combinedDetector.m

function multiObjectTracking()
% Create System objects used for reading video, detecting moving objects,
% and displaying the results.
obj = setupSystemObjects();

tracks = initializeTracks(); % Create an empty array of tracks.

nextId = 1; % ID of the next track

noOfObjects = 0;
totalObjects = 0;

position = [200 50];
box_color = {'red'};
load('ROI.mat','ROI');
% Detect moving objects, and track them across video frames.
while ~isDone(obj.reader)
    frame = readFrame();
    [centroids, bboxes, mask] = detectObjects(frame);
    predictNewLocationsOfTracks();
    [assignments, unassignedTracks, unassignedDetections] = ...
        detectionToTrackAssignment();

    updateAssignedTracks();
    updateUnassignedTracks();
    deleteLostTracks();
    createNewTracks();

    displayTrackingResults();
end

function obj = setupSystemObjects()
        % Initialize Video I/O
        % Create objects for reading a video from a file, drawing the tracked
        % objects in each frame, and playing the video.

        % Create a video file reader.
        obj.reader = vision.VideoFileReader('C:\Users\dulangakw\Dropbox\SMU\BeachStation09042016.avi');
        %obj.reader = vision.VideoFileReader('C:\Kenny Choo\3 - 1716\c1 171635-172242 twitter Copy 01.mp4');
        % Create two video players, one to display the video,
        % and one to display the foreground mask.
        %obj.videoPlayer = vision.VideoPlayer('Position', [20, 400, 700, 400]);
        %obj.maskPlayer = vision.VideoPlayer('Position', [740, 400, 700, 400]);
        
        obj.videoPlayer = vision.DeployableVideoPlayer();
        obj.maskPlayer = vision.DeployableVideoPlayer();
    
        % Create System objects for foreground detection and blob analysis

        % The foreground detector is used to segment moving objects from
        % the background. It outputs a binary mask, where the pixel value
        % of 1 corresponds to the foreground and the value of 0 corresponds
        % to the background.
        
        %NumTrainingFrame = 40
            obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
            'NumTrainingFrames', 50, 'MinimumBackgroundRatio', 0.7);

        % Connected groups of foreground pixels are likely to correspond to moving
        % objects.  The blob analysis System object is used to find such groups
        % (called 'blobs' or 'connected components'), and compute their
        % characteristics, such as area, centroid, and the bounding box.

        obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ...
            'AreaOutputPort', true, 'CentroidOutputPort', true, ...
            'MinimumBlobArea', 400);
end

function tracks = initializeTracks()
        % create an empty array of tracks
        tracks = struct(...
            'id', {}, ...
            'bbox', {}, ...
            'kalmanFilter', {}, ...
            'age', {}, ...
            'totalVisibleCount', {}, ...
            'consecutiveInvisibleCount', {});
end

function frame = readFrame()
        frame = obj.reader.step();
        %frame = imsharpen(I,'Radius',1,'Amount',0.8);
        I = imgaussfilt(frame,2);
        frame = frame - 0.2*I;
%         for layer=1:3
%             frame(:,:,layer) = medfilt2(frame(:,:,layer), [5 5]);
%         end
        
end

function [centroids, bboxes, mask] = detectObjects(frame)

        % Detect foreground.
        %type(frame)
        frame(:,:,1) = uint8(ROI).*uint8(frame(:,:,1));
        frame(:,:,2) = uint8(ROI).*uint8(frame(:,:,1));
        frame(:,:,3) = uint8(ROI).*uint8(frame(:,:,1));
        
        mask = obj.detector.step(frame);

        % Apply morphological operations to remove noise and fill in holes.
        mask = imopen(mask, strel('rectangle', [9,9]));    %[3,3]
        mask = imdilate(mask, strel('disk',10));  %[15, 15]
       mask = imfill(mask, 'holes');
        %mask = imclose(mask, strel('rectangle', [15, 15]));  %[15, 15]
        
       mask = mask.*ROI;
        
        gray = rgb2gray(frame);
        surf=detectSURFFeatures(gray);
        [surffeatures, surfpts] = extractFeatures(gray,surf);
        
        surfpts = surfpts.selectStrongest(50);
        cc = surfpts.Location;
        centroids = zeros(0,2);
        bboxes = zeros(0,4);
        for i=1:length(cc)
            if mask(floor(cc(i,2)),floor(cc(i,1)) )
                centroids = [centroids ; cc(i,:)];
                bboxes = [bboxes;[(cc(i,1)-5),(cc(i,2)-5),10,10]];
            end 
        end 
        %centroids
        bboxes = int32(bboxes)
        
        %bboxes =

        % Perform blob analysis to find connected components.
        %[~, centroids, bboxes] = obj.blobAnalyser.step(mask);
        
        
       
        
        bboxes
        noOfObjects = length(centroids);
        
end

function predictNewLocationsOfTracks()
        for i = 1:length(tracks)
            bbox = tracks(i).bbox;

            % Predict the current location of the track.
            predictedCentroid = predict(tracks(i).kalmanFilter);

