clear  
close all 
clc

% Load training and test data using |imageDatastore|.
Training_Dir   = fullfile(toolboxdir('vision'),'visiondata','digits','synthetic');
Testing_Dir = fullfile(toolboxdir('vision'),'visiondata','digits','handwritten');

% imageDatastore recursively scans the directory tree containing the
% 
trainingSet = imageDatastore(Training_Dir,'IncludeSubfolders',true,'LabelSource','foldernames');
testingSet     = imageDatastore(Testing_Dir,'IncludeSubfolders',true,'LabelSource','foldernames');
%% Show a few of the training and test images
close all 
figure;

subplot(3,3,1);
imshow(trainingSet.Files{81});

subplot(3,3,2);
imshow(trainingSet.Files{315});

subplot(3,3,3);
imshow(trainingSet.Files{810});
subplot(3,3,4);
imshow(trainingSet.Files{1000});
subplot(3,3,5);
imshow(trainingSet.Files{500});

subplot(3,3,6);
imshow(testingSet.Files{10});

subplot(3,3,7);
imshow(testingSet.Files{35});

subplot(3,3,8);
imshow(testingSet.Files{80});
subplot(3,3,9);
imshow(testingSet.Files{105});

%% Show pre-processing  and Feature Extraction results
exTestImage = readimage(testingSet,37);
Gray_IM=im2gray(exTestImage);
Binary_IM = imbinarize(Gray_IM);

figure;

subplot(1,3,1)
imshow(exTestImage)
subplot(1,3,2)
imshow(Gray_IM)
subplot(1,3,3)
imshow(Binary_IM)


% Extract HOG features from image

img = readimage(trainingSet, 206);
cellSize = [4 4];
[hog_4x4, vis4x4] = extractHOGFeatures(img,'CellSize',cellSize);
figure
plot(vis4x4); 

%% Extracting HOG Features

% HOG features from each image in training set.


hogFeatureSize = length(hog_4x4);
numImages = numel(trainingSet.Files);
trainingFeatures = zeros(numImages,hogFeatureSize,'single');
for i = 1:numImages
    img = readimage(trainingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    trainingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end

% similar procedure will be used  with testing set.

numImages_test = numel(testingSet.Files);

testingFeatures = zeros(numImages_test,hogFeatureSize,'single');
for i = 1:numImages_test
    img = readimage(testingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    testingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end
%% Train and Testing a Decision Tree Classifier


% Get labels for each image.
trainingLabels = trainingSet.Labels;
tree = fitctree(trainingFeatures,trainingLabels);

% Evaluating the the Classifier
% Get labels for each image.
testLabels = testingSet.Labels;

predictedLabels = predict(tree, testingFeatures);

% Tabulate the results using a confusion matrix.
 
cm = confusionchart(testLabels,predictedLabels,'RowSummary','row-normalized','ColumnSummary','column-normalized');

%confMat = confusionmat(testLabels, predictedLabels);

%% Testing the performance of Trained model on some testing images 

M=20;
predicted_Image = predict(tree, testingFeatures(M,:))
figure
imshow(testingSet.Files{M});