How can I change the temperature value to a base64 image in Arduino?

Other Hardware Sensors
1

I want to import image using LOLIN D32 and MLX90640 and output them to the serial monitor. Through the example code, we confirmed that the device was well connected.

mlx90640 sensor detects temperature and gets temperature array in 32*24 format

ex)
===========================WaveShare MLX90640 Thermal Camera===============================
27 28 26 28 29 29 30 30 30 30 29 29 28 28 26 25 24 26 28 29 30 29 27 27 27 26 27 28 28 28 29 32
27 26 27 28 27 28 29 29 29 28 29 29 29 29 26 24 24 25 29 29 30 30 29 28 27 27 28 27 28 28 29 29
27 27 26 28 27 27 27 28 27 27 28 29 30 29 28 26 24 25 28 29 30 30 29 29 28 28 28 29 29 29 29 30
27 27 27 27 27 27 27 26 27 27 28 28 29 30 28 27 26 25 28 29 30 30 31 30 30 30 30 30 30 30 29 29
27 27 26 27 27 27 26 27 27 27 28 28 30 30 30 29 27 26 28 30 31 31 30 30 30 30 31 31 30 31 30 31
27 27 28 27 27 27 27 27 27 27 27 27 30 30 30 29 27 27 28 30 30 30 30 29 30 30 30 30 30 31 30 30
27 27 27 28 27 27 26 27 27 26 27 27 29 30 30 30 29 28 30 30 31 30 30 30 30 30 30 31 31 31 32 33
27 27 27 27 27 27 27 26 26 27 27 27 29 29 31 30 30 30 30 30 31 30 30 30 30 30 31 30 31 31 32 33
27 27 26 27 27 27 27 27 27 27 27 28 29 30 30 31 31 31 31 32 31 30 29 30 29 29 31 31 32 32 32 33
27 27 27 27 27 27 27 27 27 28 27 27 29 30 31 31 31 31 31 31 31 30 30 29 30 30 31 31 31 32 33 32
27 27 26 27 27 27 27 27 28 28 27 27 28 29 29 30 30 30 30 31 31 30 29 30 30 30 31 31 32 32 32 32
28 27 27 26 27 28 28 27 28 28 28 27 27 27 28 28 30 30 30 31 30 31 30 30 30 30 30 31 32 32 32 33
28 27 26 27 28 27 28 28 28 27 29 29 29 28 26 28 29 30 29 30 30 30 30 30 31 31 31 32 32 32 32 31
27 27 28 28 28 28 28 28 28 28 30 30 30 29 27 27 28 30 30 30 30 30 30 30 31 31 32 32 32 32 31 31
28 27 28 28 29 28 28 29 30 30 32 32 31 31 29 29 29 29 29 30 30 29 30 30 31 31 32 33 32 31 32 32
27 27 28 28 28 28 28 29 31 31 33 32 32 31 31 30 29 29 30 29 29 29 30 30 31 32 32 32 31 31 31 31
28 27 28 28 29 28 30 31 32 33 33 33 33 33 32 32 32 31 31 31 30 30 31 31 31 32 31 31 31 31 31 31
28 29 28 28 29 29 31 31 32 33 33 33 33 33 33 33 33 32 32 32 31 31 31 31 31 31 31 31 31 30 32 31
29 30 29 30 31 32 32 33 33 33 33 34 34 34 33 33 33 33 33 33 32 31 31 31 31 31 30 31 30 30 30 31
29 30 30 30 32 32 33 33 33 33 34 34 34 34 34 34 33 33 33 33 32 32 31 31 31 32 31 30 30 31 30 31
31 31 31 32 32 32 34 33 33 33 33 34 34 34 34 34 34 33 33 33 33 32 31 32 32 31 30 31 31 30 31 31
31 32 32 32 32 33 33 33 33 34 34 34 34 34 34 34 34 34 33 33 32 32 32 31 30 30 30 31 30 31 31 33
32 33 32 33 33 33 33 34 34 34 34 35 34 34 34 34 34 34 33 34 32 32 31 31 31 31 32 32 33 32 32 34
31 32 33 33 33 33 34 34 34 35 35 34 34 35 34 35 34 34 34 34 33 32 32 32 32 32 32 33 33 33 33 33
===========================WaveShare MLX90640 Thermal Camera===============================

I want to convert these data values ​​into Image ​​and convert them into Base64 image strings.

here is my code

#include "Arduino.h"
#include "SPI.h"
#include "tft.h"
#include <Wire.h>

#include "MLX90640_API.h"
#include "MLX90640_I2C_Driver.h"

#include "Base64.h"


