%Student Dave's tutorial on: Image processing for object
%tracking (aka giving eyes to your robot :)
%Copyright Student Dave's Tutorials 2012
%if you would like to use this code, please feel free, just remember to
%reference and tell your friends! :)
%requires matlabs image processing toolbox
%What the heck does this code do!?
%the code finds the hexbug buy using a series of basic, but effective
%images processing techniques (formal talk for a second -->) :
% 1) Averaged background subtraction
% 2) Noise reduction via image smoothing using 2-d gaussian filter.
% 3) Threshold and point detection in binary image.
clear all;
close all;
set(0,'DefaultFigureWindowStyle','docked') %dock the figures..just a personal preference you don't need this.
base_dir = 'C:\Users\USER\Documents\MATLAB\hexbug_frames_compressed\hexbug_frames_compressed\';
cd(base_dir);
%% get listing of frames so that you can cycle through them easily.
f_list = dir('*png');
%% make average of background images (i.e. images with no objects of interest)
% Here we just read in a set of images (N) and then take the average of
% them so that we are confident we got a good model of what the background
% looks like (i.e. a template free from any potential weird image artifacts
N = 20; % num of frames to use to make averaged background...that is, images with no bug!
img = zeros(288,352,N); %define image stack for averaging (if you don't know what this is, just load the image and check it with size())
for i = 1:N
img_tmp = imread(f_list(i).name); %read in the given image
img(:,:,i) = img_tmp(:,:,1); % we don't really care about the rgb image values, so we just take the first dimension of the image
end
bck_img = (mean(img,3)); %take the average across the image stack..and bingo! there's your background template!
subplot(121);imagesc(bck_img)
subplot(122);imagesc(img(:,:,1))
colormap(gray)
clear img; % free up memory.
%initialize gaussian filter
%using fspecial, we will make a gaussian template to convolve (pass over)
%over the image to smooth it.
hsize = 20;
sigma = 10;
gaus_filt = fspecial('gaussian',hsize , sigma);
subplot(121); imagesc(gaus_filt)
subplot(122); mesh(gaus_filt)
colormap(jet)
%this one is just for making the coordinate locations more visible.
SE = strel('diamond', 7); %another tool make for making fun matrice :) this one makes a matrice object for passing into imdilate())
%% iteratively (frame by frame) find bug!
CM_idx = zeros(length(f_list),2); % initize the variable that will store the bug locations (x,y)
for i = 1:2:length(f_list)
img_tmp = double(imread(f_list(i).name)); %load in the image and convert to double too allow for computations on the image
img = img_tmp(:,:,1); %reduce to just the first dimension, we don't care about color (rgb) values here.
subplot(221);imagesc(img);
title('Raw');
%{
%VERY HARD TRACKING
%for frames 230:280, make the bug very hard to track
if (i > 230) && (i < 280) && (mod(i,3) == 0 )
J = imnoise(img,'speckle');
img = img+J*200;
end
%}
%subtract background from the image
sub_img = (img - bck_img);
subplot(222);imagesc(sub_img);
title('background subtracted');
%gaussian blurr the image
gaus_img = filter2(gaus_filt,sub_img,'same');
subplot(223);imagesc(gaus_img);
title('gaussian smoothed');
%threshold the image...here i just made a quick histogram to see what
%value the bug was below
subplot(224);hist(gaus_img(:));
thres_img = (gaus_img < -15);
subplot(224);imagesc(thres_img);
title('thresholded');
%% TRACKING! (i.e. get the coordinates of the bug )
%quick solution for finding center of mass for a BINARY image
%basically, just get indices of all locations above threshold (1) and
%take the average, for both the x and y directions. This will give you
%the average location in each dimension, and hence the center of the
%bug..unless of course, something else (like my hand) passes threshold
%:P
%if doesn't find anything, it randomly picks a pixel
[x,y] = find (thres_img);
if ~isempty(x)
CM_idx(i,:) = ceil([mean(x) mean(y)]+1); % i used ceiling to avoid zero indices, but it makes the system SLIGHTLY biased, meh, no biggie, not the point here :).
else
CM_idx(i,:) = ceil([rand*200 rand*200]);
end
%{
%NOT SO HARD TRACKING
%for frames 230:280, make the bugtracking just a lil noisy by randomly sampling
%around the bugtracker
if (i > 230) && (i < 280) && (mod(i,2) == 0 )
CM_idx(i,:) = [round(CM_idx(i,1) + randn*10) round(CM_idx(i,2) + randn*10)];
end
%}
%{
%NO TRACKING
%for frames 230:280, make the bugtracking just a lil noisy by randomly sampling
%around the bugtracker
if (i > 230) && (i < 280)
CM_idx(i,:) = [NaN NaN];
end
%}
%% now, we visual everything :)
%create a dilated dot at this point for visualize
%make binary image with single coordinate of bug = 1 and rest zeros.
%then dilate that point to make a more visible circle.
bug_img = zeros(size(thres_img));
bug_img(CM_idx(i,1),CM_idx(i,2)) = 1;
%{
% if you are running the "no tracking segment above, you'll need to
% skip over that segment, and thus use this code
if ~((i > 230) && (i < 280))
bug_img(CM_idx(i,1),CM_idx(i,2)) = 1;
end
%}
bug_img = imdilate(bug_img, SE);
subplot(224);imagesc(thres_img + bug_img);
title('thresholded and extracted (red diamond)');
axesHandles = get(gcf,'children');
set(axesHandles, 'XTickLabel', [], 'XTick', []);
set(axesHandles, 'YTickLabel', [], 'YTick', []) ;
pause(.01)
end
%save out the hexbug coordinates
%save('CM_idx_no.mat', 'CM_idx')
%{
%nice and elegant solution for center of mass of gray scale image (i.e. doesn't have to be binary like in our case)----------------------------------
% http://www.mathworks.com/matlabcentral/newsreader/author/109726
%These next 4 lines produce a matrix C whose rows are
% the pixel coordinates of the image A
C=cellfun(@(n) 1:n, num2cell(size(thres_img)),'uniformoutput',0);
[C{:}]=ndgrid(C{:});
C=cellfun(@(x) x(:), C,'uniformoutput',0);
C=[C{:}];
%This line computes a weighted average of all the pixel coordinates.
%The weight is proportional to the pixel value.
CenterOfMass=thres_img(:).'*C/sum(thres_img(:),'double')
%---------------------------------------------------------------------
%}
