opencv/modules/gpuimgproc/src/gftt.cpp

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#include "precomp.hpp"

using namespace cv;
using namespace cv::gpu;

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_GPUARITHM)

Ptr<gpu::CornersDetector> cv::gpu::createGoodFeaturesToTrackDetector(int, int, double, double, int, bool, double) { throw_no_cuda(); return Ptr<gpu::CornersDetector>(); }

#else /* !defined (HAVE_CUDA) */

namespace cv { namespace gpu { namespace cudev
{
    namespace gfft
    {
        int findCorners_gpu(PtrStepSzf eig, float threshold, PtrStepSzb mask, float2* corners, int max_count);
        void sortCorners_gpu(PtrStepSzf eig, float2* corners, int count);
    }
}}}

namespace
{
    class GoodFeaturesToTrackDetector : public CornersDetector
    {
    public:
        GoodFeaturesToTrackDetector(int srcType, int maxCorners, double qualityLevel, double minDistance,
                                    int blockSize, bool useHarrisDetector, double harrisK);

        void detect(InputArray image, OutputArray corners, InputArray mask = noArray());

    private:
        int maxCorners_;
        double qualityLevel_;
        double minDistance_;

        Ptr<gpu::CornernessCriteria> cornerCriteria_;

        GpuMat Dx_;
        GpuMat Dy_;
        GpuMat buf_;
        GpuMat eig_;
        GpuMat minMaxbuf_;
        GpuMat tmpCorners_;
    };

    GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector(int srcType, int maxCorners, double qualityLevel, double minDistance,
                                                             int blockSize, bool useHarrisDetector, double harrisK) :
        maxCorners_(maxCorners), qualityLevel_(qualityLevel), minDistance_(minDistance)
    {
        CV_Assert( qualityLevel_ > 0 && minDistance_ >= 0 && maxCorners_ >= 0 );

        cornerCriteria_ = useHarrisDetector ?
                    gpu::createHarrisCorner(srcType, blockSize, 3, harrisK) :
                    gpu::createMinEigenValCorner(srcType, blockSize, 3);
    }

    void GoodFeaturesToTrackDetector::detect(InputArray _image, OutputArray _corners, InputArray _mask)
    {
        using namespace cv::gpu::cudev::gfft;

        GpuMat image = _image.getGpuMat();
        GpuMat mask = _mask.getGpuMat();

        CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );

        ensureSizeIsEnough(image.size(), CV_32FC1, eig_);
        cornerCriteria_->compute(image, eig_);

        double maxVal = 0;
        gpu::minMax(eig_, 0, &maxVal, noArray(), minMaxbuf_);

        ensureSizeIsEnough(1, std::max(1000, static_cast<int>(image.size().area() * 0.05)), CV_32FC2, tmpCorners_);

        int total = findCorners_gpu(eig_, static_cast<float>(maxVal * qualityLevel_), mask, tmpCorners_.ptr<float2>(), tmpCorners_.cols);

        if (total == 0)
        {
            _corners.release();
            return;
        }

        sortCorners_gpu(eig_, tmpCorners_.ptr<float2>(), total);

        if (minDistance_ < 1)
        {
            tmpCorners_.colRange(0, maxCorners_ > 0 ? std::min(maxCorners_, total) : total).copyTo(_corners);
        }
        else
        {
            std::vector<Point2f> tmp(total);
            Mat tmpMat(1, total, CV_32FC2, (void*)&tmp[0]);
            tmpCorners_.colRange(0, total).download(tmpMat);

            std::vector<Point2f> tmp2;
            tmp2.reserve(total);

            const int cell_size = cvRound(minDistance_);
            const int grid_width = (image.cols + cell_size - 1) / cell_size;
            const int grid_height = (image.rows + cell_size - 1) / cell_size;

            std::vector< std::vector<Point2f> > grid(grid_width * grid_height);

            for (int i = 0; i < total; ++i)
            {
                Point2f p = tmp[i];

                bool good = true;

                int x_cell = static_cast<int>(p.x / cell_size);
                int y_cell = static_cast<int>(p.y / cell_size);

                int x1 = x_cell - 1;
                int y1 = y_cell - 1;
                int x2 = x_cell + 1;
                int y2 = y_cell + 1;

                // boundary check
                x1 = std::max(0, x1);
                y1 = std::max(0, y1);
                x2 = std::min(grid_width - 1, x2);
                y2 = std::min(grid_height - 1, y2);

                for (int yy = y1; yy <= y2; yy++)
                {
                    for (int xx = x1; xx <= x2; xx++)
                    {
                        std::vector<Point2f>& m = grid[yy * grid_width + xx];

                        if (!m.empty())
                        {
                            for(size_t j = 0; j < m.size(); j++)
                            {
                                float dx = p.x - m[j].x;
                                float dy = p.y - m[j].y;

                                if (dx * dx + dy * dy < minDistance_ * minDistance_)
                                {
                                    good = false;
                                    goto break_out;
                                }
                            }
                        }
                    }
                }

                break_out:

                if(good)
                {
                    grid[y_cell * grid_width + x_cell].push_back(p);

                    tmp2.push_back(p);

                    if (maxCorners_ > 0 && tmp2.size() == static_cast<size_t>(maxCorners_))
                        break;
                }
            }

            _corners.create(1, static_cast<int>(tmp2.size()), CV_32FC2);
            GpuMat corners = _corners.getGpuMat();

            corners.upload(Mat(1, static_cast<int>(tmp2.size()), CV_32FC2, &tmp2[0]));
        }
    }
}

Ptr<gpu::CornersDetector> cv::gpu::createGoodFeaturesToTrackDetector(int srcType, int maxCorners, double qualityLevel, double minDistance,
                                                                     int blockSize, bool useHarrisDetector, double harrisK)
{
    return new GoodFeaturesToTrackDetector(srcType, maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, harrisK);
}

#endif /* !defined (HAVE_CUDA) */