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jkwebco / Raspberry Pi 3 TOR Access Point Router.md
Created July 1, 2019 14:33 — forked from fire1ce/Raspberry Pi 3 TOR Access Point Router.md
Raspberry Pi 3 TOR Access Point Router

THIS GIST WON'T BE UPDATED ANY MORE (24/10/18)

Follow the progress of this project here 3os.org Raspberry Pi 3 TOR Access Point Router Project

Network: Router RJ45 <--> Ethernet Port on Raspberry <--> TOR <--> Raspberry WIFI AC <--> WIFI CLIENT

# -- Download Rasbian Strech Lite from: https://www.raspberrypi.org/downloads/raspbian/
@jkwebco
jkwebco / rest-server.py
Created February 7, 2019 15:29 — forked from miguelgrinberg/rest-server.py
The code from my article on building RESTful web services with Python and the Flask microframework. See the article here: http://blog.miguelgrinberg.com/post/designing-a-restful-api-with-python-and-flask
#!flask/bin/python
from flask import Flask, jsonify, abort, request, make_response, url_for
from flask_httpauth import HTTPBasicAuth
app = Flask(__name__, static_url_path = "")
auth = HTTPBasicAuth()
@auth.get_password
def get_password(username):
if username == 'miguel':
@jkwebco
jkwebco / face-boxer-usage.md
Created April 2, 2017 21:01 — forked from dannguyen/face-boxer-usage.md
A face-detection script in Python

This face-boxer.py script is more-or-less the same code that you'll find in the OpenCV tutorial: Face Detection using Haar Cascades. For another variation, with more explanation, check out RealPython's tutorial.

Usage

The face-boxer.py script is designed to be run from the command-line. It has two required arugments:

  1. The path to a XML file containing a Haar-cascade of visual features. In this example, it will be the features that make up a face.
  2. The path to an image file that you want to perform face-detection on. You can pass in more than one image file as space-separated arguments.
@jkwebco
jkwebco / gist:1eb94c28ade4bcc012ffc9e5ef2a33d5
Created March 31, 2017 03:41 — forked from ttezel/gist:4138642
Natural Language Processing Notes

#A Collection of NLP notes

##N-grams

###Calculating unigram probabilities:

P( wi ) = count ( wi ) ) / count ( total number of words )

In english..