人脸识别MATLAB代码 - 360文档中心

基于matlab的人脸识别源代码

function varargout = FR_Processed_histogram(varargin) %这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn', @FR_Processed_histogram_OpeningFcn.,..'gui_OutputFcn',@FR_Processed_histogram_OutputFcn.,..'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback =str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% -------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible. function FR_Processed_histogram_OpeningFcn(hObjecte, ventdata, 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 FR_Processed_histogram (see VARARGIN)% Choose default command line output forFR_Processed_histogramhandles.output = hObject;% Update handles structure guidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response(see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_numform_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;% -------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, 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 structurevarargout{1} = handles.output;% -------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_numform_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z), '\',int2str(X), '.bmp') ); [rowscols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 ) train_hist_img(max_hist_level, K)train_hist_img(max_hist_level, K) + 1;else train_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num; K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done') save'train' train_processed_bin;% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level)test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img); sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j); test_processed_bin(K) =sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname',S');getString_start=getString_start(end)+1;getString_end=strfind(pathname',\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>'num2str(subjectindex) ' matches with the image of subject >> 'num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT( 'ORL\S' ,num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')% -------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'),get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: place code in OpeningFcn to populate axes3%Programmed by Usman Qayyum。

基于肤色的人脸检测matlab代码

基于肤色的人脸检测matlab代码mainclose allclear allclc% 输入图像名字img_name = input('请输入图像名字(图像必须为RGB图像，输入0结束)：','s'); % 当输入0时结束while ~strcmp(img_name,'0')% 进行人脸识别facedetection(img_name);img_name = input('请输入图像名字(图像必须为RGB图像，输入0结束)：','s'); endfacedetectionfunctionfacedetection(img_name)% 读取RGB图像I = imread(img_name);% 转换为灰度图像gray = rgb2gray(I);% 将图像转化为YCbCr颜色空间YCbCr = rgb2ycbcr(I);% 获得图像宽度和高度heigth = size(gray,1);width = size(gray,2);% 根据肤色模型将图像二值化fori = 1:heigthfor j = 1:widthY = YCbCr(i,j,1);Cb = YCbCr(i,j,2);Cr = YCbCr(i,j,3);if(Y < 80)gray(i,j) = 0;elseif(skin(Y,Cb,Cr) == 1)gray(i,j) = 255;elsegray(i,j) = 0;endendendend% 二值图像形态学处理SE=strel('arbitrary',eye(5));%gray = bwmorph(gray,'erode');% imopen先腐蚀再膨胀gray = imopen(gray,SE);% imclose先膨胀再腐蚀%gray = imclose(gray,SE);imshow(gray);% 取出图片中所有包含白色区域的最小矩形[L,num] = bwlabel(gray,8);STATS = regionprops(L,'BoundingBox'); % 存放经过筛选以后得到的所有矩形块n = 1;result = zeros(n,4);figure,imshow(I);hold on;fori = 1:numbox = STATS(i).BoundingBox;x = box(1); %矩形坐标xy = box(2); %矩形坐标yw = box(3); %矩形宽度wh = box(4); %矩形高度h% 宽度和高度的比例ratio = h/w;ux = uint8(x);uy = uint8(y);ifux> 1ux = ux - 1;endifuy> 1uy = uy - 1;end% 可能是人脸区域的矩形应满足以下条件：% 1、高度和宽度必须都大于20，且矩形面积大于400 % 2、高度和宽度比率应该在范围（0.6,2）内% 3、函数findeye返回值为1if w < 20 || h < 20 || w*h < 400continueelseif ratio < 2 && ratio > 0.6 &&findeye(gray,ux,uy,w,h) == 1 % 记录可能为人脸的矩形区域result(n,:) = [uxuy w h];n = n+1;endend% 对可能是人脸的区域进行标记if size(result,1) == 1 && result(1,1) > 0rectangle('Position',[result(1,1),result(1,2),result(1,3),result(1, 4)],'EdgeColor','r'); else% 如果满足条件的矩形区域大于1则再根据其他信息进行筛选for m = 1:size(result,1)m1 = result(m,1);m2 = result(m,2);m3 = result(m,3);m4 = result(m,4);% 标记最终的人脸区域if m1 + m3 < width && m2 + m4 <heigth< p=""> rectangle('Position',[m1,m2,m3,m4],'EdgeColor','r');endendendfindeye% 判断二值图像中是否含有可能是眼睛的块% bImage----二值图像% x---------矩形左上角顶点X坐标% y---------矩形左上角顶点Y坐标% w---------矩形宽度% h---------矩形长度% 如果有则返回值eye等于1，否则为0function eye = findeye(bImage,x,y,w,h)% 根据矩形相关属性得到二值图像中矩形区域中的数据% 存放矩形区域二值图像信息part = zeros(h,w);% 二值化fori = y:(y+h)for j = x:(x+w)ifbImage(i,j) == 0part(i-y+1,j-x+1) = 255;elsepart(i-y+1,j-x+1) = 0;endendend[L,num] = bwlabel(part,8);% 如果区域中有两个以上的矩形则认为有眼睛ifnum< 2eye = 0;elseeye = 1;endskin% Anil K.Jain提出的基于YCbCr颜色空间的肤色模型% 根据当前点的Cb Cr值判断是否为肤色function result = skin(Y,Cb,Cr)% 参数% a = 25.39;a = 28;% b = 14.03;b=18;ecx = 1.60;ecy = 2.41;sita = 2.53;cx = 109.38;cy = 152.02;xishu = [cos(sita) sin(sita);-sin(sita) cos(sita)];% 如果亮度大于230，则将长短轴同时扩大为原来的1.1倍if(Y > 230)a = 1.1*a;b = 1.1*b;end% 根据公式进行计算Cb = double(Cb);Cr = double(Cr);t = [(Cb-cx);(Cr-cy)];temp = xishu*t;value = (temp(1) - ecx)^2/a^2 + (temp(2) - ecy)^2/b^2; % 大于1则不是肤色，返回0；否则为肤色，返回1if value > 1result = 0;elseresult = 1;end</heigth<>。

