// Copyright (C) 2017  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.

#include <dlib/python.h>
#include <boost/shared_ptr.hpp>
#include <dlib/matrix.h>
#include <boost/python/slice.hpp>
#include <dlib/geometry/vector.h>
#include <dlib/dnn.h>
#include <dlib/image_transforms.h>
#include "indexing.h"
#include <dlib/image_io.h>
#include <dlib/clustering.h>


using namespace dlib;
using namespace std;
using namespace boost::python;

typedef matrix<double,0,1> cv;


class face_recognition_model_v1
{

public:

    face_recognition_model_v1(const std::string& model_filename)
    {
        deserialize(model_filename) >> net;

        cropper = make_shared<random_cropper>();
        cropper->set_chip_dims(150,150);
        cropper->set_randomly_flip(true);
        cropper->set_max_object_size(0.99999);
        cropper->set_background_crops_fraction(0);
        cropper->set_min_object_size(0.97);
        cropper->set_translate_amount(0.02);
        cropper->set_max_rotation_degrees(3);
    }

    boost::python::list cluster(boost::python::list descriptors, float threshold)
    {
        boost::python::list clusters;

        size_t num_descriptors = len(descriptors);
        
        // In particular, one simple thing we can do is face clustering.  This next bit of code
        // creates a graph of connected faces and then uses the Chinese whispers graph clustering
        // algorithm to identify how many people there are and which faces belong to whom.
        std::vector<sample_pair> edges;
        std::vector<unsigned long> labels;
        for (size_t i = 0; i < num_descriptors; ++i)
        {
            for (size_t j = i+1; j < num_descriptors; ++j)
            {
                // Faces are connected in the graph if they are close enough.  Here we check if
                // the distance between two face descriptors is less than 0.6, which is the
                // decision threshold the network was trained to use.  Although you can
                // certainly use any other threshold you find useful.
                matrix<double,0,1> first_descriptor = boost::python::extract<matrix<double,0,1>>(descriptors[i]);
                matrix<double,0,1> second_descriptor = boost::python::extract<matrix<double,0,1>>(descriptors[j]);

                if (length(first_descriptor-second_descriptor) < threshold)
                    edges.push_back(sample_pair(i,j));
            }
        }
        const auto num_clusters = chinese_whispers(edges, labels);
        for (size_t i = 0; i < labels.size(); ++i)
        {
            clusters.append(labels[i]);
        }
        return clusters;
    }

    void save_image_chip (
        object img,
        const full_object_detection& face,
        const std::string& chip_filename
    )
    {
        std::vector<full_object_detection> faces(1, face);
        save_image_chips(img, faces, chip_filename);
        return;
    }

    void save_image_chips (
        object img,
        const std::vector<full_object_detection>& faces,
        const std::string& chip_filename
    )
    {
        int num_faces = faces.size();
        std::vector<chip_details> dets;
        for (auto& f : faces)
            dets.push_back(get_face_chip_details(f, 150, 0.25));
        dlib::array<matrix<rgb_pixel>> face_chips;
        extract_image_chips(numpy_rgb_image(img), dets, face_chips);
        int i=0;
        for (auto& chip : face_chips) {
            i++;
            if(num_faces > 1) 
            {
                const std::string& file_name = chip_filename + "_" + std::to_string(i) + ".jpg";
                save_jpeg(chip, file_name);
            }
            else
            {
                const std::string& file_name = chip_filename + ".jpg";
                save_jpeg(chip, file_name);
            }
        }
    }

    matrix<double,0,1> compute_face_descriptor (
        object img,
        const full_object_detection& face,
        const int num_jitters
    )
    {
        std::vector<full_object_detection> faces(1, face);
        return compute_face_descriptors(img, faces, num_jitters)[0];
    }

    std::vector<matrix<double,0,1>> compute_face_descriptors (
        object img,
        const std::vector<full_object_detection>& faces,
        const int num_jitters
    )
    {
        if (!is_rgb_python_image(img))
            throw dlib::error("Unsupported image type, must be RGB image.");

        for (auto& f : faces)
        {
            if (f.num_parts() != 68)
                throw dlib::error("The full_object_detection must use the iBUG 300W 68 point face landmark style.");
        }


