C++ Archives - Robotics with ROS

Finding Memory leaking, Stack and Heap overflow

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When you access an array index, C and C++ don’t do bound checking. Segmentation faults only happen when you try to read or write to a page that was not allocated (or try to do something on a page which isn’t permitted, e.g. trying to write to a read-only page), but since pages are usually […]

Finding Memory leaking, Stack and Heap overflow Read More »

Kalman Filter Explained With Python Code From Scratch

23 Comments / C++, Machine Learning, Python, Robotic, Tutorials / admin

This snippet shows tracking mouse cursor with Python code from scratch and comparing the result with OpenCV. The CSV file that has been used are being created with below c++ code. A sample could be downloaded from here 1, 2, 3. Python Kalman Filter

import numpy as np
np.set_printoptions(threshold=3)
np.set_printoptions(suppress=True)
from numpy import genfromtxt


#Notation used coming from: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/


def prediction(X_hat_t_1,P_t_1,F_t,B_t,U_t,Q_t):
    X_hat_t=F_t.dot(X_hat_t_1)+(B_t.dot(U_t).reshape(B_t.shape[0],-1) )
    P_t=np.diag(np.diag(F_t.dot(P_t_1).dot(F_t.transpose())))+Q_t
    return X_hat_t,P_t
    

def update(X_hat_t,P_t,Z_t,R_t,H_t):
    
    K_prime=P_t.dot(H_t.transpose()).dot( np.linalg.inv ( H_t.dot(P_t).dot(H_t.transpose()) +R_t ) )  
    print("K:\n",K_prime)
    
    X_t=X_hat_t+K_prime.dot(Z_t-H_t.dot(X_hat_t))
    P_t=P_t-K_prime.dot(H_t).dot(P_t)
    
    return X_t,P_t


acceleration=0
delta_t=1/20#milisecond

groundTruth = genfromtxt('data/groundTruth.csv', delimiter=',',skip_header=1)

#Observations: position_X, position_Y
measurmens = genfromtxt('data/measurmens.csv', delimiter=',',skip_header=1)


#Checking our result with OpenCV
opencvKalmanOutput = genfromtxt('data/kalmanv.csv', delimiter=',',skip_header=1)

#Transition matrix
F_t=np.array([ [1 ,0,delta_t,0] , [0,1,0,delta_t] , [0,0,1,0] , [0,0,0,1] ])

#Initial State cov
P_t= np.identity(4)*0.2

#Process cov
Q_t= np.identity(4)

#Control matrix
B_t=np.array( [ [0] , [0], [0] , [0] ])

#Control vector
U_t=acceleration

#Measurment Matrix
H_t = np.array([ [1, 0, 0, 0], [ 0, 1, 0, 0]])

#Measurment cov
R_t= np.identity(2)*5

# Initial State
X_hat_t = np.array( [[0],[0],[0],[0]] )
print("X_hat_t",X_hat_t.shape)
print("P_t",P_t.shape)
print("F_t",F_t.shape)
print("B_t",B_t.shape)
print("Q_t",Q_t.shape)
print("R_t",R_t.shape)
print("H_t",H_t.shape)

for i in range(measurmens.shape[0]):
    X_hat_t,P_hat_t = prediction(X_hat_t,P_t,F_t,B_t,U_t,Q_t)
    print("Prediction:")
    print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_t)
    
    Z_t=measurmens[i].transpose()
    Z_t=Z_t.reshape(Z_t.shape[0],-1)
    
    print(Z_t.shape)
    
    X_t,P_t=update(X_hat_t,P_hat_t,Z_t,R_t,H_t)
    print("Update:")
    print("X_t:\n",X_t,"\nP_t:\n",P_t)
    X_hat_t=X_t
    P_hat_t=P_t
    
    print("=========================================")
    print("Opencv Kalman Output:")
    print("X_t:\n",opencvKalmanOutput[i])

import numpy as np

np.set_printoptions(threshold=3)

np.set_printoptions(suppress=True)

from numpy import genfromtxt

#Notation used coming from: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

def prediction(X_hat_t_1,P_t_1,F_t,B_t,U_t,Q_t):

X_hat_t=F_t.dot(X_hat_t_1)+(B_t.dot(U_t).reshape(B_t.shape[0],-1) )

P_t=np.diag(np.diag(F_t.dot(P_t_1).dot(F_t.transpose())))+Q_t

return X_hat_t,P_t

def update(X_hat_t,P_t,Z_t,R_t,H_t):

K_prime=P_t.dot(H_t.transpose()).dot( np.linalg.inv ( H_t.dot(P_t).dot(H_t.transpose()) +R_t ) )

print("K:\n",K_prime)

X_t=X_hat_t+K_prime.dot(Z_t-H_t.dot(X_hat_t))

P_t=P_t-K_prime.dot(H_t).dot(P_t)

return X_t,P_t

acceleration=0

delta_t=1/20#milisecond

groundTruth = genfromtxt('data/groundTruth.csv', delimiter=',',skip_header=1)

#Observations: position_X, position_Y

measurmens = genfromtxt('data/measurmens.csv', delimiter=',',skip_header=1)

#Checking our result with OpenCV

opencvKalmanOutput = genfromtxt('data/kalmanv.csv', delimiter=',',skip_header=1)

#Transition matrix

F_t=np.array([ [1 ,0,delta_t,0] , [0,1,0,delta_t] , [0,0,1,0] , [0,0,0,1] ])

#Initial State cov

P_t= np.identity(4)*0.2

#Process cov

Q_t= np.identity(4)

#Control matrix

B_t=np.array( [ [0] , [0], [0] , [0] ])

#Control vector

U_t=acceleration

#Measurment Matrix

H_t = np.array([ [1, 0, 0, 0], [ 0, 1, 0, 0]])