基于matlab的人脸识别源代码

function varargout = FR_Processed_histogram(varargin) %这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn', @FR_Processed_histogram_OpeningFcn.,..'gui_OutputFcn',@FR_Processed_histogram_OutputFcn.,..'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback =str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% -------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible. function FR_Processed_histogram_OpeningFcn(hObjecte, ventdata, 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 FR_Processed_histogram (see VARARGIN)% Choose default command line output forFR_Processed_histogramhandles.output = hObject;% Update handles structure guidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response(see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_numform_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;% -------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, 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 structurevarargout{1} = handles.output;% -------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_numform_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z), '\',int2str(X), '.bmp') ); [rowscols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 ) train_hist_img(max_hist_level, K)train_hist_img(max_hist_level, K) + 1;else train_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num; K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done') save'train' train_processed_bin;% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level)test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img); sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j); test_processed_bin(K) =sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname',S');getString_start=getString_start(end)+1;getString_end=strfind(pathname',\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>'num2str(subjectindex) ' matches with the image of subject >> 'num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT( 'ORL\S' ,num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')% -------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'),get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: place code in OpeningFcn to populate axes3%Programmed by Usman Qayyum。

人脸识别核心算法及MATLAB代码

人脸识别核心算法在检测到人脸并定位面部关键特征点之后，主要的人脸区域就可以被裁剪出来，经过预处理之后，馈入后端的识别算法。

识别算法要完成人脸特征的提取，并与库存的已知人脸进行比对，完成最终的分类。

我们在这方面的主要工作包括：∙基于LGBP的人脸识别方法问题：统计学习目前已经成为人脸识别领域的主流方法，但实践表明，基于统计学习的方法往往会存在“推广能力弱”的问题，尤其在待识别图像“属性”未知的情况下，更难以确定采用什么样的训练图像来训练人脸模型。

