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How to split a large image into many small images?

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Syed JABBAR SHAH
Syed JABBAR SHAH on 17 Jun 2021
Answered: David Willingham on 17 Jun 2021
Hi, I am working on CNN and I have dataset of large images. I want to split each image into many small images to perform training. Could you please tell me how to do it? To be exact, I want 24 small samples from one 1080 x 1920 image.
Further, is it possible to perform splitting in a imageDatastore? To be exact, I want 24 small samples from one 1080 x 1920 image.
Thanks
  2 Comments
Syed JABBAR SHAH
Syed JABBAR SHAH on 17 Jun 2021
Thanks for your comment. You have asked a valid question. Unfortunately. i am still a beginner in MATLAB CNN.
I explain my objective, that would help to better understand the problem. I have a dataset of 160 images of same size with two classes and I want to use CNN for classification. Since the input size is too big, I need to split them in tiled.
I have python code, and I am trying to repicate it in matlab. Please check the below code.
....
# Four splits in row and six splits in column -> 24 small samples from one 1080 x 1920 image
n_row = 4
n_col = 6
# Resize image to 256 x 256 pixels
img_size = 256
SMALL_IMG = []
for img_array, label, name in CNT_IMG:
for i in range(n_row):
for k in range(n_col):
height = int(img_array.shape[0]/n_row)
width = int(img_array.shape[1]/n_col)
small_img = img_array[i*height:(i+1)*height, k*width:(k+1)*width] # Split -> 270 x 320
small_img = cv2.resize(small_img, (img_size, img_size)) # Resize -> 256 x 256
# Normalization
small_img = small_img / 255.0
SMALL_IMG.append([small_img, label, name])
....
I would really appricate if you point me to the right direction and resources to solve the problem.
Thanks.

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Accepted Answer

DGM
DGM on 17 Jun 2021
Edited: DGM on 17 Jun 2021
Blockwise filtering has already been mentioned; since I don't know if that applies to your needs and I have no familiarity with IMDS, I'll just throw this out there.
If you just want to split an image, there are a bunch of ways. You could do it the long way.
inpict = imread('somerandompicture.jpg');
inpict = imresize(inpict,[1080 1920]); % you assert that it's this size
s = size(inpict);
tiling = [4 6]; % i'm assuming this is what you want
f=1;
sout=s(1:2)./tiling;
outpict=zeros([sout,size(inpict,3),prod(tiling)],class(inpict));
for n=1:tiling(2)
for m=1:tiling(1)
outpict(:,:,:,f)=inpict((1:sout(1))+((m-1)*sout(1)),(1:sout(2))+((n-1)*sout(2)),:);
f=f+1;
end
end
In this case, the output is a 4D array. You could use a cell array just the same, though if the goal is to use a cell, you could just do this:
inpict = imread('somerandompicture.jpg');
inpict = imresize(inpict,[1080 1920]); % you assert that it's this size
s = size(inpict);
tiling = [4 6]; % i'm assuming this is what you want
sout=s(1:2)./tiling;
C = mat2cell(inpict,ones(1,tiling(1))*sout(1),ones(1,tiling(2))*sout(2),3)
It's worth noting that both of these will break if your image geometry isn't integer-divisible by the tiling. MIMT imdetile() handles geometry mismatches of the sort, but I doubt you need to deal with it. Just check the geometry and resize as needed.

More Answers (2)


Sulaymon Eshkabilov
Sulaymon Eshkabilov on 17 Jun 2021
  1 Comment
DGM
DGM on 17 Jun 2021
To reinforce the distinction, nlfilter() is a rectangular sliding-window filter, whereas blockproc() works on non-overlapping blocks.

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