C++ GestureRecognitionPipeline::getClassifier方法代码示例

void setup() {
    stream.setLabelsForAllDimensions({"x", "y", "z"});
    useInputStream(stream);

    DTW dtw(false, true, null_rej);
    dtw.enableTrimTrainingData(true, 0.1, 75);

    pipeline.setClassifier(dtw);
    pipeline.addPostProcessingModule(ClassLabelTimeoutFilter(timeout));
    usePipeline(pipeline);

    registerTuneable(
        null_rej, 0.1, 5.0, "Variability",
        "How different from the training data a new gesture can be and "
        "still be considered the same gesture. The higher the number, the "
        "more different it can be.",
        [](double new_null_rej) {
            pipeline.getClassifier()->setNullRejectionCoeff(new_null_rej);
            pipeline.getClassifier()->recomputeNullRejectionThresholds();
        });

    registerTuneable(
        timeout, 1, 3000, "Timeout",
        "How long (in milliseconds) to wait after recognizing a "
        "gesture before recognizing another one.",
        [](double new_timeout) {
            ClassLabelTimeoutFilter* filter =
                dynamic_cast<ClassLabelTimeoutFilter*>(
                    pipeline.getPostProcessingModule(0));
            assert(filter != nullptr);
            filter->setTimeoutDuration(new_timeout);
        });
}

void updateAlwaysPickSomething(bool new_val) {
    pipeline.getClassifier()->enableNullRejection(!new_val);
}

void updateVariability(double new_null_rej) {
    pipeline.getClassifier()->setNullRejectionCoeff(new_null_rej);
    pipeline.getClassifier()->recomputeNullRejectionThresholds();
}

bool combineModels( CommandLineParser &parser ){

    infoLog << "Combining models..." << endl;

    string directoryPath = "";
    string modelFilename = "";

    if( !parser.get("data-dir",directoryPath) ){
        errorLog << "Failed to parse data-directory from command line! You can set the data-directory using the --data-dir option." << endl;
        printUsage();
        return false;
    }

    //Get the filename
    if( !parser.get("model-filename",modelFilename) ){
        errorLog << "Failed to parse filename from command line! You can set the model filename using the --model." << endl;
        printUsage();
        return false;
    }

    Vector< string > files;

    infoLog << "- Parsing data directory: " << directoryPath << endl;

    //Parse the directory to get all the csv files
    if( !Util::parseDirectory( directoryPath, ".grt", files ) ){
        errorLog << "Failed to parse data directory!" << endl;
        return false;
    }

    RandomForests forest; //Used to validate the random forest type
    GestureRecognitionPipeline *mainPipeline = NULL; // Points to the first valid pipeline that all the models will be merged to
    Vector< GestureRecognitionPipeline* > pipelineBuffer; //Stores the pipeline for each file that is loaded
    unsigned int inputVectorSize = 0; //Set to zero to mark we haven't loaded any models yet
    const unsigned int numFiles = files.getSize();
    bool mainPipelineSet = false;
    bool combineModelsSuccessful = false;

    pipelineBuffer.reserve( numFiles );
    
    //Loop over the files, load them, and add valid random forest pipelines to the pipelineBuffer so they can be combined with the mainPipeline
    for(unsigned int i=0; i<numFiles; i++){
        infoLog << "- Loading model " << files[i] << ". File " << i+1 << " of " << numFiles << endl;

        GestureRecognitionPipeline *pipeline = new GestureRecognitionPipeline;

        if( pipeline->load( files[i] ) ){

            infoLog << "- Pipeline loaded. Number of input dimensions: " << pipeline->getInputVectorDimensionsSize() << endl;

            if( pipelineBuffer.size() == 0 ){
                inputVectorSize = pipeline->getInputVectorDimensionsSize();
            }

            if( pipeline->getInputVectorDimensionsSize() != inputVectorSize ){
                warningLog << "- Pipeline " << i+1 << " input vector size does not match the size of the first pipeline!" << endl;
            }else{

                Classifier *classifier = pipeline->getClassifier();
                if( classifier ){
                    if( classifier->getClassifierType() == forest.getClassifierType() ){ //Validate the classifier is a random forest
                        if( !mainPipelineSet ){
                            mainPipelineSet = true;
                            mainPipeline = pipeline;
                        }else pipelineBuffer.push_back( pipeline );
                    }else{
                        warningLog << "- Pipeline " << i+1 << " does not contain a random forest classifer! Classifier type: " << classifier->getClassifierType() << endl;
                    }
                }

            }
        }else{
            warningLog << "- WARNING: Failed to load model from file: " << files[i] << endl;
        }
    }

    if( mainPipelineSet ){

        //Combine the random forest models with the main pipeline model
        const unsigned int numPipelines = pipelineBuffer.getSize();
        RandomForests *mainForest = mainPipeline->getClassifier< RandomForests >();

        for(unsigned int i=0; i<numPipelines; i++){

            infoLog << "- Combing model " << i+1 << " of " << numPipelines << " with main model..." << endl;

            RandomForests *f = pipelineBuffer[i]->getClassifier< RandomForests >();

            if( !mainForest->combineModels( *f ) ){
                warningLog << "- WARNING: Failed to combine model " << i+1 << " with the main model!" << endl;
            }
        }

        if( mainPipeline->getTrained() ){
            infoLog << "- Saving combined pipeline to file..." << endl;
            combineModelsSuccessful = mainPipeline->save( modelFilename );
        }

    }else{
        errorLog << "Failed to combined models, no models were loaded!" << endl;
//.........这里部分代码省略.........