Python generation script for BitBirds

Overview

BitBirds generation script

Intro

This is published under MIT license, which means you can do whatever you want with it - entirely at your own risk.

Please don't be an asshole. This is, like, grassroots and stuff.

Specifically I'm asking you in good faith not to directly knock off the BitBirds project, or otherwise screw me over for sharing this. Do not use this for anything hateful or discriminatory.

There is a YouTube video walkthrough to complement this ReadMe ...Link....

Setting the expectations

If you're new to programming you may struggle to set up the dependencies. If you're persistent, you can do it! I believe in you.

Often in technology, setting up a pre-requisite like PIP (a python asset installation tool) isn't something the developer thinks about in a given project because it has been on their computer for months or years.

Even having set up a number of dependencies just a few weeks ago for this project, I don't remember exactly how I worked through the various error messages. When you try to run the script, if it fails there will often be some useful nugget of information buried in the cryptic response blob. As a rule for life - Google is your friend, and others have probably encountered your exact error message. When asking questions on discord, stackoverflow, or wherever, say very specifically (1) what you've tried (2) what you expect as the result and (3) what issue/error you're encountering. That'll get a lot more useful feedback than just shouting HALP.

Dependencies

The dependenices were all installed with the terminal/command line. There is documentation abound about terminal generally, and these tools specifically, but unfortunately I did not save copies of the web pages I used. From memory the things I needed to setup were:

  • Python 3 (default on my mac was python 2.7)

  • PIP - a command-line installation mechanism for python assets.

IIRC I needed to use some special command with python 3 to use pip as an installation mechanism for the items below- perhaps pip3 install ... rather than pip install ...?

  • Pillow - python library to generate images - installed via terminal/command-line with PIP

  • NumPy - python library to work with arrays - installed via terminal/command-line with PIP

  • I don't think I had to install the 'random' library (included in the script) to use the number-randomization feature, but might have.

If you encounter specific setup items I haven't mentioned here let me know, and I'll add them.

How this script works

The video I've put on YouTube complements this overview.

We are iterating through a 'loop' once for each bird. The loop starts with a 'seed number' that is used to deterministically generate pseudo-random numbers. I say 'deterministically' and 'pseudo-random' because from the same seed number the 'random' output will always be the same. It's not truly random in a security or mathematical sense. I used the most recent ETH block at the time as my seed number - 11981207.

There is then a 'chain' of additional random numbers generated that are used to define all of the various traits of the birds. Many of the attributes generate a random number between 1-1000 and use that for some sort of logical statement (e.g. to decide beak color).

Interestingly, the way I've used this random-number chain seems to have resulted in some specific behavior and combinations that I can't yet personally explain. For example the way the bird type selection random number and beak color selection random number chain from one another seems to have resulted in no red-beaked woodpeckers. I also noticed that all of the four cockatoos generated seemed to have the exact same blue crest. Because I wanted more variety than that, in the minted NFTs I ran another batch of cockatoos and replaced the second, third, and fourth cockatoos in the original sequence with others that provided more variety. I made no other changes to the randomly generated set of birds, and if you run the script yourself (without changing the seed) you should see identical matches for each number. If you spot a pattern in why the random numbers behave in this way, I would love to discuss on twitter or discord.

  • Head color and throat color are a random 1-255 number generated into each of the three RGB values in a color.
  • Eye color looks at a random number between 1-1000 and if it's 47 or less, will give the bird crazy eyes. Crazy eyes always have the same pupil color (154, 0, 0) and then generate a random color for the 'white of the eye,' in the same way the head and throat color are generated.
  • Beak color is determined by an evaluation of another 1-1000 'random' number. Grey is most common, gold is also common, red is rare, and black is very rare.

The bird images are 24x24 arrays of variables, representing every pixel in the final image. I've used variables with two letters for each type of pixel (e.g. outline ol, head color hd, beak color bk), so as to keep the 'matrix' of pixel variables easy to see and work with. If you zoom way out on the code you may even be able to see a rough picture of the birds in the code, just from the slight differences in the variables.

The script uses another 1-1000 'random' number to decide which of the bird type templates to use. Basic bird is most common, at about 75% probability. Jay has a 15% probability, woodpecker has 6%, eagle has 3.5%, and cockatoo has half a percent probability.

From there, you're just about home free. The final bit of the loop re-sizes the generated bird from 24x24 pixels up to 480x480 pixels. It generates the image file path (dynamically, wherever you have the folder using the os library), and saves the image into the included folder bird_images.

Then it goes right back to the top of the loop, and does it again for the next bird, until the number of defined loops is completed.

