Day: October 23, 2021

Blender

Cave Generation using metaballs in Blender

Cave Generation using metaballs in Blender. Originally created by sdfgeoff, Edited by Myself (Archie Jaskowicz). Installation To install this plugin, download the python script and open blender. Then go to “Edit -> Preferences -> Add-ons -> Install”. Then navigate to the python script and click “install Add-on”. Once that’s added, search for CaveGen in the search bar (located under “Install” at the top right of Preferences), make sure the “Community” box is on (shift-click it) and the enable “CaveGen” by […]

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Models

Collections of pydantic models

The pydantic-collections package provides BaseCollectionModel class that allows youto manipulate collections of pydantic models(and any other types supported by pydantic). Requirements Python >= 3.7 pydantic >= 1.8.2 Installation pip install pydantic-collections Usage Basic usage

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AntiCaptcha

A python library for anti-captcha.com

A python library for anti-captcha.com Documentation for the API Requirements Install git clone https://github.com/ShayBox/AntiCaptcha.git cd AntiCaptcha $ poetry build $ pip install dist/AntiCaptcha-0.1.0.tar.gz –user Usage from anticaptcha import AntiCaptcha captcha = AntiCaptcha(“API KEY”) captcha.solve({“TASK”: “OBJECT”}) GitHub View Github    

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Telegram

A telegram bot to monitor the latest NFT price on BSC

This is a telegram bot for monitoring price and ranking of NFT on Binance Smart Chain. Can fetch latest ranking and price in real time. .Pancake_NFT/nft-web3.py: Main file to fetch transaction and log through web3. .Pancake_NFT/pancake_nft.py: Original version to fetch log from bscscan. Now aborted. 0x00 Requirement Available for Python3.8+, recommend to use virtualenv to run this project. python-telegram-bot==13.7 web3==5.24.0 0x01 Usage Modify configuration in config.py TEST_BOT_TOKEN : tgbot token of test environment PROD_BOT_TOKEN: tgbot token of produce environment COLLECTION_CONTRACT_ADDRESS: […]

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Motion

Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction

This repository contains the code and data for the paper “Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction” by Takuma Yagi, Md. Tasnimul Hasan and Yoichi Sato. Requirements Python 3.6+ ffmpeg numpy opencv-python pillow scikit-learn python-Levenshtein pycocotools torch (1.8.1, 1.4.0- for flow generation) torchvision (0.9.1) mllogger flownet2-pytorch Caution: This repository requires ~100GB space for testing, ~200GB space for trusted label training and ~3TB space for full training. Getting Started Download the data Download EPIC-KITCHENS-100 videos from the […]

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Self-Supervised

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models, NeurIPS 2021). Setup Create the conda environment by running : conda env create -f environment.yml Alternatively, you can update an existing conda environment by running : conda env update -f environment.yml Modify the python pathexport PYTHONPATH= Export the following variables for renderingexport MUJOCO_RENDERER=egl; export MUJOCO_GL=egl Please follow these instructions to install mujoco Bibtex If you find this code useful, please cite:

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