CODEWITHSUNDEEP
matlabimage-processingsvm
user3859472
user3859472

Reputation: 29

Error in svmtrain, "Y and TRAINING must have the same number of rows"

I'm using svmtrain function to classify the images. And I'm getting an error like this.

Error using svmtrain (line 253)
Y and TRAINING must have the same number of rows.

Error in svm5 (line 80)
SVMStruct = svmtrain(Training_Set , train_label, 'kernel_function', 'linear');

Training_Set contains sets of images and train_lable is the class to which to identify the input image. The complete code for the reference

clc
clear all

% Load Datasets

Dataset = 'D:\majorproject\image\traindata\';   
Testset  = 'D:\majorproject\image\testset\';


% we need to process the images first.
% Convert your images into grayscale
% Resize the images

width=100; height=100;
DataSet      = cell([], 1);

 for i=1:length(dir(fullfile(Dataset,'*.jpg')))

     % Training set process
     k = dir(fullfile(Dataset,'*.jpg'));
     k = {k(~[k.isdir]).name};
     for j=1:length(k)
        tempImage       = imread(horzcat(Dataset,filesep,k{j}));
        imgInfo         = imfinfo(horzcat(Dataset,filesep,k{j}));

         % Image transformation
         if strcmp(imgInfo.ColorType,'grayscale')
            % array of images
            DataSet{j}   = double(imresize(tempImage,[width height])); 
         else
            % array of images
            DataSet{j}   = double(imresize((tempImage),[width height])); 
         end
     end
 end
TestSet =  cell([], 1);
  for i=1:length(dir(fullfile(Testset,'*.jpg')))

     % Training set process
     k = dir(fullfile(Testset,'*.jpg'));
     k = {k(~[k.isdir]).name};
     for j=1:length(k)
        tempImage       = imread(horzcat(Testset,filesep,k{j}));
        imgInfo         = imfinfo(horzcat(Testset,filesep,k{j}));

         % Image transformation
         if strcmp(imgInfo.ColorType,'grayscale')
            % array of images
            TestSet{j}   = double(imresize(tempImage,[width height])); 
         else
            % array of images
            TestSet{j}   = double(imresize(tempImage,[width height])); 
         end
     end
  end

    % Prepare class label for first run of svm
% I have arranged labels 1 & 2 as per my convenience.

% It is always better to label your images numerically

% Note that for every image in our Dataset we need to provide one label.

% we have 10 images and we divided it into two label groups here.

    train_label               = zeros(size(10,1),1);
    train_label(1:4,1)   = 1;         % 1 = naa
    train_label(5:10,1)  = 2;         % 2 = ta


% Prepare numeric matrix for svmtrain

    Training_Set=[];
    for i=1:length(DataSet)
    b = imresize(DataSet{i},[100 100]);
    Training_Set_tmp= b(:);
    %Training_Set_tmp   = reshape(DataSet{i},1, 100*100);
    Training_Set=[Training_Set;Training_Set_tmp];
    end

    Test_Set=[];
    for j=1:length(TestSet)
    b = imresize(TestSet{j},[100 100]);
    Test_set_tmp= b(:);
    %Test_set_tmp   = reshape(TestSet{j},1, 100*100);
    Test_Set=[Test_Set;Test_set_tmp];
    end


% Perform first run of svm

SVMStruct = svmtrain(Training_Set, train_label, 'kernel_function', 'linear');
Group     = svmclassify(SVMStruct, Test_Set);

please guide me to overcome from this. Thank you.

Upvotes: 1

Views: 1340

Answers (1)

Piiinkyy
Piiinkyy

Reputation: 365

I have recently bumped into this similar problem and after hours of checking, here is the problem I found:

% we have 10 images and we divided it into two label groups here.

train_label               = zeros(size(10,1),1);
train_label(1:4,1)   = 1;         % 1 = naa
train_label(5:10,1)  = 2;         % 2 = ta

Where I have only 499 images, while instead, I set my size to 500 (Accidentally deleted 1), and thus resulting in the exact same error as above. You did not provide your data set here so maybe you can consider checking it again.

Upvotes: 1

Related Questions