David Mashburn davidmashburn

While trying to get a local version of issue_stats running, we ran into a problem we previously experienced with respect to using an MD5 password on a Vagrant box.

Since these commands aren't the most trivial, we figure it would be a good idea to document.

Here's how to add a new user to PostgreSQL in Vagrant:

# Set up PostgreSQL 9.3 configuration
# Modified from https://github.com/twolfson/vagrant-nodebugme/blob/1.0.0/bin/bootstrap.sh#L26-L54
# If we can't open `psql` as `vagrant`

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

my database had 72k annotations at the time I ran these benchmarks, here's the result:

$ python scripts/batch_bench.py conf/development-app.ini dumb
Memory summary: start
      types |   # objects |   total size
=========== | =========== | ============
       dict |       13852 |     12.46 MB
  frozenset |         349 |     11.85 MB
VM: 327.29Mb

	import os
	from subprocess import check_call

	def post_save(model, os_path, contents_manager):
	"""post-save hook for converting notebooks to .py and .html files."""
	if model['type'] != 'notebook':
	return # only do this for notebooks
	d, fname = os.path.split(os_path)
	check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d)
	check_call(['jupyter', 'nbconvert', '--to', 'html', fname], cwd=d)

	import numpy as np
	import subprocess as sp
	import os
	DEVNULL = open(os.devnull, 'w')

	# load_audio can not detect the input type
	def ffmpeg_load_audio(filename, sr=44100, mono=False, normalize=True, in_type=np.int16, out_type=np.float32):
	channels = 1 if mono else 2
	format_strings = {
	np.float64: 'f64le',

	# VERY IMPORTANT! After each kernel update or dkms rebuild the modules must be signed again with the script
	# ~/.ssl/sign-all-modules.sh

	# Place all files in ~/.ssl folder
	mkdir ~/.ssl
	cd ~/.ssl

	# Generate custom keys with openssl
	openssl req -new -x509 -newkey rsa:2048 -keyout MOK.priv -outform DER -out MOK.der -nodes -subj "/CN=Owner/"

	/// given this uncompilable code
	pub fn main() {
	let vec0 = Vec::new();

	let mut vec1 = fill_vec(vec0);

	// Do not change the following line!
	println!("{} has length {} content `{:?}`", "vec0", vec0.len(), vec0);

	vec1.push(88);

	# Download installers
	mkdir ~/Downloads/nvidia
	cd ~/Downloads/nvidia
	wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
	wget http://us.download.nvidia.com/XFree86/Linux-x86_64/384.59/NVIDIA-Linux-x86_64-384.59.run
	sudo chmod +x NVIDIA-Linux-x86_64-384.59.run
	sudo chmod +x cuda_8.0.61_375.26_linux-run
	./cuda_8.0.61_375.26_linux-run -extract=~/Downloads/nvidia/
	# Uninstall old stuff
	sudo apt-get --purge remove nvidia-*