1.訓(xùn)練集測(cè)試集劃分（同上一篇）

2.代碼部分

%% 利用HOG + LBP分類%% 1 數(shù)據(jù)集，包括訓(xùn)練的和測(cè)試的 currentPath = pwd; % 獲得當(dāng)前的工作目錄imdsTrain = imageDatastore(fullfile(pwd,'train_images'),... 'IncludeSubfolders',true,... 'LabelSource','foldernames'); % 載入圖片集合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 對(duì)訓(xùn)練集中的每張圖像進(jìn)行hog特征提取，測(cè)試圖像一樣 % 預(yù)處理圖像,主要是得到features特征大小，此大小與圖像大小和Hog特征參數(shù)相關(guān) %% LBP參數(shù) imageSize = [256,256];% 對(duì)所有圖像進(jìn)行此尺寸的縮放 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,[]); % 對(duì)所有訓(xùn)練圖像進(jìn)行特征提取 numImages = length(imdsTrain.Files); featuresTrain1 = zeros(numImages,size(lbpFeatures,2),'single'); % featuresTrain為單精度 scaleImage = imresize(image1,imageSize); [features, visualization] = extractHOGFeatures(scaleImage,'CellSize',[8,8]); featuresTrain2 = zeros(numImages,size(features,2),'single'); % featuresTrain為單精度 for i = 1:numImages imageTrain = readimage(imdsTrain,i); imageTrain = imresize(imageTrain,imageSize); % LBPI = 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; % HOGfeaturesTrain2(i,:) = extractHOGFeatures(imageTrain,'CellSize',[8,8]); end % 特征合并 featuresTrain = [featuresTrain1,featuresTrain2];% 所有訓(xùn)練圖像標(biāo)簽 trainLabels = imdsTrain.Labels; % 開始svm多分類訓(xùn)練，注意：fitcsvm用于二分類，fitcecoc用于多分類,1 VS 1方法 classifer = fitcecoc(featuresTrain,trainLabels); correctCount = 0; %% 預(yù)測(cè)并顯示預(yù)測(cè)效果圖 numTest = length(imdsTest.Files); for i = 1:numTest testImage = readimage(imdsTest,i); % imdsTest.readimage(1)scaleTestImage = imresize(testImage,imageSize); % LBPI = 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,[]);% HOGfeatureTest2 = 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));% 統(tǒng)計(jì)正確率if strfind(tt2,char(predictIndex))==1correctCount = correctCount+1;endtitle(['predictImage: ',tt2,'--',char(predictIndex)]); fprintf('%s == %s\n',tt2,char(predictIndex)); end % 顯示正確率 fprintf('分類結(jié)束，正確了為：%.3f%%\n',correctCount * 100.0 / numTest);

　　

轉(zhuǎn)載于:https://www.cnblogs.com/xiangbin1207/p/6937017.html

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