How to reduce the number of "for" loops (while implementing an overlapping block-wise processing on an image)?
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I have the following code section to implement overlapping blocks in any square image (my image size 128*128). For each block, I would like to find HOG (Histogram of Oriented Gradients) features and then concatenate them at last to use it for classification.
The block size = 64
The step size = 16 (Both Horizontal & Vertical)
How can I implement a faster version of the following code section without using "for" loops? :
window_size=64;
step_size=16;
%the input image is stored in the variable "image"
for k=0:4
for n=0:4
for i=((step_size*n)+1):window_size+(step_size*n)
for j=((step_size*k)+1):window_size+(step_size*k)
img(p,q)=image(i,j); %get the block and store it in "img"
q=q+1;
end
p=p+1;q=1;
end
hog_block{k+1,n+1}=extractHOGFeatures(img); %storing each block's HOG features in a cell
p=1;
end
end
hog_blocks_mat=cell2mat(lbp); %convert to matrix
hog_vector=reshape(hog_blocks_mat',1,numel(hog_blocks_mat)); %convertto a row vector
Please help.
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Risposta accettata
Jan
il 13 Gen 2017
These loops:
for i=((step_size*n)+1):window_size+(step_size*n)
for j=((step_size*k)+1):window_size+(step_size*k)
img(p,q)=image(i,j); %get the block and store it in "img"
q=q+1;
end
p=p+1;q=1;
end
can be rewritten to:
iIni = (step_size*n) + 1);
iFin = window_size+(step_size*n);
jIni = (step_size * k) + 1;
jFin = window_size + (step_size * k);
img = image(iIni:iFin, jIni:jFin);
Più risposte (1)
Tohru Kikawada
il 13 Gen 2017
You can use blockproc for block processing. 'UseParallel' option enables to execute in parallel. See this link for details.
2 Commenti
Image Analyst
il 14 Gen 2017
Yes to both questions. It will move in steps/jumps of window_size. You can change the value of window_size. You can also move sliding (not full jumps). See the help.
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