pMeterloop

After completing the ROI logic, it was time to put it to the test. I ported my Python environment to Google Colab  and uploaded the images of actual gas meters to simulate a real-world scenario. The Workflow: Environment Setup:  Adjusting the Python scripts to run seamlessly in the Colab cloud environment. Model Preparation:  Training the model (leveraging the logic of MNIST but fine-tuned for meter digits) to recognize the segmented "slices" of numbers. Inference:  Feeding t

When dealing with high-resolution meter images, performing ROI (Region of Interest) localization  first is key to boosting accuracy and saving scan time. I noticed an interesting feature on most gas meters: the decimal digits on the far right are typically red . In the world of computer vision, this red color acts as a natural "anchor." My Implementation Logic: Downsample First:  To simulate a resource-constrained MCU environment, I downsample the image first to save memory.

Ever felt the headache of having to manually record meter readings?  Or worse, only realizing your pipes have been leaking for days once that massive water bill arrives? It’s the same story for expensive industrial or medical equipment. You might have a million-dollar machine that works fine but isn't "smart." Because it can’t connect to the cloud, all that precious data stays stuck on paper, making it a nightmare to track trends or automate your workflow. We are currently de