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import logging


class OneLineHandler(logging.StreamHandler):

    def emit(self, record):
        try:
            msg = self.format(record)
            stream = self.stream
            stream.write('\u001b[1000D' + msg)

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# -*- coding: utf8 -*-

import av

# Откроем ресурс на чтение
input_resource = av.open(
    'rtmp://src_stream:1935/play'
)
# Откроем ресурс на запись.
output_resource = av.open(

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"""
From http://harkablog.com/inside-the-django-orm-aggregates.html
with a couple of fixes.

Usage: MyModel.objects.all().annotate(new_attribute=GroupConcat('related__attribute', separator=':')
"""

from django.db.models import Aggregate
from django.db.models.sql.aggregates import Aggregate as SQLAggregate

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# -*- coding: utf8 -*-

from __future__ import absolute_import, division, print_function

import socket
from urlparse import urlparse


class DnsResolverMiddleware(object):
    """

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from apps.genapi import models


class RegionTreeManager(models.Manager):

    def handle_qs(self, qs):
        not_detailed_query = "not find_in_set('detailed', Options)"
        not_deleted_query = "not find_in_set('deleted', Options)"
        qs = qs.extra(where=[not_detailed_query, not_deleted_query])

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% !TeX encoding = UTF-8
\ProvidesPackage{bashline}[2016/10/24 v. 0.1]

\makeatletter
    \newcommand{\bashline@file@name}[1]{%
        /tmp/${USER}-${HOSTNAME}-\jobname-#1.tex%
    }
    \newread\bashline@file
    \newcommand{\bashline@command@one}[2][tmp]{%
        \immediate\write18{#2 > \bashline@file@name{#1}}

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# -*- coding: utf-8 -*-

from __future__ import absolute_import, division, print_function

import lxml.etree as et
import six

def dumps(data):
    name = data.keys()[0]
    value = data[name]

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import itertools
import multiprocessing as mp
from concurrent.futures import ProcessPoolExecutor, as_completed

from .base_extractor import BaseExtractor

class ParallelExtractor(BaseExtractor):
  
    POOL_SIZE = mp.cpu_count()
    IMAGE_GROUP_SIZE = 512

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import av
from av.video.frame import VideoFrame
from av.video.stream import VideoStream

# В этом списке будем хранить кадры в виде numpy-векторов.
array_list = []

# Откроем контейнер на чтение
input_container = av.open('input.mp4')

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# Python 2.* only. For Python 3.* please use concurrent.futures.ProcessPoolExecutor

def map_parallel_pymp(obj_list, filter_list, **kwargs):
    """
    Applies several filters from filter_seq
    to video frames from obj_seq in parallel manner
    using pymp (OpenMP for Python).

    Under construction