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@@ -6,18 +6,21 @@ Here this edge computing is brought into a practical oriented example, where a A
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This projects allows you to digitalize your **analoge** water, gas, power and other meters using cheap and easily available hardware.
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-All you need is an [ESP32 board with a supported camera](https://github.com/jomjol/AI-on-the-edge-device/wiki/Hardware-Compatibility) and a bit of a practical hand.
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+All you need is an [ESP32 board with a supported camera](https://jomjol.github.io/AI-on-the-edge-device-docs/Hardware-Compatibility/) and a bit of a practical hand.
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<img src="images/esp32-cam.png" width="200px">
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## Key features
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+- Tensorflow Lite (TFlite) integration - including easy to use wrapper
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+- Inline Image processing (feature detection, alignment, ROI extraction)
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- **Small** and **cheap** device (3x4.5x2 cm³, < 10 EUR)
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- camera and illumination integrated
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-- Web surface for administration and control
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+- Web surface to administrate and control
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- OTA-Interface to update directly through the web interface
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-- API for easy integration
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-- Inline Image processing (feature detection, alignment, ROI extraction)
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-- Tensorflow Lite (TFlite) integration - including easy to use wrapper
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+- Full integration into Homeassistant
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+- Support for Influx DB 1
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+- MQTT
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+- REST API
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## Workflow
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The device takes a photo of your meter at a defined interval. It then extracts the Regions of Interest (ROI's) out of it and runs them through an artificial inteligence. As a result, you get the digitalized value of your meter.
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CaCO3