训练自己的人脸检测分类器（级联+LBP的Matlab的实现）

本文通过MATLAB实现，能够实时检测识别到人脸，与OpenCV模型文件兼容，版本最好matlab2017a及其以上，老版本没试过。本文主要分为3个步骤：（1）摄像头获取人脸正样本图像；（2）摄像头获取负样本图像；（3）训练识别部分，可选择从图片，视频，摄像头实时识别。

注意事项：

（a）其中变量isSample=1时，即首次运行需要采集人脸图像，以后请把isSample置为0，表示以后不需要采集正样本；(b)负样本产生我写在另一个函数createNegativeImgs（）里面，大家运行它即可，负样本一定不要有你自己的人脸图像哦~ (c)importdata（）函数用于把正样本的标记文件导入到MATLAB工作空间中，请注意格式。

正样本可以自己手动标记人脸框，可以从trainingImageLabeler  APP交互工具获得，当然方便起见，我 从已有的人脸检测器xml文件检测人脸，从而直接得到人脸正样本，当然可以导入到trainingImageLabeler  查看预览（注意格式），我这里直接用的是lbpcascade_frontalface.xml分类器。

直接上代码，如下；

%% 用xml预训练的分类器对人脸进行筛选，记录人脸，用于训练,测试cam = webcam();% 摄像头接口，没有的话从matlab central网站搜索下载%% 收集样本isSample = 0; %这里如果现场从摄像头获取你的图像作为训练样本，请把该值置为1if isSample==1    fig = figure;    axes('parent',fig)    detector = vision.CascadeObjectDetector('lbpcascade_frontalface.xml');    detector.MinSize = [110,110];        videoPlayer = vision.VideoPlayer;        % 人脸检测与标记    if ~exist('images','file') %当前目录是否存在images文件夹，没有则新建        mkdir images    end    fid = fopen('images/face_rect.txt','a');% 以追加的方式进行写入    while  ishandle(fig)        filename = [cd,'/images/',datestr(now,'yyyy-mm-dd-HH-MM-SS-FFF'),'.png'];        frame = snapshot(cam);        bbox = step(detector,frame);        imwrite(frame,filename);        fprintf(fid,'%s  %5d%5d%5d%5d \r\n',filename,bbox);        if isempty(bbox)            fprintf(fid,'\r\n');        end                positions = bbox;        nums = size(positions,1);        strLabels = {'face'};%strEye = repmat({'eye'},1,nums-1);               RGB = insertObjectAnnotation(frame,'rectangle',positions,strLabels,'color','g');        step(videoPlayer,RGB);    end    fclose(fid);  end%% 不需要训练facerect1 = importdata();imageNames = cellstr(facerect1.imagenames);rects = [facerect1.x,facerect1.y,facerect1.w,facerect1.h];faceRect = table(imageNames,rects,'VariableNames',{'imageFilename','face'});index = ~isnan(rects(:,1));faceTrain = faceRect(index,:);% faceRect.imageNames = cellstr(imageNames);% faceRect.rects = rects;%mat2cell(rects,ones(1,length(labels.imageNames)));num = length(faceTrain.imageFilename);%% 正样本制作trainPosNums = 500; % 这里设置你的训练正样本数量，根据你的样本量适当选择newTrainLabels = faceTrain(randi(num,1,trainPosNums),:); %table类型%% 负样本制作trainNegNums = 500; % 这里设置你的训练负样本数量，根据你的样本量适当选择negativeImgDataStore = imageDatastore(fullfile(cd,'NegativeImgs'));negNUM = length(negativeImgDataStore.Files);negativeImages = negativeImgDataStore.Files( randi(negNUM,1,trainNegNums) );%% 开始训练xmlName = 'myLBPfaceDetector.xml';trainCascadeObjectDetector(xmlName,newTrainLabels,negativeImages,...    'FalseAlarmRate',0.1,'NumCascadeStages',20,...    'FeatureType','LBP');%% test ,选择跑的内容detector = vision.CascadeObjectDetector(xmlName);detector.MinSize = [100 ,100];detector.MergeThreshold = 4;videoPlayer = vision.VideoPlayer;%% flag选择平台，flag = 0为跑图片，flag = 1为跑视频文件，flag=2为跑摄像头flag = 2;% 选择index = 0;if flag == 0 %跑图片    imdsTest = imageDatastore('F:\video\patform_data\6月\06',...         'includeSubfolder',true);%图片文件，这里设置你自己的测试人脸图像路径    for i  = 1:length(imdsTest.Files)        imageTest = readimage(imdsTest,i);        bbox = step(detector,imageTest);        RGB = insertObjectAnnotation(imageTest,'rectangle',bbox,'face');        step(videoPlayer,RGB);        index = index+1;        disp(index);    endelseif flag == 1 % 跑视频    obj = vision.VideoFileReader('F:\video\smokeVideo2017_3_1\170405151456_1280328332795.mp4');%注意这里是你自己的视频文件路径    while ~isDone(obj)        frame = step(obj);        bbox = step(detector,frame);        if ~empty(bbox)            RGB = insertObjectAnnotation(frame,'rectangle',bbox,'face');        else            RGB = frame;        end        step(videoPlayer,RGB);        index = index+1;        disp(index);    endelseif flag == 2 % 跑摄像头    while 1 % command Window按ctrl+c终止循环        frame = snapshot(cam);        bbox = step(detector,frame);        RGB = insertObjectAnnotation(frame,'rectangle',bbox,'face');        step(videoPlayer,RGB);          endelse    disp('your input may be wrong!');end

另外importdata()函数和createNegativeImgs()函数如下：

function faceRect = importdata()%% Initialize variables.filename = 'E:\MATLAB\trainMyCascadeFace\images\face_rect.txt';delimiter = ' ';%% Format for each line of text:%   column1: text (%s)%column2: double (%f)%   column3: double (%f)%column4: double (%f)%   column5: double (%f)% For more information, see the TEXTSCAN documentation.formatSpec = '%s%f%f%f%f%[^\n\r]';%% Open the text file.fileID = fopen(filename,'r');%% Read columns of data according to the format.% This call is based on the structure of the file used to generate this% code. If an error occurs for a different file, try regenerating the code% from the Import Tool.dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'MultipleDelimsAsOne', true, 'TextType', 'string', 'EmptyValue', NaN,  'ReturnOnError', false);%% Close the text file.fclose(fileID);%% Post processing for unimportable data.% No unimportable data rules were applied during the import, so no post% processing code is included. To generate code which works for% unimportable data, select unimportable cells in a file and regenerate the% script.%% Create output variablefacerect1 = table(dataArray{1:end-1}, 'VariableNames', {'imagenames','x','y','w','h'});%% Clear temporary variablesclearvars filename delimiter formatSpec fileID dataArray ans;faceRect = facerect1;

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function createNegativeImgs()cam = webcam();if ~exist('NegativeImgs','file')    mkdir NegativeImgsendvideoPlayer = vision.VideoPlayer();index = 0;while 1    filename = [cd,'/NegativeImgs/',datestr(now,'yyyy-mm-dd-HH-MM-SS-FFF'),'.png'];    frame = snapshot(cam);    imwrite(frame,filename);    step(videoPlayer,frame);    index = index+1;    disp(index);end

最后给出我检测自己人脸效果图：D，打成马赛克啦~

  RGB = insertShape(frame,'FilledRectangle',bbox,'Opacity',1,'color','red');