m-radzikowski

#!/usr/bin/env bash

set -Eeuo pipefail
trap cleanup SIGINT SIGTERM ERR EXIT

script_dir=$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd -P)

usage() {
  cat <<EOF
Usage: $(basename "${BASH_SOURCE[0]}") [-h] [-v] [-f] -p param_value arg1 [arg2...]

edoakes

import argparse
import time
import random
import math

parser = argparse.ArgumentParser(description="Approximate digits of Pi using Monte Carlo simulation.")
parser.add_argument("--num-samples", type=int, default=1000000)
parser.add_argument("--parallel", default=False, action="store_true")
parser.add_argument("--distributed", default=False, action="store_true")

Hashbrownn

from __future__ import unicode_literals
import base64
from os import path
import io
from io import StringIO
import pymysql
import imaplib
import json
import smtplib,ssl
import urllib.parse

JakeSteam

# Overall config
dist: xenial
language: android

# Android version config
android:
  components:
    - build-tools-28.0.3
    - android-28

mikepfeiffer

sudo amazon-linux-extras install epel -y
sudo yum install stress -y

DrSleep

## http://cvlibs.net/datasets/kitti/eval_semantics.php
## https://omnomnom.vision.rwth-aachen.de/data/rwth_kitti_semantics_dataset.zip

### DATASET FOR SEMANTIC SEGMENTATION

lukebussey

Direct copy of pre-encoded file:

$ ffmpeg -i filename.mp4 -codec: copy -start_number 0 -hls_time 10 -hls_list_size 0 -f hls filename.m3u8

mgoodness

AWS Service annotations

• service.beta.kubernetes.io/aws-load-balancer-access-log-emit-interval (in minutes)
• service.beta.kubernetes.io/aws-load-balancer-access-log-enabled (true|false)
• service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-name
• service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-prefix
• service.beta.kubernetes.io/aws-load-balancer-additional-resource-tags (comma-separated list of key=value)
• service.beta.kubernetes.io/aws-load-balancer-backend-protocol (http|https|ssl|tcp)
• service.beta.kubernetes.io/aws-load-balancer-connection-draining-enabled (true|false)

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

kaushikgopal

#!/usr/bin/env ruby

# This pre-commit hook will prevent any commit to forbidden branches
# (by default, "staging" and "production").
# Put this file in your local repo, in the .git/hooks folder
# and make sure it is executable.
# The name of the file *must* be "pre-commit" for Git to pick it up.

def current_branch()
  branches = `git branch --no-color`.split(/\n/)