鉴于此，在对统计学习方法进行研究的同时，我们还考虑了非统计模式识别的一类方法。

思路：对于给定的人脸图像，LGBP方法首先将其与多个不同尺度和方向的Gabor滤波器卷积(卷积结果称为Gabor特征图谱)获得多分辨率的变换图像。

然后将每个Gabor特征图谱划分成若干互不相交的局部空间区域，对每个区域提取局部邻域像素的亮度变化模式，并在每个局部空间区域内提取这些变化模式的空间区域直方图，所有Gabor特征图谱的、所有区域的直方图串接为一高维特征直方图来编码人脸图像。

并通过直方图之间的相似度匹配技术（如直方图交运算）来实现最终的人脸识别。

在FERET四个人脸图像测试集合上与FERET97的结果对比情况见下表。

由此可见，该方法具有良好的识别性能。

而且LGBP方法具有计算速度快、无需大样本学习、推广能力强的优点。

参见ICCV2005表.LGBP方法与FERET'97最佳结果的对比情况∙基于AdaBoost的Gabor特征选择及判别分析方法问题：人脸描述是人脸识别的核心问题之一，人脸识别的研究实践表明：在人脸三维形状信息难以准确获取的条件下，从图像数据中提取多方向、多尺度的Gabor特征是一种合适的选择。

使用Gabor特征进行人脸识别的典型方法包括弹性图匹配方法（EGM）和Gabor特征判别分类法（GFC）。

EGM在实用中需要解决关键特征点的定位问题，而且其速度也很难提高；而GFC则直接对下采样的Gabor特征用PCA降维并进行判别分析，尽管这避免了精确定位关键特征点的难题，但下采样的特征维数仍然偏高，而且简单的下采样策略很可能遗漏了非常多的有用特征。

基于matlab的人脸识别源代码

function varargout = FR_Processed_histogram(varargin)%这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17) gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn',@FR_Processed_histogram_OpeningFcn, ...'gui_OutputFcn',@FR_Processed_histogram_OutputFcn, ...'gui_LayoutFcn', [] , ...'gui_Callback', []);if nargin && ischar(varargin{1})gui_State.gui_Callback = str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT%--------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible.function FR_Processed_histogram_OpeningFcn(hObject, eventdata, 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 toFR_Processed_histogram (see VARARGIN)% Choose default command line output for FR_Processed_histogramhandles.output = hObject;% Update handles structureguidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response (see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_num form_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;%--------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, 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;%--------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future versionof MATLAB% handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_numform_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images ofeach subjectI = imread( strcat('ORL\S',int2str(Z),'\',int2str(X),'.bmp') );[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )train_hist_img(max_hist_level, K) =train_hist_img(max_hist_level, K) + 1;elsetrain_hist_img(I(i,j), K) =train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done')save 'train'train_processed_bin;%--------------------------------------------------------------------------% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (seeGUIDATA)global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level) = test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img);sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j); test_processed_bin(K) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname,'S');getString_start=getString_start(end)+1;getString_end=strfind(pathname,'\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end ));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp'))) msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>' num2str(subjectindex) ' matches with the image of subject >> ' num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp'))) msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')%--------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end%--------------------------------------------------------------------------% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles)% hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: place code in OpeningFcn to populate axes3%Programmed by Usman Qayyum（注：可编辑下载，若有不当之处，请指正，谢谢!）。

照片人脸检测MATLAB代码(汇编)

