adlf-qarnot

#!/usr/bin/env python3

# Import the Qarnot SDK
import qarnot

# Create a connection, from which all other objects will be derived
# Enter client token here
conn = qarnot.connection.Connection(client_token="<<<MY_SECRET_TOKEN>>>")

# Create the task

chunibyo-wly

import json, os, shutil
from os.path import join, basename
from tqdm import tqdm
import numpy as np

MESHROOM = r"E:\workspace\dataset\01_DTU\cache\20\meshroom\MeshroomCache\ConvertSfMFormat\4b9ced4a64f3f996f7650f3d5ed9e27c4774a7c9\sfm.json"
COLMAP = r"E:\workspace\dataset\01_DTU\cache\20\colmap"


def make_dir(folder):

aharbick

Introduction

The Openscan project https://openscan.eu is a really cool 3D printing and scanning project. It systematically takes a series of photos that can be stitched together into a 3D model using photogrammetry. Thomas Megel the creator of OpenScan has a really cool cloud-based service that can take the raw photos and turn them an STL model for you. You can read more about it here: https://github.com/OpenScanEu/OpenScan/blob/master/temp/README.md

Another way to take advantage of photogrammetry is using AliceVision's Meshroom. Prusa Research did a nice explainer/tutorial in this blog post: https://blog.prusaprinters.org/photogrammetry-2-3d-scanning-simpler-better-than-ever_29393/

The main "catch" with Meshroom is that it requires CUDA and hence an NVIDIA graphics card. It didn't have an NVIDIA card (and didn't want to figure out how to run in the cloud) but I did have a Jetson Nano 2GB https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/education-projects/

The documentati

donmahallem

Tony607

simogasp

Setting up Travis deploy

1. Create an OAuth token from your profile settings

• under Developer select Personal access token
• generate a new one and copy it to the clipboard

2. On the travis page of the repository, go to settings --> More Options->Settings
3. Set a new Environment Variable called eg GITHUB_RELEASE_API_KEY or whatever u like, and copy the key
4. In the .travis.yaml file, under add something like that

before_deploy:
 - ARCHIVE_BASE_PATH=${INSTALL_DIR}

nikolaseu

% required dimensions
standoff = 500; % distance from camera, in [mm]
fovWidth = 320; % width of field of view, in [mm]
fovHeight = 10; % height of FOV, in [mm]

% sensor resolution
sensorXRes = 4096; % widht
sensorYRes = 3072; % height

% pixel physical size

alvinwan

docopt==0.6.2
trimesh==2.10.18

juancarlospaco

#!/usr/bin/env python3
# -*- coding: utf-8 -*-


import os, sys
from tempfile import gettempdir
from subprocess import call


def beep(waveform=(79, 45, 32, 50, 99, 113, 126, 127)):

ionelmc

The reStructuredText Cheat Sheet: Syntax Reminders

Info

See <http://docutils.sf.net/rst.html> for introductory docs.

Author

David Goodger <goodger@python.org>

Date

$Date: 2013-02-20 01:10:53 +0000 (Wed, 20 Feb 2013) $

Revision

$Revision: 7612 $

Description

This is a "docinfo block", or bibliographic field list

Note

If you are reading this as HTML, please read