final_signal.zip_practice_signal filter

%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') %--------------------------------------------------------------------- %}