#include <iostream>
#include <fstream>
#include <list>
#include <vector>
#include <chrono>
#include <ctime>
#include <climits>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/dense/linear_solver_dense.h>
#include <g2o/core/robust_kernel.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
using namespace std;
using namespace g2o;
/********************************************
* 本节演示了RGBD上的半稠密直接法
********************************************/
// 一次测量的值，包括一个世界坐标系下三维点与一个灰度值
// 一次测量的值，包括一个三维点坐标和这个点对应到灰度图上的灰度值，这里注意仅是一个点，不是一张图
//测量空间点坐标pos_world和此点在图像上的灰度值grayscale
struct Measurement
{
Measurement ( Eigen::Vector3d p, float g ) : pos_world ( p ), grayscale ( g ) {}
Eigen::Vector3d pos_world;
float grayscale;
};
inline Eigen::Vector3d project2Dto3D ( int x, int y, int d, float fx, float fy, float cx, float cy, float scale )
{
float zz = float ( d ) /scale;
float xx = zz* ( x-cx ) /fx;
float yy = zz* ( y-cy ) /fy;
return Eigen::Vector3d ( xx, yy, zz );
}
inline Eigen::Vector2d project3Dto2D ( float x, float y, float z, float fx, float fy, float cx, float cy )
{
float u = fx*x/z+cx;
float v = fy*y/z+cy;
return Eigen::Vector2d ( u,v );
}
// 直接法估计位姿
// 输入：测量值（空间点的灰度），新的灰度图，相机内参； 输出：相机位姿
// 返回：true为成功，false失败
bool poseEstimationDirect ( const vector<Measurement>& measurements, cv::Mat* gray, Eigen::Matrix3f& intrinsics, Eigen::Isometry3d& Tcw );
// project a 3d point into an image plane, the error is photometric error
//将一个3d点投影到一个图像平面，误差是光度误差
// an unary edge with one vertex SE3Expmap (the pose of camera)
class EdgeSE3ProjectDirect: public BaseUnaryEdge< 1, double, VertexSE3Expmap>
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
EdgeSE3ProjectDirect() {}
EdgeSE3ProjectDirect ( Eigen::Vector3d point, float fx, float fy, float cx, float cy, cv::Mat* image )
: x_world_ ( point ), fx_ ( fx ), fy_ ( fy ), cx_ ( cx ), cy_ ( cy ), image_ ( image )
{}
virtual void computeError()
{
const VertexSE3Expmap* v =static_cast<const VertexSE3Expmap*> ( _vertices[0] );
//从世界坐标系下坐标到像素坐标：
//位姿估计值.map()函数即为乘上位姿T,这里其实为3d点世界坐标乘上相机位姿，计算出当前相机坐标系下的坐标
Eigen::Vector3d x_local = v->estimate().map ( x_world_ );
//3d坐标投影到像素坐标
float x = x_local[0]*fx_/x_local[2] + cx_;
float y = x_local[1]*fy_/x_local[2] + cy_;
// check x,y is in the image
//检查像素是否还在图像中，这里靠近边缘有4像素时就认为已经出了图像，将误差设置为0,此条边的Level设置为１，用于区分
if ( x-4<0 || ( x+4 ) >image_->cols || ( y-4 ) <0 || ( y+4 ) >image_->rows )
{
_error ( 0,0 ) = 0.0;
this->setLevel ( 1 );
}
else
{//这里误差为标量(光度值的差值)，用估计出来的(u.v)处灰度值，减去测量值。
//这里的getPixelValue(u,v)相当于I(u,v)
_error ( 0,0 ) = getPixelValue ( x,y ) - _measurement;
}
}
// plus in manifold//计算线性增量，也就是雅克比矩阵J
virtual void linearizeOplus( )
