基于视频检测的多目标跟踪的完整matlab程序

function varargout = EasyTrack(varargin) % EASYTRACK MATLAB code for EasyTrack.fig % EASYTRACK, by itself, creates a new EASYTRACK or raises the existing % singleton*. % % H = EASYTRACK returns the handle to a new EASYTRACK or the handle to % the existing singleton*. % % EASYTRACK('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in EASYTRACK.M with the given input arguments. % % EASYTRACK('Property','Value',...) creates a new EASYTRACK or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before EasyTrack_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to EasyTrack_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help EasyTrack % Last Modified by GUIDE v2.5 12-Jun-2016 23:54:34 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @EasyTrack_OpeningFcn, ... 'gui_OutputFcn', @EasyTrack_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT % --- Executes just before EasyTrack is made visible. function EasyTrack_OpeningFcn(hObject, ~, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to EasyTrack (see VARARGIN) % Choose default command line output for EasyTrack handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes EasyTrack wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = EasyTrack_OutputFcn(~, ~, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structure varargout{1} = handles.output; % 最核心的函数,检测按钮 function mainB_Callback(~, ~, handles) %数据的前期准备 set(handles.numText,'string',0);%计数框清零 set(handles.endTime,'string',0);%结束时间清零 set(handles.startTime,'string',datestr(datetime('now'))); set(handles.infoP,'Visible','on'); %视频获取途径的选择 camSwitch=get(handles.radioCam,'value');%获取值 position=handles.position; areaSwitch=handles.areaSwitch; if areaSwitch>0; BW = handles.BW;%传递二进制图 end %保存设置； saveSwitch=handles.saveSwitch; if saveSwitch>0.5 saveName=get(handles.saveName,'String'); saveName=[saveName,'.avi']; videoFWriter = vision.VideoFileWriter(saveName);%设定记录器及名称 videoFWriter.VideoCompressor='ffdshow Video Codec';%设定水平压缩的方式 end timeLimit=str2double(get(handles.timeLimit,'String')); nextId = 1; %track的初始值ID值 uniqueReliableIds=[];%收集可信的track的唯一id startIdsCount=0;%用于统计变量的不同值 idsCount=0; obj.Dplayer=vision.DeployableVideoPlayer('Name','Detecting Video');%设定播放器 %obj.MaskDplayer=vision.DeployableVideoPlayer('Name','Detecting Mask');%设定播放器 %定义检测器 obj.detector = vision.ForegroundDetector('NumGaussians', 5, ... 'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7); %定义分析器 obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... 'AreaOutputPort', true, 'CentroidOutputPort', true, ... 'MinimumBlobArea', 400,'MaximumBlobArea',20000); %初始化跟踪路径的数据 tracks = struct(... 'id', {}, ... 'label',{},... 'time',{},... 'bbox', {}, ... 'kalmanFilter', {}, ... 'age', {}, ... 'totalVisibleCount', {}, ... 'consecutiveInvisibleCount', {}); %定义一个收集器 reliableTracksCollection=struct(... 'id', {}, ... 'label',{},... 'time',{},... 'bbox', {}, ... 'kalmanFilter', {}, ... 'age', {}, ... 'totalVisibleCount', {}, ... 'consecutiveInvisibleCount', {}); % 定义在main的坐标系中工作 axes(handles.main); runTime=0; tic;%计时开始 %重点的重点 %开始每一帧的处理 while hasFrame(handles.video)%runTime<timeLimit%~isDone(obj.reader) if camSwitch>0 frame=snapshot(handles.cam); else frame=readFrame(handles.video); end %预处理frame,就是只对框选的范围用mask if areaSwitch>0.5 [~,~,imc]=size(frame); switch imc case 1 I2=frame; I2(~BW)=0; case 2 IR=frame(:,:,1); IG=frame(:,:,2); IR(~BW)=0; IG(~BW)=0; I2=cat(3,IR,IG); case 3 IR=frame(:,:,1); IG=frame(:,:,2); IB=frame(:,:,3); IR(~BW)=0; IG(~BW)=0; IB(~BW)=0; I2=cat(3,IR,IG,IB); otherwise disp('图像为不正规图像！无法处理!') end mask = obj.detector.step(I2); else mask = obj.detector.step(frame); end %进行扑拓处理 mask = imopen(mask, strel('rectangle', [3,3])); mask = imclose(mask, strel('rectangle', [15, 15])); mask = imfill(mask, 'holes'); %获取分析图像 [~, centroids, bboxes] = obj.blobAnalyser.step(mask); %预测新的路径 for i = 1:length(tracks) bbox = tracks(i).bbox; %获取预测点的中心 predictedCentroid = predict(tracks(i).kalmanFilter); %预测点与中心点进行数值处理 predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2; tracks(i).bbox = [predictedCentroid, bbox(3:4)]; end %获得测试数量 nTracks = length(tracks); nDetections = size(centroids, 1); % 计算路径与损失路径 cost = zeros(nTracks, nDetections); for i = 1:nTracks cost(i, :) = distance(tracks(i).kalmanFilter, centroids); end % Solve the assignment problem. costOfNonAssignment = 20;%原路径与现有路径之间的距离区分的阈值 [assignments, unassignedTracks, unassignedDetections] = ... assignDetectionsToTracks(cost, costOfNonAssignment); %更新跟踪点 numAssignedTracks = size(assignments, 1); for i = 1:numAssignedTracks trackIdx = assignments(i, 1); detectionIdx = assignments(i, 2); centroid = centroids(detectionIdx, :); bbox = bboxes(detectionIdx, :); %验证数据 correct(tracks(trackIdx).kalmanFilter, centroid); %更新跟踪点 tracks(trackIdx).bbox = bbox; tracks(trackIdx).age = tracks(trackIdx).age + 1; tracks(trackIdx).totalVisibleCount = ... tracks(trackIdx).totalVisibleCount + 1; tracks(trackIdx).consecutiveInvisibleCount = 0; end %跟新没有被分配的点; for i = 1:length(unassignedTracks) ind = unassignedTracks(i); tracks(ind).age = tracks(ind).age + 1; tracks(ind).consecutiveInvisibleCount = ... tracks(ind).consecutiveInvisibleCount + 1; end %删除不匹配的点; if ~isempty(tracks)%修改这部 invisibleForTooLong = 20; ageThreshold = 8; ages = [tracks(:).age]; totalVisibleCounts = [tracks(:).totalVisibleCount]; visibility = totalVisibleCounts ./ ages;