abdularis

#!/bin/bash

# mac os is not linux and therefore it is not shipped with gnu command line tools intead with freeBSD version

tool_list=(
    # GNU programs non-existing in macOS
    "watch"
    "wget"
    "wdiff"
    # "gdb" intel only

cketti

import android.content.ContentResolver
import android.net.Uri
import okhttp3.MediaType
import okhttp3.RequestBody
import okio.BufferedSink
import okio.Okio
import java.lang.IllegalStateException

class ContentUriRequestBody(
        private val contentResolver: ContentResolver,

EricDavies

package com.priologic.easyrtcMedia;

import android.opengl.GLES11Ext;
import android.opengl.GLES20;
import java.nio.ByteBuffer;
import java.nio.FloatBuffer;
import org.webrtc.*;
/**
 * Class for converting OES textures RGBA. It should be constructed on a thread with
 * an active EGL context, and only be used from that thread. It is used by the EasyrtcSingleFrameCapturer.

Robyer

// You can use maven-publish-helper.gradle script without changes and even share it between multiple
// modules. Just place the maven-publish-helper.gradle file in the root directory of your project,
// then apply it at the bottom of your module's build.gradle file like this:

// ...content of module's build.gradle file...

apply from: '../maven-publish-helper.gradle'

publishing {
	publications {

askilondz

Adaptive Streaming has become the neccessity for streaming video and audio. Unfortantely, as of this post, there isn't a whole lot of tutorials that accumulate all of the steps to get this working. Hopefully this post achieves that. This post focuses on using Amazon Web Services (AWS) to transcode for HLS and DASH and be the Content Delivery Network (CDN) that delivers the stream to your web page. We'll be using Video.js for the HTML5 player as well as javascript support libaries to make Video.js work with HLS and DASH.

So Here's what you need:

• An AWS Account
• Video.js
• videojs-contrib-hls.min.js
• dash.all.min.js
• videojs-dash.min.js

Set up three S3 buckets

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

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

Takhion

/**
 * ArcUtils.java
 * 
 * Copyright (c) 2014 BioWink GmbH.
 * 
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

werbet

public static String integerToStringIP(int ip) {
        return ((ip >> 24 ) & 0xFF) + "." +

               ((ip >> 16 ) & 0xFF) + "." +

               ((ip >>  8 ) & 0xFF) + "." +

               ( ip        & 0xFF);
    }