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Building an AI Home Security System Using .NET, Python, CLIP, Semantic Kernel, Telegram, and Raspberry Pi 4 – Part 3: Creating a Local AI Custom Vision API for Training and Matching Images
- This article is part of a miniseries on building an AI home security system and cross-platform solution.
- The system's main requirements include motion detection, photo capture, messaging, and facial recognition.
- The .NET API implemented in this blog post provides endpoints for training and matching images using CLIP.
- The API encapsulates image processing and recognition logic for extensibility and ease of integration.
- Core functionalities of the API include getting vector embeddings, calculating cosine similarity, and finding the best match.
- API controller endpoints allow training the system with images and labels, as well as matching incoming image data.
- Testing the controllers and API can be done using tools like Postman to ensure functionality.
- The system matches incoming images with trained embeddings to determine if a match is found.
- The .NET API simplifies consumption of AI functionalities on Raspberry Pi, enhancing maintainability.
- Future plans in the series include extending the Telegram bot for managing embeddings and invoking APIs remotely.
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