# How to split a large image into many small images?

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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 CommentsShowHide 1 older comment
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.

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 = 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 = 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.

David Willingham on 17 Jun 2021
Hi Syed,
I'd encourge you to use blockedImage along with blockedImagedatastore, it will help you perform the block operations for you.
David Willingham

Sulaymon Eshkabilov on 17 Jun 2021
Here is a nice discussion on how to split images with nlfilter() suggested by Rik:
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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