currentPath = pwd; % 获得当前的工作目录 imdsTrain = imageDatastore(fullfile(pwd,'train_images'),... 'IncludeSubfolders',true,... 'LabelSource','foldernames'); % 载入图片集合 %% 2 对训练集中的每张图像进行hog特征提取，测试图像一样 % 预处理图像,主要是得到features特征大小，此大小与图像大小和Hog特征参数相关 imageSize = [256,256];% 对所有图像进行此尺寸的缩放 I = readimage(imdsTrain,1); I = imresize(I,imageSize); I = rgb2gray(I); lbpFeatures = extractLBPFeatures(I,'CellSize',[16 16],'Normalization','None'); numNeighbors = 8; % Upright = false; numBins = numNeighbors*(numNeighbors-1)+3; % numNeighbors+2; lbpCellHists = reshape(lbpFeatures,numBins,[]); lbpCellHists = bsxfun(@rdivide,lbpCellHists,sum(lbpCellHists)); lbpFeatures = reshape(lbpCellHists,1,[]); % 提示信息 disp('开始训练数据...'); % 对所有训练图像进行特征提取 numImages = length(imdsTrain.Files); featuresTrain = zeros(numImages,size(lbpFeatures,2),'single'); % featuresTrain为单精度 for i = 1:numImages imageTrain = readimage(imdsTrain,i); imageTrain = imresize(imageTrain,imageSize); I = rgb2gray(imageTrain); lbpFeatures = extractLBPFeatures(I,'CellSize',[16 16],'Normalization','None'); % numNeighbors = 8; % numBins = numNeighbors*(numNeighbors-1)+3; lbpCellHists = reshape(lbpFeatures,numBins,[]); lbpCellHists = bsxfun(@rdivide,lbpCellHists,sum(lbpCellHists)); lbpFeatures = reshape(lbpCellHists,1,[]); featuresTrain(i,:) = lbpFeatures; % 所有训练图像标签 trainLabels = imdsTrain.Labels; % 开始svm多分类训练，注意：fitcsvm用于二分类，fitcecoc用于多分类,1 VS 1方法 classifer = fitcecoc(featuresTrain,trainLabels); save classifer % 提示信息 disp('训练阶段结束！！！');

2）测试部分代码： classify.m

%% 该函数用来对图片进项分类 LBP + SVM
%% 1.读入待分类的图片集合
currentPath = pwd;
imdsTest = imageDatastore(fullfile(pwd,'test_image')); 
%% 2.分类，预测并显示预测效果图  
% 载入分类器
load classifer
correctCount = 0;
%% 预测并显示预测效果图  
numTest = length(imdsTest.Files);  
for i = 1:numTest  
    testImage = readimage(imdsTest,i);  %  imdsTest.readimage(1)
    scaleTestImage = imresize(testImage,imageSize);  
    I = rgb2gray(scaleTestImage);
    lbpFeatures = extractLBPFeatures(I,'CellSize',[16 16],'Normalization','None');
    numNeighbors = 8;
    numBins = numNeighbors*(numNeighbors-1)+3;
    lbpCellHists = reshape(lbpFeatures,numBins,[]);
    lbpCellHists = bsxfun(@rdivide,lbpCellHists,sum(lbpCellHists));
    featureTest = reshape(lbpCellHists,1,[]);
    [predictIndex,score] = predict(classifer,featureTest);  
    figure;imshow(imresize(testImage,[256,256]));
    imgName = imdsTest.Files(i);
    tt = regexp(imgName,'\','split');
    cellLength =  cellfun('length',tt);
    tt2 = char(tt{1}(1,cellLength));
     % 统计正确率
    if strfind(tt2,char(predictIndex))==1
        correctCount = correctCount+1;
    title(['分类结果: ',tt2,'--',char(predictIndex)]);  
    fprintf('%s == %s \n',tt2,char(predictIndex));
% 显示正确率
fprintf('分类结束，正确了为：%.1f%%\n',correctCount * 100.0 / numTest);