#Measurment cov

R_t= np.identity(2)*5

# Initial State

X_hat_t = np.array( [[0],[0],[0],[0]] )

print("X_hat_t",X_hat_t.shape)

print("P_t",P_t.shape)

print("F_t",F_t.shape)

print("B_t",B_t.shape)

print("Q_t",Q_t.shape)

print("R_t",R_t.shape)

print("H_t",H_t.shape)

for i in range(measurmens.shape[0]):

X_hat_t,P_hat_t = prediction(X_hat_t,P_t,F_t,B_t,U_t,Q_t)

print("Prediction:")

print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_t)

Z_t=measurmens[i].transpose()

Z_t=Z_t.reshape(Z_t.shape[0],-1)

print(Z_t.shape)

X_t,P_t=update(X_hat_t,P_hat_t,Z_t,R_t,H_t)

print("Update:")

print("X_t:\n",X_t,"\nP_t:\n",P_t)

X_hat_t=X_t

P_hat_t=P_t

print("=========================================")

print("Opencv Kalman Output:")

print("X_t:\n",opencvKalmanOutput[i])

C++ and OpenCV Kalman Filter Rapidcsv has been downloaded from

Kalman Filter Explained With Python Code From Scratch Read More »

Eigen Memory Mapping

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struct point {
    double a;
    double b;
};

void EigenMapExample()
{
    ////////////////////////////////////////First Example/////////////////////////////////////////
    Eigen::VectorXd solutionVec(12,1);
    solutionVec<<1,2,3,4,5,6,7,8,9,10,11,12;
    Eigen::Map<Eigen::MatrixXd> solutionColMajor(solutionVec.data(),4,3);

    Eigen::Map<Eigen::Matrix<double, 3, 4, Eigen::RowMajor> >solutionRowMajor (solutionVec.data());


    std::cout << "solutionColMajor: "<< std::endl;
    std::cout << solutionColMajor<< std::endl;

    std::cout << "solutionRowMajor"<< std::endl;
    std::cout << solutionRowMajor<< std::endl;

    ////////////////////////////////////////Second Example/////////////////////////////////////////

    // https://stackoverflow.com/questions/49813340/stdvectoreigenvector3d-to-eigenmatrixxd-eigen

    int array[9];
    for (int i = 0; i < 9; ++i) {
        array[i] = i;
    }

    Eigen::MatrixXi a(9, 1);
    a = Eigen::Map<Eigen::Matrix3i>(array);
    std::cout << a << std::endl;

    std::vector<point> pointsVec;
    point point1, point2, point3;

    point1.a = 1.0;
    point1.b = 1.5;

    point2.a = 2.4;
    point2.b = 3.5;

    point3.a = -1.3;
    point3.b = 2.4;

    pointsVec.push_back(point1);
    pointsVec.push_back(point2);
    pointsVec.push_back(point3);

    Eigen::Matrix2Xd pointsMatrix2d = Eigen::Map<Eigen::Matrix2Xd>(
        reinterpret_cast<double*>(pointsVec.data()), 2,  long(pointsVec.size()));

    Eigen::MatrixXd pointsMatrixXd = Eigen::Map<Eigen::MatrixXd>(
        reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

    std::cout << pointsMatrix2d << std::endl;
    std::cout << "==============================" << std::endl;
    std::cout << pointsMatrixXd << std::endl;
    std::cout << "==============================" << std::endl;

    std::vector<Eigen::Vector3d> eigenPointsVec;
    eigenPointsVec.push_back(Eigen::Vector3d(2, 4, 1));
    eigenPointsVec.push_back(Eigen::Vector3d(7, 3, 9));
    eigenPointsVec.push_back(Eigen::Vector3d(6, 1, -1));
    eigenPointsVec.push_back(Eigen::Vector3d(-6, 9, 8));

    Eigen::MatrixXd pointsMatrix = Eigen::Map<Eigen::MatrixXd>(eigenPointsVec[0].data(), 3, long(eigenPointsVec.size()));

    std::cout << pointsMatrix << std::endl;
    std::cout << "==============================" << std::endl;

    pointsMatrix = Eigen::Map<Eigen::MatrixXd>(reinterpret_cast<double*>(eigenPointsVec.data()), 3, long(eigenPointsVec.size()));

    std::cout << pointsMatrix << std::endl;

    std::vector<double> aa = { 1, 2, 3, 4 };
    Eigen::VectorXd b = Eigen::Map<Eigen::VectorXd, Eigen::Unaligned>(aa.data(), long(aa.size()));
}

struct point {

double a;

double b;

};

void EigenMapExample()

{

////////////////////////////////////////First Example/////////////////////////////////////////

Eigen::VectorXd solutionVec(12,1);

solutionVec<<1,2,3,4,5,6,7,8,9,10,11,12;

Eigen::Map<Eigen::MatrixXd> solutionColMajor(solutionVec.data(),4,3);

Eigen::Map<Eigen::Matrix<double, 3, 4, Eigen::RowMajor> >solutionRowMajor (solutionVec.data());

std::cout << "solutionColMajor: "<< std::endl;

std::cout << solutionColMajor<< std::endl;

std::cout << "solutionRowMajor"<< std::endl;

std::cout << solutionRowMajor<< std::endl;

////////////////////////////////////////Second Example/////////////////////////////////////////

// https://stackoverflow.com/questions/49813340/stdvectoreigenvector3d-to-eigenmatrixxd-eigen

int array[9];

for (int i = 0; i < 9; ++i) {

array[i] = i;

}

Eigen::MatrixXi a(9, 1);

a = Eigen::Map<Eigen::Matrix3i>(array);

std::cout << a << std::endl;

std::vector<point> pointsVec;

point point1, point2, point3;

point1.a = 1.0;

point1.b = 1.5;

point2.a = 2.4;

point2.b = 3.5;

point3.a = -1.3;

point3.b = 2.4;

pointsVec.push_back(point1);

pointsVec.push_back(point2);

pointsVec.push_back(point3);

