Analysis

Static Code Analysis: a code comprehensive analysis platform

TCA, Tencent Cloud Code Analysis English | 简体中文 What is TCA Tencent Cloud Code Analysis (TCA for short, code-named CodeDog inside the company early) is a code comprehensive analysis platform, which includes three components: server, web and client. It supports the integration of common code analysis tools in the industry. Its main function is to ensure the code quality under agile iterations of multiple engineering projects, and Support the team to inherit the code culture. Code analysis is to analyze […]

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Calculator

Calculates the dates and times when the sun or moon is in a given position in the sky

I wrote this script after I was busy updating my article on chronolocation, which can be found here. It calculates the dates and times when the sun or moon is in a given position in the sky. It can be used during chronolocation tasks, to quickly find possible dates and times within a 15 minutes accuracy. For finding a more precise moment, one should use a tool like suncalc.org to find a closer match, or use the option –accurate. Installation […]

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Natural Language Processing

BDD-driven natural language automated testing framework, present by Trip Flight

行为驱动开发（Behavior-driven development，缩写BDD），是一种软件过程的思想或者方法，是一种敏捷软件开发的技术. Flybird是基于BDD模式的前端UI自动化测试框架,提供了一系列开箱即用的工具和完善的文档。 基于Behave，实现BDD中“自然语言测试用例文档”和“自动化测试代码”关联需要用到支持BDD工具。 基于Airtest，实现BDD中“测试用例能在自动化测试平台上执行”需要用到UI自动化测试框架。 特性 使用Flybird你能够完成大部分的手机端自动化操作，以下是一些帮助入门的特性描述： 基于BDD模式，类自然语言语法 支持自动化APP操作、表单提交、UI元素校验、键盘输入、Deeplink跳转等 默认支持英文、中文两种语言，支持更多语言扩展 插件式设计，支持用户自定义自动化扩展 提供cli脚手架，快速搭建项目 提供html报告 环境要求 python(3.7-3.9) nodejs(12+) 快速开始 1. 环境搭建 使用pip安装flybird框架，过程中会自动安装所需的 依赖包 在Mac/Linux系统下，需要手动赋予adb可执行权限 cd {your_python_path}/site-packages/airtest/core/android/static/adb/mac chmod +x adb cd {your_python_path}/site-packages/airtest/core/android/static/adb/linux chmod +x adb 使用脚手架创建项目 创建过程中会提示输入以下信息 项目名称 测试平台：Android / iOS 测试设备名称（ 可跳过，后续可在config中配置deviceId节点 ） webDriverAgent的BundleID（ 可跳过，ios设备连接使用，后续可在config中配置webDriverAgent节点 ） APP测试包名称（可跳过，默认为ctrip演示包，后续可在config中配置packageName节点） 为了帮助使用，项目创建时，会在test目录下生成演示features，后续可自行删除 2. 测试执行 请确保配置的测试设备能够正常连接 Android: 执行命令 adb devices , 检查设备列表中是否包含测试设备 iOS：以tidevice库举例，执行命令 tidevice list，检查设备列表中是否包含测试设备 Android设备连接 Q&A 请先安装手机对应品牌的官方驱动，确保能使用电脑对手机进行USB调试 确保已经打开了手机中的”开发者选项”，并且打开”开发者选项”内的”允许USB调试” 部分手机需要打开”允许模拟位置”、”允许通过USB安装应用” 关闭电脑上已经安装的手机助手软件，能避免绝大多数问题，请务必在任务管理器中手工结束手机助手进程 iOS设备连接 Q&A 请先准备一台macOS ，使用xcode部署 iOS-Tagent 成功后，能够在mac或windows机器上连接到iOS手机。请点击链接下载项目代码到本地进行部署。 mac 环境通过 Homebrew 安装iproxy brew install libimobiledevice windows 环境安装itunes 下载安装测试包 […]

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mosaic

Mosaic blur processing is applied to multiple faces appearing in the video

face_recognitionを使用して得られる顔座標は長方形であり、この座標をそのまま用いてぼかし処理を行った場合得られる画像は醜い。それに対してモザイクによってぼかし処理を行うと自然に見える。これを静止画だけでなく動画にも適用したい。同じ働きを持つものはORB-HD/defaceが既に存在する。defaceがcenterface.onnxを用いるのに比べこちらはdlibを使用する。これらは顔検出のみか顔認識まで行うかで使い分けすると良いと思う。 静止画の場合 face_recognitionのメソッドであるface_recognition.face_locationsの返り値は(top, right, bottom, left)の配列。 顔周りの長方形をぼかす original.png

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Server

Distance to Closest Road Feature Server

Milliarium Aerum, the zero of all Roman roads This is a feature server that computes the distance to the closest road as feature for a given coordinate. It can be used command-line or with the provided Flask microframework server. Roadster uses .SHP files from the Open Street Map project, as generously provided by Geofabrik GmbH. It can provide the value of the distance-to-closest-road feature for a given coordinate or compute the value of the feature for all points on a […]

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Tool

SimBiber: A tool for simplifying bibtex with official info

We often need to simplify the official bib that consists of many information into a shorter version that only maintains necessary information (e.g., author, title, conference/journal name and etc) due to page limitation. We introduce SimBiber, a simple tool in Python to simplify them automatically. Hope it’s helpful for you. We also highly recommend another wonderful tool for you Rebiber, which is a tool for normalizing bibtex with official info. Changelog 2021.12.31We build the first version and release it. Installation […]

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Converter

Convert any given animated gif file into an animation in GIFT CSV format

This repo is made to participate in Matt Parker’s XmasTree 2021 event. Convert any given animated gif file into an animation in GIFT CSV format. Convert a Gif: First, run pip3 install -r requirements.txt to install the necessary packages. To convert a gif you will need a CSV file with coordinates of the Xmas tree’s LED lights; Matt Parker’s 2021 coordinates are given in examples/coords_2021.csv. Run this command: python3 convert.py The output will be a CSV file in GIFT format […]

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Generator

A WIP Code Generator tool. This is the way

Django Fett is an incomplete code generator used on several projects.This is an attempt to clean it up and make it public for consumption. Django Fett is different because it leverages Frontmatter Metadata and exposes the extra metadata into the template context.Frontmatter Metadata may also contain variables that make setting an output filename cleaner than trying to save file or folder names with Jinja syntax. TODO: Quick start Templates A Fett template is a header of a markdown-like frontmatter and […]

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Tool

0xFalcon Tool For Python

Installation Install 0xFalcone Tool: apt install git git clone https://github.com/hassanalharbi123/0xFalcon cd 0xFlacon python3 install.py Usage python3 exploit.py -h Example XSS Tool: python3 exploit.py -u xss -T http://example.com -L list.txt -m GET -n username Windows Reverse Shell: python3 exploit.py -u shell -y exe -I 127.0.0.1 -P 1111 -F payloadexe Subdomains Brute Force: python3 exploit.py -u subdomain -T http://example.com -L list.txtand you can also Brute Force Attack (Files and Folders), Extract Picture metadata. GitHub View Github    

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Machine Learning

Putting a Machine Learning Model into Production with Flask and Heroku

La creación de un proyecto de aprendizaje automático en un jupyter notebook ejecutaándose en local para unos datos de entrada controlados es una cosa, pero implementar el modelo como una aplicación web y su posterior puesta en producción como servicio para usuarios en la red es otra cosa muy distinta. Para que un producto basado en el aprendizaje automático tenga éxito, es necesario crear servicios que otros equipos puedan usar o un producto donde los usuarios puedan interactuar. Para ello, […]

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