The following code sample will be pushed into OpenCV in the near future and is part of the OpenCV 3 Blueprints book, Chapter 5 - written by me -, where these models are evaluated in detail. I have used it on all my models and none of them have features like that, it is not possible with the included training software to have values like that...

More software can be found on our books GitHub repo.

/****************************************************************************************************
This code is part of the code supplied with the OpenCV Blueprints book.
It was written by Steven Puttemans, who can be contacted via steven.puttemans[at]kuleuven.be

License can be found at https://github.com/OpenCVBlueprints/OpenCVBlueprints/blob/master/license.txt
*****************************************************************************************************
This is a software alpha release of the OpenCV interface for visualising object models and
the used features to perform their detections.

USAGE:
./visualise_models -model <model.xml> -image <ref.png> -data <output folder>

LIMITS
- Only handles cascade classifier models
- Handles stumps only for the moment
- Needs a valid training/test sample window with the original model dimensions
- Can handle HAAR and LBP features
***********************************************************************************************/

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

#include <fstream>
#include <iostream>

using namespace std;
using namespace cv;

struct rect_data{
    int x;
    int y;
    int w;
    int h;
    float weight;
};

int main( int argc, const char** argv )
{
    // If no parameters are give, then a usage template should be provided
    if(argc == 1){
    cout << "This is a software alpha release of the OpenCV interface for visualising object models and the used features to perform their detections." << endl;
        cout << "USAGE ./visualise_models -model <model.xml> -image <ref.png> -data <output folder>" << endl;
    cout << "LIMITS: only cascade classifier models / only stump features for now / valid sample window with model dimensions needed / HAAR and LBP features" << endl;
        return 0;
    }

    // Read in the input arguments
    string model = "";
    string output_folder = "";
    string image_ref = "";
    for(int i = 1; i < argc; ++i )
    {
        if( !strcmp( argv[i], "-model" ) )
        {
            model = argv[++i];
        }else if( !strcmp( argv[i], "-image" ) ){
            image_ref = argv[++i];
        }else if( !strcmp( argv[i], "-data" ) ){
            output_folder = argv[++i];
        }
    }

    // Value for timing
    int timing = 1;

    // Value for cols of storing elements
    int cols_prefered = 5;

    // Open the XML model
    FileStorage fs;
    fs.open(model, FileStorage::READ);

    // Get a the required information
    // First decide which feature type we are using
    FileNode cascade = fs["cascade"];
    string feature_type = cascade["featureType"];
    bool haar = false, lbp = false;
    if (feature_type.compare("HAAR") == 0){
        haar = true;
    }
    if (feature_type.compare("LBP") == 0){
        lbp = true;
    }
    if ( feature_type.compare("HAAR") != 0 && feature_type.compare("LBP")){
        cerr << "The model is not an HAAR or LBP feature based model!" << endl;
        cerr << "Please select a model that can be visualized by the software." << endl;
        return -1;
    }

    // We make a visualisation mask - which increases the window to make it at least a bit more visible
    int resize_factor = 10;
    int resize_storage_factor = 10;
    Mat reference_image = imread(image_ref, CV_LOAD_IMAGE_GRAYSCALE);
    Mat visualization;
    resize(reference_image, visualization, Size(reference_image.cols * resize_factor, reference_image.rows * resize_factor), 0, 0, CV_INTER_CUBIC);

    // First recover for each stage the number of weak features and their index
    // Important since it is NOT sequential when using LBP features
    vector< vector<int> > stage_features;
    FileNode stages = cascade["stages"];
    FileNodeIterator it_stages = stages.begin(), it_stages_end = stages.end();
    int idx = 0;
    for( ; it_stages != it_stages_end; it_stages++, idx++ ){
        vector<int> current_feature_indexes;
        FileNode weak_classifiers = (*it_stages)["weakClassifiers"];
        FileNodeIterator it_weak = weak_classifiers.begin(), it_weak_end = weak_classifiers.end();
        vector<int> values;
        for(int idy = 0; it_weak != it_weak_end; it_weak++, idy++ ){
            (*it_weak)["internalNodes"] >> values;
            current_feature_indexes.push_back( (int)values[2] );
        }
        stage_features.push_back(current_feature_indexes);
    }

