M5 StickC Thermal Camera Hat (MLX90640)

M5 StickC Thermal Camera Hat (MLX90640) Documentation

Overview

The M5 StickC Thermal Camera Hat is a thermographic camera module based on the MLX90640 sensor, designed for thermal imaging applications. It is a compact, low-power consumption device that can be easily integrated with the M5 StickC microcontroller board. This module provides high-resolution thermal images, making it suitable for various IoT applications, such as temperature monitoring, predictive maintenance, and thermal anomaly detection.

Technical Specifications

MLX90640 thermal sensor with 32x24 pixels resolution
 Temperature range: -40C to 300C
 Accuracy: 1.5C (typical)
 Resolution: 12-bit
 Frame rate: up to 64 Hz
 Interface: I2C
 Power consumption: 1.8V, <100 mA

Software Libraries and Examples

The M5 StickC Thermal Camera Hat can be used with the M5 StickC microcontroller board, which is based on the ESP32 chip. The following examples demonstrate how to use this component in various contexts:

Example 1: Basic Thermal Imaging

This example uses the `M5StickC_MLX90640` library to read thermal data from the MLX90640 sensor and display it on the M5 StickC's built-in LCD screen.

```c
#include <M5StickC.h>
#include <M5StickC_MLX90640.h>

M5StickC_MLX90640 thermalCamera;

void setup() {
  M5.begin();
  thermalCamera.begin();
}

void loop() {
  thermalCamera.readSensor();
  uint16_t thermalData = thermalCamera.getThermalData();
  
  M5.Lcd.clear(BLACK);
  M5.Lcd.setCursor(0, 0);
  M5.Lcd.print("Thermal Image:");
  
  for (int i = 0; i < 768; i++) {
    uint16_t temperature = thermalData[i];
    M5.Lcd.setCursor(i % 32, i / 32);
    M5.Lcd.print(temperature, DEC);
  }
  
  delay(1000);
}
```

Example 2: Temperature Monitoring with Wi-Fi Connectivity

This example uses the `WiFi` and `M5StickC_MLX90640` libraries to connect to a Wi-Fi network and send thermal data to a remote server using HTTP requests.

```c
#include <WiFi.h>
#include <M5StickC.h>
#include <M5StickC_MLX90640.h>

const char ssid = "your_ssid";
const char password = "your_password";
const char serverUrl = "http://your_server_url.com/thermal_data";

M5StickC_MLX90640 thermalCamera;
WiFiClient client;

void setup() {
  M5.begin();
  thermalCamera.begin();
  
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi...");
  }
  Serial.println("Connected to WiFi");
  Serial.println("Initializing HTTP connection...");
}

void loop() {
  thermalCamera.readSensor();
  uint16_t thermalData = thermalCamera.getThermalData();
  
  String thermalDataString = "";
  for (int i = 0; i < 768; i++) {
    thermalDataString += String(thermalData[i], DEC) + ",";
  }
  
  client.setServer(serverUrl, 80);
  client.println("POST /thermal_data HTTP/1.1");
  client.println("Host: " + String(serverUrl));
  client.println("Connection: close");
  client.println("Content-Type: application/x-www-form-urlencoded");
  client.println("Content-Length: " + String(thermalDataString.length()));
  client.println();
  client.print(thermalDataString);
  
  delay(10000);
}
```

Example 3: Integration with a Machine Learning Model

This example uses the `TensorFlow Lite` library to integrate the M5 StickC Thermal Camera Hat with a machine learning model for thermal anomaly detection.

```c
#include <TensorFlowLite.h>
#include <M5StickC.h>
#include <M5StickC_MLX90640.h>

M5StickC_MLX90640 thermalCamera;
tflite::MicroInterpreter interpreter;

void setup() {
  M5.begin();
  thermalCamera.begin();
  
  // Load the TensorFlow Lite model
  tflite::Model model = tflite::LoadModelFromFile("thermal_anomaly_detection.tflite");
  interpreter = new tflite::MicroInterpreter(model, 1);
}

void loop() {
  thermalCamera.readSensor();
  uint16_t thermalData = thermalCamera.getThermalData();
  
  // Preprocess thermal data for input to the machine learning model
  float inputData = new float[768];
  for (int i = 0; i < 768; i++) {
    inputData[i] = thermalData[i] / 100.0;
  }
  
  // Run the machine learning model
  TfLiteTensor inputTensor = interpreter->input(0);
  inputTensor->data.f = inputData;
  interpreter->Invoke();
  
  TfLiteTensor outputTensor = interpreter->output(0);
  float outputData = outputTensor->data.f;
  
  // Check for thermal anomalies
  bool isAnomaly = false;
  for (int i = 0; i < 1; i++) {
    if (outputData[i] > 0.5) {
      isAnomaly = true;
      break;
    }
  }
  
  if (isAnomaly) {
    Serial.println("Thermal anomaly detected!");
  } else {
    Serial.println("No thermal anomaly detected.");
  }
  
  delay(10000);
}
```

These examples demonstrate the capabilities of the M5 StickC Thermal Camera Hat and its potential applications in various IoT contexts.