osr_demo.m

%% Optical Shape Recognition
%
% Created by Javier Matos Odut
%
% This is a demonstration about an optical shape recognition created with
% MATLAB using different toolboxes:
%
% * Neural Network - the recognition engine of this tool was created using
% a multilayer neural network.
% * Image Acquisition/Image Processing - with this two toolboxes we can
% extract shapes within images and analyze their properties to create the
% input needed to the neural network.
%
% I have created some useful functions to extract advanced properties of
% shapes explained in the literature of computer vision and image
% processing. Harris corner detector is an example of function done to
% extract properties from shapes.
%
% http://en.wikipedia.org/wiki/Corner_detection
%

%% 1. Extract properties from shapes
% Suppose that we have a folder with binary images. Each image contains a
% shape that we want to recognize. The first step is to extract
% characteristics from shapes within images.

% A binary image example
imshow('F.png')

% Characteristics extraction
imagesPath = '/home/myuser/symbols/characters';
exhaustive = true; % extract more characteristics from shapes
progressBar = false; % show a progress bar
verbose = true; % verbose output

[Map,Data] = performCharacteristicsExtraction(imagesPath, exhaustive, progressBar, verbose);

%% 2. Analyze data acquired from shapes
% We can perform a principal component analysis using extracted data from
% shapes to know if differences are enough to recognize shapes.
%
% http://en.wikipedia.org/wiki/Principal_component_analysis

% PCA computation
[coeff, score] = princomp(Data.input');

clf; hold on;

% Colors
colorBase = [ 'b' ; 'g' ; 'r' ; 'c' ; 'm' ; 'y' ];

for i=1:unique(max(Data.output))
    plot(  score(Data.output == i,1), ...
           score(Data.output == i,2), ...
           '.','Color',colorBase(mod(i-1,length(colorBase))+1));
end

%% 3. Training neural Network
% Now that we have the input and the desired output we can perform the
% learning process using the neural network.

% Generates the trained neural network
Engine = performLearning(Data);

%% 4. Shape recognition
% Now we load the image that contains shapes that we want to recognize.

% Read input image
image = imread('test.png');

% Extract shapes from input image
Shapes = performRecognition(Engine, Map, image);

%% 5. Show results

clf; imshow(image); hold on;

% For every shape in the image we show the associated label
for i=1:length(Shapes)
    text(Shapes(i).Centroid(1),Shapes(i).Centroid(2),[' \leftarrow ',Shapes(i).Label],'FontSize',18,'Color',colorBase(mod(i-1,length(colorBase))+1));
end

%% Graphical User Interface
% The graphical user interface let you perform previous steps in a friendly
% way. To start it run `launch.m` file that is in the main directory.
%
% <<gui.png>>