ialhashim

If you want to connect to a local workstation behind a firewall you can do that by bouncing traffic off of an AWS instance.

An example would be accessing a Jupyter Lab from the outside world to a machine with no static IP or behind a firewall.

The steps are:

1. Create a paid AWS Ubuntu instance and save the '.pem' file.
2. Make sure the AWS instance's Security Rules allows traffic in.
3. Test that you can connect to this instance from the outside world (e.g. python3 -m http.server or jupyter lab --ip=*) then visit the instance using its public IP.
4. (optionally) Enable password login in /etc/ssh/sshd_config by setting PasswordAuthentication yes and sudo passwd $USER.
5. Enable GatewayPorts yes and restart sshd. E.g. sudo nano /etc/ssh/sshd_config then sudo systemctl restart ssh.service.

ialhashim

template<class Vector3>
std::pair<Vector3, Vector3> best_plane_from_points(const std::vector<Vector3> & c)
{
	// copy coordinates to  matrix in Eigen format
	size_t num_atoms = c.size();
	Eigen::Matrix< Vector3::Scalar, Eigen::Dynamic, Eigen::Dynamic > coord(3, num_atoms);
	for (size_t i = 0; i < num_atoms; ++i) coord.col(i) = c[i];

	// calculate centroid
	Vector3 centroid(coord.row(0).mean(), coord.row(1).mean(), coord.row(2).mean());

ialhashim

#pragma once

// Code adapted from https://github.com/propanoid/DBSCAN

#include <vector>
#include <algorithm>
#include <omp.h>

// Any basic vector/matrix library should also work
#include <Eigen/Core>

ialhashim

# Original Matlab code https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
#
#
# Python port of depth filling code from NYU toolbox
# Speed needs to be improved
#
# Uses 'pypardiso' solver 
#
import scipy
import skimage

ialhashim

==============================

Expression: (id=\d)(.+?)(")

==============================

Python script to download:


import os.path

ialhashim

// Note this is just a workaround until the offical support of web

// We would need to add these as well
//// ignore_for_file: avoid_web_libraries_in_flutter
//import 'dart:html';
//import 'package:mime/mime.dart';
//import 'package:firebase/firebase.dart' as fb;

// Some upload button is pressed
onPressed: () async {          

ialhashim

#include "screengrab.h"

void capture()
{
    CComPtr<ID3D11Device> pDevice;
    CComPtr<IDXGIOutputDuplication> pDeskDupl;
    //(...)create devices and duplication interface etc here..

    DXGI_OUTDUPL_DESC OutputDuplDesc;

ialhashim

from OpenGL import GL, GLU
from OpenGL.arrays import vbo

# Prepare point cloud once
def makeCloud(self):    
    width, height = self.rgb.shape[1], self.rgb.shape[0] 
    
    # ...
    # Compute 3D positions from depth
    # ...

ialhashim

// Portable version of powercrust, adapted from https://github.com/timhutton/vtkpowercrust

/*===================================================================================================

vtkPowerCrustSurfaceReconstruction algorithm reconstructs surfaces from unorganized point data.
Copyright (C) 2014  Arash Akbarinia, Tim Hutton, Bruce Lamond Dieter Pfeffer, Oliver Moss

====================================================================================================*/
/*
#include "CellArray.h"

ialhashim

def split_to_batches(img, trained_size=512):
    img = skimage.img_as_float32(img)
    direction = 'landscape'
    if img.shape[0] > img.shape[1]: direction = 'portrait'
    
    # resize into 512x[width|height]
    width, height = img.shape[1], img.shape[0]    
    if direction == 'landscape': 
        new_height = trained_size
        new_width = int(new_height * (width / height))