ESPHome Meter Reader TFLite Component

General-purpose TensorFlow Lite Micro implementation with image processing support for ESPHome

🚀 Overview

This project provides a robust, modular framework for running TensorFlow Lite Micro models on ESP32 devices within the ESPHome ecosystem. While originally designed for analog/digital meter reading, it is suitable for various computer vision tasks.

New in v2.0: The project has been refactored into modular components for better maintainability and reusability.

📦 Components

The repository allows you to use specific components based on your needs:

Core Reading Components

Component	Description
meter_reader_tflite	AI-powered meter reader using TensorFlow Lite models. Orchestrates capture, inference, and reporting for digital meters.
ssocr_reader	(Alpha dev !) Seven-segment OCR reader using SSOCR algorithm. Reads digital displays without AI models.
analog_reader	(Alpha dev!) Analog dial/gauge reader using radial intensity sum algorithm. Reads pointer positions without AI.

Supporting Components

Component	Description
value_validator	Robust validation engine for meter readings. Eliminates outliers, tracks history, and prevents impossible value jumps.
esp32_camera_utils	Powerful image processing utilities. Handles cropping, scaling, rotation (JPEG/Raw), and format conversion using esp_new_jpeg library.
tflite_micro_helper	Wrapper for TensorFlow Lite Micro runtime (checking model CRC32, etc..) and esp-nn optimizations. Manages tensor arena and model loading.
flash_light_controller	Manages flash light timing for optimal image capture conditions.

Legacy

Component	Description
legacy_meter_reader_tflite	The previous monolithic version, kept for backward compatibility.

🏁 Quick Start

1. Installation

A detailed procedure for setting up is available in wiki/setup page

Add the components to your ESPHome configuration:

external_components:
  - source:
      type: git
      url: https://github.com/nliaudat/esphome_ai_component
      ref: main
    components: 
      # Choose the reader type you need:
      - meter_reader_tflite   # AI-powered digit recognition
      # - ssocr_reader        # Seven-segment OCR (no AI)
      # - analog_reader       # Analog gauge/dial reader (no AI)
      
      # Supporting components:
      - value_validator       # Validation engine (recommended)
      - tflite_micro_helper   # Required for meter_reader_tflite
      - esp32_camera_utils    # Image processing utilities
      - flash_light_controller

2. Basic Configuration

Option A: AI-Powered Meter Reader (TFLite)

esp32_camera:
  name: "My Camera"
  resolution: 640x480
  pixel_format: JPEG

value_validator:
  id: my_validator
  allow_negative_rates: false
  max_absolute_diff: 300

meter_reader_tflite:
  id: my_meter_reader
  model: "digit_recognizer.tflite"
  camera_id: my_camera
  # Optional: Validator
  validator: my_validator

  # If no validator is set, use simple threshold:
  # confidence_threshold: 0.85 

  update_interval: 60s
  
  # Optional: Link to other components
  flash_light_controller: my_flash_controller
  crop_zones_global: my_crop_zones
  
  # Image Rotation (Dev)
  rotation: "90" # Options: "0", "90", "180", "270"

Option B: Seven-Segment OCR Reader (No AI)

value_validator:
  id: my_validator
  allow_negative_rates: false
  max_absolute_diff: 50

ssocr_reader:
  id: my_ssocr
  camera_id: my_camera
  validator: my_validator
  update_interval: 60s
  threshold_level: 128
  digit_count: 8

Option C: Analog Gauge Reader (No AI)

value_validator:
  id: my_validator
  allow_negative_rates: true  # Analog gauges can go both ways
  max_rate_change: 0.25

analog_reader:
  id: my_analog
  camera_id: my_camera
  validator: my_validator
  update_interval: 60s
  dials:
    - min_value: 0
      max_value: 100
      radius_min: 20
      radius_max: 80

Note

Rotation behavior: The rotation setting only affects the AI processing. The internal webserver and Home Assistant live view will likely remain unrotated (sideways) as the camera hardware only supports 180° flips. This is normal behavior.

3. Runtime Configuration (Home Assistant)

Most parameters are now exposed to Home Assistant for real-time adjustment without recompiling:

• Calibration: Camera Window (Offset X/Y, Width, Height)
• Timing: Flash Pre/Post times, Update Interval
• Debug: Toggle debug logging and image generation
• Settings: Meter units and thresholds

These entities normally appear under the device in Home Assistant.

✨ Key Features

• 🤖 TensorFlow Lite Micro: Full TFLite Micro runtime support (with operators detection and auto loading)
• 📷 Camera Integration: State of the art ESP32 camera integration with TrackedBuffer for better memory management and windowing for OV2640 like camera
• 🖼️ Image Preprocessing: Automatic cropping, scaling, and format conversion
• ⚡ Optimized Performance: ESP-NN accelerated operations
• 🎯 Multi-Zone Processing: Process multiple regions of interest
• 🔧 Flexible Configuration: Support for various model types and input formats
• 🐛 Advanced Debugging: Real-time image analysis and model output inspection
• 🔄 Image Rotation (Dev): Full support for 0°, 90°, 180°, 270° rotation on both JPEG and Raw formats (via esp32_camera_utils).

🎯 Use Cases

Meter Reading

• Water, electricity, gas meter digit recognition
• Analog gauge reading
• Digital display extraction

Computer Vision Applications

• Object Detection: Identify objects in camera frames
• Image Classification: Categorize images into classes
• Anomaly Detection: Detect unusual patterns or events
• Quality Control: Inspect products or components

🔍 Troubleshooting

Common Issues

❌ Model loading fails

• Cause: Tensor arena size is too small.
• Solution: Increase tensor_arena_size in meter_reader_tflite config.

meter_reader_tflite:
  tensor_arena_size: 768KB  # Default is 512KB

❌ Poor inference results

• Cause: Input image poor quality or wrong crop.
• Solution: Enable debug mode to see what the model actually "sees".

meter_reader_tflite:
  debug: true

❌ Camera frame issues

• Cause: Low memory or bandwidth issues.
• Solution: Lower resolution or framerate in esp32_camera.

Performance Tips

1. Use quantized models (int8) for better performance
2. Enable ESP-NN optimizations (enabled by default)
3. Use appropriate tensor_arena_size (already calculated per model)

🔄 Backward Compatibility

If you need to use the old version, specify the legacy component:

external_components:
  - source:
      type: git
      url: https://github.com/nliaudat/esphome_ai_component
      ref: main
    components: [legacy_meter_reader_tflite]

🧠 Optimized Models

New optimized models are available for digit recognition that significantly improve performance:

• Source: nliaudat/digit_recognizer
• Performance: Capable of full image processing and 8-digit inference in less than 270 ms [esp32-S3] (2700 ms for legacy esp32).

These models are recommended for faster response times and lower power consumption.

📄 License

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA)
• No commercial use