October 23, 2021 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

October 23, 2021 Recorder

Python Screen Recorder using Python

October 23, 2021 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

October 23, 2021 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: […]

October 23, 2021 tables

Python table of data with styles

October 23, 2021 Scripts

DRF magic links with python

Installation pip install drf-magic-links Add URL patterns # urls.py from django.urls import path urlpatterns = [ path(‘/api/’, include(‘magic_links.urls.api’)), … ] Override settings as needed

October 23, 2021 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 […]

October 23, 2021 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: