Yuukiy/JavSP
JavSP
JavSP is a media library organizer and video metadata scraper designed to sort and categorize video files. It extracts identifiers from filenames to aggregate media information from online sources and generates standardized NFO metadata files compatible with media servers.
The utility features automated cover art processing that fetches high-definition images and crops non-standard posters into consistent dimensions. It also includes a multilingual metadata translator to convert video titles and summaries into different target languages.
The system provides capabilities for media library organization and multi-source web scraping to maintain structured digital archives. Operational parameters can be adjusted via command-line arguments or configuration files.
The application can be deployed via a container to isolate dependencies and ensure consistent behavior across different hosts.
Features
- Automated File Organizers - Sorts and categorizes video files based on automated naming and classification rules to maintain a structured archive.
- Library Organizers - Provides a comprehensive system for sorting video files and processing cover imagery into standardized poster dimensions.
- Media Servers - Creates standardized information files used by self-hosted media servers to organize video collections.
- Metadata Organizers - Generates standardized NFO files and arranges media assets for compatibility with popular media server software.
- Automated Metadata Fetching - Automatically fetches detailed video information from online sources using unique identifiers found in filenames.
- Multi-Source Content Extraction - Retrieves detailed video information from various online databases using targeted web requests and specialized parsers.
- Metadata Aggregators - Extracts unique identifiers from filenames to aggregate media metadata from multiple external web sources into unified records.
- Media Metadata Extraction - Extracts descriptive and technical metadata from media files to create standardized information files.
- Metadata Automators - Automatically fetches and standardizes video metadata from online databases to create server-compatible information files.
- Video Metadata Scraping - Extracts identifiers from filenames to automatically gather and aggregate content details from multiple web databases.
- Media Metadata Scrapers - Extracts identifiers from filenames to scrape descriptive tags and technical details from media databases.
- Web Scraping - Extracts structured metadata from online sources to automate the cataloging of video libraries.
- Language Translation & Detection - Identifies and translates video metadata into different languages to improve library discoverability.
- Multilingual Content Translation - Provides AI-powered translation of video titles and summaries into multiple target languages.
- Imagery Processing - Processes high-definition images to generate standardized cover art for video libraries.
- Language and Translation - Translates video titles and summaries into various languages to improve library discoverability.
- Automated Cover Art Processing - Automates the retrieval and cropping of high-definition cover art to ensure posters fit standard media server dimensions.
- Image Cropping Tools - Automatically enforces aspect ratios for video posters to ensure consistent dimensions across media servers.
- Intelligent Cropping Engines - Implements an autonomous engine to crop high-definition posters into consistent dimensions using image analysis.
- External Metadata File Generation - Generates standardized external NFO files to provide media server organization and rich library details.
- File Template Generators - Generates standardized metadata files from aggregated web data using predefined file templates.
- Metadata Template Resolvers - Transforms aggregated web data into standardized NFO formats compatible with media server organization rules.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
