opencv/modules/stitching/exposure_compensate.cpp

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#include "exposure_compensate.hpp"
#include "util.hpp"

using namespace std;
using namespace cv;
using namespace cv::gpu;


Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type)
{
    if (type == NO)
        return new NoExposureCompensator();
    if (type == GAIN)
        return new GainCompensator();
    if (type == GAIN_BLOCKS)
        return new BlocksGainCompensator();
    CV_Error(CV_StsBadArg, "unsupported exposure compensation method");
    return NULL;
}


void ExposureCompensator::feed(const vector<Point> &corners, const vector<Mat> &images, 
                               const vector<Mat> &masks)
{
    vector<pair<Mat,uchar> > level_masks;
    for (size_t i = 0; i < masks.size(); ++i)
        level_masks.push_back(make_pair(masks[i], 255));
    feed(corners, images, level_masks);
}


void GainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images, 
                           const vector<pair<Mat,uchar> > &masks)
{
    CV_Assert(corners.size() == images.size() && images.size() == masks.size());

    const int num_images = static_cast<int>(images.size());
    Mat_<int> N(num_images, num_images); N.setTo(0);
    Mat_<double> I(num_images, num_images); I.setTo(0);

    Rect dst_roi = resultRoi(corners, images);
    Mat subimg1, subimg2;
    Mat_<uchar> submask1, submask2, intersect;

    for (int i = 0; i < num_images; ++i)
    {
        for (int j = i; j < num_images; ++j)
        {
            Rect roi;
            if (overlapRoi(corners[i], corners[j], images[i].size(), images[j].size(), roi))
            {
                subimg1 = images[i](Rect(roi.tl() - corners[i], roi.br() - corners[i]));
                subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j]));

                submask1 = masks[i].first(Rect(roi.tl() - corners[i], roi.br() - corners[i]));
                submask2 = masks[j].first(Rect(roi.tl() - corners[j], roi.br() - corners[j]));
                intersect = (submask1 == masks[i].second) & (submask2 == masks[j].second);

                N(i, j) = N(j, i) = max(1, countNonZero(intersect));

                double Isum1 = 0, Isum2 = 0;
                for (int y = 0; y < roi.height; ++y)
                {
                    const Point3_<uchar>* r1 = subimg1.ptr<Point3_<uchar> >(y);
                    const Point3_<uchar>* r2 = subimg2.ptr<Point3_<uchar> >(y);
                    for (int x = 0; x < roi.width; ++x)
                    {
                        if (intersect(y, x))
                        {
                            Isum1 += sqrt(static_cast<double>(sqr(r1[x].x) + sqr(r1[x].y) + sqr(r1[x].z)));
                            Isum2 += sqrt(static_cast<double>(sqr(r2[x].x) + sqr(r2[x].y) + sqr(r2[x].z)));
                        }
                    }
                }
                I(i, j) = Isum1 / N(i, j);
                I(j, i) = Isum2 / N(i, j);
            }
        }
    }

    double alpha = 0.01;
    double beta = 100;

    Mat_<double> A(num_images, num_images); A.setTo(0);
    Mat_<double> b(num_images, 1); b.setTo(0);
    for (int i = 0; i < num_images; ++i)
    {
        for (int j = 0; j < num_images; ++j)
        {
            b(i, 0) += beta * N(i, j);
            A(i, i) += beta * N(i, j);
            if (j == i) continue;
            A(i, i) += 2 * alpha * I(i, j) * I(i, j) * N(i, j);
            A(i, j) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j);
        }
    }

    solve(A, b, gains_);
}


void GainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
{
    image *= gains_(index, 0);
}


vector<double> GainCompensator::gains() const
{
    vector<double> gains_vec(gains_.rows);
    for (int i = 0; i < gains_.rows; ++i)
        gains_vec[i] = gains_(i, 0);
    return gains_vec;
}


void BlocksGainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images, 
                                const vector<pair<Mat,uchar> > &masks)
{
    CV_Assert(corners.size() == images.size() && images.size() == masks.size());

    const int num_images = static_cast<int>(images.size());

    vector<Size> bl_per_imgs(num_images);
    vector<Point> block_corners;
    vector<Mat> block_images;
    vector<pair<Mat,uchar> > block_masks;

    // Construct blocks for gain compensator
    for (int img_idx = 0; img_idx < num_images; ++img_idx)
    {
        Size bl_per_img((images[img_idx].cols + bl_width_ - 1) / bl_width_,
                        (images[img_idx].rows + bl_height_ - 1) / bl_height_);
        int bl_width = (images[img_idx].cols + bl_per_img.width - 1) / bl_per_img.width;
        int bl_height = (images[img_idx].rows + bl_per_img.height - 1) / bl_per_img.height;
        bl_per_imgs[img_idx] = bl_per_img;
        for (int by = 0; by < bl_per_img.height; ++by)
        {
            for (int bx = 0; bx < bl_per_img.width; ++bx)
            {
                Point bl_tl(bx * bl_width, by * bl_height);
                Point bl_br(min(bl_tl.x + bl_width, images[img_idx].cols),
                            min(bl_tl.y + bl_height, images[img_idx].rows));

                block_corners.push_back(corners[img_idx] + bl_tl);
                block_images.push_back(images[img_idx](Rect(bl_tl, bl_br)));
                block_masks.push_back(make_pair(masks[img_idx].first(Rect(bl_tl, bl_br)), 
                                                masks[img_idx].second));
            }
        }
    }

    GainCompensator compensator;
    compensator.feed(block_corners, block_images, block_masks);
    vector<double> gains = compensator.gains();
    gain_maps_.resize(num_images);

    Mat_<float> ker(1, 3); 
    ker(0,0) = 0.25; ker(0,1) = 0.5; ker(0,2) = 0.25;

    int bl_idx = 0;
    for (int img_idx = 0; img_idx < num_images; ++img_idx)
    {
        Size bl_per_img = bl_per_imgs[img_idx];
        gain_maps_[img_idx].create(bl_per_img);

        for (int by = 0; by < bl_per_img.height; ++by)
            for (int bx = 0; bx < bl_per_img.width; ++bx, ++bl_idx)
                gain_maps_[img_idx](by, bx) = static_cast<float>(gains[bl_idx]);
        
        sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
        sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
    }
}


void BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
{
    CV_Assert(image.type() == CV_8UC3);

    Mat_<float> gain_map;
    if (gain_maps_[index].size() == image.size())
        gain_map = gain_maps_[index];
    else
        resize(gain_maps_[index], gain_map, image.size(), 0, 0, INTER_LINEAR);

    for (int y = 0; y < image.rows; ++y)
    {
        const float* gain_row = gain_map.ptr<float>(y);
        Point3_<uchar>* row = image.ptr<Point3_<uchar> >(y);
        for (int x = 0; x < image.cols; ++x)
        {
            row[x].x = saturate_cast<uchar>(row[x].x * gain_row[x]);
            row[x].y = saturate_cast<uchar>(row[x].y * gain_row[x]);
            row[x].z = saturate_cast<uchar>(row[x].z * gain_row[x]);
        }
    }
}