archenroot

To generate a pom.xml file just run gradle writeNewPom

If you want to generate it as pom.xml in the root of the project, replace writeTo("$buildDir/newpom.xml") with writeTo("pom.xml")

archenroot

In order to clone the TVN application from sourceforge to github I performed the following steps.

rsync -av rsync://tnv.cvs.sourceforge.net/cvsroot/tnv/* .
svn export --username=guest http://cvs2svn.tigris.org/svn/cvs2svn/trunk cvs2svn-trunk
cp ./cvs2svn-trunk/cvs2git-example.options ./cvs2git.options
vim cvs2git.options
cvs2svn-trunk/cvs2git --options=cvs2git.options --fallback-encoding utf-8
[email protected]:binarytemple/tnv.git tnv-github
git clone [email protected]:binarytemple/tnv.git tnv-github

archenroot

# cat /etc/logrotate.d/mysqld

# This logname can be set in /etc/my.cnf
# by setting the variable "err-log"
# in the [safe_mysqld] section as follows:
#
# [safe_mysqld]
# err-log=/var/log/mysqld.log
#
# If the root user has a password you have to create a

archenroot

docker network ls
docker network create --driver bridge isolated_network   ## create ~ create custom network, bridge ~ use a bridge network, isolated_network ~ name of the custom network
docker network isolated_network
docker network inspect isolated_network
docker run -d --net=isolated_network --name nodeapp -p 3000:3000 abhinavkorpal/node  ## net ~ run container in network, mongodb ~ link to this containe by name

archenroot

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

archenroot

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

archenroot

This gist is an implementation of http://sirile.github.io/2015/05/18/using-haproxy-and-consul-for-dynamic-service-discovery-on-docker.html on top of Docker Machine and Docker Swarm.

archenroot

import kafka.server.KafkaConfig;
import kafka.server.KafkaServer;

import java.io.File;
import java.io.FileNotFoundException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Properties;

archenroot

/*
 * Parallel bitonic sort using CUDA.
 * Compile with
 * nvcc -arch=sm_11 bitonic_sort.cu
 * Based on http://www.tools-of-computing.com/tc/CS/Sorts/bitonic_sort.htm
 * License: BSD 3
 */

#include <stdlib.h>
#include <stdio.h>

archenroot

#!/bin/bash
# Cisco Anyconnect CSD wrapper for OpenConnect

# Enter your vpn host here
CSD_HOSTNAME=
if [[ -z ${CSD_HOSTNAME} ]]
then
    echo "Define CSD_HOSTNAME with vpn-host in script text. Exiting."
    exit 1
fi