#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
/*#include <Eigen/Core>
#include <Eigen/Geometry>
#include <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/csparse/linear_solver_csparse.h>
#include <g2o/types/sba/types_six_dof_expmap.h>*/
#include <chrono>

#include "pnp.h"
#include "../Steger.h"
using namespace std;
using namespace cv;

void find_feature_matches (
    const Mat& img_1, const Mat& img_2,
    std::vector<KeyPoint>& keypoints_1,
    std::vector<KeyPoint>& keypoints_2,
    std::vector< DMatch >& matches );



void bundleAdjustment (
    const vector<Point3f> points_3d,
    const vector<Point2f> points_2d,
    const Mat& K,
    Mat& R, Mat& t
);

void verify_result(Mat R,Mat t,vector<Point2f> pts_2d,Mat K,vector<Point3f> pts_3d)
{
    if(pts_2d.size()!=pts_3d.size())
    {
        printf(" pts_2d.size()!=pts_3d.size() \n");
        return;
    }

    Mat RINV=R.inv();

    for(int i=0;i<pts_3d.size();++i)
    {
        float dx=pts_3d[i].x+t.at<double>(0,0);
        float dy=pts_3d[i].y+t.at<double>(1,0);
        float dz=pts_3d[i].z+t.at<double>(2,0);
        float distance=sqrt(dx*dx+dy*dy+dz*dz);

        Point2d p1 = pixel2cam ( pts_2d[i], K );
        Point3f pose3d ( Point3f ( p1.x*distance, p1.y*distance, distance ) );



        Mat pose3d_m=Mat::zeros(3,1,R.type());

        pose3d_m.at<double>(0,0)=pose3d.x-t.at<double>(0,0);
        pose3d_m.at<double>(1,0)=pose3d.y-t.at<double>(1,0);
        pose3d_m.at<double>(2,0)=pose3d.z-t.at<double>(2,0);

        Mat o_pose=RINV*pose3d_m;

        printf(" cal :%f, %f, %f   org:%f, %f, %f\n",o_pose.at<double>(0,0),o_pose.at<double>(1,0),o_pose.at<double>(2,0),
                pts_3d[i].x,pts_3d[i].y,pts_3d[i].z);

    }


}



void test_pnp(){
    Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    cv::Mat R=(Mat_<double> ( 3,3 ) <<0.9979193252225095, -0.05138618904650328, 0.03894200717385427,
            0.05033852907733768, 0.9983556574295407, 0.02742286944795593,
            -0.04028712992732941, -0.02540552801471822, 0.998865109165653);

    cv::Mat T=(Mat_<double>(3,1)<<-0.1255867099750184,-0.007363525258815341,0.0609992658867812);

    P3d2d p3d2d(K);
    p3d2d.R_=R;
    p3d2d.t_=T;


    cv::Mat src = cv::imread("../images/3.bmp", 0);
    cv::Mat image = src(cv::Rect(0, 0, src.cols, src.rows)).clone();
    for (int i = 0; i < image.rows; i++)
    {
        for (int j = 0; j < image.cols; j++)
        {
            if (image.at<uchar>(i, j) < 20)
                image.at<uchar>(i, j) = 0;
        }
    }
    cv::Mat out= cv::Mat::zeros(image.size(), CV_8UC1);
    CSteger steger;
    cv::GaussianBlur(image, image, cv::Size(3, 3), 3, 3);
    std::vector<cv::Point2d> points = steger.StripCenter(image);

    auto t1=std::chrono::steady_clock::now();
    p3d2d.execute(points);

    auto t2=std::chrono::steady_clock::now();
    double ms=std::chrono::duration<double,std::milli>(t2-t1).count();
    printf("points :%d  time :%lf\n",points.size(),ms);


}

int main ( int argc, char** argv )
{
    test_pnp();
    return 0;

    if ( argc != 4 )
    {
        cout<<"usage: pose_estimation_3d2d img1 img2 depth1"<<endl;
        return 1;
    }
    //-- 读取图像
    Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
    Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );

    vector<KeyPoint> keypoints_1, keypoints_2;
    vector<DMatch> matches;
    find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
    cout<<"一共找到了"<<matches.size() <<"组匹配点"<<endl;

    // 建立3D点
    Mat d1 = imread ( argv[3], CV_LOAD_IMAGE_UNCHANGED );       // 深度图为16位无符号数，单通道图像
    Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    vector<Point3f> pts_3d;
    vector<Point2f> pts_2d;
    for ( DMatch m:matches )
    {
        ushort d = d1.ptr<unsigned short> (int ( keypoints_1[m.queryIdx].pt.y )) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
        if ( d == 0 )   // bad depth
            continue;
        float dd = d/5000.0;
        Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
        pts_3d.push_back ( Point3f ( p1.x*dd, p1.y*dd, dd ) );
        pts_2d.push_back ( keypoints_2[m.trainIdx].pt );

        printf("image1 key 3d :%f, %f, %f   image2 key 2d:%f, %f\n",p1.x*dd, p1.y*dd, dd ,
                keypoints_2[m.trainIdx].pt.x,keypoints_2[m.trainIdx].pt.y);
    }

    cout<<"3d-2d pairs: "<<pts_3d.size() <<endl;

