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)

结果

在这里插入图片描述

haihong.png