``` function varargout = track(varargin) gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @track_OpeningFcn, ... 'gui_OutputFcn', @track_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 function track_OpeningFcn(hObject, eventdata, handles, varargin) handles.output = hObject; guidata(hObject, handles); function varargout = track_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output; function pushbutton1_Callback(hObject, eventdata, handles) function pushbutton2_Callback(hObject, eventdata, handles) global obj; DIRS=dir('.\picture\'); n=length(DIRS); for i=1:n if (~DIRS(i).isdir && ~strcmp(DIRS(i).name,'.') && ~strcmp(DIRS(i).name,'..') ) delete(['./picture/' DIRS(i).name]);%删除文件夹里面之前的图像 end end numFrames = obj.NumFrames;%视频总的帧数 for k = 1: numFrames frame = read(obj,k); imwrite(frame,strcat('.\原始帧图像\',num2str(k),'.jpg'),'jpg'); I=rgb2gray(frame);%加权平均法 %保存每一帧图片. imwrite(I,strcat('.\picture\',num2str(k),'.jpg'),'jpg'); end msgbox('分解完成','结果');```function pushbutton4_Callback(hObject, eventdata, handles) file_path = '.\picture\';% 图像文件夹路径 img_path_list = dir(strcat(file_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像 img_num = length(img_path_list);%获取图像总数量 for j=2:img_num-1 sourceimage1=imread( [ '.\picture\',int2str(j-1),'.jpg'] ); sourceimage2=imread( [ '.\picture\',int2str(j),'.jpg'] ); sourceimage3=imread( [ '.\picture\',int2str(j+1),'.jpg'] ); image1=medfilt2(sourceimage1);%图像中值滤波，去除部分干扰 image2=medfilt2(sourceimage2); image3=medfilt2(sourceimage3); imwrite(image2,['.\三帧差分\中值滤波\' num2str(j) '.jpg']); doubleimage1 = im2double(image1);%将图像转换为双精度类型 doubleimage2 = im2double(image2); doubleimage3 = im2double(image3); frame1 = doubleimage2 - doubleimage1;%帧间差分 frame2 = doubleimage3 - doubleimage2; imwrite(frame1,['.\三帧差分\一二帧差分\' num2str(j) '.jpg']); imwrite(frame2,['.\三帧差分\二三帧差分\' num2str(j) '.jpg']); t = 10/256;%阈值选取 %根据阈值分割图像 I1 = find(abs(frame1)>=t); I2 = find(abs(frame2)>=t); frame1(I1) = 255; frame2(I2) = 255; I1 = find(abs(frame1)<t); I2 = find(abs(frame2)<t); frame1(I1) = 0; frame2(I2) = 0; finalimage = frame1 & frame2;%图像组合成一幅图像 imwrite( finalimage,['.\三帧差分\阈值分割\' num2str(j) '.jpg']); finalimage = bwareaopen(finalimage,40); SE = strel('disk',7); BWCuterodil= imclose(finalimage,SE); BWCuterodi2= imfill(BWCuterodil,'holes'); imwrite( BWCuterodi2,['.\三帧差分\形态学处理\' num2str(j) '.jpg']); axes(handles.axes2);%%使用图像，操作在坐标1 imshow(sourceimage2); [L,nm] = bwlabel(BWCuterodi2,8);%找出图像中的八连通区域，视为车辆所在的区域 for j = 1:nm [r,c] = find(L == j); left= min(c); right= max(c); top= min(r); buttom= max(r); width=right - left + 1; height = buttom - top + 1; rectangle('Position',[left,top,width,height],'EdgeColor','r');%对车辆用矩形标记 pause(0.01); end end