opencv_contrib/modules/xobjdetect/src/icfdetector.cpp

/*

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                          License Agreement
               For Open Source Computer Vision Library
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#include <sstream>
using std::ostringstream;

#include <iomanip>
using std::setfill;
using std::setw;

#include <iostream>
using std::cout;
using std::endl;

#include "precomp.hpp"

using std::vector;
using std::string;

using std::min;
using std::max;

namespace cv
{
namespace xobjdetect
{

void ICFDetector::train(const String& pos_path,
                        const String& bg_path,
                        ICFDetectorParams params)
{
    vector<String> pos_filenames;
    glob(pos_path + "/*.png", pos_filenames);

    vector<String> bg_filenames;
    glob(bg_path + "/*.png", bg_filenames);

    model_n_rows_ = params.model_n_rows;
    model_n_cols_ = params.model_n_cols;

    Size model_size(params.model_n_cols, params.model_n_rows);

    vector<Mat> samples; /* positive samples + negative samples */
    Mat sample, resized_sample;
    int pos_count = 0;

    for( size_t i = 0; i < pos_filenames.size(); ++i, ++pos_count )
    {
        cout << setw(6) << (i + 1) << "/" << pos_filenames.size() << "\r";
        Mat img = imread(pos_filenames[i]);
        resize(img, resized_sample, model_size);
        samples.push_back(resized_sample.clone());
    }
    cout << "\n";

    int neg_count = 0;
    RNG rng;
    for( size_t i = 0; i < bg_filenames.size(); ++i )
    {
        cout << setw(6) << (i + 1) << "/" << bg_filenames.size() << "\r";
        Mat img = imread(bg_filenames[i]);
        for( int j = 0; j < params.bg_per_image; ++j, ++neg_count)
        {
            Rect r;
            r.x = rng.uniform(0, img.cols);
            r.width = rng.uniform(r.x + 1, img.cols);
            r.y = rng.uniform(0, img.rows);
            r.height = rng.uniform(r.y + 1, img.rows);

            sample = img.colRange(r.x, r.width).rowRange(r.y, r.height);
            resize(sample, resized_sample, model_size);
            samples.push_back(resized_sample.clone());
        }
    }
    cout << "\n";

    Mat_<int> labels(1, pos_count + neg_count);
    for( int i = 0; i < pos_count; ++i)
        labels(0, i) = 1;
    for( int i = pos_count; i < pos_count + neg_count; ++i )
        labels(0, i) = -1;

    vector<vector<int> > features = generateFeatures(model_size, "icf",
        params.feature_count);
    Ptr<FeatureEvaluator> evaluator = createFeatureEvaluator(features, "icf");

    Mat_<int> data = Mat_<int>::zeros((int)features.size(), (int)samples.size());
    Mat_<int> feature_col(1, (int)samples.size());

    vector<Mat> channels;
    for( int i = 0; i < (int)samples.size(); ++i )
    {
        cout << setw(6) << i << "/" << samples.size() << "\r";
        computeChannels(samples[i], channels);
        evaluator->setChannels(channels);
        //evaluator->assertChannels();
        evaluator->evaluateAll(feature_col);

        CV_Assert(feature_col.cols == (int)features.size());

        for( int j = 0; j < feature_col.cols; ++j )
            data(j, i) = feature_col(0, j);
    }
    cout << "\n";
    samples.clear();

    WaldBoostParams wparams;
    wparams.weak_count = params.weak_count;
    wparams.alpha = 0.02f;

    waldboost_ = createWaldBoost(wparams);
    vector<int> indices = waldboost_->train(data, labels);
    cout << "indices: ";
    for( size_t i = 0; i < indices.size(); ++i )
        cout << indices[i] << " ";
    cout << endl;

    features_.clear();
    for( size_t i = 0; i < indices.size(); ++i )
        features_.push_back(features[indices[i]]);
}

void ICFDetector::write(FileStorage& fs) const
{
    fs << "{";
    fs << "model_n_rows" << model_n_rows_;
    fs << "model_n_cols" << model_n_cols_;
    fs << "waldboost" << *waldboost_;
    fs << "features" << "[";
    for( size_t i = 0; i < features_.size(); ++i )
    {
        fs << features_[i];
    }
    fs << "]";
    fs << "}";
}

void ICFDetector::read(const FileNode& node)
{
    waldboost_ = Ptr<WaldBoost>(createWaldBoost(WaldBoostParams()));
    node["model_n_rows"] >> model_n_rows_;
    node["model_n_cols"] >> model_n_cols_;
    node["waldboost"] >> *waldboost_;
    FileNode features = node["features"];
    features_.clear();
    vector<int> p;
    for( FileNodeIterator n = features.begin(); n != features.end(); ++n )
    {
        (*n) >> p;
        features_.push_back(p);
    }
}

void ICFDetector::detect(const Mat& img, vector<Rect>& objects,
    double scaleFactor, Size minSize, Size maxSize, float threshold)
{
    float scale_from = min(model_n_cols_ / (float)maxSize.width,
                           model_n_rows_ / (float)maxSize.height);
    float scale_to = max(model_n_cols_ / (float)minSize.width,
                         model_n_rows_ / (float)minSize.height);
    objects.clear();
    Ptr<FeatureEvaluator> evaluator = createFeatureEvaluator(features_, "icf");
    Mat rescaled_image;
    int step = 8;
    vector<Mat> channels;
    for( float scale = scale_from; scale < scale_to + 0.001; scale *= scaleFactor )
    {
        cout << "scale " << scale << endl;
        int new_width = img.cols * scale;
        new_width -= new_width % 4;
        int new_height = img.rows * scale;
        new_height -= new_height % 4;

        resize(img, rescaled_image, Size(new_width, new_height));
        computeChannels(rescaled_image, channels);
        evaluator->setChannels(channels);
        for( int row = 0; row <= rescaled_image.rows - model_n_rows_; row += step)
        {
            for( int col = 0; col <= rescaled_image.cols - model_n_cols_;
                col += step )
            {
                evaluator->setPosition(Size(row, col));
                float value = waldboost_->predict(evaluator);
                if( value > threshold )
                {
                    int x = (int)(col / scale);
                    int y = (int)(row / scale);
                    int width = (int)(model_n_cols_ / scale);
                    int height = (int)(model_n_rows_ / scale);
                    cout << value << " " << x << " " << y << " " << width << " "
                         << height << endl;
                    objects.push_back(Rect(x, y, width, height));
                }
            }
        }

    }
}

void write(FileStorage& fs, String&, const ICFDetector& detector)
{
    detector.write(fs);
}

void read(const FileNode& node, ICFDetector& d,
    const ICFDetector& default_value)
{
    if( node.empty() )
        d = default_value;
    else
        d.read(node);
}

} /* namespace xobjdetect */
} /* namespace cv */