ghandic

import boto3
from PIL import Image
from io import BytesIO
import os

class S3ImagesInvalidExtension(Exception):
    pass

class S3ImagesUploadFailed(Exception):
    pass

duhaime

import warnings
from skimage.measure import compare_ssim
from skimage.transform import resize
from scipy.stats import wasserstein_distance
from scipy.misc import imsave
from scipy.ndimage import imread
import numpy as np
import cv2

##

kylehounslow

from __future__ import print_function
import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'

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

dannyvassallo

# db/migrate/filename.rb
# generate with >> rails g model image alt:string hint:string file:string

class CreateImages < ActiveRecord::Migration[5.0]
  def change
    create_table :images do |t|
      t.string :alt
      t.string :hint
      t.string :file

parmentf

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

steerapi

BOOST_INC_DIR = []                                                              
BOOST_LIB_DIR = []                                                              
BOOST_COMPILER = 'gcc43'                                                        
USE_SHIPPED_BOOST = True                                                        
BOOST_PYTHON_LIBNAME = ['boost_python-py27']                                    
BOOST_THREAD_LIBNAME = ['boost_thread']                                         
CUDA_TRACE = False                                                              
CUDA_ROOT = '/usr/local/cuda'                                                   
CUDA_ENABLE_GL = False                                                          
CUDA_ENABLE_CURAND = True                                                       

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

yono

前提

• [local] -> [admin] -> [prod]
• [admin] -> [prod] の鍵は事前に配置済み
• $HOME/.ssh/config で設定
• 踏み台サーバ(admin)、目的のサーバ(prod) どちらにも鍵認証でログイン

結果

• $HOME/.ssh/config だけだと実行終了時の DONE で固まる

crmccreary

from Crypto.Cipher import AES
from Crypto import Random

BS = 16
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS) 
unpad = lambda s : s[0:-ord(s[-1])]

class AESCipher:
    def __init__( self, key ):
        """