{
if ( level() == 1 )
{
_jacobianOplusXi = Eigen::Matrix<double, 1, 6>::Zero();
return;
}
VertexSE3Expmap* vtx = static_cast<VertexSE3Expmap*> ( _vertices[0] );
Eigen::Vector3d xyz_trans = vtx->estimate().map ( x_world_ ); // q in book
double x = xyz_trans[0];
double y = xyz_trans[1];
double invz = 1.0/xyz_trans[2];
double invz_2 = invz*invz;
float u = x*fx_*invz + cx_;
float v = y*fy_*invz + cy_;
// jacobian from se3 to u,v
// NOTE that in g2o the Lie algebra is (\omega, \epsilon), where \omega is so(3) and \epsilon the translation
Eigen::Matrix<double, 2, 6> jacobian_uv_ksai;
jacobian_uv_ksai ( 0,0 ) = - x*y*invz_2 *fx_;
jacobian_uv_ksai ( 0,1 ) = ( 1+ ( x*x*invz_2 ) ) *fx_;
jacobian_uv_ksai ( 0,2 ) = - y*invz *fx_;
jacobian_uv_ksai ( 0,3 ) = invz *fx_;
jacobian_uv_ksai ( 0,4 ) = 0;
jacobian_uv_ksai ( 0,5 ) = -x*invz_2 *fx_;
jacobian_uv_ksai ( 1,0 ) = - ( 1+y*y*invz_2 ) *fy_;
jacobian_uv_ksai ( 1,1 ) = x*y*invz_2 *fy_;
jacobian_uv_ksai ( 1,2 ) = x*invz *fy_;
jacobian_uv_ksai ( 1,3 ) = 0;
jacobian_uv_ksai ( 1,4 ) = invz *fy_;
jacobian_uv_ksai ( 1,5 ) = -y*invz_2 *fy_;
Eigen::Matrix<double, 1, 2> jacobian_pixel_uv;
jacobian_pixel_uv ( 0,0 ) = ( getPixelValue ( u+1,v )-getPixelValue ( u-1,v ) ) /2;
jacobian_pixel_uv ( 0,1 ) = ( getPixelValue ( u,v+1 )-getPixelValue ( u,v-1 ) ) /2;
_jacobianOplusXi = jacobian_pixel_uv*jacobian_uv_ksai;
}
// dummy read and write functions because we don't care...
virtual bool read ( std::istream& in ) {}
virtual bool write ( std::ostream& out ) const {}
protected:
// get a gray scale value from reference image (bilinear interpolated)
inline float getPixelValue ( float x, float y )
//取得变换后的图中对应像素坐标处的灰度值，这里并不是返回一张图像的灰度值，而是就是写死了，就是类构造里传入的那张图
{
//这里先说一下各个参数的类型：
//image_为Mat*类型，图像指针，所以调用data时用->符号，
//data为图像矩阵首地址，支持数组形式访问，data[]就是访问到像素的值了，此处为像素的灰度值，类型为uchar
//关于step有点复杂，data[]中括号的式子有点复杂，总的意思就是y行乘上每行内存数，定位到行，然后在加上x，定位到像素
//step具体解释在最后面有一些资料
//image_->data[int(y)*image_->step + int(x)]这一步读到了x,y处的灰度值，类型为uchar，
//但是后面由于线性插值，需要定位这个像素的位置，而不是他的灰度值，所以取其地址，赋值给data_ptr，记住它的位置，后面使用
uchar* data = & image_->data[ int ( y ) * image_->step + int ( x ) ];
//由于x,y这里有可能带小数，但是像素位置肯定是整数，所以，问题来了，(1.2, 4.5)像素坐标处的灰度值为多少呢?OK,线性插值！
//说一下floor(),std中的cmath函数。向下取整,返回不大于x的整数。例floor(4.9)=4
//xx和yy，就是取到小数部分。例：x=4.9的话，xx=x-floor(x)就为0.9。y同理
float xx = x - floor ( x );
float yy = y - floor ( y );
return float (
( 1-xx ) * ( 1-yy ) * data[0] +
xx* ( 1-yy ) * data[1] +
( 1-xx ) *yy*data[ image_->step ] +
xx*yy*data[image_->step+1]
);
}
//这里说一下自定义边类型时的成员变量怎么来，不是随便写，而是误差需要哪些变量算出来，就定义哪些。
//这里需要世界坐标系下的空间点坐标，相机内参，和第二帧图
//这里说一下这个第二帧图：空间点经RT,经内参投影到第二帧图(image_)上，在这个image_上找像素的灰度值，这个灰度值是估计值