        std::vector<chip_details> dets;
        for (auto& f : faces)
            dets.push_back(get_face_chip_details(f, 150, 0.25));
        dlib::array<matrix<rgb_pixel>> face_chips;
        extract_image_chips(numpy_rgb_image(img), dets, face_chips);

        std::vector<matrix<double,0,1>> face_descriptors;
        face_descriptors.reserve(face_chips.size());

        if (num_jitters <= 1)
        {
            // extract descriptors and convert from float vectors to double vectors
            for (auto& d : net(face_chips,16))
                face_descriptors.push_back(matrix_cast<double>(d));
        }
        else
        {
            for (auto& fimg : face_chips)
                face_descriptors.push_back(matrix_cast<double>(mean(mat(net(jitter_image(fimg,num_jitters),16)))));
        }

        return face_descriptors;
    }

private:

    std::shared_ptr<random_cropper> cropper;

    std::vector<matrix<rgb_pixel>> jitter_image(
        const matrix<rgb_pixel>& img,
        const int num_jitters
    )
    {
        std::vector<mmod_rect> raw_boxes(1), ignored_crop_boxes;
        raw_boxes[0] = shrink_rect(get_rect(img),3);
        std::vector<matrix<rgb_pixel>> crops; 

        matrix<rgb_pixel> temp; 
        for (int i = 0; i < num_jitters; ++i)
        {
            (*cropper)(img, raw_boxes, temp, ignored_crop_boxes);
            crops.push_back(move(temp));
        }
        return crops;
    }


    template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
    using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;

    template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
    using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;

    template <int N, template <typename> class BN, int stride, typename SUBNET> 
    using block  = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;

    template <int N, typename SUBNET> using ares      = relu<residual<block,N,affine,SUBNET>>;
    template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;

    template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>;
    template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>;
    template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>;
    template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>;
    template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>;

    using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything<
                                alevel0<
                                alevel1<
                                alevel2<
                                alevel3<
                                alevel4<
                                max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2,
                                input_rgb_image_sized<150>
                                >>>>>>>>>>>>;
    anet_type net;
};


// ----------------------------------------------------------------------------------------

void bind_face_recognition()
{
    using boost::python::arg;
    {
    class_<face_recognition_model_v1>("face_recognition_model_v1", "This object maps human faces into 128D vectors where pictures of the same person are mapped near to each other and pictures of different people are mapped far apart.  The constructor loads the face recognition model from a file. The model file is available here: http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2", init<std::string>())
        .def("compute_face_descriptor", &face_recognition_model_v1::compute_face_descriptor, (arg("img"),arg("face"),arg("num_jitters")=0),
            "Takes an image and a full_object_detection that references a face in that image and converts it into a 128D face descriptor. "
            "If num_jitters>1 then each face will be randomly jittered slightly num_jitters times, each run through the 128D projection, and the average used as the face descriptor."
            )
        .def("compute_face_descriptor", &face_recognition_model_v1::compute_face_descriptors, (arg("img"),arg("faces"),arg("num_jitters")=0),
            "Takes an image and an array of full_object_detections that reference faces in that image and converts them into 128D face descriptors.  "
            "If num_jitters>1 then each face will be randomly jittered slightly num_jitters times, each run through the 128D projection, and the average used as the face descriptor."
            )
        .def("save_image_chip", &face_recognition_model_v1::save_image_chip, (arg("img"),arg("face"),arg("chip_filename")),
            "Takes an image and a full_object_detection that references a face in that image and saves the face with the specified file name prefix"
            )
        .def("save_image_chips", &face_recognition_model_v1::save_image_chips, (arg("img"),arg("faces"),arg("chip_filename")),
            "Takes an image and a full_object_detections object that reference faces in that image and saves the faces with the specified file name prefix"
            )
        .def("cluster", &face_recognition_model_v1::cluster, (arg("descriptors"), arg("threshold")),
            "Takes a list of descriptors and returns a list that contains a label for each descriptor. Clustering is done using chinese_whispers."
            );
    }

    {
    typedef std::vector<full_object_detection> type;
    class_<type>("full_object_detections", "An array of full_object_detection objects.")
        .def(vector_indexing_suite<type>())
        .def("clear", &type::clear)
        .def("resize", resize<type>)
        .def_pickle(serialize_pickle<type>());
    }
}