From ec8de6287f7ef0709025d3529a1143b135f7645f Mon Sep 17 00:00:00 2001 From: CaCO3 Date: Sat, 14 Jan 2023 00:44:39 +0100 Subject: [PATCH] Update README.md --- README.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 570db9d8..df1c8909 100644 --- a/README.md +++ b/README.md @@ -6,18 +6,21 @@ Here this edge computing is brought into a practical oriented example, where a A This projects allows you to digitalize your **analoge** water, gas, power and other meters using cheap and easily available hardware. -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. +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. ## Key features +- Tensorflow Lite (TFlite) integration - including easy to use wrapper +- Inline Image processing (feature detection, alignment, ROI extraction) - **Small** and **cheap** device (3x4.5x2 cm³, < 10 EUR) - camera and illumination integrated -- Web surface for administration and control +- Web surface to administrate and control - OTA-Interface to update directly through the web interface -- API for easy integration -- Inline Image processing (feature detection, alignment, ROI extraction) -- Tensorflow Lite (TFlite) integration - including easy to use wrapper +- Full integration into Homeassistant +- Support for Influx DB 1 +- MQTT +- REST API ## Workflow 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.