The NCA Toolkit API eliminates monthly subscription fees by consolidating common API functionalities into a single FREE API. Designed for businesses, creators, and developers, it streamlines advanced media processing, including video editing and captioning, image transformations, cloud storage, and Python code execution.

The power of machine learning allows us to change long-standing computing paradigms. One of these is the age-old password-based authentication system common to most apps. With fast real-time facial recognition, we can easily dispense with text-based verification and allow users to log in just by showing their faces to a webcam. In this session, we’ll show how to do this in Flutter, Google’s popular open-source UI toolkit for developing apps for web, Android, iOS, Fuchsia, and many other platforms with a single codebase. We’ll first build a simple authentication-based Android app, and then deploy the Firebase ML Vision model for face ID & image processing; as well as the MobileFaceNet CNN model through TensorFlow Lite for structured verification. Once all these parts are in place, our solution will work seamlessly and can easily be ported to other apps. Pre-requisites: ✅ Android Studio (https://developer.android.com/studio) — you can also use other IDEs/platforms if you’d rather not use Android - Flutter documentation below guides on the same. ✅ Flutter SDK (https://flutter.dev/docs/get-started/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #TensorFlow #Flutter #MachineLearning

⚡A multithreaded toolkit for digital media processing using ffmpeg. It provides both a CLI and a GUI. If ffmpeg can do it, ffzap can do it - as many files in parallel as your system can handle.

After last month’s workshop with Tkinter, we’ll show you another great new option for developing GUIs in Python. PyQt5 is the latest iteration of the Python binding for the cross-platform Qt GUI toolkit, implemented as a plugin. In this session, we’ll use PyQt5 to code our own customizable rich text editor a la Microsoft Word. We’ll implement file system interactions, selective text formatting, and other features one expects from a modern word processor. Prerequisites: — Python (https://www.python.org/downloads/) — Visual Studio Code (https://code.visualstudio.com/download) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings

Creative coding, interactive new-media art, and computational design - specifically working with popular open-source arts-engineering toolkits, including p5.js (JavaScript), Processing (Java), and openFrameworks (C++), applied to interactive art and design, information visualization, generative media, and other creative cultural practices.

EncodeForge — Cross-platform media processing toolkit with hardware-accelerated video encoding, AI subtitle generation, and smart file renaming.

ImagePipes is a modular Python-based toolkit developed for high-resolution 3D image processing and analysis of micro-CT datasets. Tailored for porous media and geoscience applications, this collection of scripts offers robust, automated workflows for denoising, masking, registration, segmentation, clustering, resampling, and 3D mesh generation.

Memory-safe media processing toolkit in Rust

A web-based toolkit for media processing powered by FFmpeg WASM.

Media processing toolkit for presentation localization using Google Gemini AI

A comprehensive Python toolkit for AI, data processing, media manipulation, and system utilities.

An integrated toolkit platform for audio and video processing,cover the needs of regular self-media

Media Toolkit. A production-grade media processing system that runs 100% locally using Node.js. No cloud services, no uploads to third parties, complete control over your media files.

Lua script for MPV media player to automatically generate subtitles for playing media using NVIDIA's Parakeet ASR model (via NeMo toolkit) and FFmpeg for audio processing.

A comprehensive toolkit for downloading audio and video content from YouTube, social media platforms, and various streaming services. This tool integrates advanced AI functionalities for enhanced media processing.

An all-in-one self-media toolkit. Features include AI-assisted writing, image processing, multi-platform publishing, and automated analytics. Built for content creators.

A cross-platform toolkit of Python scripts to convert videos into HLS format. Includes both automated and manual options for flexible video processing, simplifying HLS generation for all media projects.

AI-powered video processing toolkit for content creators. Streamlines transcription workflows, automates formatting, and generates optimized social media content using Go and YAML-defined pipelines. Perfect for YouTubers, podcasters, and video professionals.

DotNetCampus.MediaConverters is a media conversion toolkit focused on image optimization and effect processing. It supports both direct library usage (via NuGet) and command-line invocation as a standalone process. The conversion capabilities were originally designed for Office image effects, and can also be used independently in other scenarios.

A large amount of data that is generated today is unstructured, which requires processing to generate insights. Some examples of unstructured data are news articles, posts on social media, and search history. The process of analyzing natural language and making sense out of it falls under the field of Natural Language Processing (NLP). Sentiment analysis is a common NLP task, which involves classifying texts or parts of texts into a pre-defined sentiment. You will use the Natural Language Toolkit (NLTK), a commonly used NLP library in Python, to analyze textual data. In this tutorial, you will prepare a dataset of sample tweets from the NLTK package for NLP with different data cleaning methods. Once the dataset is ready for processing, you will train a model on pre-classified tweets and use the model to classify the sample tweets into negative and positives sentiments. This article assumes that you are familiar with the basics of Python (see our How To Code in Python 3 series), primarily the use of data structures, classes, and methods. The tutorial assumes that you have no background in NLP and nltk, although some knowledge on it is an added advantage.

In the era of technology and internet, people use online social media services like Twitter, Instagram, Facebook, Reddit, etc. to express their emotions. The idea behind this paper is to understand people’s emotion on Twitter and their opinion towards Presidential Election 2020. We collected 1.2 million tweets in total with keyword like “RealDonaldTrump”, “JoeBiden”, “Election2020” and other election related keywords using Twitter API and then processed them with natural language processing toolkit. A Bidirectional Long Short-Term Memory (BiLSTM) model has been trained and we have achieved 93.45% accuracy on our test dataset. We then used our trained model to perform sentiment analysis on the rest of our dataset. With the sentiment analysis results and comparison with 2016 Presidential Election, we have made predictions on who could win the US Presidential Election in 2020 with pre-election twitter data. We have also analyzed the impact of COVID-19 on people’s sentiment about the election.

Sentiment analysis is part of the Natural Language Processing (NLP) techniques that consists in extracting emotions related to some raw texts. This is usually used on social media posts and customer reviews in order to automatically understand if some users are positive or negative and why. The goal of this study is to show how sentiment analysis can be performed using python. Here are some of the main libraries we will use: NLTK: the most famous python module for NLP techniques Gensim: a topic-modelling and vector space modelling toolkit Scikit-learn: the most used python machine learning library We will use here some hotel reviews data. Each observation consists in one customer review for one hotel. Each customer review is composed of a textual feedback of the customer's experience at the hotel and an overall rating

Full-stack web app for PDF processing, OCR text extraction, video compression, and audio merging

A comprehensive suite of tools for processing and analyzing media files, including images, videos, and metadata. Designed for photographers, content creators, and developers working with media assets.

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A flexible FFmpeg-based CLI toolkit for advanced media processing workflows.

Modular Python toolkit for computer vision, media processing, and system automation.

Professional digital toolkit with file conversion, media processing, and content generation tools

A modern, cross-platform media processing toolkit built with Tauri 2.0 + React + Python.

Advanced image processing toolkit with GUI and Python API. Batch processing, social media optimization, smart cropping, and professional editing tools.