//而测量值在前一帧上，也就是上面的_measurement，在main()函数中直接赋值给到。
public:
Eigen::Vector3d x_world_; // 3D point in world frame
float cx_=0, cy_=0, fx_=0, fy_=0; // Camera intrinsics
cv::Mat* image_=nullptr; // reference image
};
int main ( int argc, char** argv )
{
if ( argc != 2 )
{
cout<<"usage: useLK path_to_dataset"<<endl;
return 1;
}
srand ( ( unsigned int ) time ( 0 ) );
string path_to_dataset = argv[1];
string associate_file = path_to_dataset + "/associate.txt";
ifstream fin ( associate_file );
string rgb_file, depth_file, time_rgb, time_depth;
cv::Mat color, depth, gray;
vector<Measurement> measurements;
// 相机内参
float cx = 325.5;
float cy = 253.5;
float fx = 518.0;
float fy = 519.0;
float depth_scale = 1000.0;
Eigen::Matrix3f K;
K<<fx,0.f,cx,0.f,fy,cy,0.f,0.f,1.0f;
Eigen::Isometry3d Tcw = Eigen::Isometry3d::Identity();
cv::Mat prev_color;
// 我们以第一个图像为参考，对后续图像和参考图像做直接法
//跟稀疏的相比就是在循环中的第一帧取一些点的时候，又稀疏的特征点取成了有明显梯度的点，
//就是增加了一些点。同样，点的增加就会往g2o中增加一些边，在后面绘制对比图时，也没有了线，而只有点，因为太多了，画线看不清了。
for ( int index=0; index<10; index++ )
{
cout<<"*********** loop "<<index<<" ************"<<endl;
fin>>time_rgb>>rgb_file>>time_depth>>depth_file;
color = cv::imread ( path_to_dataset+"/"+rgb_file );
depth = cv::imread ( path_to_dataset+"/"+depth_file, -1 );
if ( color.data==nullptr || depth.data==nullptr )
continue;
cv::cvtColor ( color, gray, cv::COLOR_BGR2GRAY );
if ( index ==0 )
{
// select the pixels with high gradiants
for ( int x=10; x<gray.cols-10; x++ )//双层循环遍历像素点,边缘的就不要了
for ( int y=10; y<gray.rows-10; y++ )
{//这里就是梯度向量delta，可以看一下，
//它是以(x,y)像素右减左像素灰度差为x方向梯度值，
// 以(x,y)像素下减上像素灰度差为y方向梯度值。
//发现(x,y)处的像素梯度跟(x,y)处的灰度值没啥关系，只跟它的上下左右的像素有关
Eigen::Vector2d delta (
gray.ptr<uchar>(y)[x+1] - gray.ptr<uchar>(y)[x-1],
gray.ptr<uchar>(y+1)[x] - gray.ptr<uchar>(y-1)[x]
);
if ( delta.norm() < 50 )//如果模长小于50,即任务就是梯度不明显，continue掉，其他的就开始对应深度和空间点，往measurements中push了
//说白了跟稀疏的比就是在第一帧中多取了一些点而已。稠密的就是不用说了，所有点全push进measurements
continue;
ushort d = depth.ptr<ushort> (y)[x];
if ( d==0 )
continue;
Eigen::Vector3d p3d = project2Dto3D ( x, y, d, fx, fy, cx, cy, depth_scale );
float grayscale = float ( gray.ptr<uchar> (y) [x] );
measurements.push_back ( Measurement ( p3d, grayscale ) );
}
prev_color = color.clone();
cout<<"add total "<<measurements.size()<<" measurements."<<endl;
continue;
}
// 使用直接法计算相机运动
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
poseEstimationDirect ( measurements, &gray, K, Tcw );
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> ( t2-t1 );
cout<<"direct method costs time: "<<time_used.count() <<" seconds."<<endl;
cout<<"Tcw="<<Tcw.matrix() <<endl;
// plot the feature points
cv::Mat img_show ( color.rows*2, color.cols, CV_8UC3 );
