README_BLOBTRACK.m

% NOTE
%
% OPENING MATLAB
% --------------
% In order to open Matlab in the server if we intend to use opencv,
% we need to switch off the Matlab internal opencv libraries. 
% This ammounts to opening matlab with this order from the command line:

LD_PRELOAD=/opt/opencv/lib/libopencv_core.so:/opt/opencv/lib/libopencv_highgui.so:/opt/opencv/lib/libopencv_imgproc.so matlab


% 
% adds the mbtools to your path
% 
addpath(genpath('.'));

% Read the provided video
g = mbvid.mbVideoReader('wt80_test_1-10-14.mp4');

% first we need a mask of the interior of the cage
someFrame = g.read(1);
mymask = createCageMask(someFrame);

% (you can save the mask as a file with
%  mbio.write(mymask,'mymaskfile.tiff');
% This mask will help mbtools discarding  big darks spots outside the cage
% (like the reflection of the mouse!)

% In order to track the mouse all across the video we can proceed with: 
%
%
tracking = blobTrack(g,'show',0,'frames',[],'erosion',8,'mask',mymask,...
    'binarizationThreshold',0.9,'NumWorkers',12);

% Also, note that the results of this computation are provided as
% load 'wt80_test_1-10-14_tracking.mat'

%  NOTE: Performance
% 'NumWorkers' is the number of cores you want to use in parallel!
%  The code is slow. If the algorithm seems to work on your type of data,
%  it will be possible to accelerate it:
%  * creating an opencv version (software solution) 
%  * creating a GPU version (hardware solution)
% 
% An opencv version is already available:
% 
% tracking = blobTrackopencv('video',g,'show',0,'frames',[],'erosion',8,'mask',mymask,...
%    'binarizationThreshold',0.9,'NumWorkers',12);



% NOTE: Video linking problems
% Possibly you need to refresh the video linkage in your system.
% This just means that the results that you loaded from the mat file
% contain a link to the  video and that link maybe broken. Check with:
% tracking.video.show(1);
% If you don't get an image of the first frame, just write on the matlab
% prompt:
% tracking.video = g;



% Now back to the image processing. We were using an "erosion" of 8
% pixels and some "binarizationThreshold" of 12 pixels.
% But where do we have those parameters from?
% Just trust me... or play a little bit first before computing the full 
% video.
% You can visualize the effect on each frame like this:
% 
trackingOneImage = blobTrack(g,'show',1,'frames',50,'erosion',1,'mask',mymask, 'binarizationThreshold',0.95);

% once you have an idea on which parameters should work, run the command...
%
% ... wait some time....
%
% You can check a posteriori if the results are correct, i.e., if the mouse
% gets tracked most of the time
%
% % generates a mbtools.mbVideoReader speciallized in patches: 
% gpatch = mbvid.patchReader(g,'track',tracking);
% gpatch.patchWidth   = 128;
% gpatch.patchHeight  = 128;
% % and makes a montage on the created patches
% gpatch.montage('frames',1:500:20000);

% If you are satisfied with the tracking, you can compute the distances
% between frames:

magnitudes    = blobTrackMotionVector(tracking.getArray,'compareWith',30);
trackMagnitudes  = mbvid.videotrack.scalar('video',g,'data',magnitudes);

% You can interact with this plot in order to get a feeling on which are
% the regions of interest and which parametes will reveal them.


trackMagnitudes.plot();

% there are two ways to interact:
% 1 : main clik on the plot to show the corresponding frame
%     the frame is then controllable with arrow keys in order to navigate
%     inside the video
% 2 : use [Tracktools] to create a draggable box
%     This can be used:
%     a)  as  dynamic zoom (magnifying glass option)
%     b) to check as montage the contained images



r = mbvid.freezing.findRegions('videotrack',trackMagnitudes,'addDelay',30,'maxScore',5,'minimumLength',30);
% just to facilitate depiction (showing only one frame of every tenth in the
% montages)
r.depictionIntervalInFrames = 10;

% Checks the results in the command line
r.globalReport();

% Interactive plot
r.plotFragments();

% * secondary click to see a montage of the individual region
% - main click to see basic information of the individual region


% Check one region
r.fragments{1}.show();
% % You can set additional flags:
% r.fragments{1}.show('interval',20);

% % In order to see the previous frames in the image
%  r.fragments{21}.show('interval',10,'buffer',50);


% Check one by one
r.showFragmentSequence('pause',true);


% Compares  with the recoreded track of trials
% We read the file and convert it directly into an mbtools object
rtrials = mbvid.videotrack.fragmentSet('video',g,'fragmentsAsPartitionInSeconds','trialmatrix_test_basic.dlm');
rtrials.depictionIntervalInFrames = 15;

% We can depict the fragment distribution
rtrials.depictionIntervalInFrames = 15;
rtrials.plotFragments; % remember that this is interactive,
% you can click onto the bars


% but you can also compare the intervals side by side
 mbvid.videotrack.fragmentSet.comparePlotFragments({rtrials,r},'colors',{'r','g'},'nfig',2);


% Finally you can overimpose the score used for our freezing identification
% (i.e, the monovariate function)
% with the available information about the trial duration (as bar plot)
figure(3);clf;
trackMagnitudes.plot();
rtrials.plotFragments('nfig',3,'hold',1,'adjustRange',1); 



% In order to get a final statistics on how many frames on each trial
% detect frame activity (i.e.: " freezing event")

o = trialPercentages(r,rtrials);