%% Tutorial on leaf recognition
% In this tutorial, we shall explain the basics of leaf recognition based on its shape and color statistics.
% The dataset is availabe at <http://flavia.sourceforge.net http://flavia.sourceforge.net>. 
%% Preprocessing
% Before we start, let's add necessary toolboxes to the search path of MATLAB:
addpath d:/users/jang/matlab/toolbox/utility
addpath d:/users/jang/matlab/toolbox/machineLearning
%%
% For compatibility, here we list the platform and MATLAB version that we used to run this script:
fprintf('Platform: %s\n', computer);
fprintf('MATLAB version: %s\n', version);
scriptStartTime=tic;
%% Dataset construction
% First of all, we shall collect all the image data from the image directory:
imDir='D:\users\jang\books\dcpr\appNote\leafId\Leaves';
opt=mmDataCollect('defaultOpt');
opt.extName='jpg';
imageData=mmDataCollect(imDir, opt);
for i=1:length(imageData)
	[~, imageData(i).mainName]=fileparts(imageData(i).name);
	imageData(i).mainName=eval(imageData(i).mainName);
end
%%
% Now we can read the class information from the file class.txt, which is collected from the
% website of the dataset.
classFile='class.txt';
classData=tableRead(classFile, 2);
for i=1:length(classData)
	items=split(classData(i).filename, '-');
	classData(i).range=[eval(items{1}), eval(items{2})];
end
fprintf('%d classes are collected from %s.\n', length(classData), classFile);
%%
% We can print the common names of these plants (or classes of leaves):
for i=1:length(classData)
	fprintf('id=%d, common name=%s\n', i, classData(i).commonName);
end
%%
% (Of course, the above 3 statements can be omited if we want to use all
% classes of leaves for further exploration.)
%%
% Based on the class information, we need to assign the class label for each leaf (or image):
allMainName=[imageData.mainName];
for i=1:length(imageData)
	imageData(i).classId=0;	% Default value, indicating no class being assigned yet
end
for i=1:length(classData)
	range=classData(i).range;
	for j=range(1):range(2);
		index=find(allMainName==j);
		if length(index)~=1, keyboard; end
		imageData(index).classId=i;
		imageData(index).class=classData(i).commonName;
	end
end
imageData([imageData.classId]==0)=[];	% Delete image with no class info
fprintf('%d images are retained for further analysis.\n', length(imageData));
%%
% Dispatch to sub-folder
outputDir='leafSorted';
if ~exist(outputDir, 'dir'), fprintf('Creating %s...\n', outputDir); mkdir(outputDir); end
for i=1:length(imageData)
	leafDir=[outputDir, '/', imageData(i).class];
	if ~exist(leafDir, 'dir'),  fprintf('Creating %s...\n', leafDir); mkdir(leafDir); end
	copyfile(imageData(i).path, leafDir);
end