prev_color.copyTo ( img_show ( cv::Rect ( 0,0,color.cols, color.rows ) ) );
color.copyTo ( img_show ( cv::Rect ( 0,color.rows,color.cols, color.rows ) ) );
for ( Measurement m:measurements )
{
if ( rand() > RAND_MAX/5 )
continue;
Eigen::Vector3d p = m.pos_world;
Eigen::Vector2d pixel_prev = project3Dto2D ( p ( 0,0 ), p ( 1,0 ), p ( 2,0 ), fx, fy, cx, cy );
Eigen::Vector3d p2 = Tcw*m.pos_world;
Eigen::Vector2d pixel_now = project3Dto2D ( p2 ( 0,0 ), p2 ( 1,0 ), p2 ( 2,0 ), fx, fy, cx, cy );
if ( pixel_now(0,0)<0 || pixel_now(0,0)>=color.cols || pixel_now(1,0)<0 || pixel_now(1,0)>=color.rows )
continue;
float b = 0;
float g = 250;
float r = 0;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3] = b;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3+1] = g;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3+2] = r;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3] = b;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3+1] = g;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3+2] = r;
cv::circle ( img_show, cv::Point2d ( pixel_prev ( 0,0 ), pixel_prev ( 1,0 ) ), 4, cv::Scalar ( b,g,r ), 2 );
cv::circle ( img_show, cv::Point2d ( pixel_now ( 0,0 ), pixel_now ( 1,0 ) +color.rows ), 4, cv::Scalar ( b,g,r ), 2 );
}
cv::imshow ( "result", img_show );
cv::waitKey ( 0 );
}
return 0;
}
bool poseEstimationDirect ( const vector< Measurement >& measurements, cv::Mat* gray, Eigen::Matrix3f& K, Eigen::Isometry3d& Tcw )
{
// 初始化g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6,1>> DirectBlock; // 求解的向量是6＊1的
DirectBlock::LinearSolverType* linearSolver = new g2o::LinearSolverDense< DirectBlock::PoseMatrixType > ();
DirectBlock* solver_ptr = new DirectBlock ( linearSolver );
// g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton( solver_ptr ); // G-N
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr ); // L-M
g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm ( solver );
optimizer.setVerbose( true );
g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap();
pose->setEstimate ( g2o::SE3Quat ( Tcw.rotation(), Tcw.translation() ) );
pose->setId ( 0 );
optimizer.addVertex ( pose );
// 添加边
int id=1;
for ( Measurement m: measurements )
{
EdgeSE3ProjectDirect* edge = new EdgeSE3ProjectDirect (
m.pos_world,
K ( 0,0 ), K ( 1,1 ), K ( 0,2 ), K ( 1,2 ), gray
);
edge->setVertex ( 0, pose );
edge->setMeasurement ( m.grayscale );
edge->setInformation ( Eigen::Matrix<double,1,1>::Identity() );
edge->setId ( id++ );
optimizer.addEdge ( edge );
}
cout<<"edges in graph: "<<optimizer.edges().size() <<endl;
optimizer.initializeOptimization();
optimizer.optimize ( 30 );
Tcw = pose->estimate();
}