Matlabscript Botsingsdetectie

function Collision_detection()
   clc
   % 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


   % Detect moving objects, and track them across video frames.
   fr=0;
   disp('Starting...');


   f = step(obj.reader);
   obj.videoPlayer.step(f); release(obj.videoPlayer);


   Lastpos = [0 0];
   Currentpos = [0 0];
   colvar = 1;
   SHOW = 1;  %turn on/off message box


   while fr < 41 && isOpen(obj.videoPlayer)
       fr;
       frame = readFrame();
       [centroids, bboxes, mask] = detectObjects(frame, blob);
       predictNewLocationsOfTracks();
       [assignments, ~ , unassignedDetections] = ...
           detectionToTrackAssignment();
       updateAssignedTracks();  %position
       createNewTracks();
       displayTrackingResults();
       fr=fr+1;
   end


   release(obj.videoPlayer);
   obj.videoPlayer.hide();
   close all;


   %% Create System Objects
   % Create System objects used for reading the video frames, detecting
   % colored objects, and displaying results.


   function obj = setupSystemObjects()
       % Initialize Video
       % Create objects for reading a video from a file, drawing the tracked
       % objects in each frame, and playing the video. No live camera data
       % can be analyzed yet
       obj.reader = vision.VideoFileReader('botsing1.mp4');

       
       % Create a video player
       obj.videoPlayer = vision.VideoPlayer('Position', [200, 400, 700, 400]);


       % Create System objects based on movement and blob analysis
       obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
           'NumTrainingFrames', 5, 'MinimumBackgroundRatio', 0.8);


       blob = vision.BlobAnalysis('BoundingBoxOutputPort', true, 'ExcludeBorderBlobs',true, ...
           'MajorAxisLengthOutputPort',true,'EccentricityOutputPort', true, 'CentroidOutputPort', true, ...
           'MinimumBlobArea', 2100, 'MaximumBlobArea',50000);
   end


   %% Initialize Tracks
   % The structure contains the following fields:
   %
   % * |id| :                        the integer ID of the track
   % * |bbox| :                      the current bounding box of the object; used
   %                                 for display
   % * |kalmanFilter| :              a Kalman filter object used for motion-based
   %                                 tracking
   % * |age| :                       the number of frames since the track was first
   %                                 detected
   % * |totalVisibleCount| :         the total number of frames in which the track
   %                                 was detected (visible)
   % * |consecutiveInvisibleCount| : the number of consecutive frames for 
   %                                 which the track was not detected (invisible).
   %                                 This results in deleting the track if the
   %                                 threshold is reached


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


   %% Read a Video Frame
   % Read the next video frame from the video file.
   function frame = readFrame()
       frame = obj.reader.step();
       %frame = snapshot(cam);
   end


   %% Detect Objects
   % The |detectObjects| function returns the centroids and the bounding boxes
   % of the detected objects. It also returns the binary mask, which has the 
   % same size as the input frame. Pixels with a value of 1 correspond to the
   % foreground, and pixels with a value of 0 correspond to the background. 


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

      
   % Use color to identify turtles from each team. Only these colors will
   % be taken into account. A distinctive top color results in more 
   % accurate tracking.
   Red = frame*255;
   Red = Red(:,:,1)>Red(:,:,2)*2 & Red(:,:,1)>Red(:,:,2)*2;


   mask = obj.detector.step(frame);
 % Apply morphological operations to remove noise and fill in holes.
   mask = imopen(mask, strel('rectangle', [9,9]));
   mask = imclose(mask, strel('rectangle', [15, 15])); 
   mask = imfill(mask, 'holes');


  % detect blobs, return centroids, bounding boxes, eccentricity and diameter
  [~,centroids,bboxes,diam,ecc] = step(blob,Red);              


  % maximize for most round object
  if ~isempty(centroids)    
       [~,I] = max(ecc,[],1);
       bboxes = bboxes(I,:);
       centroids = centroids(I,:);
       diam = diam(I);
  end
  %check if max is indeed round (i.e. if anything useful detected)
  if ecc > 1
       ecc = [];
       centroids = [];
       bboxes = [];
       diam = [];
  end


   end
   %% Predict New Locations of Existing Tracks
   % Use the Kalman filter to predict the centroid of each track in the
   % current frame, and update its bounding box accordingly.


   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


   %% Assign Detections to Tracks
   % Assigning object detections in the current frame to existing tracks is
   % done by minimizing cost. The cost is defined as the negative
   % log-likelihood of a detection corresponding to a track.  


   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);
       if nTracks>0
           cost(1, :) = distance(tracks(1).kalmanFilter, centroids);
       end


       % Solve the assignment problem.
       costOfNonAssignment = 20;
       [assignments, unassignedTracks, unassignedDetections] = ...
           assignDetectionsToTracks(cost, costOfNonAssignment);
   end


   %% Update Assigned Tracks
   % The |updateAssignedTracks| function updates each assigned track with the
   % corresponding detection. It calls the |correct| method of
   % |vision.KalmanFilter| to correct the location estimate. Next, it stores
   % the new bounding box, and increases the age of the track and the total
   % visible count by 1. Finally, the function sets the invisible count to 0.


   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. This will give the current position
           Currentpos = 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;
              checkcollision();
              saveposition();
       end
   end


   %% Create New Tracks
   % Create new tracks from unassigned detections. Assume that any unassigned
   % detection is a start of a new track. In practice, you can use other cues
   % to eliminate noisy detections, such as size, location, or appearance.


   function createNewTracks()
       centroids = centroids(unassignedDetections, :);
       bboxes = bboxes(unassignedDetections, :);


       for i = 1:size(centroids, 1)


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


           % Create a Kalman filter object.
           kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
               centroid, [200, 50], [100, 25], 200);


           % Create a new track.
           newTrack = struct(...
               'id', nextId, ...
               'bbox', bbox, ...
               'kalmanFilter', kalmanFilter, ...
               'age', 1, ...
               'totalVisibleCount', 4, ...
               'consecutiveInvisibleCount', 0);


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


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


   %% Display Tracking Results
   % The |displayTrackingResults| function draws a bounding box and label ID 
   % for each track on the video frame and the foreground mask. It then 
   % displays the frame and the mask in their respective video players. 


   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);


               labels = 'Red Ball';
               % 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


       % Display the mask and the frame.      
       obj.videoPlayer.step(frame);
   end


   %% Store previous location
   % The |saveposition| function stores the location of the previous frame to
   % allow for the calculation of a direction vector created from consecutive
   % frames


   function saveposition()
       if fr <2
          Lastpos = [0 0];
       elseif fr >= 2
          Lastpos = Currentpos;
       end
   end


   %% Check for a collision
   % The |checkcollision| function checks every frame if there is a sudden
   % change in the direction compared to its previous. If more elaborate 
   % collision rules apply, one can look to find the acceleration


   function checkcollision()
       if ~isempty(Lastpos) && fr >17  %% Bounding box drawn
           if (Currentpos(1) > Lastpos(1))&&(Currentpos(2) > Lastpos(2)) ...
                   && colvar == 1
               fprintf('BOTSING in frame %u \n', fr);
               if SHOW == 1
                   h =msgbox('Collision occured!');
               end
               colvar = colvar + 1;
           end
       end
   end


end