OpenCV 3 KNN 实现答案

【问题标题】：OpenCV 3 KNN implementationOpenCV 3 KNN 实现
【发布时间】：2015-03-18 02:22:56
【问题描述】：

您可能知道，OpenCV 3 发生了很多变化。在以前的 OpenCV 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

我也找到了一个例子，但是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);

能否请您提供信息，如何正确地将 KNearest 的实际代码重写为 openCV 3？

【问题讨论】：

标签： c++ opencv knn opencv3.0

【解决方案1】：

自@aperture-laboratories 回答以来，API 再次发生了变化。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
    <<"getNSamples\t"<<trainingData->getNSamples()<<endl
    <<"getSamples\n"<<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

【讨论】：

【解决方案2】：

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，所以最好的方法是做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，被证明是有效的。希望对您有所帮助。

【讨论】：