Results for media_analyzer

Analyze video/image with machine learning methods, exif data, and other file based information.

Extract file-based information from photo and video files.

AI-native Rust media framework — container parsing, audio/video decoding, codec bindings, media analysis

Public Relations MCP server — brand monitoring, media sentiment, crisis detection, journalist discovery, press release formatting

Command-line interface for OxiMedia

Intelligent media file organizer following Plex naming conventions

Rust SDK for the VynCo Swiss Corporate Intelligence API

The autonomous, self-improving AI agent. Single Rust binary. Every channel. Install with: cargo install opencrabs

The PostBoost REST API lets you publish, schedule, and analyze social media posts across 12+ platforms from a single integration. No OAuth apps to maintain — PostBoost handles platform authorization for you. ## Base URL All workspace-scoped endpoints are prefixed with `/{workspaceUuid}`. Panel/admin endpoints are prefixed with `/panel`. ## Authentication All requests require a Bearer token in the `Authorization` header. Generate tokens in your PostBoost dashboard under **Settings → Access Tokens**. ``` Authorization: Bearer YOUR_API_TOKEN ```

Core media, timing, detection, and analyzer contracts for video-analysis.

Core media, timing, detection, and analyzer contracts for video-analysis.

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to: Simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) Programmatically construct pipeline topologies and live pipelines

High-level workflow operations for Anki via AnkiConnect