Wrap up

I sincerely hope this inspires someone to learn a new skill, take up coding, or generally expand their horizons! I won't profess to be a professional coder, but I am a technologist in my day job and have found it to be a fulfilling and rewarding life path. This BitBirds project has been a joy to be involved in. The community that has popped up around it already has been inspiring, and I'm excited to see it grow in the years to come.

If would like to show your thanks for this shared asset I'd encourage you to plant some trees! https://onetreeplanted.org/collections/all

If you feel absolutly compelled to send ETH or NFTs to me directly, please know that it is not necessary, but the BitBirds project hardware wallet address is: 0x1fd146a5e6152c5ACd3A013fBC42A243e4DfCe63

Thanks for everything!

Owner
BitBirds generation script!
Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

TextDistance TextDistance -- python library for comparing distance between two or more sequences by many algorithms. Features: 30+ algorithms Pure pyt

Life4 3k Jan 06, 2023
Interactive Jupyter Notebook Environment for using the GPT-3 Instruct API

gpt3-instruct-sandbox Interactive Jupyter Notebook Environment for using the GPT-3 Instruct API Description This project updates an existing GPT-3 san

312 Jan 03, 2023
Sample data associated with the Aurora-BP study

The Aurora-BP Study and Dataset This repository contains sample code, sample data, and explanatory information for working with the Aurora-BP dataset

Microsoft 16 Dec 12, 2022
To be a next-generation DL-based phenotype prediction from genome mutations.

Sequence -----------+-- 3D_structure -- 3D_module --+ +-- ? | |

Eric Alcaide 18 Jan 11, 2022
A linter to manage all your python exceptions and try/except blocks (limited only for those who like dinosaurs).

Manage your exceptions in Python like a PRO Currently in BETA. Inspired by this blog post. I shared the building process of this tool here. “For those

Guilherme Latrova 353 Dec 31, 2022
This is a NLP based project to extract effective date of the contract from their text files.

Date-Extraction-from-Contracts This is a NLP based project to extract effective date of the contract from their text files. Problem statement This is

Sambhav Garg 1 Jan 26, 2022
English loanwords in the world's languages

Wiktionary as CLDF Content cldf1 and cldf2 contain cldf-conform data sets with a total of 2 377 756 entries about the vocabulary of all 1403 languages

Viktor Martinović 3 Jan 14, 2022
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai

Amazon Web Services - Labs 124 Jan 03, 2023
Long text token classification using LongFormer

Long text token classification using LongFormer

abhishek thakur 161 Aug 07, 2022
Python bindings to the dutch NLP tool Frog (pos tagger, lemmatiser, NER tagger, morphological analysis, shallow parser, dependency parser)

Frog for Python This is a Python binding to the Natural Language Processing suite Frog. Frog is intended for Dutch and performs part-of-speech tagging

Maarten van Gompel 46 Dec 14, 2022
CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus

CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus CVSS is a massively multilingual-to-English speech-to-speech translation corpus, co

Google Research Datasets 118 Jan 06, 2023
code for modular summarization work published in ACL2021 by Krishna et al

This repository contains the code for running modular summarization pipelines as described in the publication Krishna K, Khosla K, Bigham J, Lipton ZC

Approximately Correct Machine Intelligence (ACMI) Lab 21 Nov 24, 2022
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
Winner system (DAMO-NLP) of SemEval 2022 MultiCoNER shared task over 10 out of 13 tracks.

KB-NER: a Knowledge-based System for Multilingual Complex Named Entity Recognition The code is for the winner system (DAMO-NLP) of SemEval 2022 MultiC

116 Dec 27, 2022
Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together

SpeechMix Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together. Introduction For the same input: from datas

Eric Lam 31 Nov 07, 2022
Various capabilities for static malware analysis.

Malchive The malchive serves as a compendium for a variety of capabilities mainly pertaining to malware analysis, such as scripts supporting day to da

MITRE Cybersecurity 64 Nov 22, 2022
Meta learning algorithms to train cross-lingual NLI (multi-task) models

Meta learning algorithms to train cross-lingual NLI (multi-task) models

M.Hassan Mojab 4 Nov 20, 2022
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind

Jonas Djondo 1 Nov 18, 2021
GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model

GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.

Nathan Cooper 2.3k Jan 01, 2023
A NLP program: tokenize method, PoS Tagging with deep learning

IRIS NLP SYSTEM A NLP program: tokenize method, PoS Tagging with deep learning Report Bug · Request Feature Table of Contents About The Project Built

Zakaria 7 Dec 13, 2022