This is how to REAL install Remix OS on VirtualBox. rootfs could be writable!!!
- any linux liveCD iso file (Xubuntu here)
- Remix OS iso file ("Remix_OS_for_PC_Android_M_32bit_B2016092201.iso" here)
- VirtualBox
# got this from http://stackoverflow.com/questions/8672809/use-ffmpeg-to-add-text-subtitles | |
ffmpeg -i infile.mp4 -f srt -i infile.srt -c:v copy -c:a copy -c:s mov_text outfile.mp4 | |
# confirmed working with the following ffmpeg | |
# (installed using `brew 'ffmpeg', args: ['with-libvorbis', 'with-libvpx']` ) | |
ffmpeg version 3.1.2 Copyright (c) 2000-2016 the FFmpeg developers | |
built with Apple LLVM version 7.3.0 (clang-703.0.31) | |
configuration: --prefix=/usr/local/Cellar/ffmpeg/3.1.2 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-opencl --enable-libx264 --enable-libmp3lame --enable-libxvid --enable-libvorbis --enable-libvpx --disable-lzma --enable-vda |
Preamble:
In this post I will explore how to stream a video and audio capture from one computer to another using ffmpeg and netcat, with a latency below 100ms, which is good enough for presentations and general purpose remote display tasks on a local network.
The problem:
Streaming low-latency live content is quite hard, because most software-based video codecs are designed to achieve the best compression and not best latency. This makes sense, because most movies are encoded once and decoded often, so it is a good trade-off to use more time for the encoding than the decoding.
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:
#!/usr/bin/env python | |
""" | |
Twitter's API doesn't allow you to get replies to a particular tweet. Strange | |
but true. But you can use Twitter's Search API to search for tweets that are | |
directed at a particular user, and then search through the results to see if | |
any are replies to a given tweet. You probably are also interested in the | |
replies to any replies as well, so the process is recursive. The big caveat | |
here is that the search API only returns results for the last 7 days. So |
/* | |
// retrieved from http://dublincore.org/documents/dcmi-terms/ | |
// with the following Chrome console code: | |
(function() { | |
Element.prototype.getAxis = function(axis) { | |
var td = this.querySelector('td[axis=' + axis + ']'); | |
return td ? td.innerText : ""; | |
} | |
var tbodies = $$('#H2 tbody'), |
// This doesn't work or at least it didn't for the shader i was trying to use it with. | |
// Just Sharing Becasue i got farther with this than other version I found around the internet. | |
// | |
// Quit working on this because the shader i was trying to convert to a Standard Unity Shader | |
// Really could not be done as it was based reflections of orginal object (PolyWorld). | |
//orginal - http://wiki.unity3d.com/index.php/Bake_Material_to_Texture | |
using UnityEngine; | |
using UnityEditor; |
from __future__ import print_function | |
import imageio | |
from PIL import Image | |
import numpy as np | |
import keras | |
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation | |
from keras.models import Model | |
from keras.regularizers import l2 | |
from keras.optimizers import SGD |
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
'''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 |