Eigen::Matrix2Xd pointsMatrix2d = Eigen::Map<Eigen::Matrix2Xd>(

reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

Eigen::MatrixXd pointsMatrixXd = Eigen::Map<Eigen::MatrixXd>(

reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

std::cout << pointsMatrix2d << std::endl;

std::cout << "==============================" << std::endl;

std::cout << pointsMatrixXd << std::endl;

std::cout << "==============================" << std::endl;

std::vector<Eigen::Vector3d> eigenPointsVec;

eigenPointsVec.push_back(Eigen::Vector3d(2, 4, 1));

eigenPointsVec.push_back(Eigen::Vector3d(7, 3, 9));

eigenPointsVec.push_back(Eigen::Vector3d(6, 1, -1));

eigenPointsVec.push_back(Eigen::Vector3d(-6, 9, 8));

Eigen::MatrixXd pointsMatrix = Eigen::Map<Eigen::MatrixXd>(eigenPointsVec[0].data(), 3, long(eigenPointsVec.size()));

std::cout << pointsMatrix << std::endl;

std::cout << "==============================" << std::endl;

pointsMatrix = Eigen::Map<Eigen::MatrixXd>(reinterpret_cast<double*>(eigenPointsVec.data()), 3, long(eigenPointsVec.size()));

std::cout << pointsMatrix << std::endl;

std::vector<double> aa = { 1, 2, 3, 4 };

Eigen::VectorXd b = Eigen::Map<Eigen::VectorXd, Eigen::Unaligned>(aa.data(), long(aa.size()));

}

Eigen Memory Mapping Read More »

Decomposing Projection Using OpenCV and C++

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#include <opencv/cv.hpp>
#include <opencv2/core.hpp>
#include <opencv2/calib3d.hpp>
#include "transformation.hpp"

//https://www.cnblogs.com/shengguang/p/5932522.html
void HouseHolderQR(const cv::Mat &A, cv::Mat &Q, cv::Mat &R)
{
    assert ( A.channels() == 1 );
    assert ( A.rows >= A.cols );
    auto sign = [](double value) { return value >= 0 ? 1: -1; };
    const auto totalRows = A.rows;
    const auto totalCols = A.cols;
    R = A.clone();
    Q = cv::Mat::eye ( totalRows, totalRows, A.type() );
    for ( int col = 0; col < A.cols; ++ col )
    {
        cv::Mat matAROI = cv::Mat ( R, cv::Range ( col, totalRows ), cv::Range ( col, totalCols ) );
        cv::Mat y = matAROI.col ( 0 );
        auto yNorm = norm ( y );
        cv::Mat e1 = cv::Mat::eye ( y.rows, 1, A.type() );
        cv::Mat w = y + sign(y.at<double>(0,0)) *  yNorm * e1;
        cv::Mat v = w / norm( w );
        cv::Mat vT; cv::transpose(v, vT );
        cv::Mat I = cv::Mat::eye( matAROI.rows, matAROI.rows, A.type() );
        cv::Mat I_2VVT = I - 2 * v * vT;
        cv::Mat matH = cv::Mat::eye ( totalRows, totalRows, A.type() );
        cv::Mat matHROI = cv::Mat(matH, cv::Range ( col, totalRows ), cv::Range ( col, totalRows ) );
        I_2VVT.copyTo ( matHROI );
        R = matH * R;
        Q = Q * matH;
    }
}

int main()
{


/*
Thera are two notations for Projection Matrix

1)P=K[R|t]
                                                              ┌X┐
                                                      ┌R,t┐   |Y|
    X_Cam=R*X+t  ==> Homogeneous Coordinate ==> X_Cam=|0,1|   |Z|
                                                      └   ┘4x4└1┘4x1

                                         ┌X┐
                                ┌   ┐    |Y|
    image plane  <--x=K[I|0]3x4 |R,t|    |Z|
                                └0,1┘4x4 └1┘4x1

    P=K[R|t]

2)P=KR[I|-X0]
                                                              ┌X┐
                                                       ┌R -Rc┐|Y|
    X_Cam=R*[X_w-C]  ==> Homogeneous Coordinate ==>    |     ||Z|
                                                       └0  1 ┘└1┘
                                       ┌X┐
                                ┌R -Rc┐|Y|
    image plane  <--x=K[I|0]3x4 |0  1 ||Z|
                                └     ┘└1┘
    P=K[R|-RC]= KR[I|-C]


(1) and (2) ==> t=-RC

*/
    int numberOfPixelInHeight,numberOfPixelInWidth;
    double heightOfSensor, widthOfSensor;
    double focalLength=1.50;
    double mx, my, U0, V0;
    numberOfPixelInHeight=600;
    numberOfPixelInWidth=800;

    heightOfSensor=10;
    widthOfSensor=10;


    my=(numberOfPixelInHeight)/heightOfSensor ;
    U0=(numberOfPixelInHeight)/2 ;

    mx=(numberOfPixelInWidth)/widthOfSensor;
    V0=(numberOfPixelInWidth)/2;

    cv::Mat cameraMatrix = (cv::Mat_<double>(3,3) <<
    focalLength*mx, 0, V0,
    0,focalLength*my,U0,
    0,0,1);

    double tx,ty,tz,roll,pitch,yaw;

    tx=1.0;
    ty=2.1;
    tz=-1.4;

    cv::Mat translation = (cv::Mat_<double>(3,1) <<tx,ty,tz);

    cv::Vec3d theta;

    roll=+M_PI/4 ;
    pitch=+M_PI/10;
    yaw=-+M_PI/6;

    theta[0]=roll;
    theta[1]=pitch;
    theta[2]=yaw;
    cv::Mat rotation=eulerAnglesToRotationMatrix(theta);
    //1)P=K[R|t]
    cv::Mat R_T;
    cv::hconcat(rotation, translation, R_T);
    cv::Mat projectionMatrix=cameraMatrix*R_T;



