karlos g liberal karlosgliberal

Twitter abuses all media file uploads, each type in its own way. If we want to upload a good looking animation loop from some low-color, high-detail generative art, we have to game their system's mechanisms.

don't upload a video file, they will re-encode it into absolute 💩
create a GIF, which they will auto-convert into a video file 😱
The frames of the GIF will be resized to an even-sized width using an extremely naive algorithm. Your GIF should be an even size (1000, 2000,

Quick, Draw! ndjson data

The Quick, Draw! dataset uses ndjson as one of the formats to store its millions of drawings.

We can use the ndjons-cli utility to quickly create interesting subsets of this dataset.

The drawings (stroke data and associated metadata) are stored as one JSON object per line. e.g.:

Instructions for making a Neural-Style movie

The following instructions are for creating your own animations using the style transfer technique described by Gatys, Ecker, and Bethge, and implemented by Justin Johnson. To see an example of such an animation, see this video of Alice in Wonderland re-styled by 17 paintings.

Setting up the environment

The easiest way to set up the environment is to simply load Samim's a pre-built Terminal.com snap or use another cloud service like Amazon EC2. Unfortunately the g2.2xlarge GPU instances cost $0.99 per hour, and depending on parameters selected, it may take 10-15 minutes to produce a 512px-wide image, so it can cost $2-3 to generate 1 sec of video at 12fps.

If you do load the

	import os
	import pickle
	import requests

	#download cached token data here:
	#https://drive.google.com/file/d/1g_4w_Re5Y0NmcS2Y55WQzESWDeL2dey6/view?usp=sharing
	#and put it into the same folder as this file

	cachedTokenData = {"maxTokenID":-1,"knownTokenIds":{},"data":[]}
	if os.path.exists("cached_token_data.pickle"):

	<script>
	import delaunay from 'delaunay-triangulate';
	import { Slider, Color } from 'texel/ui';

	// This will get passed in with the P5 instance
	export let p5;

	// Size of the canvas in pixels
	export let width = 256;
	export let height = 256;

	#!/usr/bin/env python3

	from PIL import Image
	import numpy as np
	import tensorflow as tf
	import tensorflow_hub as hub

	# smooth values from point a to point b.
	STEPS = 100
	pt_a = np.random.normal(size=(512))

	# git clone from https://github.com/tkarras/progressive_growing_of_gans
	# download the snapshot from their Google drive
	# use the following code in the same directory to generate random faces
	import os
	import sys
	import time
	import glob
	import shutil
	import operator
	import theano

	from gensim.models import KeyedVectors

	# Load gensim word2vec
	w2v_path = '<Gensim File Path>'
	w2v = KeyedVectors.load_word2vec_format(w2v_path)

	import io

	# Vector file, `\t` seperated the vectors and `\n` seperate the words
	"""

	///////////////////////////////////
	// PARTICLE / WIRING CODE //
	///////////////////////////////////

	int photoResistor = A0;
	int power = A5;
	int lightValue;

	void setup() {
	pinMode(photoResistor,INPUT);

	from multiprocessing.dummy import Pool
	from tqdm import tqdm
	from urllib.parse import urlsplit
	import urllib3
	import itertools, functools, operator
	import os
	import sys
	import re
	import errno
	import argparse