andreasvc

unicolet

@ in soa localhost. root 1 3H 15M 1W 1D
  ns localhost.
{% set nets=salt['publish.publish']('*','network.interfaces') %}{% for n in nets %}{% set pdata=salt['publish.publish'](n,'pillar.data') %}{% if pdata.has_key(n) %}{% if pdata[n].has_key('zone') %}{% if pdata[n]['zone']=='linuxservers' %}
{{ n }} IN      A       {{ nets[n]['eth0']['inet'][0]['address'] }} 
{% endif %}{% endif %}{% endif %}{% endfor %}

Kami

#!/usr/bin/env python
# Licensed to Tomaz Muraus under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# Tomaz muraus licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#

ogrisel

*.pyc
*.npy
*.pkl
*.mmap

fsodogandji

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

taku-y

nlm

from functools import wraps
from types import FunctionType, GeneratorType
import logging
import time

def coroutine(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        logging.debug('coroutine starting: {}'.format(f.__name__))
        return f(*args, **kwargs)

TomAugspurger

stared

Sequential model in Keras -> ASCII

by Piotr Migdał from deepsense.io

EDIT: Now at: https://github.com/stared/keras-sequential-ascii, pip-installable.

Usage

from keras_ascii_sequential import sequential_model_to_ascii_printout

kevin-keraudren

#!/usr/bin/python

import numpy as np
import cv2
import densecrf as dcrf
from skimage.segmentation import relabel_sequential
import sys

# Usage:
# python dense_inference.py image annotations output