    cv::Mat calculatedCameraMatrix,calculatedRotation,calculatedTranslation;

    cv::decomposeProjectionMatrix(projectionMatrix,calculatedCameraMatrix,calculatedRotation,calculatedTranslation);


    std::cout<<"==============================Ground Truth==============================" <<std::endl;

    std::cout<<"Rotation Matrix (Ground Truth)" <<std::endl;
    std::cout<<rotation <<std::endl;

    std::cout<<"Translation Matrix (Ground Truth)" <<std::endl;
    std::cout<<translation <<std::endl;

    std::cout<<"Camera Matrix (Ground Truth)" <<std::endl;
    std::cout<<cameraMatrix <<std::endl;

    std::cout<<"==============================Decomposing Using OpenCV==============================" <<std::endl;


    std::cout<<"Computed Rotation Matrix (OpenCV)" <<std::endl;
    std::cout<<calculatedRotation <<std::endl;

    std::cout<<"Computed Translation Matrix (OpenCV)" <<std::endl;
    //std::cout<<calculatedTranslation/calculatedTranslation.at<double>(3,0) <<std::endl;
    cv::Mat tempT=(cv::Mat_<double>(3,1)<<
                   calculatedTranslation.at<double>(0,0)/calculatedTranslation.at<double>(3,0),
                   calculatedTranslation.at<double>(1,0)/calculatedTranslation.at<double>(3,0),
                   calculatedTranslation.at<double>(2,0)/calculatedTranslation.at<double>(3,0));
    std::cout<<-rotation*tempT<<std::endl;

    std::cout<<"Computed Camera Matrix (OpenCV)" <<std::endl;
    std::cout<<calculatedCameraMatrix <<std::endl;

///////////////////////////////////////decomposing the projection matrix///////////////////////////////////////////

/*

    P=KR[I|-X0]=[H_inf3x3|h3x1]
    KR=H_inf3x3
    1)X0
    -KRX0=h3x1 => X0=-(KR)^-1*h3x1 ==>X0=-(H_inf3x3)^-1*h3x1
    2)K,R

    KR=H_inf3x3 =>(KR)^-1= H_inf3x3^-1 =>R^-1*K^-1=H_inf3x3^-1 | R^-1*K^-1=Q*R => R=Q^-1, K=R^-1
                                         H_inf3x3^-1=Q*R       |

*/


    cv::Mat H_inf3x3=(cv::Mat_<double>(3,3)<< projectionMatrix.at<double>(0,0),projectionMatrix.at<double>(0,1),projectionMatrix.at<double>(0,2)
                      ,projectionMatrix.at<double>(1,0),projectionMatrix.at<double>(1,1),projectionMatrix.at<double>(1,2)
                      ,projectionMatrix.at<double>(2,0),projectionMatrix.at<double>(2,1),projectionMatrix.at<double>(2,2));

    cv::Mat h3x1=(cv::Mat_<double>(3,1)<<projectionMatrix.at<double>(0,3),projectionMatrix.at<double>(1,3),projectionMatrix.at<double>(2,3) );



    cv::Mat Q,R;
    cv::Mat H_inf3x3_inv=H_inf3x3.inv();
    //R=Q^-1, K=R^-1


    HouseHolderQR(H_inf3x3_inv, Q, R);
    cv::Mat K=R.inv();

    std::cout<<"==============================Decomposing Using My Code==============================" <<std::endl;
    //due to homogeneity we divide it by last element
    std::cout<<"Estimated Camera Matrix\n"<<K/K.at<double>(2,2) <<std::endl;

    cv::Mat rotationMatrix=Q.inv();
    std::cout<<"Estimated Camera Rotation\n"<<rotationMatrix*-1 <<std::endl;

    std::cout<<"Estimated Camera Translation" <<std::endl;

    //t=-R*C, Q.inv()=R
    std::cout<<-1*(-Q.inv()*(-H_inf3x3.inv()*h3x1 ))<<std::endl;

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#include <opencv/cv.hpp>

#include <opencv2/core.hpp>

#include <opencv2/calib3d.hpp>

#include "transformation.hpp"

//https://www.cnblogs.com/shengguang/p/5932522.html

void HouseHolderQR(const cv::Mat &A, cv::Mat &Q, cv::Mat &R)

{

assert ( A.channels() == 1 );

assert ( A.rows >= A.cols );

auto sign = [](double value) { return value >= 0 ? 1: -1; };

const auto totalRows = A.rows;

const auto totalCols = A.cols;

R = A.clone();

Q = cv::Mat::eye ( totalRows, totalRows, A.type() );

for ( int col = 0; col < A.cols; ++ col )

{

cv::Mat matAROI = cv::Mat ( R, cv::Range ( col, totalRows ), cv::Range ( col, totalCols ) );

cv::Mat y = matAROI.col ( 0 );

auto yNorm = norm ( y );

cv::Mat e1 = cv::Mat::eye ( y.rows, 1, A.type() );

cv::Mat w = y + sign(y.at<double>(0,0)) * yNorm * e1;

cv::Mat v = w / norm( w );

cv::Mat vT; cv::transpose(v, vT );

cv::Mat I = cv::Mat::eye( matAROI.rows, matAROI.rows, A.type() );

cv::Mat I_2VVT = I - 2 * v * vT;

cv::Mat matH = cv::Mat::eye ( totalRows, totalRows, A.type() );

cv::Mat matHROI = cv::Mat(matH, cv::Range ( col, totalRows ), cv::Range ( col, totalRows ) );

I_2VVT.copyTo ( matHROI );

R = matH * R;

Q = Q * matH;

}

int main()

{

Thera are two notations for Projection Matrix

1)P=K[R|t]

┌X┐

┌R,t┐ |Y|

X_Cam=R*X+t ==> Homogeneous Coordinate ==> X_Cam=|0,1| |Z|

└ ┘4x4└1┘4x1

┌X┐

┌ ┐ |Y|

image plane <--x=K[I|0]3x4 |R,t| |Z|

└0,1┘4x4 └1┘4x1

P=K[R|t]