% 载入图像Img = imread('star1.jpg');if ndims(Img) == 3I=rgb2gray(Img);elseI = Img;endBW = im2bw(I, graythresh(I)); % 二值化figure;subplot(2, 2, 1); imshow(Img);title('原图像', 'FontWeight', 'Bold');subplot(2, 2, 2); imshow(Img);title('网格标记图像', 'FontWeight', 'Bold');hold on;[xt, yt] = meshgrid(round(linspace(1, size(I, 1), 10)), ...round(linspace(1, size(I, 2), 10)));mesh(yt, xt, zeros(size(xt)), 'FaceColor', ...'None', 'LineWidth', 3, ...'EdgeColor', 'r');subplot(2, 2, 3); imshow(BW);title('二值图像', 'FontWeight', 'Bold');[n1, n2] = size(BW);r = floor(n1/10); % 分成10块，行c = floor(n2/10); % 分成10块，列x1 = 1; x2 = r; % 对应行初始化s = r*c; % 块面积for i = 1:10y1 = 1; y2 = c; % 对应列初始化for j = 1:10if (y2<=c || y2>=9*c) || (x1==1 || x2==r*10)% 如果是在四周区域loc = find(BW(x1:x2, y1:y2)==0);[p, q] = size(loc);pr = p/s*100; % 黑色像素所占的比例数if pr <= 100BW(x1:x2, y1:y2) = 0;endendy1 = y1+c; % 列跳跃y2 = y2+c; % 列跳跃endx1 = x1+r; % 行跳跃x2 = x2+r; % 行跳跃end[L, num] = bwlabel(BW, 8); % 区域标记stats = regionprops(L, 'BoundingBox'); % 得到包围矩形框Bd = cat(1, stats.BoundingBox);[s1, s2] = size(Bd);mx = 0;for k = 1:s1p = Bd(k, 3)*Bd(k, 4); % 宽*高if p>mx && (Bd(k, 3)/Bd(k, 4))<1.8% 如果满足面积块大，而且宽/高<1.8mx = p;j = k;endendsubplot(2, 2, 4);imshow(I); hold on;rectangle('Position', Bd(j, :), ...'EdgeColor', 'r', 'LineWidth', 3);title('标记图像', 'FontWeight', 'Bold');if ndims(Img) == 3I=rgb2gray(Img);elseI = Img;endBW = im2bw(I, graythresh(I)); % 二值化figure;subplot(2, 2, 1); imshow(Img);title('原图像', 'FontWeight', 'Bold');subplot(2, 2, 2); imshow(Img);title('网格标记图像', 'FontWeight', 'Bold');hold on;[xt, yt] = meshgrid(round(linspace(1, size(I, 1), 10)), ...round(linspace(1, size(I, 2), 10)));mesh(yt, xt, zeros(size(xt)), 'FaceColor', ...'None', 'LineWidth', 3, ...'EdgeColor', 'r');subplot(2, 2, 3); imshow(BW);title('二值图像', 'FontWeight', 'Bold');[n1, n2] = size(BW);r = floor(n1/10); % 分成10块，行c = floor(n2/10); % 分成10块，列x1 = 1; x2 = r; % 对应行初始化s = r*c; % 块面积for i = 1:10y1 = 1; y2 = c; % 对应列初始化for j = 1:10if (y2<=c || y2>=9*c) || (x1==1 || x2==r*10)% 如果是在四周区域loc = find(BW(x1:x2, y1:y2)==0);[p, q] = size(loc);pr = p/s*100; % 黑色像素所占的比例数if pr <= 100BW(x1:x2, y1:y2) = 0;endendy1 = y1+c; % 列跳跃y2 = y2+c; % 列跳跃endx1 = x1+r; % 行跳跃x2 = x2+r; % 行跳跃end[L, num] = bwlabel(BW, 8); % 区域标记stats = regionprops(L, 'BoundingBox'); % 得到包围矩形框Bd = cat(1, stats.BoundingBox);[s1, s2] = size(Bd);mx = 0;for k = 1:s1p = Bd(k, 3)*Bd(k, 4); % 宽*高if p>mx && (Bd(k, 3)/Bd(k, 4))<1.8% 如果满足面积块大，而且宽/高<1.8mx = p;j = k;endendsubplot(2, 2, 4);imshow(I); hold on;rectangle('Position', Bd(j, :), ...'EdgeColor', 'r', 'LineWidth', 3);title('标记图像', 'FontWeight', 'Bold');。

