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Add Makefile and Markdown linter (#73)

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Adriaan Van Niekerk
2025-10-31 01:06:36 +02:00
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@@ -8,6 +8,7 @@ Artificial intelligence based systems have been established in our everyday live
Here this edge computing is brought into a practice-oriented example, where a AI network is implemented on a ESP32 device so: **AI on the edge**.
## 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)
@@ -23,22 +24,19 @@ Here this edge computing is brought into a practice-oriented example, where a AI
![Idea](img/idea.jpg){: style="width:600px"}
### Hardware
![](img/watermeter_all.jpg){: style="width:200px"}
![](img/main.jpg){: style="width:200px"}
![](img/size.png){: style="width:200px"}
![Watermeter All](img/watermeter_all.jpg){: style="width:200px"}
![Main](img/main.jpg){: style="width:200px"}
![Size](img/size.png){: style="width:200px"}
### Web interface
![](img/watermeter.jpg){: style="width:600px"}
![Watermeter](img/watermeter.jpg){: style="width:600px"}
### Configuration Interface
![](img/edit_reference.jpg){: style="width:600px"}
![Edit Reference](img/edit_reference.jpg){: style="width:600px"}
**Have fun in studying the new possibilities and ideas**
@@ -53,6 +51,7 @@ There are two types of CNN implemented, a classification network for reading the
This project is an evolution of the [water-meter-system-complete](https://github.com/jomjol/water-meter-system-complete), which uses ESP32-CAM just for taking the image and a 1GB-Docker image to run the neural network's backbone. Here everything is integrated in an ESP32-CAM module with 8MB of RAM and a SD card as data storage.
# Additional Tutorials
A lot of people created useful Youtube videos which might help you getting started.
Here a small selection: