>> % Object for reading video file.
filename = 'viptraffic.avi';
hVidReader = vision.VideoFileReader(filename, 'ImageColorSpace', 'RGB',...
'VideoOutputDataType', 'single');
>> hOpticalFlow = vision.OpticalFlow( ...
'OutputValue', 'Horizontal and vertical components in complex form', ...
'ReferenceFrameDelay', 3);
>> hMean1 = vision.Mean;
hMean2 = vision.Mean('RunningMean', true);
>> hMedianFilt = vision.MedianFilter;
>> hclose = vision.MorphologicalClose('Neighborhood', strel('line',5,45));
>> hblob = vision.BlobAnalysis(...
'CentroidOutputPort', false, 'AreaOutputPort', true, ...
'BoundingBoxOutputPort', true, 'OutputDataType', 'double', ...
'MinimumBlobArea', 250, 'MaximumBlobArea', 3600, 'MaximumCount', 80);
>> herode = vision.MorphologicalErode('Neighborhood', strel('square',2));
>> hshapeins1 = vision.ShapeInserter('BorderColor', 'Custom', ...
'CustomBorderColor', [0 1 0]);
hshapeins2 = vision.ShapeInserter( 'Shape','Lines', ...
'BorderColor', 'Custom', ...
'CustomBorderColor', [255 255 0]);
>> htextins = vision.TextInserter('Text', '%4d', 'Location', [1 1], ...
'Color', [1 1 1], 'FontSize', 12);
>> sz = get(0,'ScreenSize');
pos = [20 sz(4)-300 200 200];
hVideo1 = vision.VideoPlayer('Name','Original Video','Position',pos);
pos(1) = pos(1)+220; % move the next viewer to the right
hVideo2 = vision.VideoPlayer('Name','Motion Vector','Position',pos);
pos(1) = pos(1)+220;
hVideo3 = vision.VideoPlayer('Name','Thresholded Video','Position',pos);
pos(1) = pos(1)+220;
hVideo4 = vision.VideoPlayer('Name','Results','Position',pos);
% Initialize variables used in plotting motion vectors.
lineRow = 22;
firstTime = true;
motionVecGain = 20;
borderOffset = 5;
decimFactorRow = 5;
decimFactorCol = 5;
>> while ~isDone(hVidReader) % Stop when end of file is reached
frame = step(hVidReader); % Read input video frame
grayFrame = rgb2gray(frame);
ofVectors = step(hOpticalFlow, grayFrame); % Estimate optical flow
% The optical flow vectors are stored as complex numbers. Compute their
% magnitude squared which will later be used for thresholding.
y1 = ofVectors .* conj(ofVectors);
% Compute the velocity threshold from the matrix of complex velocities.
vel_th = 0.5 * step(hMean2, step(hMean1, y1));
% Threshold the image and then filter it to remove speckle noise.
segmentedObjects = step(hMedianFilt, y1 >= vel_th);
% Thin-out the parts of the road and fill holes in the blobs.
segmentedObjects = step(hclose, step(herode, segmentedObjects));
% Estimate the area and bounding box of the blobs.
[area, bbox] = step(hblob, segmentedObjects);
% Select boxes inside ROI (below white line).
Idx = bbox(:,1) > lineRow;
% Based on blob sizes, filter out objects which can not be cars.
% When the ratio between the area of the blob and the area of the
% bounding box is above 0.4 (40%), classify it as a car.
ratio = zeros(length(Idx), 1);
ratio(Idx) = single(area(Idx,1))./single(bbox(Idx,3).*bbox(Idx,4));
ratiob = ratio > 0.4;
count = int32(sum(ratiob)); % Number of cars
bbox(~ratiob, :) = int32(-1);
% Draw bounding boxes around the tracked cars.
y2 = step(hshapeins1, frame, bbox);
% Display the number of cars tracked and a white line showing the ROI.
y2(22:23,:,:) = 1; % The white line.
y2(1:15,1:30,:) = 0; % Background for displaying count
result = step(htextins, y2, count);
% Generate coordinates for plotting motion vectors.
if firstTime
[R C] = size(ofVectors); % Height and width in pixels
RV = borderOffset:decimFactorRow:(R-borderOffset);
CV = borderOffset:decimFactorCol:(C-borderOffset);
[Y X] = meshgrid(CV,RV);
firstTime = false;
end
% Calculate and draw the motion vectors.
tmp = ofVectors(RV,CV) .* motionVecGain;
lines = [Y(:), X(:), Y(:) + real(tmp(:)), X(:) + imag(tmp(:))];
motionVectors = step(hshapeins2, frame, lines);
% Display the results
step(hVideo1, frame); % Original video
step(hVideo2, motionVectors); % Video with motion vectors
step(hVideo3, segmentedObjects); % Thresholded video
step(hVideo4, result); % Video with bounding boxes
end
release(hVidReader);
>>
