Cryptocurrency

Bitcoin brainwallet recovery tool with python

Bitcoin-brainwallet-recovery Bitcoin brainwallet recovery tool. Programmed in Python | PySimpleGUI From seed phrase create bitcoin privatekey and then convert it to Wallet Interchange Format key (WiF) format, which is a Base-58 form for the random key. This is the format that is stored in the Bitcoin Wallet. For example a sample private key is generated from: seed phrase: alfanumerico Privatekey: 05d95cf373f63b44267a193a1ef875c6758996e6c8ab5e049c48eb444206fce8 We then convert this into WiF format (Base-58): 5HrrzGnmMMr6fwkftrtL6AEfkkwWfgFQ8a2Lh8tAtF3MkXF9bPD This can be stored in a Bitcoin wallet. Next we […]

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Security

Pylint plugin to enforce some secure coding standards for Python

Pylint Secure Coding Standard Plugin pylint plugin that enforces some secure coding standards. Installation pip install pylint-secure-coding-standard Pylint codes Code Description R8000 Use os.path.realpath() instead of os.path.abspath() and os.path.relpath() E8001 Avoid using exec() and eval() E8002 Avoid using os.sytem() E8003 Avoid using shell=True in subprocess functions or using functions that internally set this R8004 Avoid using tempfile.mktemp(), prefer tempfile.mkstemp() instead E8005 Avoid using unsafe PyYAML loading functions E8006 Avoid using jsonpickle.decode() C8007 Avoid debug statement in production code C8008 Avoid […]

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Excel

Example Python codes that works with MySQL and Excel files

Python x MySQL x Excel by Zinglecode Example Python codes that do the processes between MySQL database and Excel spreadsheet files. YouTube videos Setup database table products table structure product_notes table structure categories table structure hashtags table structure products_hashtags table structure Install Python 3 and pipenv Download Python 3 installation file from https://www.python.org/ Install pipenv as global package by this command. pip install pipenv Note: for macOS with pre-installed Python 2, use pip3 instead of pip. Install and    

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Python

Python’s collections: A Buffet of Specialized Data Types

Python’s collections module provides a rich set of specialized container data types carefully designed to approach specific programming problems in a Pythonic and efficient way. The module also provides wrapper classes that make it safer to create custom classes that behave similar to the built-in types dict, list, and str. Learning about the data types and classes in collections will allow you to grow your programming tool kit with a valuable set of reliable and efficient tools. In this tutorial, […]

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Neural Network

Learning to Tile with Self-Supervised Graph Neural Network

TilinGNN TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: given a tile set (a) and a 2D region to be filled (b), we aim to produce a tiling (c) that maximally covers the interior of the given region without overlap or hole between the tile instances. Dependencies: This project is implemented in Python 3.7. You need to install the following packages to run our program. Pytorch (tested […]

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Network

Riggable 3D Face Reconstruction via In-Network Optimization

INORig Source code for CVPR 2021 paper “Riggable 3D Face Reconstruction via In-Network Optimization”. Installation (1) Create an Anaconda environment. conda env create -f env.yaml conda activate INORig (2) Clone the repository and install dependencies. git clone https://github.com/zqbai-jeremy/INORig.git cd INORig pip install -r requirements_pip.txt (3) Setup 3DMM mkdir external cd external git clone https://github.com/zqbai-jeremy/face3d.git cd face3d Setup face3d as in YadiraF/face3d. Download “Exp_Pca.bin” from Guo et al. (in “CoarseData” link of their repository)and copy to “/external/face3d/examples/Data/BFM/Out/”. Download “std_exp.txt” from Deng […]

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Machine Learning

Spatially-invariant Style-codes Controlled Makeup Transfer in python

SCGAN Implementation of CVPR 2021 paper “Spatially-invariant Style-codes Controlled Makeup Transfer” Prepare The pre-trained model is avaiable at https://drive.google.com/file/d/1t1Hbgqqzc_rV5v3gF7HuJ-xiuEVNb8sh/view?usp=sharing. vgg_conv.pth:https://drive.google.com/file/d/1JNrSVZrK4TfC7pFG-r7AOmGvBXF2VFOt/view?usp=sharing Put the G.pth and VGG weights in “./checkpoints” and “./” respectively. Environments:python=3.8, pytorch=1.6.0, Ubuntu=20.04.1 LTS Train Put the train-list of makeup images in “./MT-Dataset/makeup.txt” and the train-list of non-makeup images in “./MT-Dataset/non-makeup.txt” Use the “./scripts/handle_parsing.py” to convert the origin MT-Dataset’s seg labels Use python sc.py –phase train to train Test 1.Global Makeup Transfer python sc.py –phase test 2.Part-specific Makeup Transfer […]

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Natural Language Processing

Bi-encoder based entity linker for japanese with python

jel: Japanese Entity Linker jel – Japanese Entity Linker – is Bi-encoder based entity linker for japanese. Currently, link and question methods are supported. el.link This returnes named entity and its candidate ones from Wikipedia titles. from jel import EntityLinker el = EntityLinker() el.link(‘今日は東京都のマックにアップルを買いに行き、スティーブジョブスとドナルドに会い、堀田区に引っ越した。’) >> [ { “text”: “東京都”, “label”: “GPE”, “span”: [ 3, 6 ], “predicted_normalized_entities”: [ [ “東京都庁”, 0.1084 ], [ “東京”, 0.0633 ], [ “国家地方警察東京都本部”, 0.0604 ], [ “東京都”, 0.0598 ], … ] }, { “text”: “アップル”, […]

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Deep Learning

Website which uses Deep Learning to generate horror stories

Creepypasta – Text Generator Website which uses Deep Learning to generate horror stories. There are two parts to the project. One is the Deep Learning model which generates the text. The other is the website which uses the model to generate text. I have used Deep Learning model to generate text. It is a Neural Network which uses Recurrent Neural Network to generate text. I have hosted a model on Algorithmia and used it’s API to generate text. Built With […]

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Simulation

An open-source computational electrodynamics Python simulator

PyCharge PyCharge is an open-source computational electrodynamics Python simulator that can calculate the electromagnetic fields and potentials generated by moving point charges and can self-consistently simulate dipoles modeled as Lorentz oscillators. PyCharge was designed to be accessible for a wide range of use cases: it can be used as a pedagogical tool for undergraduate and graduate-level EM theory courses to provide an intuitive understanding of the EM waves generated by moving point charges, and can also be used by researchers […]

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