matlab CVdemo：Edge Detection Tracking Cars Using Optical Flow.doc

Edge DetectionThis demo uses the Computer Vision System Toolbox System objects to find the edges of objects in a video stream.IntroductionThis demo illustrates how to use the EdgeDetector System object to identify the edges of objects in a video stream using the Prewitt method. The demo also shows the use of the AlphaBlender System object to overlay the edges on the original video frames.InitializationUse these next sections of code to initialize the required System objects.%Create a VideoFileReader System object to read video from a file.hVideoSrc = vision.VideoFileReader(vipmen.avi, . ImageColorSpace, Intensity);%Create an EdgeDetector System object to detect the edges in the video frame.hEdge = vision.EdgeDetector( . Method, Prewitt, . ThresholdSource, Property, . Threshold, 15/256, . EdgeThinning, true);%Create an AlphaBlender System object to overlay the edges on the original video frame.hAB = vision.AlphaBlender(Operation, Highlight selected pixels);%Create three VideoPlayer System objects to display the original video, the edges, and the edges overlaying the original video.WindowSize = 190 150;hVideoOrig = vision.VideoPlayer(Name, Original);hVideoOrig.Position = 10 hVideoOrig.Position(2) WindowSize;hVideoEdges = vision.VideoPlayer(Name, Edges);hVideoEdges.Position = 210 hVideoOrig.Position(2) WindowSize;hVideoOverlay = vision.VideoPlayer(Name, Overlay);hVideoOverlay.Position = 410 hVideoOrig.Position(2) WindowSize;Stream Processing Loop%Create a processing loop to perform edge detection on the input video. This loop uses the objects you instantiated above.while isDone(hVideoSrc) frame = step(hVideoSrc); % Read input video edges = step(hEdge, frame); % Edge detection composite = step(hAB, frame, edges); % AlphaBlender step(hVideoOrig, frame); % Display original step(hVideoEdges, edges); % Display edges step(hVideoOverlay, composite); % Display edges overlayedendRelease%Here you call the release method on the object to close any open files and devices.release(hVideoSrc);SummaryThe EdgeDetector object outputs a binary image with the edges shown in white. This output is displayed in the Edges window. The inputs to the AlphaBlender object are the original video frames, shown in the Original window, and the binary image output by the EdgeDetector object. The AlphaBlender object uses this binary image as a mask that specifies which original pixels to overwrite. The AlphaBlender object outputs a composite image, shown in the Overlay window, where the original pixel values are overwritten by the white edge values.Tracking Cars Using Optical FlowThis demo tracks cars in a video by detecting motion using optical flow. The cars are segmented from the background by thresholding the motion vector magnitudes. Then, blob analysis is used to identify the cars.Initialization%Create the System objects outside of the main video processing loop.% Object for reading video file.filename = viptraffic.avi;hVidReader = vision.VideoFileReader(filename, ImageColorSpace, RGB,. VideoOutputDataType, single);%Optical flow object for estimating direction and speed of object motion.hOpticalFlow = vision.OpticalFlow( . OutputValue, Horizontal and vertical components in complex form, . ReferenceFrameDelay, 3);%Create two objects for analyzing optical flow vectors.hMean1 = vision.Mean;hMean2 = vision.Mean(RunningMean, true);%Filter object for removing speckle noise introduced during segmentation.hMedianFilt = vision.MedianFilter;%Morphological closing object for filling holes in blobs.hclose = vision.MorphologicalClose(Neighborhood, strel(line,5,45);%Create a blob analysis System object to segment cars in the video.hblob = vision.BlobAnalysis(. CentroidOutputPort, false, AreaOutputPort, true, . BoundingBoxOutputPort, true, OutputDataType, double, . MinimumBlobArea, 250, MaximumBlobArea, 3600, MaximumCount, 80);%Morphological erosion object for removing portions of the road and other unwanted objects.herode = vision.MorphologicalErode(Neighborhood, strel(square,2);%Create objects for drawing the bounding boxes and motion vectors.hshapeins1 = vision.ShapeInserter(BorderColor, Custom, . CustomBorderColor, 0 1 0);hshapeins2 = vision.ShapeInserter( Shape,Lines, . BorderColor, Custom, . CustomBorderColor, 255 255 0);%This object will write the number of tracked cars in the output image.htextins = vision.TextInserter(Text, %4d, Location, 1 1, . Color, 1 1 1, FontSize, 12);%Create System objects to display the original video, motion vector video, the thresholded video and the final result.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 righthVideo2 = 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;Tracking Cars in Video%Create the processing loop to track the cars in video.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(