Brainiarc7

Using VAAPI's hardware accelerated video encoding on Linux with Intel's hardware on FFmpeg and libav

Hello, brethren :-)

As it turns out, the current version of FFmpeg (version 3.1 released earlier today) and libav (master branch) supports full H.264 and HEVC encode in VAAPI on supported hardware that works reliably well to be termed "production-ready".

SteffenBlake

Cheap gear to get the job done

1. Reolink PoE 5MP RLC-522 - https://www.amazon.ca/gp/product/B01AL5D85W/ref=ppx_yo_dt_b_asin_title_o06_s00
2. Mokerlink 1 to 4 port PoE switch - https://www.amazon.ca/MokerLink-Ethernet-Support-IEEE802-3af-Fanless/dp/B07SGBQNLM/ref=sr_1_4
3. (Kind of works) Raspberry Pi 4 - https://www.amazon.ca/CanaKit-Raspberry-Starter-Kit-4GB/dp/B07WRMR2CX/ref=sr_1_4 3a. (Recommended) Nanopi R4S (Way better performance but likely has to ship from China, but it is the far superior device for same price)
4. Recommended: Also get yourself a USB 3.0 portable hard drive, preferably 2TB+, of your preferred brand

Also likely needed:

xdamman

#!/bin/bash

# Bash script to install latest version of ffmpeg and its dependencies on Ubuntu 12.04 or 14.04
# Inspired from https://gist.github.com/faleev/3435377

# Remove any existing packages:
sudo apt-get -y remove ffmpeg x264 libav-tools libvpx-dev libx264-dev

# Get the dependencies (Ubuntu Server or headless users):
sudo apt-get update

pliablepixels

All the stuff to get CUDA 10.1 working with NVIDIA GPUs on Ubuntu 18.04. My notes.

Step 1: Install NVIDIA Driver

• sudo apt install nvidia-driver-430
• reboot
• run nvidia-smi. If it does not show your GPU, stop, fix. If this doesn't work, nothing else will (the rest of the stuff will compile, but won't work)

kevinGodell

Live streaming mpeg-dash video using ffmpeg and dash.js

Use ffmpeg to connect to an ip cctv camera and create video files on the fly that can be viewed in an mpeg-dash compatible browser using dash.js and an html5 video element.

Prerequisites

A linux server, such as Ubuntu

Apache web server installed, running, and reachable via its ip address

aruld

main() {
    List<String> list = new List<String>();
    list.add('one');
    list.add('two');
    list.add('twelve');
    list.forEach((element) => print(element));

    Set<String> set = Set.from(list);
    set.forEach((element) => print(element));    
}

Brainiarc7

FFmpeg and libav's playbook: Advanced encoding options with hardware-based acceleration, NVIDIA's NVENC and Intel's VAAPI-based encoder.

Hello guys,

Continuing from this guide to building ffmpeg and libav with NVENC and VAAPI enabled, this snippet will cover advanced options that you can use with ffmpeg and libav on both NVENC and VAAPI hardware-based encoders.

For ffmpeg:

jgornick

/**
 * Parses a string containing one or multiple JSON encoded objects in the string.
 * The result is always an array of objects.
 *
 * @param  {String} data
 * @return {Array}
 */
function parseJson(data) {
  data = data.replace('\n', '', 'g');

Jiab77

Compilation FFMpeg / NVENC + NVRESIZE + QSV + VAAPI + VDPAU + OpenCL

nVidia nvresize patch is outdated and not more compatible to the latest version of FFmpeg, so it's not included in this documentation.

(even if I've passed a lot of time at trying to make it compile... without any success)

Please don't rely on this page: https://developer.nvidia.com/ffmpeg, the implementation is a hack and was never been added to the main FFmpeg tree.

See:

• https://ffmpeg.org/pipermail/ffmpeg-devel/2015-November/182781.html

muendelezaji

#!/bin/bash

# =============================================================
# UPDATE SOURCE
# =============================================================
# git clone https://github.com/tensorflow/tensorflow
# git checkout -- .
# git pull origin master
# TF_BRANCH=r1.8
TF_ROOT=/home/tensorflow