opencv/modules/features2d/src/akaze.cpp

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/*
OpenCV wrapper of reference implementation of
[1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces.
Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli.
In British Machine Vision Conference (BMVC), Bristol, UK, September 2013
http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf
@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
*/

#include "precomp.hpp"
#include "akaze/AKAZEFeatures.h"

namespace cv
{
    AKAZE::AKAZE()
        : descriptor(DESCRIPTOR_MLDB)
        , descriptor_channels(3)
        , descriptor_size(0)
    {
    }

    AKAZE::AKAZE(DESCRIPTOR_TYPE descriptor_type, int _descriptor_size, int _descriptor_channels)
        : descriptor(descriptor_type)
        , descriptor_channels(_descriptor_channels)
        , descriptor_size(_descriptor_size)
    {

    }

    AKAZE::~AKAZE()
    {

    }

    // returns the descriptor size in bytes
    int AKAZE::descriptorSize() const
    {
        switch (descriptor)
        {
        case cv::AKAZE::DESCRIPTOR_KAZE:
        case cv::AKAZE::DESCRIPTOR_KAZE_UPRIGHT:
            return 64;

        case cv::AKAZE::DESCRIPTOR_MLDB:
        case cv::AKAZE::DESCRIPTOR_MLDB_UPRIGHT:
            // We use the full length binary descriptor -> 486 bits
            if (descriptor_size == 0)
            {
                int t = (6 + 36 + 120) * descriptor_channels;
                return (int)ceil(t / 8.);
            }
            else
            {
                // We use the random bit selection length binary descriptor
                return (int)ceil(descriptor_size / 8.);
            }

        default:
            return -1;
        }
    }

    // returns the descriptor type
    int AKAZE::descriptorType() const
    {
        switch (descriptor)
        {
        case cv::AKAZE::DESCRIPTOR_KAZE:
        case cv::AKAZE::DESCRIPTOR_KAZE_UPRIGHT:
                return CV_32F;

        case cv::AKAZE::DESCRIPTOR_MLDB:
        case cv::AKAZE::DESCRIPTOR_MLDB_UPRIGHT:
                return CV_8U;

            default:
                return -1;
        }
    }

    // returns the default norm type
    int AKAZE::defaultNorm() const
    {
        switch (descriptor)
        {
        case cv::AKAZE::DESCRIPTOR_KAZE:
        case cv::AKAZE::DESCRIPTOR_KAZE_UPRIGHT:
            return cv::NORM_L2;

        case cv::AKAZE::DESCRIPTOR_MLDB:
        case cv::AKAZE::DESCRIPTOR_MLDB_UPRIGHT:
            return cv::NORM_HAMMING;

        default:
            return -1;
        }
    }


    void AKAZE::operator()(InputArray image, InputArray mask,
        std::vector<KeyPoint>& keypoints,
        OutputArray descriptors,
        bool useProvidedKeypoints) const
    {
        cv::Mat img = image.getMat();
        if (img.type() != CV_8UC1)
            cvtColor(image, img, COLOR_BGR2GRAY);

        Mat img1_32;
        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);

        cv::Mat& desc = descriptors.getMatRef();

        AKAZEOptions options;
        options.descriptor = static_cast<DESCRIPTOR_TYPE>(descriptor);
        options.descriptor_channels = descriptor_channels;
        options.descriptor_size = descriptor_size;
        options.img_width = img.cols;
        options.img_height = img.rows;

        AKAZEFeatures impl(options);
        impl.Create_Nonlinear_Scale_Space(img1_32);

        if (!useProvidedKeypoints)
        {
            impl.Feature_Detection(keypoints);
        }

        if (!mask.empty())
        {
            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
        }

        impl.Compute_Descriptors(keypoints, desc);

        CV_Assert((!desc.rows || desc.cols == descriptorSize()));
        CV_Assert((!desc.rows || (desc.type() == descriptorType())));
    }

    void AKAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
    {
        cv::Mat img = image.getMat();
        if (img.type() != CV_8UC1)
            cvtColor(image, img, COLOR_BGR2GRAY);

        Mat img1_32;
        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);

        AKAZEOptions options;
        options.descriptor = static_cast<DESCRIPTOR_TYPE>(descriptor);
        options.descriptor_channels = descriptor_channels;
        options.descriptor_size = descriptor_size;
        options.img_width = img.cols;
        options.img_height = img.rows;

        AKAZEFeatures impl(options);
        impl.Create_Nonlinear_Scale_Space(img1_32);
        impl.Feature_Detection(keypoints);

        if (!mask.empty())
        {
            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
        }
    }

    void AKAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
    {
        cv::Mat img = image.getMat();
        if (img.type() != CV_8UC1)
            cvtColor(image, img, COLOR_BGR2GRAY);

        Mat img1_32;
        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);

        cv::Mat& desc = descriptors.getMatRef();

        AKAZEOptions options;
        options.descriptor = static_cast<DESCRIPTOR_TYPE>(descriptor);
        options.descriptor_channels = descriptor_channels;
        options.descriptor_size = descriptor_size;
        options.img_width = img.cols;
        options.img_height = img.rows;

        AKAZEFeatures impl(options);
        impl.Create_Nonlinear_Scale_Space(img1_32);
        impl.Compute_Descriptors(keypoints, desc);

        CV_Assert((!desc.rows || desc.cols == descriptorSize()));
        CV_Assert((!desc.rows || (desc.type() == descriptorType())));
    }
}