jakeonrails

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christianroman

#! /bin/bash

# build the environment
mkdir tessenv; cd tessenv 
TROOT=`pwd` 
mkdir $TROOT/stockfonts; mkdir $TROOT/build; mkdir $TROOT/build/eng 
echo "Environment built"
# Get the stock english fonts from Google (old, but they work)
cd $TROOT/stockfonts
GET http://tesseract-ocr.googlecode.com/files/boxtiff-2.01.eng.tar.gz > boxtiff-2.01.eng.tar.gz

robmathers

#!/usr/bin/env python
import plistlib
from shutil import copy
import subprocess
import os
from tempfile import gettempdir
import sys
import atexit

BOOKMARKS_PLIST = '~/Library/Safari/Bookmarks.plist'

JeffPaine

Moved

Now located at https://github.com/JeffPaine/beautiful_idiomatic_python.

Why it was moved

Github gists don't support Pull Requests or any notifications, which made it impossible for me to maintain this (surprisingly popular) gist with fixes, respond to comments and so on. In the interest of maintaining the quality of this resource for others, I've moved it to a proper repo. Cheers!

nickynicolson

import urllib
import urllib2

try:
  # Base url for the TNRS match_names API
  url = 'http://api.opentreeoflife.org/v2/tnrs/match_names'
  # Encode data value to be looked up as an array of names:
  data = '{"names": ["'+value+'"]}'
  print "Looking up: " + data
  # Set HTTP headers:

beezly

function performLogin(email, password) {
  var payload = {
    "username" : email,
    "password" : password
  };
  
  var options = {
    "method"  : "post",
    "payload" : payload
  };

dannguyen

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

kaspermeerts

import collections
import math
import os
import cv2
import numpy as np
import time

MAX_LINES = 4000
N_PINS = 36*8
MIN_LOOP = 20               # To avoid getting stuck in a loop

joelbarmettlerUZH

from os import listdir, rename
from os.path import isfile, join
import tkinter as tk
from PIL import ImageTk, Image

IMAGE_FOLDER = "./images/unclassified"

images = [f for f in listdir(IMAGE_FOLDER) if isfile(join(IMAGE_FOLDER, f))]
unclassified_images = filter(lambda image: not (image.startswith("0_") or image.startswith("1_")), images)
current = None

lwrubel

#!/usr/bin/env python3

import webvtt
from datetime import datetime, timedelta
import argparse

parser = argparse.ArgumentParser(description="Shift caption start \
				     and end times in a .vtt file")
parser.add_argument("inputfile", help="input filename, must be VTT format")
parser.add_argument("outputfile", help="output filename")