crizCraig

import json
import os
import time
import requests
from PIL import Image
from StringIO import StringIO
from requests.exceptions import ConnectionError

def go(query, path):
  """Download full size images from Google image search.

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

46bit

import sys, cv2

# Refactored https://realpython.com/blog/python/face-recognition-with-python/

def cascade_detect(cascade, image):
  gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  return cascade.detectMultiScale(
    gray_image,
    scaleFactor = 1.15,
    minNeighbors = 5,

dynamicguy

# install dependencies
sudo apt-get update
sudo apt-get install -y build-essential
sudo apt-get install -y cmake
sudo apt-get install -y libgtk2.0-dev
sudo apt-get install -y pkg-config
sudo apt-get install -y python-numpy python-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev libjasper-dev
 

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

BenElgar

import urllib, StringIO
from math import log, exp, tan, atan, pi, ceil
from PIL import Image

EARTH_RADIUS = 6378137
EQUATOR_CIRCUMFERENCE = 2 * pi * EARTH_RADIUS
INITIAL_RESOLUTION = EQUATOR_CIRCUMFERENCE / 256.0
ORIGIN_SHIFT = EQUATOR_CIRCUMFERENCE / 2.0

def latlontopixels(lat, lon, zoom):

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

genekogan

from bs4 import BeautifulSoup
import requests
import re
import urllib2
import os
import argparse
import sys
import json

# adapted from http://stackoverflow.com/questions/20716842/python-download-images-from-google-image-search

geffy

# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking. 

margaretmeehan

def unet(input_shape):
    '''
    Params: input_shape -- the shape of the images that are input to the model
                           in the form (width_or_height, width_or_height,
                           num_color_channels)

    Returns: model -- a model that has been defined, but not yet compiled.
                      The model is an implementation of the Unet paper
                      (https://arxiv.org/pdf/1505.04597.pdf) and comes
                      from this repo https://github.com/zhixuhao/unet. It has