(完整版)人脸识别MATLAB代码

1.色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread('D:\matlab\皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1); t2=imread('D:\matlab\皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2); t3=imread('D:\matlab\皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3); t4=imread('D:\matlab\皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4); t5=imread('D:\matlab\皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5); t6=imread('D:\matlab\皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6); t7=imread('D:\matlab\皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7); t8=imread('D:\matlab\皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);t9=imread('D:\matlab\皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);t10=imread('D:\matlab\皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);t11=imread('D:\matlab\皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);t12=imread('D:\matlab\皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);t13=imread('D:\matlab\皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);t14=imread('D:\matlab\皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);t15=imread('D:\matlab\皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);t16=imread('D:\matlab\皮肤库\16.jpg');[r16,g16]=rgb_RGB(t16);t17=imread('D:\matlab\皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);t18=imread('D:\matlab\皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);t19=imread('D:\matlab\皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);t20=imread('D:\matlab\皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);t21=imread('D:\matlab\皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);t22=imread('D:\matlab\皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);t23=imread('D:\matlab\皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);t24=imread('D:\matlab\皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);t25=imread('D:\matlab\皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);t26=imread('D:\matlab\皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);t27=imread('D:\matlab\皮肤库\27.jpg');[r27,g27]=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20 ,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3.求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw);bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)^2 * bw(i,j);end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1)break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0)down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval) newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7.模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); % 旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},'Please choose a color picture...'); % 返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j);changelist = [];for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread('E:\我的照片\人脸模板.jpg'); % 读入人脸模板照片FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分,L为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray));% 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度/宽度比例大于0.8且小于2.0，则将其选出进行下一步运算S=ly*wx; % 计算包含此区域矩形的面积A=bwarea(bwsegment); % 计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray));final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。

肤色分割人脸检测matlab代码

image = imread('im.jpg');figure,imshow(image);red = double(image(:,:,1));green = double(image(:,:,2));blue = double(image(:,:,3));[m n]=size(red);Y = zeros(m,n);Cb = zeros(m,n);Cr = zeros(m,n);I = zeros(m,n);Q = zeros(m,n);red_gama = zeros(m,n);green_gama = zeros(m,n);blue_gama = zeros(m,n);for i=1:m %gamma矫正for j=1:nif red(i,j)>0 && red(i,j)<90fai=pi*red(i,j)/180;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);elseif red(i,j)>=90 && red(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);elseif red(i,j)>170 && red(i,j)<=255fai=pi-pi*(255-red(i,j))/170;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);endif green(i,j)>0 && green(i,j)<90fai=pi*green(i,j)/180;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);elseif green(i,j)>=90 && green(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);elseif green(i,j)>170 && green(i,j)<=255fai=pi-pi*(255-green(i,j))/170;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);endif blue(i,j)>0 && blue(i,j)<90fai=pi*blue(i,j)/180;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);elseif blue(i,j)>=90 && blue(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);elseif blue(i,j)>170 && blue(i,j)<=255fai=pi-pi*(255-blue(i,j))/170;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);endendendfor i=1:mfor j=1:nY(i,j)=0.2989*red_gama(i,j)+0.5866*green_gama(i,j)+0.1145*blue_gama(i,j) ;Cb(i,j)=-0.1688*red_gama(i,j)-0.3312*green_gama(i,j)+0.5000*blue_gama(iCr(i,j)=0.5000*red_gama(i,j)-0.4184*green_gama(i,j)-0.0817*blue_gama(i,j) ;endendemp=zeros(m,n);sita=zeros(m,n);for i=1:mfor j=1:nif Cr(i,j)>0 && Cb(i,j)>0sita(i,j)=atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elseif Cr(i,j)>0 && Cb(i,j)<0sita(i,j)=180-atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elseif Cr(i,j)<0 && Cb(i,j)<0sita(i,j)=180 + atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elsesita(i,j)=0;endendendfor i=1:mfor j=1:nif sita(i,j)>105 && sita(i,j)<150emp(i,j)=sita(i,j);elseemp(i,j)=0;Y(i,j)=0;endendfigure,imshow(emp); figure,imshow(uint8(Y));原图像分割结果分割结果。