| --- |
| license: apache-2.0 |
| library_name: onnx |
| tags: |
| - meter-reading |
| - water-meter |
| - gas-meter |
| - home-assistant |
| pipeline_tag: image-classification |
| --- |
| |
| # VisionState wheel reader |
|
|
| The model [VisionState](https://github.com/oleost/VisionState) — a Home Assistant app that turns |
| camera images into sensors, locally — reads mechanical counters with (water and gas meters with |
| rolling digit wheels). |
|
|
| It looks at **one wheel** and tells how far it has turned: a distribution over 100 positions |
| around the wheel (0.0, 0.1, … 9.9), so a wheel half way between two digits is read as such. |
| VisionState splits the counter into one cell per wheel and picks the most likely value whose |
| wheel positions fit together (a wheel only turns while the wheel to its right goes from 9 to 0). |
|
|
| | File | Size | Input | Output | |
| |---|---|---|---| |
| | `wheels-v1.onnx` | 2.2 MB, 565 k parameters | `cells`: N × 1 × 48 × 32, float, 0…1 | `logits`: N × 100 | |
|
|
| **Preprocessing of a cell:** grey from each pixel's darkest colour channel (coloured wheels look |
| like black ones), resized to 32 × 48 (bilinear), autocontrast with 1 % cut-off, divided by 255. |
| About 2 ms for eight wheels on one CPU thread. |
|
|
| ## Training data |
|
|
| All of it may be used for anything: |
|
|
| - Drawn wheels (600 000 cells) with fonts from Google Fonts (SIL Open Font License): many |
| colours, lighting, blur, noise and JPEG artefacts, with exactly known positions. |
| - [Word-Wheel Water Meter Dataset](https://doi.org/10.5061/dryad.7d7wm3860) (Scientific Data, |
| 2026), recognition crops — CC0. |
| - Checked readings shared by VisionState users in GitHub issues #32 and #40 — CC0. |
|
|
| Training code, data sources and how to make the model again: |
| [`tools/wheelreader`](https://github.com/oleost/VisionState/tree/beta/tools/wheelreader). |
|
|
| ## Results |
|
|
| Whole reading exact (last wheel rounded), read with VisionState's decoding: |
|
|
| - A water meter with red decimal wheels seen by an ESP32 camera with its flash, held out from |
| training: 17 of 17. |
| - Two water meters never seen in training: 30–33 of 35 and 15–18 of 18, no value too high. |
| - Word-Wheel test set (2,400 photos): 97.7 % when the photo is the right way up. |
|
|
| Not for pointer dials. Licence: Apache-2.0. |
|
|
