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می تونید تو این برنامه کمکم کنید لطفا ببینیدش
این برنامه تعداد خودرو ها رو شمارش می کند و به صورت کلی بیان می کند
من می خوام این شمارش روی هر خودرو نوشته بشه مثل همون باکس های سبزی که دور هر خودرو میکشه ممنون
می تونید تو این برنامه کمکم کنید لطفا ببینیدش
این برنامه تعداد خودرو ها رو شمارش می کند و به صورت کلی بیان می کند
من می خوام این شمارش روی هر خودرو نوشته بشه مثل همون باکس های سبزی که دور هر خودرو میکشه ممنون
کد:
clc;clear;close all %Tracking Cars Using Optical Flow %This 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 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; %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(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);
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