2)P=KR[I|-X0]

┌X┐

┌R -Rc┐|Y|

X_Cam=R*[X_w-C] ==> Homogeneous Coordinate ==> | ||Z|

└0 1 ┘└1┘

┌X┐

┌R -Rc┐|Y|

image plane <--x=K[I|0]3x4 |0 1 ||Z|

└ ┘└1┘

P=K[R|-RC]= KR[I|-C]

(1) and (2) ==> t=-RC

int numberOfPixelInHeight,numberOfPixelInWidth;

double heightOfSensor, widthOfSensor;

double focalLength=1.50;

double mx, my, U0, V0;

numberOfPixelInHeight=600;

numberOfPixelInWidth=800;

heightOfSensor=10;

widthOfSensor=10;

my=(numberOfPixelInHeight)/heightOfSensor ;

U0=(numberOfPixelInHeight)/2 ;

mx=(numberOfPixelInWidth)/widthOfSensor;

V0=(numberOfPixelInWidth)/2;

cv::Mat cameraMatrix = (cv::Mat_<double>(3,3) <<

focalLength*mx, 0, V0,

0,focalLength*my,U0,

0,0,1);

double tx,ty,tz,roll,pitch,yaw;

tx=1.0;

ty=2.1;

tz=-1.4;

cv::Mat translation = (cv::Mat_<double>(3,1) <<tx,ty,tz);

cv::Vec3d theta;

roll=+M_PI/4 ;

pitch=+M_PI/10;

yaw=-+M_PI/6;

theta[0]=roll;

theta[1]=pitch;

theta[2]=yaw;

cv::Mat rotation=eulerAnglesToRotationMatrix(theta);

//1)P=K[R|t]

cv::Mat R_T;

cv::hconcat(rotation, translation, R_T);

cv::Mat projectionMatrix=cameraMatrix*R_T;

cv::Mat calculatedCameraMatrix,calculatedRotation,calculatedTranslation;

cv::decomposeProjectionMatrix(projectionMatrix,calculatedCameraMatrix,calculatedRotation,calculatedTranslation);

std::cout<<"==============================Ground Truth==============================" <<std::endl;

std::cout<<"Rotation Matrix (Ground Truth)" <<std::endl;

std::cout<<rotation <<std::endl;

std::cout<<"Translation Matrix (Ground Truth)" <<std::endl;

std::cout<<translation <<std::endl;

std::cout<<"Camera Matrix (Ground Truth)" <<std::endl;

std::cout<<cameraMatrix <<std::endl;

std::cout<<"==============================Decomposing Using OpenCV==============================" <<std::endl;

std::cout<<"Computed Rotation Matrix (OpenCV)" <<std::endl;

std::cout<<calculatedRotation <<std::endl;

std::cout<<"Computed Translation Matrix (OpenCV)" <<std::endl;

//std::cout<<calculatedTranslation/calculatedTranslation.at<double>(3,0) <<std::endl;

cv::Mat tempT=(cv::Mat_<double>(3,1)<<

calculatedTranslation.at<double>(0,0)/calculatedTranslation.at<double>(3,0),

calculatedTranslation.at<double>(1,0)/calculatedTranslation.at<double>(3,0),

calculatedTranslation.at<double>(2,0)/calculatedTranslation.at<double>(3,0));

std::cout<<-rotation*tempT<<std::endl;

std::cout<<"Computed Camera Matrix (OpenCV)" <<std::endl;

std::cout<<calculatedCameraMatrix <<std::endl;

///////////////////////////////////////decomposing the projection matrix///////////////////////////////////////////

P=KR[I|-X0]=[H_inf3x3|h3x1]

KR=H_inf3x3

1)X0

-KRX0=h3x1 => X0=-(KR)^-1*h3x1 ==>X0=-(H_inf3x3)^-1*h3x1

2)K,R

KR=H_inf3x3 =>(KR)^-1= H_inf3x3^-1 =>R^-1*K^-1=H_inf3x3^-1 | R^-1*K^-1=Q*R => R=Q^-1, K=R^-1

H_inf3x3^-1=Q*R |

cv::Mat H_inf3x3=(cv::Mat_<double>(3,3)<< projectionMatrix.at<double>(0,0),projectionMatrix.at<double>(0,1),projectionMatrix.at<double>(0,2)

,projectionMatrix.at<double>(1,0),projectionMatrix.at<double>(1,1),projectionMatrix.at<double>(1,2)

,projectionMatrix.at<double>(2,0),projectionMatrix.at<double>(2,1),projectionMatrix.at<double>(2,2));

cv::Mat h3x1=(cv::Mat_<double>(3,1)<<projectionMatrix.at<double>(0,3),projectionMatrix.at<double>(1,3),projectionMatrix.at<double>(2,3) );

cv::Mat Q,R;

cv::Mat H_inf3x3_inv=H_inf3x3.inv();

//R=Q^-1, K=R^-1

HouseHolderQR(H_inf3x3_inv, Q, R);

cv::Mat K=R.inv();

std::cout<<"==============================Decomposing Using My Code==============================" <<std::endl;

//due to homogeneity we divide it by last element

std::cout<<"Estimated Camera Matrix\n"<<K/K.at<double>(2,2) <<std::endl;

cv::Mat rotationMatrix=Q.inv();

std::cout<<"Estimated Camera Rotation\n"<<rotationMatrix*-1 <<std::endl;

std::cout<<"Estimated Camera Translation" <<std::endl;

//t=-R*C, Q.inv()=R

std::cout<<-1*(-Q.inv()*(-H_inf3x3.inv()*h3x1 ))<<std::endl;

And the output is:

