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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 "kaze/AKAZEFeatures.h"

#include <iostream>

namespace cv
{
    using namespace std;

    class AKAZE_Impl : public AKAZE
    {
    public:
        AKAZE_Impl(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
                 float _threshold, int _octaves, int _sublevels, int _diffusivity)
        : descriptor(_descriptor_type)
        , descriptor_channels(_descriptor_channels)
        , descriptor_size(_descriptor_size)
        , threshold(_threshold)
        , octaves(_octaves)
        , sublevels(_sublevels)
        , diffusivity(_diffusivity)
        {
        }

        virtual ~AKAZE_Impl()
        {

        }

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

            case DESCRIPTOR_MLDB:
            case 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 descriptorType() const
        {
            switch (descriptor)
            {
            case DESCRIPTOR_KAZE:
            case DESCRIPTOR_KAZE_UPRIGHT:
                    return CV_32F;

            case DESCRIPTOR_MLDB:
            case DESCRIPTOR_MLDB_UPRIGHT:
                    return CV_8U;

                default:
                    return -1;
            }
        }

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

            case DESCRIPTOR_MLDB:
            case DESCRIPTOR_MLDB_UPRIGHT:
                return NORM_HAMMING;

            default:
                return -1;
            }
        }

        void detectAndCompute(InputArray image, InputArray mask,
                              std::vector<KeyPoint>& keypoints,
                              OutputArray descriptors,
                              bool useProvidedKeypoints)
        {
            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 = descriptor;
            options.descriptor_channels = descriptor_channels;
            options.descriptor_size = descriptor_size;
            options.img_width = img.cols;
            options.img_height = img.rows;
            options.dthreshold = threshold;
            options.omax = octaves;
            options.nsublevels = sublevels;
            options.diffusivity = diffusivity;

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

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

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

            if( descriptors.needed() )
            {
                Mat& desc = descriptors.getMatRef();
                impl.Compute_Descriptors(keypoints, desc);

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

        int descriptor;
        int descriptor_channels;
        int descriptor_size;
        float threshold;
        int octaves;
        int sublevels;
        int diffusivity;
    };

    Ptr<AKAZE> AKAZE::create(int descriptor_type,
                             int descriptor_size, int descriptor_channels,
                             float threshold, int octaves,
                             int sublevels, int diffusivity)
    {
        return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
                                   threshold, octaves, sublevels, diffusivity);
    }
}