score_freezing.m

%% 1. Load video and create mask 
%Open the FreezingAnalysis folder
%If the video is in the same folder as the analysis package:
addpath(genpath('.'));
% Load video by entering file name
g = mbvid.mbVideoReader('wt80_test_1-10-14.mp4');

% If the video is not in the same folder as the analysis package:
%addpath(genpath(pwd));

% Load video by entering file name
%g = mbvid.mbVideoReader('examplePV2.mp4');
%g = mbvid.mbVideoReader('~/wt80_test_1-10-14.mp4');

%NOTE: if using a Mac, a good way of telling whether Matlab will be able to
%read the video is whether or not Quicktime is able to play the video. If
%Quicktime cannot play the video, then convert the video using Handbrake
%(free to download) - we have to convert all our videos using handbrake for
%Matlab to be able to read them. 

% Draw mask
someFrame = g.read(1);
mymask = createCageMask(someFrame);
%% 2. Save mask
mbio.write(mymask,'mymaskfile.tiff');
%% 3. Set tracking parameters
% Key parameters: 'erosion' and 'binarizationThreshold'
% We use 'erosion' 8, 'binarizationThreshold' 0.9, but you may need to
% adjust depending on the lighting conditions in your experiment
trackingOneImage = blobTrack(g,'show',1,'frames',50,'erosion',8,'mask',mymask, 'binarizationThreshold',0.9);
%% 4. Track video
%Basic version:
tracking = blobTrack(g,'show',0,'frames',[],'erosion',8,'mask',mymask,...
'binarizationThreshold',0.9,'NumWorkers',12);

%OpenCV (compute machine - much faster!):
%tracking = blobTrackopencv('video',g,'show',0,'frames',[],'erosion',8,'mask',mymask,...
%'binarizationThreshold',0.9,'NumWorkers',8);
% NOTE: save the tracking as a .mat file. This can then be loaded later 
%(see analyse_saved_track.m) for further analysis without having to do the 
%tracking again - especially if using the basic version!!)  
%% 5. Check tracking parameters
% This is to check that the mouse was successfully tracked (ensure that the mouse is in every frame displayed in the montage). Using the current settings the montage
% displays every 100th frame between frame 1 and frame 27000 (first frame:number of frames as interval:last frame).
gpatch = mbvid.patchReader(g,'track',tracking);
gpatch.patchWidth   = 128;
gpatch.patchHeight  = 128;
gpatch.montage('frames',1:100:27000);
%% 6. Compute and plot pixel changes between frames 
% Compute pixel changes between frames. 'compareWith' parameter corresponds
% to the number of frames between the two frames that are being compared
% (i.e. it should be 30 when video is 30 frames per second).
magnitudes = blobTrackMotionVector(tracking.getArray,'compareWith',30);
trackMagnitudes = mbvid.videotrack.scalar('video',g,'data',magnitudes);

% Make interactive plot of pixels changed (y axis) x frame number (x axis).
trackMagnitudes.plot();

%The plot displays pixel changes relative to the frame 30 frames beforehand
%on the y axis and frame number on the x axis. That is, low plateaus
%correspond to little pixel change (= little movement of the mouse).
%It is possible to double click anywhere on the plot, and this will display
%the frame corresponding to the location on which you clicked
%% 7. Find and plot freezing episodes and stimulus trials
% Find all freezing episodes.
r = mbvid.freezing.findRegions('videotrack',trackMagnitudes,'addDelay',30,'maxScore',5,'minimumLength',30);

% Freezing episodes are classified as a maximum of 5 pixels ('maxScore')
% changing over 30 consecutive frames compared to the preceding 30 consecutive 
%frames (since our frame rate is 30 frames per second, this corresponds to
%a period of 2 seconds - hence when freezing is defined as the absence of
%movement for minimum of 2 seconds, the 'minimumLength' should be equal to the frame
%rate, and the 'compareWith' value and the section above)
% to a frame 30 frames [1 second] beforehand, 30 consecutive frames
% corresponds to a period of 2 seconds. 
% Plot freezing episodes, displayed as red bars. Freezing episodes can be inspected by double clicking on the red bars and are
% displayed at the frame rate defined by 'r.depictionIntervalInFrames'. In
% this way one can check whether the mouse is actually freezing during the
% detected freezing episodes.
r.depictionIntervalInFrames = 10;

% Plot freezing episodes alongside trials. Trial times are obtained from
% the 'trialmatrix_test_basic.dlm' file. Trials are displayed as the green
% bars. In this case, the first four green bars are four 30 second baseline
% periods, the next four green bars are the CS- trials, and the last four
% green bars are the CS+ trials. If changes are made to the fear
% conditioning test protocol (e.g. the inter-trial intervals, number of
% trials, then a new trialmatrix.dlm file must be generated to be able to
% calculate the freezing percentages. It is also possible to double click on the 
%green bars, and this displays every 15th frame
%(rtrials.depictionIntervalInFrames = 15). This can be done to check
%whether any potential freezing episodes could have been missed (e.g. if
%the pixel change threshold was too stringent). 
rtrials = mbvid.videotrack.fragmentSet('video',g,'fragmentsAsPartitionInSeconds','trialmatrix_test_basic.dlm');
rtrials.depictionIntervalInFrames = 15;
mbvid.videotrack.fragmentSet.comparePlotFragments({rtrials,r},'colors',{'g','r'},'nfig',2);
%% 8. Obtain freezing percentages
o = trialPercentages(r,rtrials);
bar(o.percentage);
%Gives the percentage of time spent freezing in each trial, also plots a
%rudimentary bar graph.