==============================Ground Truth==============================
Rotation Matrix (Ground Truth)
[0.823639103546332, 0.5427868801100539, -0.1643199010764935;
 -0.4755282581475767, 0.5031184295835893, -0.7216264418079997;
 -0.3090169943749474, 0.6724985119639573, 0.6724985119639574]
Translation Matrix (Ground Truth)
[1;
 2.1;
 -1.4]
Camera Matrix (Ground Truth)
[120, 0, 400;
 0, 90, 300;
 0, 0, 1]
==============================Decomposing Using OpenCV==============================
Computed Rotation Matrix (OpenCV)
[0.823639103546332, 0.5427868801100539, -0.1643199010764936;
 -0.4755282581475769, 0.5031184295835893, -0.7216264418079997;
 -0.3090169943749474, 0.6724985119639573, 0.6724985119639574]
Computed Translation Matrix (OpenCV)
[0.9999999999999988;
 2.1;
 -1.4]
Computed Camera Matrix (OpenCV)
[120, -1.4210854715202e-14, 400;
 0, 90.00000000000001, 300;
 0, 0, 1]
==============================Decomposing Using My Code==============================
Estimated Camera Matrix
[120, -5.776629175002766e-14, 399.9999999999999;
 -5.608036291626306e-15, 89.99999999999994, 299.9999999999999;
 -1.49192016182457e-17, -1.561251128379126e-16, 1]
Estimated Camera Rotation
[0.8236391035463322, 0.5427868801100542, -0.1643199010764936;
 -0.4755282581475767, 0.5031184295835892, -0.7216264418079998;
 -0.3090169943749475, 0.6724985119639573, 0.6724985119639575]
Estimated Camera Translation
[1;
 2.099999999999999;
 -1.4]

==============================Ground Truth==============================

Rotation Matrix (Ground Truth)

[0.823639103546332, 0.5427868801100539, -0.1643199010764935;

-0.4755282581475767, 0.5031184295835893, -0.7216264418079997;

-0.3090169943749474, 0.6724985119639573, 0.6724985119639574]

Translation Matrix (Ground Truth)

[1;

2.1;

-1.4]

Camera Matrix (Ground Truth)

[120, 0, 400;

0, 90, 300;

0, 0, 1]

==============================Decomposing Using OpenCV==============================

Computed Rotation Matrix (OpenCV)

[0.823639103546332, 0.5427868801100539, -0.1643199010764936;

-0.4755282581475769, 0.5031184295835893, -0.7216264418079997;

-0.3090169943749474, 0.6724985119639573, 0.6724985119639574]

Computed Translation Matrix (OpenCV)

[0.9999999999999988;

2.1;

-1.4]

Computed Camera Matrix (OpenCV)

[120, -1.4210854715202e-14, 400;

0, 90.00000000000001, 300;

0, 0, 1]

==============================Decomposing Using My Code==============================

Estimated Camera Matrix

[120, -5.776629175002766e-14, 399.9999999999999;

-5.608036291626306e-15, 89.99999999999994, 299.9999999999999;

-1.49192016182457e-17, -1.561251128379126e-16, 1]

Estimated Camera Rotation

[0.8236391035463322, 0.5427868801100542, -0.1643199010764936;

-0.4755282581475767, 0.5031184295835892, -0.7216264418079998;

-0.3090169943749475, 0.6724985119639573, 0.6724985119639575]

Estimated Camera Translation

[1;

2.099999999999999;

-1.4]

Decomposing Projection Using OpenCV and C++ Read More »

How to use image_geometry and camera_info_manager in ROS

Leave a Comment / C++, Computer Vision, ROS, Tutorials / admin

camera_info_publisher.cpp:

#include <camera_info_manager/camera_info_manager.h>
int main(int argc, char** argv)
{
    ros::init(argc, argv, "camera_info_publisher");
    ros::NodeHandle nh;

    const std::string cname="prosilica";
    const std::string url="file://${ROS_HOME}/camera_info/prosilica.yaml";
    ros::Publisher pubCameraInfo = nh.advertise<sensor_msgs::CameraInfo>("camera/camera_info", 1);
    camera_info_manager::CameraInfoManager cinfo(nh,cname,url);
    sensor_msgs::CameraInfo msgCameraInfo;
    ros::Rate loop_rate(5);
    while (nh.ok())
    {
        msgCameraInfo=cinfo.getCameraInfo();
        pubCameraInfo.publish(msgCameraInfo);
        ros::spinOnce();
        loop_rate.sleep();
    }
}

#include <camera_info_manager/camera_info_manager.h>

int main(int argc, char** argv)

{

ros::init(argc, argv, "camera_info_publisher");

ros::NodeHandle nh;

const std::string cname="prosilica";

const std::string url="file://${ROS_HOME}/camera_info/prosilica.yaml";

ros::Publisher pubCameraInfo = nh.advertise<sensor_msgs::CameraInfo>("camera/camera_info", 1);

camera_info_manager::CameraInfoManager cinfo(nh,cname,url);

sensor_msgs::CameraInfo msgCameraInfo;

ros::Rate loop_rate(5);

while (nh.ok())

{

msgCameraInfo=cinfo.getCameraInfo();

pubCameraInfo.publish(msgCameraInfo);

ros::spinOnce();

loop_rate.sleep();

}

image_geometry_demo.cpp:

#include <ros/ros.h>
#include <image_geometry/pinhole_camera_model.h>

void cameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& info_msg)
{
    std::cout<<"===============================cameraInfoCallback===============================" <<std::endl;
    image_geometry::PinholeCameraModel cam_model_;
    cam_model_.fromCameraInfo(info_msg);
    // projection_matrix is the matrix you should use if you don't want to use project3dToPixel() and want to use opencv API
    cv::Matx34d projection_matrix=cam_model_.fullProjectionMatrix();
    std::cout<<cam_model_.project3dToPixel(cv::Point3d(-0.1392072,-0.02571392, 2.50376511) )<<std::endl;
}

int main(int argc,char ** argv)
{
    ros::init(argc,argv,"image_geometry_demo",1);
    ros::NodeHandle nh;
    ros::Subscriber sub = nh.subscribe("camera/camera_info", 1000, cameraInfoCallback);
    ros::spin();
}