#define EMMISIVITY 0.95
#define INTERPOLATE false
#define min(a,b) ((a)<(b)?(a):(b))
#define max(a,b) ((a)>(b)?(a):(b))
#define TA_SHIFT 8 //Default shift for MLX90640 in open air

// Output size
// #define O_WIDTH 224
//  #define O_HEIGHT 168

//memory
#define O_WIDTH 112
#define O_HEIGHT 84

// #define O_WIDTH 32
// #define O_HEIGHT 24


#define O_RATIO O_WIDTH/32

paramsMLX90640 mlx90640;
uint8_t MLX90640_address = 0x33; //Default 7-bit unshifted address of the MLX90640

// Added for measure Temp
boolean measure = true;
float centerTemp;
unsigned long tempTime = millis();
unsigned long tempTime2 = 0;

// start with some initial colors
float minTemp = 20.0;
float maxTemp = 40.0;

// variables for interpolated colors
uint8_t red, green, blue;

// variables for row/column interpolation
float intPoint, val, a, b, c, d, ii;
int x, y, i, j;

// array for the 32 x 24 measured tempValues
static float tempValues[32 * 24];

float **interpolated = NULL;
uint16_t *imageData = NULL;

uint8_t* pixelData = (uint8_t*)malloc(O_WIDTH * O_HEIGHT * 3);

TFT tft;

void setup() {
  Serial.begin(115200);
  Serial.println("Hello.");

  // Connect thermal sensor.
  Wire.begin();
  Wire.setClock(400000); // Increase I2C clock speed to 400kHz
  Wire.beginTransmission((uint8_t)MLX90640_address);
  if (Wire.endTransmission() != 0) {
    Serial.println("MLX90640 not detected at default I2C address. Please check wiring.");
  }
  else {
    Serial.println("MLX90640 online!");
  }
  // Get device parameters - We only have to do this once
  int status;
  uint16_t eeMLX90640[832];
  status = MLX90640_DumpEE(MLX90640_address, eeMLX90640);
  if (status != 0) Serial.println("Failed to load system parameters");
  status = MLX90640_ExtractParameters(eeMLX90640, &mlx90640);
  if (status != 0) Serial.println("Parameter extraction failed");
  // Set refresh rate
  MLX90640_SetRefreshRate(MLX90640_address, 0x05); // Set rate to 8Hz effective - Works at 800kHz
  // Once EEPROM has been read at 400kHz we can increase
  Wire.setClock(800000);

  SPI.begin();
  tft.begin();
  tft.setRotation(0); // Use landscape format
  tft.fillScreen(TFT_BLACK);

  tft.setCursor(55, 228);
  tft.setTextColor(TFT_WHITE);
  tft.setTextSize(4);
  tft.print("WaveShare");

  // Prepare interpolated array
  interpolated = (float **)malloc(O_HEIGHT * sizeof(float *));
  for (int i = 0; i < O_HEIGHT; i++) {
    interpolated[i] = (float *)malloc(O_WIDTH * sizeof(float));
  }

  // Prepare imageData array
  imageData = (uint16_t *)malloc(O_WIDTH * O_HEIGHT * sizeof(uint16_t));

  // get the cutoff points for the color interpolation routines
  // note this function called when the temp scale is changed
}

void loop(void) {
  tempTime = millis();

  readTempValues();
  setTempScale();
  drawPicture();


  for (int y = 0; y < O_HEIGHT; y++) {
    for (int x = 0; x < O_WIDTH; x++) {
      uint16_t color565 = imageData[(y * O_WIDTH) + x];  
      convertToRGB(color565, &red, &green, &blue);  
      pixelData[(y * O_WIDTH + x) * 3] = red;
      pixelData[(y * O_WIDTH + x) * 3 + 1] = green;
      pixelData[(y * O_WIDTH + x) * 3 + 2] = blue;
    }
  }

  char *input = (char *)pixelData;
  char output[base64_enc_len(3)];
  String imageFile = "data:image/jpeg;base64,";
  for (int i=0;i<O_WIDTH * O_HEIGHT * 3;i++) {
    base64_encode(output, (input++), 3);
    if (i%3==0) imageFile += String(output);
  }
  int fbLen = imageFile.length();
  Serial.print("fbLen=");
  Serial.println(fbLen);


  Serial.println(imageFile);


  delay(3000);
}

void convertToRGB(uint16_t color565, uint8_t* r, uint8_t* g, uint8_t* b) {
    *r = (color565 >> 11) & 0x1F;  
    *r = (*r * 255) / 31;           
    
    *g = (color565 >> 5) & 0x3F;    
    *g = (*g * 255) / 63;           
    
