Use a neural network for image recognition to digitize your water meter and store the values in InfluxDB. Tools used in this workflow:
You might want to check out AI-on-the-edge-device which is way more advanced and easiert to use water meter read-out approach using a ESP32-CAM.
Attach two webcams to e.g. a RaspberryPi, aim them at your cold and warm water meters
- Raspberry Pi 4
- Logitech C925e
- Adjust monitor-water.py to fit your needs (camera device, crop, etc)
- Capture a bunch of images using monitor-water.py (set SAVE_FOR_TRAINING to True to capter just everything)
- Training should be done on a system with CUDA enabled GPU.
- Use the scripts in the "train" folder in order:
- To rsync the images generated by monitor-water.py to the "training" system
- View all images and assign them a value (We will use OpenCV do display the extracted features to manually assign a value according to what is in the picture)
- Review your work (I will display all characterized images to spot eventually miscahractericed images)
- Fix image <-> value asignment in case you screwd up in step 1 and found the mistake in step 2
- Append new images to the an existing image set (Python pickle file)
- Get some info about exiting image sets
- Train your model with the images in the image set
- Rsync test data (images which have not yet been seen by the model) to the training system
- Test your model
Step 1a
Step 1b
Step 2
Step 3
Step 4
Step 5
Step 6
Step 8
