223,:);
% for j = 1:640
% int_frame_all(:,j) = trapz(singleframe(:,j));
% end
% The loop is uncessary as trapz can operate on the entire matrix at once.
int_frame_all = trapz(singleframe,1);
%I think this is what you really want...
conc(i,:) = int_frame_all;
end
It looks like you're processing frames in a video.
The most efficent approach in my experience would be to reshape datimf to be 3-dimensional. This can easily be achieved with the reshape command.
something along the line of vid=reshape(datimf,224,640,[]); should get you far in this regard, where the 3rd dimension is time. vid(:,:,1) then would display the first frame of the video.
I have the following function that works perfectly, but I would like to apply vectorization to it...
for i = 1:size(centroids,1)
centroids(i, :) = mean(X(idx == i, :));
end
It checks if idx matches the current index and if it does, it calculates the mean value for all the X values that correspond to that index.
This is my attempt at vectorization, my solution does not work and I know why...
centroids = mean(X(idx == [1:size(centroids,1)], :));
The following idx == [1:size(centroids,1)] breaks the code. I have no idea how to check if idx equals to either of the numbers from 1 to size(centroids,1).
tl:dr
Get rid of the for loop through vectorization
One option is to use arrayfun;
nIdx = size(centroids,1);
centroids = arrayfun(#(ii) mean(X(idx==ii,:)),1:nIdx, 'UniformOutput', false);
centroids = vertcat(centroids{:})
Since the output of a single function call is not necessarily a scalar, the UniformOutput option has to be set to false. Thus, arrayfun returns a cell array and you need to vertcat it to get the desired double array.
you can split the matrix into cells and take the mean from each cell using cellfun (which applies a loop in its inner operation):
generate data:
dim = 10;
N = 400;
nc = 20;
idx = randi(nc,[N 1]);
X = rand(N,dim);
centroids = zeros(nc,dim);
mean using loop (the question's method)
for i = 1:size(centroids,1)
centroids(i, :) = mean(X(idx == i, :));
end
vectorizing:
% split X into cells by idx
A = accumarray(idx, (1:N)', [nc,1], #(i) {X(i,:)});
% mean of each cell
C = cell2mat(cellfun(#(x) mean(x,1),A,'UniformOutput',0));
maximum absolute error between the methods:
max(abs(C(:) - centroids(:))) % about 1e-16
I have written the 3x3 average filter. It works fine but it shows the same output image three times instead of one. How to resolve the problem?
The code is
function [filtr_image] = avgFilter(noisy_image)
[x,y] = size(noisy_image);
filtr_image = zeros(x,y);
for i = 2:x-1
for j =2:y-1
sum = 0;
for k = i-1:i+1
for l = j-1:j+1
sum = sum+noisy_image(k,l);
end
end
filtr_image(i,j) = sum/9.0;
filtr_image = uint8(filtr_image);
end
end
end
thanks in advance
What is most likely happening is the fact that you are supplying a colour image when the code is specifically meant for grayscale. The reason why you see "three" is because when you do this to allocate your output filtered image:
[x,y] = size(noisy_image)
If you have a 3D matrix, the number of columns reported by size will be y = size(noisy_image,2)*size(noisy_image,3);. As such, when you are iterating through each pixel in your image, in column major order each plane would be placed side by side each other. What you should do is either convert your image into grayscale from RGB or filter each plane separately.
Also, you have an unnecessary casting performed in the loop. Just do it once outside of the loop.
Option #1 - Filter per plane
function [filtr_image] = avgFilter(noisy_image)
[x,y,z] = size(noisy_image);
filtr_image = zeros(x,y,z,'uint8');
for a = 1 : z
for i = 2:x-1
for j =2:y-1
sum = 0;
for k = i-1:i+1
for l = j-1:j+1
sum = sum+noisy_image(k,l,a);
end
end
filtr_image(i,j,a) = sum/9.0;
end
end
end
end
Then you'd call it by:
filtr_image = avgFilter(noisy_image);
Option #2 - Convert to grayscale
filtr_image = avgFilter(rgb2gray(noisy_image));
Minor Note
You are using sum as a variable. sum is an actual function in MATLAB and you would be overshadowing this function with your variable. This will have unintended consequences if you have other functions that rely on sum later down the line.
