MATLAB--数字图像处理 HOG+SVM识别手写数字

源代码

syntheticDir   = fullfile(toolboxdir('vision'), 'visiondata','digits','synthetic');
handwrittenDir = fullfile(toolboxdir('vision'), 'visiondata','digits','handwritten');

% |imageDatastore| recursively scans the directory tree containing the
% images. Folder names are automatically used as labels for each image.
trainingSet = imageDatastore(syntheticDir,   'IncludeSubfolders', true, 'LabelSource', 'foldernames');
testSet = imageDatastore(handwrittenDir, 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
img = readimage(trainingSet, 206);

% Extract HOG features and HOG visualization
[hog_2x2, vis2x2] = extractHOGFeatures(img,'CellSize',[2 2]);
[hog_4x4, vis4x4] = extractHOGFeatures(img,'CellSize',[4 4]);
[hog_8x8, vis8x8] = extractHOGFeatures(img,'CellSize',[8 8]);
cellSize = [4 4];
hogFeatureSize = length(hog_4x4);
numImages = numel(trainingSet.Files);
trainingFeatures = zeros(numImages, hogFeatureSize, 'single');

for i = 1:numImages img = readimage(trainingSet, i); img = rgb2gray(img); % Apply pre-processing steps img = imbinarize(img); trainingFeatures(i, :) = extractHOGFeatures(img, 'CellSize', cellSize);  
end

% Get labels for each image.
trainingLabels = trainingSet.Labels;
classifier = fitcecoc(trainingFeatures, trainingLabels);
[testFeatures, testLabels] = helperExtractHOGFeaturesFromImageSet(testSet, hogFeatureSize, cellSize);

% Make class predictions using the test features.
predictedLabels = predict(classifier, testFeatures);

% Tabulate the results using a confusion matrix.
confMat = confusionmat(testLabels, predictedLabels);

helperDisplayConfusionMatrix(confMat)

  

 

  
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结果

文章来源: haihong.blog.csdn.net，作者：海轰Pro，版权归原作者所有，如需转载，请联系作者。

原文链接：haihong.blog.csdn.net/article/details/102945680