    *b = color565 & 0x1F;         
    *b = (*b * 255) / 31;       
}

// Read pixel data from MLX90640.
void readTempValues() {
  for (uint8_t x = 0 ; x < 2 ; x++) // Read both subpages
  {
    uint16_t mlx90640Frame[834];
    int status = MLX90640_GetFrameData(MLX90640_address, mlx90640Frame);
    if (status < 0)
    {
      Serial.print("GetFrame Error: ");
      Serial.println(status);
    }

    float vdd = MLX90640_GetVdd(mlx90640Frame, &mlx90640);
    float Ta = MLX90640_GetTa(mlx90640Frame, &mlx90640);

    float tr = Ta - TA_SHIFT; //Reflected temperature based on the sensor ambient temperature

    MLX90640_CalculateTo(mlx90640Frame, &mlx90640, EMMISIVITY, tr, tempValues);
  }
}


int row;
float temp, temp2;

void interpolate() {
  for (row = 0; row < 24; row++) {
    for (x = 0; x < O_WIDTH; x++) {
      temp  = tempValues[(31 - (x / 7)) + (row * 32) + 1];
      temp2 = tempValues[(31 - (x / 7)) + (row * 32)];
      interpolated[row * 7][x] = lerp(temp, temp2, x % 7 / 7.0);
    }
  }
  for (x = 0; x < O_WIDTH; x++) {
    for (y = 0; y < O_HEIGHT; y++) {
      temp  = interpolated[y - y % 7][x];
      temp2 = interpolated[min((y - y % 7) + 7, O_HEIGHT - 7)][x];
      interpolated[y][x] = customLerp(temp, temp2, 1);//y%7/7.0);
    }
  }
}


// Linear interpolation
float customLerp(float v0, float v1, float t) {
  return v0 + t * (v1 - v0);
}


void drawPicture() {
  if (INTERPOLATE) {
    interpolate();
    for (y = 0; y < O_HEIGHT; y++) {
      for (x = 0; x < O_WIDTH; x++) {
        imageData[(y * O_WIDTH) + x] = getColor(interpolated[y][x]);
      }
    }
    //tft.pushImage(8, 8, O_WIDTH, O_HEIGHT, imageData);
  }
  else {
    for (y = 0; y < 24; y++) {
      for (x = 0; x < 32; x++) {
        tft.fillRect(8 + x * 7, 8 + y * 7, 7, 7, getColor(tempValues[(31 - x) + (y * 32)]));
      }
    }
  }
}



// Get color for temp value.
uint16_t getColor(float val) {
  /*
    pass in value and figure out R G B
    several published ways to do this I basically graphed R G B and developed simple linear equations
    again a 5-6-5 color display will not need accurate temp to R G B color calculation

    equations based on
    http://web-tech.ga-usa.com/2012/05/creating-a-custom-hot-to-cold-temperature-color-gradient-for-use-with-rrdtool/index.html

  */

  red = constrain(255.0 / (c - b) * val - ((b * 255.0) / (c - b)), 0, 255);

  if ((val > minTemp) & (val < a)) {
    green = constrain(255.0 / (a - minTemp) * val - (255.0 * minTemp) / (a - minTemp), 0, 255);
  }
  else if ((val >= a) & (val <= c)) {
    green = 255;
  }
  else if (val > c) {
    green = constrain(255.0 / (c - d) * val - (d * 255.0) / (c - d), 0, 255);
  }
  else if ((val > d) | (val < a)) {
    green = 0;
  }

  if (val <= b) {
    blue = constrain(255.0 / (a - b) * val - (255.0 * b) / (a - b), 0, 255);
  }
  else if ((val > b) & (val <= d)) {
    blue = 0;
  }
  else if (val > d) {
    blue = constrain(240.0 / (maxTemp - d) * val - (d * 240.0) / (maxTemp - d), 0, 240);
  }

  // use the displays color mapping function to get 5-6-5 color palet (R=5 bits, G=6 bits, B-5 bits)
  //Serial.println(String(val) + " -> " + String(red) + "|" + String(green) + "|" + String(blue));
  return tft.color565(red, green, blue);
}

void setTempScale() {
  minTemp = 255;
  maxTemp = 0;

  for (i = 0; i < 768; i++) {
    minTemp = min(minTemp, tempValues[i]);
    maxTemp = max(maxTemp, tempValues[i]);
  }
}

result :

data:image/jpeg;base64,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
=

How can I convert this data into a base64 image?

2

Sorry, the code is a mess.
I don't know what kind of board you have, but even on ESP32 it's not worth wasting memory so senselessly. You create about 80K intermediate arrays, and then try to put 30K binary data into a String class.

Some $0.02 about the code:

i) By the way, your image file is not jpeg:

ii) Why this strange multiplication?

Is it not the same as *r = *r << 3; ?

And finally, what's the point of converting a binary array to base64?

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