#include <ros/ros.h>

#include <image_geometry/pinhole_camera_model.h>

void cameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& info_msg)

{

std::cout<<"===============================cameraInfoCallback===============================" <<std::endl;

image_geometry::PinholeCameraModel cam_model_;

cam_model_.fromCameraInfo(info_msg);

// projection_matrix is the matrix you should use if you don't want to use project3dToPixel() and want to use opencv API

cv::Matx34d projection_matrix=cam_model_.fullProjectionMatrix();

std::cout<<cam_model_.project3dToPixel(cv::Point3d(-0.1392072,-0.02571392, 2.50376511) )<<std::endl;

}

int main(int argc,char ** argv)

{

ros::init(argc,argv,"image_geometry_demo",1);

ros::NodeHandle nh;

ros::Subscriber sub = nh.subscribe("camera/camera_info", 1000, cameraInfoCallback);

ros::spin();

}

CMakeLists.txt

find_package(catkin REQUIRED COMPONENTS image_geometry camera_info_manager roscpp)

1	find_package(catkin REQUIRED COMPONENTS image_geometry camera_info_manager roscpp)

How to use image_geometry and camera_info_manager in ROS Read More »

Open source Structure-from-Motion and Multi-View Stereo tools with C++

Leave a Comment / C++, Computer Vision, Image Processing, Linear Algebra, Tutorials / admin

Structure-from-Motion (SFM) is genuinely an interesting topic in computer vision, Basically making 3D structure from something 2D is absolutely mesmerizing 🙂 There two open source yet very robust tools for SFM, which sometimes compiling them might be complicated, here I will share my experience with you. 1)VisualSFM Prerequisite: 1)Glew Download the glew from SF at http://glew.sourceforge.net/. Do NOT get it from Github as

Open source Structure-from-Motion and Multi-View Stereo tools with C++ Read More »

Examples of Dynamic Programming with C++ and Matlab

Leave a Comment / C++, Machine Learning, Matlab, Tutorials / admin

In this tutorial, I will give you examples of using dynamic programming for solving the following problems: 1)Minimum number of coins for summing X.

int c[]={1,3,4}; // value of coins
int k=3; //number of cpins that we have

/*
The d[20] array is for memorization, for instance if we computed f_coin_problem_memo(3),
we put this value in d[3] and we don't compute it again, we just use d[3] instead of f_coin_problem_memo(3)
*/
int d[20];
int f_coin_problem_memo(int x)
{
    if (x < 0) return 1e9;
    if (x == 0) return 0;
    if (d[x]) return d[x];
    int u = 1e9;
    int u_old = 1e9;
    for (int i = 0; i < k; i++)
    {
        u_old=u;
        u = std::min(u, f_coin_problem_memo(x-c[i]) +1);
    }
    d[x] = u;
    return d[x];
}

int c[]={1,3,4}; // value of coins

int k=3; //number of cpins that we have

The d[20] array is for memorization, for instance if we computed f_coin_problem_memo(3),

we put this value in d[3] and we don't compute it again, we just use d[3] instead of f_coin_problem_memo(3)

int d[20];

int f_coin_problem_memo(int x)

{

if (x < 0) return 1e9;

if (x == 0) return 0;

if (d[x]) return d[x];

int u = 1e9;

int u_old = 1e9;

for (int i = 0; i < k; i++)

{

u_old=u;

u = std::min(u, f_coin_problem_memo(x-c[i]) +1);

}

d[x] = u;

return d[x];

}

The minimum number of coins from the set {1,3,4} to sum up value of 12 is:
3

1 2	The minimum number of coins from the set {1,3,4} to sum up value of 12 is: 3

2)The most (least) costly path on a grid (dynamic time warping).

finding a path in an n × n grid from the upper-left corner to the lower-right corner. We are only allowed
 to move right or down.

1,1  1,2 .... 1,X
2,1  2,2 .... 2,X
.
.
.
Y,1  Y,2 .... Y,X


3 7 9 2 7
9 8 3 5 5
1 7 9 8 5
3 8 6 4 10
6 3 9 7 8

*/

int grid[5][5]={{3,7, 9, 2, 7},{9, 8, 3 ,5 ,5},{1, 7 ,9 ,8 ,5},{3, 8, 6 ,4 ,10},{6, 3 ,9 ,7 ,8}};
int memo[5][5]{{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1}};

std::vector<int>x_solution,y_solution;

int f_paths_in_grid(int y,int x)
{
    if((x==0)&& (y==0))
    {
        memo[y][x]=grid[0][0];
    }
    else if (x==0)
    {
        if(memo[y][x]<0)
        {
            memo[y][x] =f_paths_in_grid(y-1,x) +grid[y][x];
        }
    }
    else if(y==0)
    {
       if(memo[y][x]<0)
       {
            memo[y][x]=f_paths_in_grid(y,x-1) +grid[y][x];
       }
    }
   else if(memo[y][x]<0)
   {
        memo[y][x]=std::max(f_paths_in_grid(y,x-1)  ,f_paths_in_grid(y-1,x)  )+grid[y][x];
    }
    return  memo[y][x];
}

finding a path in an n × n grid from the upper-left corner to the lower-right corner. We are only allowed

to move right or down.

