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Highlights from
Algorithm Development with MATLAB

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Algorithm Development with MATLAB

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21 May 2013 (Updated )

Demo files to go with recorded webinar.

findBallFcn(greenBall1, thresh, imageType, axH)
function centroid = findBallFcn(greenBall1, thresh, imageType, axH)

%% Find Green Object
% This script reads in an image file and then attempts to find a green
% object in the image. It is designed to find one green ball and highlight
% that ball on the original image

% Copyright 2013 The MathWorks, Inc.

error(nargchk(3, 4, nargin, 'struct'));

if isempty(greenBall1)
    return;
end
if ischar(greenBall1)
    greenBall1 = imread(greenBall1);
end
%% Step 1: Read image into MATLAB 
% First we read the specified image from the file and bring it into MATLAB
% as a variable. We also display the image to ensure it is correct.
% greenBall1 = imread('greenBall3.jpg');
% imtool(greenBall1);

%% Step 2: Identify Unique Characteristics of Object of Interest

%%
% Extract each color
% Next we using indexing to extract three 2D matrices from the 3D image
% data corresponding to the red, green, and blue components of the image.
r = greenBall1(:, :, 1);
g = greenBall1(:, :, 2);
b = greenBall1(:, :, 3);

%% 
% View different color planes
% figure
% subplot(2,2,1),imagesc(r)
% subplot(2,2,2),imagesc(g)
% subplot(2,2,3),imagesc(b)

%%
% Calculate Green
% Then we perform an arithmetic operation on the matrices as a whole to try
% to create one matrix that represents an intensity of green.
justGreen = g - r/2 - b/2;
% colorPlanesPlot(r,g,b,justGreen);

%%
% close all

%% Step 3: Isolate Object of Interest

%% 
% Threshold the image
% Now we can set a threshold to separate the parts of the image that we
% consider to be green from the rest.
% bw = justGreen > 50;
% imagesc(bw);
% colormap(gray);
if nargin == 4
    bw = justGreen > thresh;
else
    bw = justGreen > 80;
end
%%
% Remove small unwanted objects
% We can use special functions provided by the Image Processing toolbox to
% quickly perform common image processing tasks. Here we are using
% BWAREAOPEN to remove groups of pixels less than 30.
ball1 = bwareaopen(bw, 30);
% imagesc(ball1);

%% Step 4: Find center of green object
% Now we are using REGIONPROPS to extract the centroid of the group of
% pixels representing the ball.
% s  = regionprops(ball1, {'centroid','area'});
% imshow(greenBall1); 
% if isempty(s)
%   title('No ball found!');
% else
%   [~, id] = max([s.Area]);
%   hold on, plot(s(id).Centroid(1),s(id).Centroid(2),'wp','MarkerSize',20,'MarkerFaceColor','r'), hold off
%   title(['Center location is (',num2str(s(id).Centroid(1),4),', ',num2str(s(id).Centroid(2),4),')'])
% end
s  = regionprops(ball1, {'centroid','area'});
if isempty(s)
    centroid = [];
else
    [maxArea, id] = max([s.Area]); %#ok<ASGLU>
    centroid = s(id).Centroid;
end
switch imageType
   case 'video'
      imshow(greenBall1, 'Parent', axH);
      if ~isempty(centroid)
         line(centroid(1), centroid(2), 'Parent', axH, 'Color', 'w', 'Marker', 'p', 'MarkerSize', 20, 'MarkerFaceColor', 'r')
      end
   case 'bw'
      imshow(ball1, 'Parent', axH);
end
%% Step 5: Verify estimated location
% Finally we will plot the center on the original image to clearly evaluate
% how well we have found the center.
% imagesc(greenBall1);
% hold on, plot(s(id).Centroid(1),s(id).Centroid(2),'wp','MarkerSize',20,'MarkerFaceColor','r'), hold off

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