splitting dataset into training set and testing set

I have 400 images in my dataset(images).I want to split the dataset into 80% for training and 20% for testing.the below attached code works but , test_idx is empty?why?

train_idx contains 320 images.test_idx is empty.

clc
clear

% Load Image dataset
faceDatabase = imageSet('facedatabaseatt','recursive');

%splitting into training and testing sets
N = 400;  % number of images 
idx = 1:N ;
PD = 0.80 ;
train_idx = idx(1:round(PD*N));    % training indices 
test_idx = idx(round(PD*N)+1:end,:) ; % test indices

Answer 1

[Akira Agata]

You can split your dataset by using partition function, like:

[setTrain, setTest] = partition(faceDatabase, [0.8, 0.2], 'randomized');

[Munshida P]

I have to split the dataset manually, without using built-in functions.

[Akira Agata]

OK. Then, how about the following?

N = imgSet.Count; % number of images
PD = 0.8;

train_idx = sort(randperm(N,round(N*PD)));
test_idx = setxor(train_idx,1:N);

setTrain = select(faceDatabase,train_idx);
setTest = select(faceDatabase,test_idx);

[Munshida P]

Thank you sir. The above code works if i copy all 400 images in a single folder.

But, the original dataset ORL Facedatabaseatt contains 40 folders s1,s2,.....,s40(10 images each of 40 persons).

like that 10x40=400 images.i have to choose 8 images from each person for testing and 2 images for testing.

the following error was occured .

Index exceeds the number of array elements (10).

Error in imageSet/selectProperties (line 526)
            imgSet.ImageLocation = imgSet.ImageLocation(index);

Error in imageSet/select (line 394)
            out(n) = selectProperties(this(end), index);

Error in split (line 12)
setTrain = select(faceDatabase,train_idx);
 
>> 

[Rachel Shively]

I'm trying to use this to divide my dataset and getting the following error:

[imdsTrain, imdsTest] = partition(imds [0.8, 0.2], 'randomized');

Error using matlab.io.datastore.ImageDatastore/partition

Too many output arguments.

Any alternative suggestions?

[Akira Agata]

>the original dataset ORL Facedatabaseatt contains 40 folders s1,s2,.....,s40(10 images each of 40 persons).

> like that 10x40=400 images.i have to choose 8 images from each person for training and 2 images for testing.

OK. in that case, I would recommend using imageDataStore function, like:

dataFolder = pwd; % if your 40 folders are stored in different folder, please change.
imgSet = imageDatastore(dataFolder,...
    'IncludeSubfolders',    true,...
    'LabelSource',          'foldernames');

% Choose first 8 images from each folder and set them to training dataset, and 2 images for test dataset
[imgSetTrain, imgSetTest] = splitEachLabel(imgSet,0.8);

% If you want to choose 8 and 2 images from each folder randomly, please set 'randomized' option
[imgSetTrain, imgSetTest] = splitEachLabel(imgSet,0.8,'randomized');

[Ruaa waleed]

please i split image data base but icant find [imgSetTrain, imgSetTest] how ican make output .txt

clc

F = fullfile('g:','iris-recognition---pm-diseased-human-driven-bsif-main','casia 2 device1');

imds = imageDatastore(F,'IncludeSubfolders',true,'LabelSource','foldernames');

labelCount = countEachLabel (imds)

% Choose first 8 images from each folder and set them to training dataset, and 2 images for test dataset

numTrainFiles= 0.8

% If you want to choose 8 and 2 images from each folder randomly, please set 'randomized' option

[imgSetTrain, imgSetTest] = splitEachLabel(imds,numTrainFiles);