1,1 1,2 .... 1,X

2,1 2,2 .... 2,X

Y,1 Y,2 .... Y,X

3 7 9 2 7

9 8 3 5 5

1 7 9 8 5

3 8 6 4 10

6 3 9 7 8

int grid[5][5]={{3,7, 9, 2, 7},{9, 8, 3 ,5 ,5},{1, 7 ,9 ,8 ,5},{3, 8, 6 ,4 ,10},{6, 3 ,9 ,7 ,8}};

int memo[5][5]{{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1}};

std::vector<int>x_solution,y_solution;

int f_paths_in_grid(int y,int x)

{

if((x==0)&& (y==0))

{

memo[y][x]=grid[0][0];

}

else if (x==0)

{

if(memo[y][x]<0)

{

memo[y][x] =f_paths_in_grid(y-1,x) +grid[y][x];

}

else if(y==0)

{

if(memo[y][x]<0)

{

memo[y][x]=f_paths_in_grid(y,x-1) +grid[y][x];

}

else if(memo[y][x]<0)

{

memo[y][x]=std::max(f_paths_in_grid(y,x-1) ,f_paths_in_grid(y-1,x) )+grid[y][x];

}

return memo[y][x];

}

maximum sum on a path from the upper-left corner to the lower-right corner is:
67
3|10|19|21|28|
---------------
12|20|23|28|33|
---------------
13|27|36|44|49|
---------------
16|35|42|48|59|
---------------
22|38|51|58|67|
---------------

maximum sum on a path from the upper-left corner to the lower-right corner is:

3|10|19|21|28|

---------------

12|20|23|28|33|

---------------

13|27|36|44|49|

---------------

16|35|42|48|59|

---------------

22|38|51|58|67|

---------------

3)Levenshtein edit distance.

int LevenshteinDistance(std::string s, std::string t )
{
  int cost;

  /* base case: empty strings */
  if (s.length() == 0) return t.length();
  if (t.length() == 0) return s.length();

  /* test if last characters of the strings match */
  if (s.at(s.length()-1)  == t.at(t.length()-1) )
      cost = 0;
  else
      cost = 1;

  /* return minimum of delete char from s, delete char from t, and delete char from both */

  return std::min(   std::min(LevenshteinDistance(s.substr(0, s.size()-1 ),t ) + 1,LevenshteinDistance(s  , t.substr(0, t.size()-1 )) + 1 )    ,  LevenshteinDistance(s.substr(0, s.size()-1 ), t.substr(0, t.size()-1 )) + cost );
}

int LevenshteinDistance(std::string s, std::string t )

{

int cost;

/* base case: empty strings */

if (s.length() == 0) return t.length();

if (t.length() == 0) return s.length();

/* test if last characters of the strings match */

if (s.at(s.length()-1) == t.at(t.length()-1) )

cost = 0;

else

cost = 1;

/* return minimum of delete char from s, delete char from t, and delete char from both */

return std::min( std::min(LevenshteinDistance(s.substr(0, s.size()-1 ),t ) + 1,LevenshteinDistance(s , t.substr(0, t.size()-1 )) + 1 ) , LevenshteinDistance(s.substr(0, s.size()-1 ), t.substr(0, t.size()-1 )) + cost );

}

The Levenshtein distance between saturday and sunday is:
3

1 2	The Levenshtein distance between saturday and sunday is: 3

4)Seam Carving. I have written a tutorial on that

Examples of Dynamic Programming with C++ and Matlab Read More »

Peak Signal-to-Noise Ratio (PSNR) in Image using OpenCV and Matlab

Leave a Comment / C++, Computer Vision, Image Processing, Matlab, Tutorials / admin

Peak signal-to-noise ratio (PSNR) shows the ratio between the maximum possible power of a signal and the power of the same image with noise. PSNR is usually expressed in logarithmic decibel scale. $ MSE =1/m*n \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} [ Image( i,j) -NoisyImage( i,j) ] ^2 $ $ PSNR =10* \log_{10} \Bigg(MAXI^2/MSE\Bigg) $ MSE is Mean Square Error and MAXI is

Peak Signal-to-Noise Ratio (PSNR) in Image using OpenCV and Matlab Read More »

Finding roll, pitch yaw from 3X3 rotation matrix with Eigen

Leave a Comment / C++, ROS, Tutorials / admin

/*  http://planning.cs.uiuc.edu/node103.html

    |r11 r12 r13 |
    |r21 r22 r23 |
    |r31 r32 r33 |

    yaw: alpha=arctan(r21/r11)
    pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )
    roll: gamma=arctan(r32/r33)
*/
    double roll, pitch, yaw;
    roll=M_PI/3;
    pitch=M_PI/4;
    yaw=M_PI/6;
    Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());
    Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());
    Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

    Eigen::Quaternion<double> q =  yawAngle*pitchAngle *rollAngle;
    Eigen::Matrix3d rotationMatrix = q.matrix();



    std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;
    std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5  )  ) <<std::endl;
    std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

/* http://planning.cs.uiuc.edu/node103.html

|r11 r12 r13 |

|r21 r22 r23 |

|r31 r32 r33 |

yaw: alpha=arctan(r21/r11)

pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )

roll: gamma=arctan(r32/r33)

double roll, pitch, yaw;

roll=M_PI/3;

pitch=M_PI/4;

yaw=M_PI/6;

Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());

Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());

Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle;

Eigen::Matrix3d rotationMatrix = q.matrix();

std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;

std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5 ) ) <<std::endl;

std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

Finding roll, pitch yaw from 3X3 rotation matrix with Eigen Read More »

pkg-config and CMake

Leave a Comment / C++, Tutorials / admin

pkg-config and CMake If the library that you want to use is not coming with Find<package>. cmake you can use pkg-config files which are stored in “.pc”. The environmental variable PKG_CONFIG_PATH is the place that should point to the “.pc” files. Type in the shell:

PKG_CONFIG_PATH=<path-to-directory-containing-the-.pc-file>:${PKG_CONFIG_PATH}
export PKG_CONFIG_PATH
printenv PKG_CONFIG_PATH

PKG_CONFIG_PATH=<path-to-directory-containing-the-.pc-file>:${PKG_CONFIG_PATH}

export PKG_CONFIG_PATH

printenv PKG_CONFIG_PATH

To get the list of all available package configs, you can

pkg-config and CMake Read More »

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