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

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.........

bool computeFeatureWeights( CommandLineParser &parser ){

    infoLog << "Computing feature weights..." << endl;

    string resultsFilename = "";
    string modelFilename = "";
    bool combineWeights = false;

    //Get the model 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;
    }

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

    //Get the results filename
    parser.get("combine-weights",combineWeights);

    //Load the model
    GestureRecognitionPipeline pipeline;

    if( !pipeline.load( modelFilename ) ){
        errorLog << "Failed to load model from file: " << modelFilename << endl;
        printUsage();
        return false;
    }

    //Make sure the pipeline contains a random forest model and that it is trained
    RandomForests *forest = pipeline.getClassifier< RandomForests >();

    if( !forest ){
        errorLog << "Model loaded, but the pipeline does not contain a RandomForests classifier!" << endl;
        printUsage();
        return false;
    }

    if( !forest->getTrained() ){
        errorLog << "Model loaded, but the RandomForests classifier is not trained!" << endl;
        printUsage();
        return false;
    }

    //Compute the feature weights
    if( combineWeights ){
        VectorFloat weights = forest->getFeatureWeights();
        if( weights.getSize() == 0 ){
            errorLog << "Failed to compute feature weights!" << endl;
            printUsage();
            return false;
        }

        //Save the results to a file
        fstream file;
        file.open( resultsFilename.c_str(), fstream::out );
        
        const unsigned int N = weights.getSize();
        for(unsigned int i=0; i<N; i++){
            file << weights[i] << endl;
        }
        
        file.close();
    }else{

        double norm = 0.0;
        const unsigned int K = forest->getForestSize();
        const unsigned int N = forest->getNumInputDimensions();
        VectorFloat tmp( N, 0.0 );
        MatrixDouble weights(K,N);

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

            DecisionTreeNode *tree = forest->getTree(i);
            tree->computeFeatureWeights( tmp );
            norm = 1.0 / Util::sum( tmp );
            for(unsigned int j=0; j<N; j++){
                tmp[j] *= norm;
                weights[i][j] = tmp[j];
                tmp[j] = 0;
            }
        }

        //Save the results to a file
        weights.save( resultsFilename );
    }
    

    return true;
}

bool test( CommandLineParser &parser ){

    infoLog << "Testing model..." << endl;

    string datasetFilename = "";
    string modelFilename = "";
    string resultsFilename = "";

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

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

    //Get the model filename
    parser.get("results-filename",resultsFilename,string("results.txt"));

    //Load the pipeline from a file
    GestureRecognitionPipeline pipeline;

    infoLog << "- Loading model..." << endl;

    if( !pipeline.load( modelFilename ) ){
        errorLog << "Failed to load model from file: " << modelFilename << endl;
        printUsage();
        return false;
    }

    infoLog << "- Model loaded!" << endl;

    //Load the data to test the classifier
    ClassificationData data;

    infoLog << "- Loading Training Data..." << endl;
    if( !data.load( datasetFilename ) ){
        errorLog << "Failed to load data!\n";
        return false;
    }

    const unsigned int N = data.getNumDimensions();
    infoLog << "- Num training samples: " << data.getNumSamples() << endl;
    infoLog << "- Num dimensions: " << N << endl;
    infoLog << "- Num classes: " << data.getNumClasses() << endl;

    //Test the classifier
    if( !pipeline.test( data ) ){
        errorLog << "Failed to test pipeline!" << endl;
        return false;
    }

    infoLog << "- Test complete in " << pipeline.getTestTime()/1000.0 << " seconds with and accuracy of: " << pipeline.getTestAccuracy() << endl;

    return saveResults( pipeline, resultsFilename );
}