opencv_contrib/modules/xobjdetect/src/acffeature.cpp

/*

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                          License Agreement
               For Open Source Computer Vision Library
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*/

#include "precomp.hpp"

using std::vector;

using std::min;

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

namespace cv
{
namespace xobjdetect
{

static bool isNull(const Mat_<int> &m)
{
    bool null_data = true;
    for( int row = 0; row < m.rows; ++row )
    {
        for( int col = 0; col < m.cols; ++col )
            if( m.at<int>(row, col) )
                null_data = false;
    }
    return null_data;
}

class FeatureEvaluatorImpl : public FeatureEvaluator
{
public:
    FeatureEvaluatorImpl(const vector<vector<int> >& features):
        features_(features), channels_(), position_()
    {
        CV_Assert(features.size() > 0);
    }

    virtual void assertChannels()
    {
        bool null_data = true;
        for( size_t i = 0; i < channels_.size(); ++i )
            null_data &= isNull(channels_[i]);
        CV_Assert(!null_data);
    }

    virtual void evaluateAll(OutputArray feature_values) const
    {
        Mat_<int> feature_vals(1, (int)features_.size());
        for( int i = 0; i < (int)features_.size(); ++i )
        {
            feature_vals(0, i) = evaluate(i);
        }
        feature_values.assign(feature_vals);
    }

protected:
    /* Features to evaluate */
    vector<vector<int> > features_;
    /* Channels for feature evaluation */
    std::vector<Mat> channels_;
    /* Channels window position */
    Size position_;
};

class ICFFeatureEvaluatorImpl : public FeatureEvaluatorImpl
{
public:
    ICFFeatureEvaluatorImpl(const vector<vector<int> >& features):
        FeatureEvaluatorImpl(features)
    {
    }

    virtual void setChannels(InputArrayOfArrays channels);
    virtual void setPosition(Size position);
    virtual int evaluate(size_t feature_ind) const;
};

void ICFFeatureEvaluatorImpl::setChannels(InputArrayOfArrays channels)
{
    channels_.clear();
    vector<Mat> ch;
    channels.getMatVector(ch);
    CV_Assert(ch.size() == 10);

    for( size_t i = 0; i < ch.size(); ++i )
    {
        const Mat &channel = ch[i];
        Mat integral_channel;
        integral(channel, integral_channel, CV_32F);
        Mat_<int> chan(integral_channel.rows, integral_channel.cols);
        for( int row = 0; row < integral_channel.rows; ++row )
            for( int col = 0; col < integral_channel.cols; ++col )
                chan(row, col) = (int)integral_channel.at<float>(row, col);
        channels_.push_back(chan.clone());
    }
}

void ICFFeatureEvaluatorImpl::setPosition(Size position)
{
    position_ = position;
}

int ICFFeatureEvaluatorImpl::evaluate(size_t feature_ind) const
{
    CV_Assert(channels_.size() == 10);
    CV_Assert(feature_ind < features_.size());

    const vector<int>& feature = features_[feature_ind];
    int x = feature[0] + position_.height;
    int y = feature[1] + position_.width;
    int x_to = feature[2] + position_.height;
    int y_to = feature[3] + position_.width;
    int n = feature[4];
    const Mat_<int>& ch = channels_[n];
    return ch(y_to + 1, x_to + 1) - ch(y, x_to + 1) - ch(y_to + 1, x) + ch(y, x);
}

class ACFFeatureEvaluatorImpl : public FeatureEvaluatorImpl
{
public:
    ACFFeatureEvaluatorImpl(const vector<vector<int> >& features):
        FeatureEvaluatorImpl(features)
    {
    }

    virtual void setChannels(InputArrayOfArrays channels);
    virtual void setPosition(Size position);
    virtual int evaluate(size_t feature_ind) const;
};

void ACFFeatureEvaluatorImpl::setChannels(InputArrayOfArrays channels)
{
    channels_.clear();
    vector<Mat> ch;
    channels.getMatVector(ch);
    CV_Assert(ch.size() == 10);

    for( size_t i = 0; i < ch.size(); ++i )
    {
        const Mat &channel = ch[i];
        Mat_<int> acf_channel = Mat_<int>::zeros(channel.rows / 4, channel.cols / 4);
        for( int row = 0; row < channel.rows; row += 4 )
        {
            for( int col = 0; col < channel.cols; col += 4 )
            {
                int sum = 0;
                for( int cell_row = row; cell_row < row + 4; ++cell_row )
                    for( int cell_col = col; cell_col < col + 4; ++cell_col )
                        sum += (int)channel.at<float>(cell_row, cell_col);

