C++ Ptr::train方法代码示例

    void train( std::vector<cv::Mat> &images, std::vector<int> &labels )
    {

//        //turn all images to grey and same size
//        for( cv::Mat image : images )
//        {
//            cv::cvtColor(image, image, CV_BGR2GRAY);
//            cv::resize(image, image, cv::Size(500, 500));
//
//            cv::imshow("face", image);
//            cv::waitKey();
//        }


        eigenfaceRecognizor->train(images, labels);
        fisherfaceRecognizor->train(images, labels);
        LBPHRecognizor->train(images, labels);
    }

void TellThatToMyCamera_v1_0App::setup()
{
    mExpressionsCascade.load(getAssetPath("haarcascade_frontalface_alt.xml").string());
    mPath= getAssetPath("ppdtest.csv").string();
    
	mCapture = Capture( 640, 480 );                 // Camera settings
	mCapture.start();
    
    read_csv(mPath, mDBimgFaces, mDBLabels);        // Read DB of faces for FaceRec algorithm
    mFisherFaceRec->train(mDBimgFaces, mDBLabels);  // Train the Fisher Face Recognizer algorithm
}

bool loadKNNDataAndTrainKNN(void) {

    // read in training classifications ///////////////////////////////////////////////////

    cv::Mat matClassificationInts;              // we will read the classification numbers into this variable as though it is a vector

    cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::READ);        // open the classifications file

    if (fsClassifications.isOpened() == false) {                                                        // if the file was not opened successfully
        std::cout << "error, unable to open training classifications file, exiting program\n\n";        // show error message
        return(false);                                                                                  // and exit program
    }

    fsClassifications["classifications"] >> matClassificationInts;          // read classifications section into Mat classifications variable
    fsClassifications.release();                                            // close the classifications file

    // read in training images ////////////////////////////////////////////////////////////

    cv::Mat matTrainingImagesAsFlattenedFloats;         // we will read multiple images into this single image variable as though it is a vector

    cv::FileStorage fsTrainingImages("images.xml", cv::FileStorage::READ);              // open the training images file

    if (fsTrainingImages.isOpened() == false) {                                                 // if the file was not opened successfully
        std::cout << "error, unable to open training images file, exiting program\n\n";         // show error message
        return(false);                                                                          // and exit program
    }

    fsTrainingImages["images"] >> matTrainingImagesAsFlattenedFloats;           // read images section into Mat training images variable
    fsTrainingImages.release();                                                 // close the traning images file

    // train //////////////////////////////////////////////////////////////////////////////

            // finally we get to the call to train, note that both parameters have to be of type Mat (a single Mat)
            // even though in reality they are multiple images / numbers
    kNearest->setDefaultK(1);

    kNearest->train(matTrainingImagesAsFlattenedFloats, cv::ml::ROW_SAMPLE, matClassificationInts);

    return true;
}

void ICPApp::setup()
{
    mExpressionsCascade.load(getAssetPath("haarcascade_frontalface_alt.xml").string());
    mPath= getAssetPath("ppdtest.csv").string();
    
	mCapture = Capture( 640, 480 );                 // Camera settings
	mCapture.start();
    
    read_csv(mPath, mDBimgFaces, mDBLabels);        // Read DB of faces for FaceRec algorithm
    mFisherFaceRec->train(mDBimgFaces, mDBLabels);  // Train the Fisher Face Recognizer algorithm
    
    // FOR TESTING PURPOSES
    //    mSurf=(loadImage("/Users/PpD/Desktop/EcA - Pp DanY/MSc ICP/Semester 2/ICP 3/Faces DB Original/hugh_laurie_extra1.jpg"));
    //  mTexture = gl::Texture(mCinderDBimgFaces);
    //  mTexture = gl::Texture( fromOcv( input ) );
    //  cv::Mat output;
    //  mTexture = gl::Texture( fromOcv( loadImage("/Users/PpD/Desktop/emotionsrec2/data/emotions/0neutral/amy_adams_neutral.jpg") ) );    
    //  mDBLabelsTEST.push_back(0);
    //  mDBLabelsTEST.push_back(1);
    //  mFisherFaceRec->train(mDBimgFaces, mDBLabelsTEST);
    //  mFisherFaceRec->train(mDBimgFaces, mDBLabels);
}

/*
* Train any given model if it is not already trained
*/
void ModelTrainer::trainModel(cv::Ptr<cv::ml::StatModel> model) {
	if (model && !model->isTrained()) {
		cv::Ptr<cv::ml::TrainData> trainData = loadTrainingData();
		model->train(trainData);
	}
}