I can't see why your code would repeat the image (unless it's a pattern cause by an integer overflow :/ ) but here are some suggestions:
if you want to use loops, at least drop the inner loops:
[x,y] = size(noisy_image);
filtr_image = zeros(x,y);
for i = 2:x-1
for j =2:y-1
% // you could do this in 1 line if you use mean2(...) instead
sub = noisy_image(i-1:i+1, j-1:j+1);
filtr_image = uint8(mean(sub(:)));
end
end
However do you know about convolution? Matlab has a built in function for this:
filter = ones(3)/9;
filtr_image = uint8(conv2(noisy_image, filter, 'same'));
I've got 3 functions, oe1(n), oe2(n) and oe3(n).
I want to create a matrix representing the function values.
The structure of the matrix should be like this:
A = [oe1(0) oe2(0) oe3(0); oe1(1) oe2(1) od3(1); ...... ; oe1(N-1), oe2(N-1), oe3(N-1)];
I've tried filling it with a for loop, but it does not work.
Is there a standard Matlab operation for this? I really can't figure out how to do it.
Anders.
oe1(n1) = sin(2*pi*F*n1+phi)
oe2(n1) = ones(length(n1),1);
oe3(n1) = n1*Ts
pol = (oe2)'
vector_x = [a; b; c];
vector_veardier = [oe1(n1), 1, oe3(n1)]
xi = 1:N-1;
for i = 2:N-1;
for j = 1:3
vector_veardier(i, j) = oe1(j);
end
end
Do your functions accept vectors? If so you can use:
A = [oe1((1:N)'), oe2((1:N)'), oe3((1:N)')];
but otherwise you might have to use arrayfun:
A = [arrayfun(#oe1, (1:N)'), arrayfun(#oe2, (1:N)'), arrayfun(#oe3, (1:N)')];
Note that in your provided code you have not defined oeN as functions, but as some kind of array with a value inserted at position n1.
One way to do it with a for loop would however be:
A = zeros(N,3);
for i = 1:N,
A(i,:) = [oe1(i), oe2(i) oe3(i)];
end
I have a code with two for loops. The code is working properly. The problem is that at the end I would like to get a variable megafinal with the results for all the years. The original varaible A has 3M rows, so it gives me an error because the size of the megafinal changes with each loop iteration and matlab stops running the code. I guess it’s a problem of inefficiency. Does anyone know a way to get this final variable despite of the size?
y = 1997:2013;
for i=1:length(y)
A=b(cell2mat(b(:,1))==y(i),:);
%Obtain the absolute value of the difference
c= cellfun(#minus,A(:,3),A(:,4));
c=abs(c);
c= num2cell(c);
A(:,end+1) = c;
%Delete rows based on a condition
d = (abs(cell2mat(A(:,8)) - cell2mat(A(:,7))));
[~, ind1] = sort(d);
e= A(ind1(end:-1:1),:);
[~, ind2,~] = unique(strcat(e(:,2),e(:, 6)));
X= e(ind2,:);
(…)
for j = 2:length(X)
if strcmp(X(j,2),X(j-1,2)) == 0
lin2 = j-1;
%Sort
X(lin1:lin2,:) = sortrows(X(lin1:lin2,:),13);
%Rank
[~,~,f]=unique([X{lin1:lin2,13}].');
g=accumarray(f,(1:numel(f))',[],#mean);
X(lin1:lin2,14)=num2cell(g(f));
%Score
out1 = 100 - ((cell2mat(X(lin1:lin2,14))-1) ./ size(X(lin1:lin2,:),1))*100;
X(lin1:lin2,15) = num2cell(out1);
lin1 = j;
end
end
%megafinal(i)=X
end
Make megafinal a cell array. This will account for the varying sizes of X at each iteration. As such, simply do this:
megafinal{i} = X;
To access a cell element, you just have to do megafinal{num}, where num is any index you want.