    Mat r, t;
    solvePnP ( pts_3d, pts_2d, K, Mat(), r, t, false ); // 调用OpenCV 的 PnP 求解，可选择EPNP，DLS等方法
    Mat R;
    cv::Rodrigues ( r, R ); // r为旋转向量形式，用Rodrigues公式转换为矩阵

    cout<<"R="<<endl<<R<<endl;
    cout<<"t="<<endl<<t<<endl;

    verify_result(R,t,pts_2d,K,pts_3d);
    cout<<"calling bundle adjustment"<<endl;

    //bundleAdjustment ( pts_3d, pts_2d, K, R, t );
}

void find_feature_matches ( const Mat& img_1, const Mat& img_2,
                            std::vector<KeyPoint>& keypoints_1,
                            std::vector<KeyPoint>& keypoints_2,
                            std::vector< DMatch >& matches )
{
    //-- 初始化
    Mat descriptors_1, descriptors_2;
    // used in OpenCV3
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr<DescriptorMatcher> matcher  = DescriptorMatcher::create ( "BruteForce-Hamming" );
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector<DMatch> match;
    // BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, match );

    //-- 第四步:匹配点对筛选
    double min_dist=10000, max_dist=0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = match[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            matches.push_back ( match[i] );
        }
    }
}


void bundleAdjustment (
    const vector< Point3f > points_3d,
    const vector< Point2f > points_2d,
    const Mat& K,
    Mat& R, Mat& t )
{
    // 初始化g2o
    /*typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3
    Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse<Block::PoseMatrixType>(); // 线性方程求解器
    Block* solver_ptr = new Block ( linearSolver );     // 矩阵块求解器
    g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr );
    g2o::SparseOptimizer optimizer;
    optimizer.setAlgorithm ( solver );

    // vertex
    g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose
    Eigen::Matrix3d R_mat;
    R_mat <<
          R.at<double> ( 0,0 ), R.at<double> ( 0,1 ), R.at<double> ( 0,2 ),
               R.at<double> ( 1,0 ), R.at<double> ( 1,1 ), R.at<double> ( 1,2 ),
               R.at<double> ( 2,0 ), R.at<double> ( 2,1 ), R.at<double> ( 2,2 );
    pose->setId ( 0 );
    pose->setEstimate ( g2o::SE3Quat (
                            R_mat,
                            Eigen::Vector3d ( t.at<double> ( 0,0 ), t.at<double> ( 1,0 ), t.at<double> ( 2,0 ) )
                        ) );
    optimizer.addVertex ( pose );

    int index = 1;
    for ( const Point3f p:points_3d )   // landmarks
    {
        g2o::VertexSBAPointXYZ* point = new g2o::VertexSBAPointXYZ();
        point->setId ( index++ );
        point->setEstimate ( Eigen::Vector3d ( p.x, p.y, p.z ) );
        point->setMarginalized ( true ); // g2o 中必须设置 marg 参见第十讲内容
        optimizer.addVertex ( point );
    }

    // parameter: camera intrinsics
    g2o::CameraParameters* camera = new g2o::CameraParameters (
        K.at<double> ( 0,0 ), Eigen::Vector2d ( K.at<double> ( 0,2 ), K.at<double> ( 1,2 ) ), 0
    );
    camera->setId ( 0 );
    optimizer.addParameter ( camera );

    // edges
    index = 1;
    for ( const Point2f p:points_2d )
    {
        g2o::EdgeProjectXYZ2UV* edge = new g2o::EdgeProjectXYZ2UV();
        edge->setId ( index );
        edge->setVertex ( 0, dynamic_cast<g2o::VertexSBAPointXYZ*> ( optimizer.vertex ( index ) ) );
        edge->setVertex ( 1, pose );
        edge->setMeasurement ( Eigen::Vector2d ( p.x, p.y ) );
        edge->setParameterId ( 0,0 );
        edge->setInformation ( Eigen::Matrix2d::Identity() );
        optimizer.addEdge ( edge );
        index++;
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    optimizer.setVerbose ( true );
    optimizer.initializeOptimization();
    optimizer.optimize ( 100 );
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> ( t2-t1 );
    cout<<"optimization costs time: "<<time_used.count() <<" seconds."<<endl;

    cout<<endl<<"after optimization:"<<endl;
    cout<<"T="<<endl<<Eigen::Isometry3d ( pose->estimate() ).matrix() <<endl;*/
}