    // If the output option has been chosen than we will store a combined image plane for
    // each stage, containing all weak classifiers for that stage.
    bool draw_planes = false;
    stringstream output_video;
    output_video << output_folder << "model_visualization.avi";
    VideoWriter result_video;
    if( output_folder.compare("") != 0 ){
        draw_planes = true;
        result_video.open(output_video.str(), CV_FOURCC('X','V','I','D'), 15, Size(reference_image.cols * resize_factor, reference_image.rows * resize_factor), false);
    }

    if(haar){
        // Grab the corresponding features dimensions and weights
        FileNode features = cascade["features"];
        vector< vector< rect_data > > feature_data;
        FileNodeIterator it_features = features.begin(), it_features_end = features.end();
        for(int idf = 0; it_features != it_features_end; it_features++, idf++ ){
            vector< rect_data > current_feature_rectangles;
            FileNode rectangles = (*it_features)["rects"];
            int nrects = (int)rectangles.size();
            for(int k = 0; k < nrects; k++){
                rect_data current_data;
                FileNode single_rect = rectangles[k];
                current_data.x = (int)single_rect[0];
                current_data.y = (int)single_rect[1];
                current_data.w = (int)single_rect[2];
                current_data.h = (int)single_rect[3];
                current_data.weight = (float)single_rect[4];
                current_feature_rectangles.push_back(current_data);
            }
            feature_data.push_back(current_feature_rectangles);
        }

        // Loop over each possible feature on its index, visualise on the mask and wait a bit,
        // then continue to the next feature.
        // If visualisations should be stored then do the in between calculations
        Mat image_plane;
        Mat metadata = Mat::zeros(150, 1000, CV_8UC1);
        vector< rect_data > current_rects;
        for(int sid = 0; sid < (int)stage_features.size(); sid ++){
            if(draw_planes){
                int features = stage_features[sid].size();
                int cols = cols_prefered;
                int rows = features / cols;
                if( (features % cols) > 0){
                    rows++;
                }
                image_plane = Mat::zeros(reference_image.rows * resize_storage_factor * rows, reference_image.cols * resize_storage_factor * cols, CV_8UC1);
            }
            for(int fid = 0; fid < (int)stage_features[sid].size(); fid++){
                stringstream meta1, meta2;
                meta1 << "Stage " << sid << " / Feature " << fid;
                meta2 << "Rectangles: ";
                Mat temp_window = visualization.clone();
                Mat temp_metadata = metadata.clone();
                int current_feature_index = stage_features[sid][fid];
                current_rects = feature_data[current_feature_index];
                Mat single_feature = reference_image.clone();
                resize(single_feature, single_feature, Size(), resize_storage_factor, resize_storage_factor);
                for(int i = 0; i < (int)current_rects.size(); i++){
                    rect_data local = current_rects[i];
                    if(draw_planes){
                        if(local.weight >= 0){
                            rectangle(single_feature, Rect(local.x * resize_storage_factor, local.y * resize_storage_factor, local.w * resize_storage_factor, local.h * resize_storage_factor), Scalar(0), CV_FILLED);
                        }else{
                            rectangle(single_feature, Rect(local.x * resize_storage_factor, local.y * resize_storage_factor, local.w * resize_storage_factor, local.h * resize_storage_factor), Scalar(255), CV_FILLED);
                        }
                    }
                    Rect part(local.x * resize_factor, local.y * resize_factor, local.w * resize_factor, local.h * resize_factor);
                    meta2 << part << " (w " << local.weight << ") ";
                    if(local.weight >= 0){
                        rectangle(temp_window, part, Scalar(0), CV_FILLED);
                    }else{
                        rectangle(temp_window, part, Scalar(255), CV_FILLED);
                    }
                }
                imshow("features", temp_window);
                putText(temp_window, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                result_video.write(temp_window);
                // Copy the feature image if needed
                if(draw_planes){
                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
                }
                putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                putText(temp_metadata, meta2.str(), Point(15,40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                imshow("metadata", temp_metadata);
                waitKey(timing);
            }
            //Store the stage image if needed
            if(draw_planes){
                stringstream save_location;
                save_location << output_folder << "stage_" << sid << ".png";
                imwrite(save_location.str(), image_plane);
            }
        }
    }

    if(lbp){
        // Grab the corresponding features dimensions and weights
        FileNode features = cascade["features"];
        vector<Rect> feature_data;
        FileNodeIterator it_features = features.begin(), it_features_end = features.end();
        for(int idf = 0; it_features != it_features_end; it_features++, idf++ ){
            FileNode rectangle = (*it_features)["rect"];
            Rect current_feature ((int)rectangle[0], (int)rectangle[1], (int)rectangle[2], (int)rectangle[3]);
            feature_data.push_back(current_feature);
        }