            % Shift the bounding box so that its center is at
            % the predicted location.
            predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2;
            tracks(i).bbox = [predictedCentroid, bbox(3:4)];
        end
end

function [assignments, unassignedTracks, unassignedDetections] = ...
            detectionToTrackAssignment()

        nTracks = length(tracks);
        nDetections = size(centroids, 1);

        % Compute the cost of assigning each detection to each track.
        cost = zeros(nTracks, nDetections);
        for i = 1:nTracks
            cost(i, :) = distance(tracks(i).kalmanFilter, centroids);
        end

        % Solve the assignment problem.
        costOfNonAssignment = 10;
        [assignments, unassignedTracks, unassignedDetections] = ...
            assignDetectionsToTracks(cost, costOfNonAssignment);
end

function updateAssignedTracks()
        numAssignedTracks = size(assignments, 1);
        for i = 1:numAssignedTracks
            trackIdx = assignments(i, 1);
            detectionIdx = assignments(i, 2);
            centroid = centroids(detectionIdx, :);
            bbox = bboxes(detectionIdx, :);

            % Correct the estimate of the object's location
            % using the new detection.
            correct(tracks(trackIdx).kalmanFilter, centroid);

            % Replace predicted bounding box with detected
            % bounding box.
            tracks(trackIdx).bbox = bbox;

            % Update track's age.
            tracks(trackIdx).age = tracks(trackIdx).age + 1;

            % Update visibility.
            tracks(trackIdx).totalVisibleCount = ...
                tracks(trackIdx).totalVisibleCount + 1;
            tracks(trackIdx).consecutiveInvisibleCount = 0;
        end
end

function updateUnassignedTracks()
        for i = 1:length(unassignedTracks)
            ind = unassignedTracks(i);
            tracks(ind).age = tracks(ind).age + 1;
            tracks(ind).consecutiveInvisibleCount = ...
                tracks(ind).consecutiveInvisibleCount + 1;
        end
end

function deleteLostTracks()
        if isempty(tracks)
            return;
        end

        invisibleForTooLong = 5;  %20  %10
        ageThreshold = 8;  %8

        % Compute the fraction of the track's age for which it was visible.
        ages = [tracks(:).age];
        totalVisibleCounts = [tracks(:).totalVisibleCount];
        visibility = totalVisibleCounts ./ ages;

        % Find the indices of 'lost' tracks.
        lostInds = (ages < ageThreshold & visibility < 0.6) | ...
            [tracks(:).consecutiveInvisibleCount] >= invisibleForTooLong;

        % Delete lost tracks.
        tracks = tracks(~lostInds);
end

function createNewTracks()
        centroids = centroids(unassignedDetections, :);
        bboxes = bboxes(unassignedDetections, :);
        
        totalObjects =  length(centroids) + totalObjects
        
        for i = 1:size(centroids, 1)

            centroid = centroids(i,:);
            bbox = bboxes(i, :);

            % Create a Kalman filter object.
            kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
                centroid, [400, 100], [200, 50], 1);

            % Create a new track.
            newTrack = struct(...
                'id', nextId, ...
                'bbox', bbox, ...
                'kalmanFilter', kalmanFilter, ...
                'age', 1, ...
                'totalVisibleCount', 1, ...
                'consecutiveInvisibleCount', 0);

            % Add it to the array of tracks.
            tracks(end + 1) = newTrack;

            % Increment the next id.
            nextId = nextId + 1;
        end
end

function displayTrackingResults()
        % Convert the frame and the mask to uint8 RGB.
        frame = im2uint8(frame);
        mask = uint8(repmat(mask, [1, 1, 3])) .* 255;

        minVisibleCount = 8;
        if ~isempty(tracks)

            % Noisy detections tend to result in short-lived tracks.
            % Only display tracks that have been visible for more than
            % a minimum number of frames.
            reliableTrackInds = ...
                [tracks(:).totalVisibleCount] > minVisibleCount;
            reliableTracks = tracks(reliableTrackInds);

            % Display the objects. If an object has not been detected
            % in this frame, display its predicted bounding box.
            if ~isempty(reliableTracks)
                % Get bounding boxes.
                bboxes = cat(1, reliableTracks.bbox);

                % Get ids.
                ids = int32([reliableTracks(:).id]);

                % Create labels for objects indicating the ones for
                % which we display the predicted rather than the actual
                % location.
                labels = cellstr(int2str(ids'));
                predictedTrackInds = ...
                    [reliableTracks(:).consecutiveInvisibleCount] > 0;
                isPredicted = cell(size(labels));
                isPredicted(predictedTrackInds) = {''};  %{' predicted'}
                labels = strcat(labels, isPredicted);

                % Draw the objects on the frame.
                frame = insertObjectAnnotation(frame, 'rectangle', ...
                    bboxes, labels);

                % Draw the objects on the mask.
                mask = insertObjectAnnotation(mask, 'rectangle', ...
                    bboxes, labels);
            end
        end
        
        frame = insertText(frame,position,strcat('No Of Objects Detected: ',int2str(noOfObjects)),'FontSize',18,'BoxColor',...
    box_color,'BoxOpacity',0.4,'TextColor','white');
        
        
        
        % Display the mask and the frame.
        obj.maskPlayer.step(mask);
        obj.videoPlayer.step(frame);
end
end