OpenCV 3 KNN implementation

OpenCV 3 KNN implementation

本文关键字：implementation KNN OpenCV      更新时间：2023-10-16

如您所知，OpenCV 3 中的许多内容都发生了变化。在OpenCV的先前版本中，我曾经这样做过：

Mat trainData(classes * samples, ImageSize, CV_32FC1);
Mat trainClasses(classes * samples, 1, CV_32FC1);
KNNLearning(&trainData, &trainClasses); //learning function
KNearest knearest(trainData, trainClasses); //creating
//loading input image
Mat input = imread("input.jpg");
//digital recognition
learningTest(input, knearest);//test

我还找到了一个如何弄清楚它的示例，但我在创建函数中仍然有错误：

Ptr<KNearest> knearestKdt = KNearest::create(ml::KNearest::Params(10, true, INT_MAX, ml::KNearest::KDTREE));
knearestKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
knearestKdt->findNearest(testData, 4, bestLabels);

您能否为我提供信息，如何正确重写 KNearest 的实际代码以打开CV 3？

自@aperture实验室回答以来，API 再次发生变化。我希望他们在将来发布新功能或更改时跟上文档。

一个工作示例如下

using namespace cv::ml;
//Be sure to change number_of_... to fit your data!
Mat matTrainFeatures(0,number_of_train_elements,CV_32F);
Mat matSample(0,number_of_sample_elements,CV_32F);
Mat matTrainLabels(0,number_of_train_elements,CV_32F);
Mat matSampleLabels(0,number_of_sample_elements,CV_32F);
Mat matResults(0,0,CV_32F);
//etcetera code for loading data into Mat variables suppressed
Ptr<TrainData> trainingData;
Ptr<KNearest> kclassifier=KNearest::create();
trainingData=TrainData::create(matTrainFeatures,
                        SampleTypes::ROW_SAMPLE,matTrainLabels);

kclassifier->setIsClassifier(true);
kclassifier->setAlgorithmType(KNearest::Types::BRUTE_FORCE);
kclassifier->setDefaultK(1);
kclassifier->train(trainingData);
kclassifier->findNearest(matSample,kclassifier->getDefaultK(),matResults);
//Just checking the settings
cout<<"Training data: "<<endl
    <<"getNSamplest"<<trainingData->getNSamples()<<endl
    <<"getSamplesn"<<trainingData->getSamples()<<endl
    <<endl;
cout<<"Classifier :"<<endl
    <<"kclassifier->getDefaultK(): "<<kclassifier->getDefaultK()<<endl
    <<"kclassifier->getIsClassifier()   : "<<kclassifier->getIsClassifier()<<endl   
    <<"kclassifier->getAlgorithmType(): "<<kclassifier->getAlgorithmType()<<endl
    <<endl;
//confirming sample order
cout<<"matSample: "<<endl
    <<matSample<<endl
    <<endl;
//displaying the results
cout<<"matResults: "<<endl
    <<matResults<<endl
    <<endl;
//etcetera ending for main function

KNearest::Params params;
params.defaultK=5;
params.isclassifier=true;
    //////// Train and find with knearest
        Ptr<TrainData> knn;
        knn= TrainData::create(AmatOfFeatures,ROW_SAMPLE,AmatOfLabels);
        Ptr<KNearest> knn1;
        knn1=StatModel::train<KNearest>(knn,params);
        knn1->findNearest(AmatOfFeaturesToTest,4,ResultMatOfNearestNeighbours);
        /////////////////

这些函数的名称将帮助您在文档中找到它们。但是，在完全更新之前，文档可能会有点混乱，因此完全按照自己的要求进行操作的最佳方法是制作一个小玩具示例并使用试错法。这是一个工作示例，直接从我自己的代码中粘贴出来，事实证明这是有效的。 希望有帮助。

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