olzhas

#include <Eigen/Dense>

template <class MatT>
Eigen::Matrix<typename MatT::Scalar, MatT::ColsAtCompileTime, MatT::RowsAtCompileTime>
pseudoinverse(const MatT &mat, typename MatT::Scalar tolerance = typename MatT::Scalar{1e-4}) // choose appropriately
{
    typedef typename MatT::Scalar Scalar;
    auto svd = mat.jacobiSvd(Eigen::ComputeFullU | Eigen::ComputeFullV);
    const auto &singularValues = svd.singularValues();
    Eigen::Matrix<Scalar, MatT::ColsAtCompileTime, MatT::RowsAtCompileTime> singularValuesInv(mat.cols(), mat.rows());

olzhas

ffmpeg -i 1.mov -i 2.mov -i 3.mov -filter_complex "hstack=3" output.mp4

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CMake

target_link_library(something private LIB)

The thing above is only applicable for shared libraries

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sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-5.0 100
sudo update-alternatives --install /usr/bin/clang clang /usr/bin/clang-5.0 100

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#ifndef TIMER_H
#define TIMER_H

#include <chrono>
#include <thread>

using timer = std::chrono::high_resolution_clock::time_point;

inline void tic(timer &t){
    t = std::chrono::high_resolution_clock::now();

olzhas

The document provides description on calibration of three Kinect for Microsoft sensors connected to one computer with several usb controllers. Three cameras setup is shown below:

Intrinsic, extrinsic, and Kinect2Kinect calibration is performed to know the position of each sensor in the space. Our setup is ROS Indigo with Ubuntu 14.04. freenect_launch and camera_pose ROS packages are used. Camera_pose package provides the pipeline to calibrate the relative 6D poses between multiple camera's. freenect_launch package contains launch files for using OpenNI-compliant devices in ROS. It creates a nodelet graph to transform raw data from the device driver into point clouds, disparity images, and other products suitable for processing and visualization. It is installed with catkin as follows:

# Prepa

olzhas

1. Turn off all swap processes

sudo swapoff -a

2. Resize the swap

sudo dd if=/dev/zero of=/swapfile bs=1G count=8

• if = input file
• of = output file

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To forward X11 from inside a docker container to a host running macOS

1. Install XQuartz: https://www.xquartz.org/
2. Launch XQuartz. Under the XQuartz menu, select Preferences
3. Go to the security tab and ensure "Allow connections from network clients" is checked.
4. Run xhost + ${hostname} to allow connections to the macOS host *
5. Setup a HOSTNAME env var export HOSTNAME=`hostname`*
6. Add the following to your docker-compose:

 environment:

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https://github.com/aancel/admin/wiki/VirtualGL-on-Ubuntu

https://virtualgl.org/About/Introduction

https://www.turbovnc.org

VirtualGL

When you use ssh with X forwarding, you might have noticed that you cannot execute programs that require 3D acceleration. That's where VirtualGL comes into play.

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import numpy as NP
import casadi as C
def qpsolve(H,g,lbx,ubx,A=NP.zeros((0,0)),lba=NP.zeros(0),uba=NP.zeros(0)):
    # Convert to CasADi types
    H = C.DMatrix(H)
    g = C.DMatrix(g)
    lbx = C.DMatrix(lbx)
    ubx = C.DMatrix(ubx)
    A = C.DMatrix(A)
    A = A.reshape((A.size1(),H.size1())) # Make sure matching dimensions