使用HOG+LBP实现动物分类:matlab版本
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1.训练集测试集划分(同上一篇)
2.代码部分
%% 利用HOG + LBP分类 %% 1 数据集,包括训练的和测试的 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 imdsTest = imageDatastore(fullfile(pwd,‘test_image‘)); % imdsTrain = imageDatastore(‘C:\Program Files\MATLAB\R2017a\bin\proj_xiangbin\train_images‘,... % ‘IncludeSubfolders‘,true,... % ‘LabelSource‘,‘foldernames‘); % imdsTest = imageDatastore(‘C:\Program Files\MATLAB\R2017a\bin\proj_xiangbin\test_image‘); %% 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 %% LBP参数 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,[]); % 对所有训练图像进行特征提取 numImages = length(imdsTrain.Files); featuresTrain1 = zeros(numImages,size(lbpFeatures,2),‘single‘); % featuresTrain为单精度 scaleImage = imresize(image1,imageSize); [features, visualization] = extractHOGFeatures(scaleImage,‘CellSize‘,[8,8]); featuresTrain2 = zeros(numImages,size(features,2),‘single‘); % featuresTrain为单精度 for i = 1:numImages imageTrain = readimage(imdsTrain,i); imageTrain = imresize(imageTrain,imageSize); % LBP 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,[]); featuresTrain1(i,:) = lbpFeatures; % HOG featuresTrain2(i,:) = extractHOGFeatures(imageTrain,‘CellSize‘,[8,8]); end % 特征合并 featuresTrain = [featuresTrain1,featuresTrain2]; % 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); 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); % LBP 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)); featureTest1 = reshape(lbpCellHists,1,[]); % HOG featureTest2 = extractHOGFeatures(scaleTestImage,‘CellSize‘,[8,8]); % 合并 featureTest = [featureTest1,featureTest2]; [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; end title([‘predictImage: ‘,tt2,‘--‘,char(predictIndex)]); fprintf(‘%s == %s\n‘,tt2,char(predictIndex)); end % 显示正确率 fprintf(‘分类结束,正确了为:%.3f%%\n‘,correctCount * 100.0 / numTest);
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