                acf_channel(row / 4, col / 4) = sum;
            }
        }

        channels_.push_back(acf_channel.clone());
    }
}

void ACFFeatureEvaluatorImpl::setPosition(Size position)
{
    position_ = Size(position.width / 4, position.height / 4);
}

int ACFFeatureEvaluatorImpl::evaluate(size_t feature_ind) const
{
    CV_Assert(channels_.size() == 10);
    CV_Assert(feature_ind < features_.size());

    const vector<int>& feature = features_[feature_ind];
    int x = feature[0];
    int y = feature[1];
    int n = feature[2];
    return channels_[n].at<int>(y + position_.width, x + position_.height);
}

Ptr<FeatureEvaluator> createFeatureEvaluator(
    const vector<vector<int> >& features, const std::string& type)
{
    FeatureEvaluator *evaluator = NULL;
    if( type == "acf" )
        evaluator = new ACFFeatureEvaluatorImpl(features);
    else if( type == "icf" )
        evaluator = new ICFFeatureEvaluatorImpl(features);
    else
        CV_Error(CV_StsBadArg, "type value is either acf or icf");

    return Ptr<FeatureEvaluator>(evaluator);
}

vector<vector<int> > generateFeatures(Size window_size, const std::string& type,
                                      int count, int channel_count)
{
    CV_Assert(count > 0);
    vector<vector<int> > features;
    if( type == "acf" )
    {
        int cur_count = 0;
        int max_count = window_size.width * window_size.height / 16;
        count = min(count, max_count);
        for( int x = 0; x < window_size.width / 4; ++x )
            for( int y = 0; y < window_size.height / 4; ++y )
                for( int n = 0; n < channel_count; ++n )
                {
                    int f[] = {x, y, n};
                    vector<int> feature(f, f + sizeof(f) / sizeof(*f));
                    features.push_back(feature);
                    if( (cur_count += 1) == count )
                        break;
                }
    }
    else if( type == "icf" )
    {
        RNG rng;
        for( int i = 0; i < count; ++i )
        {
            int x = rng.uniform(0, window_size.width - 1);
            int y = rng.uniform(0, window_size.height - 1);
            int x_to = rng.uniform(x, window_size.width - 1);
            int y_to = rng.uniform(y, window_size.height - 1);
            int n = rng.uniform(0, channel_count - 1);
            int f[] = {x, y, x_to, y_to, n};
            vector<int> feature(f, f + sizeof(f) / sizeof(*f));
            features.push_back(feature);
        }
    }
    else
        CV_Error(CV_StsBadArg, "type value is either acf or icf");

    return features;
}

void computeChannels(InputArray image, vector<Mat>& channels)
{
    Mat src(image.getMat().rows, image.getMat().cols, CV_32FC3);
    image.getMat().convertTo(src, CV_32FC3, 1./255);

    Mat_<float> grad;
    Mat luv, gray;
    cvtColor(src, gray, CV_RGB2GRAY);
    cvtColor(src, luv, CV_RGB2Luv);

    Mat_<float> row_der, col_der;
    Sobel(gray, row_der, CV_32F, 0, 1);
    Sobel(gray, col_der, CV_32F, 1, 0);

    magnitude(row_der, col_der, grad);

    Mat_<Vec6f> hist = Mat_<Vec6f>::zeros(grad.rows, grad.cols);
    const float to_deg = 180 / 3.1415926f;
    for (int row = 0; row < grad.rows; ++row) {
        for (int col = 0; col < grad.cols; ++col) {
            float angle = atan2(row_der(row, col), col_der(row, col)) * to_deg;
            if (angle < 0)
                angle += 180;
            int ind = (int)(angle / 30);

            // If angle == 180, prevent index overflow
            if (ind == 6)
                ind = 5;

            hist(row, col)[ind] = grad(row, col) * 255;
        }
    }

    channels.clear();

    Mat luv_channels[3];
    split(luv, luv_channels);
    for( int i = 0; i < 3; ++i )
        channels.push_back(luv_channels[i]);

    channels.push_back(grad);

    vector<Mat> hist_channels;
    split(hist, hist_channels);

    for( size_t i = 0; i < hist_channels.size(); ++i )
        channels.push_back(hist_channels[i]);
}

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