Caleb Adams piepieninja

Open OVA/OVF file with VirtualBox
Right click the virtual machine and select 'Clone'
Import the generated .vbox file with Parallels Desktop

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]

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

	// munged from https://github.com/simontime/Resead

	namespace sead
	{
	class Random
	{

	0x00004e1e movw r2, #0xc977
	0x00004e26 movw r1, #0xc977
	0x00004e3e movw r2, #0xc977
	0x00004e46 movw r1, #0xc977
	0x00004e5c movw r3, #0xc977

	#!/usr/bin/env python
	# MIT License

	# Copyright (c) 2017 Jetsonhacks

	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is

	/*
	Example code for displaying gstreamer video from the CSI port of the Nvidia Jetson in OpenCV.
	Created by Peter Moran on 7/29/17.
	https://gist.github.com/peter-moran/742998d893cd013edf6d0c86cc86ff7f
	*/

	#include <opencv2/opencv.hpp>

	std::string get_tegra_pipeline(int width, int height, int fps) {
	return "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)" + std::to_string(width) + ", height=(int)" +

	#!/bin/bash
	# A script to backup GitLab repositories.

	GLAB_BACKUP_DIR=${GLAB_BACKUP_DIR-"gitlab_backup"} # where to place the backup files
	GLAB_TOKEN=${GLAB_TOKEN-"YOUR_TOKEN"} # the access token of the account
	GLAB_GITHOST=${GLAB_GITHOST-"gitlab.com"} # the GitLab hostname
	GLAB_PRUNE_OLD=${GLAB_PRUNE_OLD-true} # when `true`, old backups will be deleted
	GLAB_PRUNE_AFTER_N_DAYS=${GLAB_PRUNE_AFTER_N_DAYS-7} # the min age (in days) of backup files to delete
	GLAB_SILENT=${GLAB_SILENT-false} # when `true`, only show error messages
	GLAB_API=${GLAB_API-"https://gitlab.com/api/v3"} # base URI for the GitLab API

	Result: 1
	Items {
	TemplateId: "BADGE_BATTLE_ATTACK_WON"
	Badge {
	BadgeType: BADGE_BATTLE_ATTACK_WON
	BadgeRanks: 4
	Targets: "\nd\350\007"
	}
	}
	Items {

	require 'pp'

	# parameters
	minutes = 30 # how long to run the sim
	packet_size_bytes = 25
	packet_size_bits = packet_size_bytes * 8
	channels = [ 12, 11, 10, 9, 8, 7, 6 ]
	channel_bits_per_second = [ 0.293 * 1000, 0.537 * 1000, 0.976 * 1000, 1.757 * 1000, 3.125 * 1000, 5.468 * 1000, 9.375 * 1000 ]
	channel_time_per_packet = channel_bits_per_second.map{ \|bps\| packet_size_bits / bps }

	/**
	* Compute the (Moore-Penrose) pseudo-inverse of a libgsl matrix in plain C.
	*
	* Compile uding:
	*
	* gcc moore_penrose_pseudoinverse.c -lgsl -lblas
	*
	* Dependencies:
	* - libgsl (GNU Scientific Library)
	* - libblas (Basic Linear Algebra Subprograms)