maheshgawali

root@f827ea48f84f:/code# ffmpeg -h full
ffmpeg version 3.4.2-2 Copyright (c) 2000-2018 the FFmpeg developers
  built with gcc 7 (Ubuntu 7.3.0-16ubuntu2)
  configuration: --prefix=/usr --extra-version=2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --e

hakib

class TimeLimitedPaginator(Paginator):
    """
    Paginator that enforced a timeout on the count operation.
    When the timeout is reached a "fake" large value is returned instead,

    Why does this hack exist? On every admin list view, Django issues a
    COUNT on the full queryset. There is no simple workaround. On big tables,
    this COUNT is extremely slow and makes things unbearable. This solution
    is what we came up with.
    """

maheshgawali

#!/usr/bin/env python3

import requests
import json
import argparse

PAGE_LENGTH = '50'
PR_STATE = 'OPEN'

maheshgawali

#!/usr/bin/env python3

# OBJECTIVE 
# 1. script should be able to pick up from where it left off, assume that state is saved extrenally in some datastore
# 2. order of execution of methods matters

class statefulDispatcherUsingChaining():

    def __init__(self, current_state=None):
        self.current_state = "one" if not current_state else current_state

jackton1

from django.db import ProgrammingError, models
from django.db.models.constants import LOOKUP_SEP
from django.db.models.query import normalize_prefetch_lookups
from rest_framework import serializers
from rest_framework.utils import model_meta

class OptimizeModelViewSetMetaclass(type):
    """
    This metaclass optimizes the REST API view queryset using `prefetch_related` and `select_related`
    if the `serializer_class` is an instance of `serializers.ModelSerializer`.

docPhil99

#Capture and stream a webcam To capture using the iSight camera on a Mac, or infact any other webcam connected to the Mac, we can use FFmpeg. First get a list of the devices installed.

ffmpeg -f avfoundation -list_devices true -i "" 

This will list the aviable video and audio devices.

The below will capture at 30fps and the set video size to a file. ffmpeg -f avfoundation -framerate 30 -video_size 640x480 -i "0:none" out.avi

maheshgawali

#!/bin/bash

echo -n $'#####################################################\n'
echo -n $'### Find all git branches merged into this branch ###\n'
echo -n $'#####################################################\n'

response=
echo -n $'\nEnter name of the git branch > '
read response
if [ -n "$response" ]; then

fdemmer

# -*- coding: utf-8 -*-
import logging
import os
from tempfile import gettempdir

import boto3
import botocore
from django.db.backends.sqlite3.base import DatabaseWrapper

log = logging.getLogger(__name__)

EKami

Building TensorFlow 1.3.0-rc1 for Raspberry Pi/Ubuntu 16.04: a Step-By-Step Guide

Here you'll learn how to build Tensorflow for the raspberry pi 3 with either the Python API or as a standalone shared library which can be interfaced from the C++ API and eventually as a library which can be used in other languages.

For the C++ library this tutorial will show you how extract tensorflow library and headers to use in any environment you want.

(This tutorial couldn't be possible without the help of the people from the References section)

What You Need

Spaider

If you use Amazon AWS for nearly anything, then you are probably familiar with KMS, the Amazon Key Management Service.

KMS is a service which allows API-level access to cryptographic primitives without the expense and complexity of a full-fledged HSM or CloudHSM implementation. There are trade-offs in that the key material does reside on servers rather than tamper-proof devices, but these risks should be acceptable to a wide range of customers based on the care Amazon has put into the product. You should perform your own diligence on whether KMS is appropriate for your environment. If the security profile is not adequate, you should consider a stronger product such as CloudHSM or managing your own HSM solutions.

The goal here is to provide some introductory code on how to perform envelope encrypt a message using the AWS KMS API.

KMS allows you to encrypt messages of up to 4kb in size directly using the encrypt()/decrypt() API. To exceed these limitations, you must use a technique called "envelope encryptio