Code covered by the BSD License

# Algorithm Development with MATLAB

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)
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.
% 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'});
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
```