        // Loop over each possible feature on its index, visualise on the mask and wait a bit,
        // then continue to the next feature.
        Mat image_plane;
        Mat metadata = Mat::zeros(150, 1000, CV_8UC1);
        for(int sid = 0; sid < (int)stage_features.size(); sid ++){
            if(draw_planes){
                int features = stage_features[sid].size();
                int cols = cols_prefered;
                int rows = features / cols;
                if( (features % cols) > 0){
                    rows++;
                }
                image_plane = Mat::zeros(reference_image.rows * resize_storage_factor * rows, reference_image.cols * resize_storage_factor * cols, CV_8UC1);
            }
            for(int fid = 0; fid < (int)stage_features[sid].size(); fid++){
                stringstream meta1, meta2;
                meta1 << "Stage " << sid << " / Feature " << fid;
                meta2 << "Rectangle: ";
                Mat temp_window = visualization.clone();
                Mat temp_metadata = metadata.clone();
                int current_feature_index = stage_features[sid][fid];
                Rect current_rect = feature_data[current_feature_index];
                Mat single_feature = reference_image.clone();
                resize(single_feature, single_feature, Size(), resize_storage_factor, resize_storage_factor);

                // VISUALISATION
                // The rectangle is the top left one of a 3x3 block LBP constructor
                Rect resized(current_rect.x * resize_factor, current_rect.y * resize_factor, current_rect.width * resize_factor, current_rect.height * resize_factor);
                meta2 << resized;
                // Top left
                rectangle(temp_window, resized, Scalar(0), 1);
                // Top middle
                rectangle(temp_window, Rect(resized.x + resized.width, resized.y, resized.width, resized.height), Scalar(0), 1);
                // Top right
                rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y, resized.width, resized.height), Scalar(0), 1);
                // Middle left
                rectangle(temp_window, Rect(resized.x, resized.y + resized.height, resized.width, resized.height), Scalar(0), 1);
                // Middle middle
                rectangle(temp_window, Rect(resized.x + resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(0), CV_FILLED);
                // Middle right
                rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(0), 1);
                // Bottom left
                rectangle(temp_window, Rect(resized.x, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);
                // Bottom middle
                rectangle(temp_window, Rect(resized.x + resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);
                // Bottom right
                rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);

                if(draw_planes){
                    Rect resized(current_rect.x * resize_storage_factor, current_rect.y * resize_storage_factor, current_rect.width * resize_storage_factor, current_rect.height * resize_storage_factor);
                    // Top left
                    rectangle(single_feature, resized, Scalar(0), 1);
                    // Top middle
                    rectangle(single_feature, Rect(resized.x + resized.width, resized.y, resized.width, resized.height), Scalar(0), 1);
                    // Top right
                    rectangle(single_feature, Rect(resized.x + 2*resized.width, resized.y, resized.width, resized.height), Scalar(0), 1);
                    // Middle left
                    rectangle(single_feature, Rect(resized.x, resized.y + resized.height, resized.width, resized.height), Scalar(0), 1);
                    // Middle middle
                    rectangle(single_feature, Rect(resized.x + resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(0), CV_FILLED);
                    // Middle right
                    rectangle(single_feature, Rect(resized.x + 2*resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(0), 1);
                    // Bottom left
                    rectangle(single_feature, Rect(resized.x, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);
                    // Bottom middle
                    rectangle(single_feature, Rect(resized.x + resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);
                    // Bottom right
                    rectangle(single_feature, Rect(resized.x + 2*resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(0), 1);

                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
                }

                putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                putText(temp_metadata, meta2.str(), Point(15,40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                imshow("metadata", temp_metadata);
                imshow("features", temp_window);
                putText(temp_window, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                result_video.write(temp_window);

                waitKey(timing);
            }

            //Store the stage image if needed
            if(draw_planes){
                stringstream save_location;
                save_location << output_folder << "stage_" << sid << ".png";
                imwrite(save_location.str(), image_plane);
            }
        }
    }
    return 0;
}

And this will give you results like found here:

• Video: https://github.com/OpenCVBlueprints/OpenCVBlueprints/blob/master/chapter_5/samples/HAAR/model_visualization.avi
• Images of stages: https://github.com/OpenCVBlueprints/OpenCVBlueprints/blob/master/chapter_5/samples/HAAR/stage_0.png

Good luck with it!