rezonn

ffmpeg -f avfoundation -framerate 30 -s "640x480" -i "0" -c:v rawvideo -pix_fmt rgb24 -f caca -s "640x480" output4.mp4

brew install homebrew-ffmpeg/ffmpeg/ffmpeg --with-libcaca

ffmpeg -i vitrine.mp4 -vf scale=1024:-1 -q:vscale 0 vitrine.avi

Parameters

Parameter	YouTube recommends setting
-movflags faststart	moov atom at the front of the file (Fast Start)

By Ryan Aunur Rassyid

Simply create RESTful API with Google Script and store it to Google SpreadSheet like a Pro.

	# Modify this file accordingly for your specific requirement.
	# http://www.thegeekstuff.com
	# 1. Delete all existing rules
	iptables -F

	# 2. Set default chain policies
	iptables -P INPUT DROP
	iptables -P FORWARD DROP
	iptables -P OUTPUT DROP

	@if(0)==(0) @echo off & rem -- Javascript --
	CScript.exe //NoLogo //E:JScript "%~f0" %*
	exit /b 0
	@end

	var excel = new ActiveXObject("Excel.Application"),
	WshShell = WScript.CreateObject("WScript.Shell"),
	BIN_DIR = WScript.ScriptFullName.replace(WScript.ScriptName, "");
	WINDOW_CLASS = "HTML Application Host Window Class",
	//WINDOW_CLASS = "Internet Explorer_Server",

	###############################################################################
	# The MIT License
	#
	# Copyright 2012-2014 Jakub Jirutka <jakub@jirutka.cz>.
	#
	# 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

	// MIT License - Copyright (c) 2016 Can Güney Aksakalli
	// https://aksakalli.github.io/2014/02/24/simple-http-server-with-csparp.html

	using System;
	using System.Collections.Generic;
	using System.Linq;
	using System.Text;
	using System.Net.Sockets;
	using System.Net;
	using System.IO;

	import * as tf from "@tensorflow/tfjs-node";

	// In my case I have 256x256 pictures
	const IMG_SIZE = 256;
	const NUMBER_OF_CHANNELS = 3;

	// Batch normalisation and ReLU always go together, let's add them to the separate function
	const batchNormRelu = (input: tf.Tensor4D) => {
	const batch = tf.layers.batchNormalization().apply(input);
	return tf.layers.reLU().apply(batch);