Kutluhan Aktar
Published © CC BY

AI-driven Dental Cast (Model) Classifier w/ Edge Impulse

Via Spresense; collect dental cast images on an SD card to train an object detection model, display video stream, and run the model directly

ExpertFull instructions provided3,326
Grand Prize
Spresense Developer Challenge 2022

Things used in this project

Hardware components

Spresense boards (main & extension)
Sony Spresense boards (main & extension)
×1
Spresense camera board
Sony Spresense camera board
×1
DFRobot Tiny (Embedded) Thermal Printer
×1
2.8'' 240x320 TFT LCD Touch Screen (ILI9341)
×1
Creality CR-200B 3D Printer
×1
Creality HALOT-ONE CL-60 SLA 3D Printer
×1
Flash Memory Card, MicroSD Card
Flash Memory Card, MicroSD Card
×1
Keyes 10mm RGB LED Module (140C05)
×1
SparkFun Button (6x6)
×3
Xiaomi 20000 mAh 3 Pro Type-C Power Bank
×1
USB Buck-Boost Converter Board
×1
Solderless Breadboard Half Size
Solderless Breadboard Half Size
×1
Mini Breadboard
×1
10mm M3 Male-Female Brass Hex Spacer Standoff
×4
Machine Screw, M3
Machine Screw, M3
×1
Jumper wires (generic)
Jumper wires (generic)
×1

Software apps and online services

Edge Impulse Studio
Edge Impulse Studio
Arduino IDE
Arduino IDE
Fusion
Autodesk Fusion
Ultimaker Cura
HALOT BOX

Hand tools and fabrication machines

Hot glue gun (generic)
Hot glue gun (generic)

Story

Read more

Custom parts and enclosures

Dental_Model_Classifier_v1.stl

Dental_Model_Classifier_side_cover_v1.stl

Edge Impulse Model (Arduino Library)

data_collect.c

dental.c

dental_logo.h

Schematics

Schematic-1

Schematic-2

Code

dental_model_classifier_collect.ino

Arduino
         /////////////////////////////////////////////  
        //    AI-driven Dental Model Classifier    //
       //            w/ Edge Impulse              //
      //             ---------------             //
     //             (Sony Spresense)            //           
    //             by Kutluhan Aktar           // 
   //                                         //
  /////////////////////////////////////////////

//
// Via Spresense; collect dental cast images on an SD card to train an object detection model, display video stream, and run the model directly.
//
// For more information:
// https://www.theamplituhedron.com/projects/AI_driven_Dental_Model_Classifier_w_Edge_Impulse
//
//
// Connections
// Sony Spresense (w/ Extension Board) :  
//                                2.8'' 240x320 TFT LCD Touch Screen (ILI9341)
// D7   --------------------------- CS 
// D8   --------------------------- RESET 
// D9   --------------------------- D/C
// MOSI --------------------------- SDI (MOSI)
// SCK  --------------------------- SCK 
// 3.3V --------------------------- LED 
// MISO --------------------------- SDO(MISO) 
//                                Tiny (Embedded) Thermal Printer
// TX   --------------------------- RX
// RX   --------------------------- TX
// GND  --------------------------- GND
//                                Control Button (A)
// D2   --------------------------- +
//                                Control Button (B)
// D4   --------------------------- +
//                                Control Button (C)
// D14  --------------------------- +
//                                Keyes 10mm RGB LED Module (140C05)
// D3   --------------------------- R
// D5   --------------------------- G
// D6   --------------------------- B  


// Include the required libraries:
#include <Camera.h>
#include <SDHCI.h>
#include <RTC.h>
#include <Adafruit_GFX.h>
#include <Adafruit_ILI9341.h>

// Include graphics (color bitmaps):
#include "data_collect.c"

// Define camera settings:
int               g_pict_id = 0;
int               g_width   = CAM_IMGSIZE_QUADVGA_H;
int               g_height  = CAM_IMGSIZE_QUADVGA_V;
CAM_IMAGE_PIX_FMT g_img_fmt = CAM_IMAGE_PIX_FMT_JPG;
CAM_WHITE_BALANCE g_wb      = CAM_WHITE_BALANCE_FLUORESCENT;
CAM_COLOR_FX      g_cfx     = CAM_COLOR_FX_NONE;
int               g_divisor = 7;

// Define the camera error object.
CamErr err;

// Initialize the SD class.
SDClass  theSD;

// Define the required pins for the 240x320 TFT LCD Touch Screen (ILI9341):
#define TFT_CS   7
#define TFT_RST  8
#define TFT_DC   9

// Use hardware SPI (on Spresense, SCK, MISO, MOSI) and the above for DC/CS/RST.
Adafruit_ILI9341 tft = Adafruit_ILI9341(TFT_CS, TFT_DC, TFT_RST);

// Define the control button pins:
#define button_A   2
#define button_B   4
#define button_C   14

// Define the RGB LED pins:
#define redPin     3
#define greenPin   5
#define bluePin    6

// Define the data holders:
#define TFT_ROTATION 1

void setup(){
  Serial.begin(115200);

  pinMode(button_A, INPUT_PULLUP);
  pinMode(button_B, INPUT_PULLUP);
  pinMode(button_C, INPUT_PULLUP);
  pinMode(redPin, OUTPUT);
  pinMode(greenPin, OUTPUT);
  pinMode(bluePin, OUTPUT);

  // Initialize the RTC timer and set the date and time as the compiled date and time.
  RTC.begin();
  RtcTime compiledDateTime(__DATE__, __TIME__);
  RTC.setTime(compiledDateTime);

  // Initialize the TFT LCD Touch Screen (ILI9341):
  tft.begin();
  tft.setRotation(TFT_ROTATION);
  tft.fillScreen(ILI9341_NAVY);
  tft.setTextColor(ILI9341_WHITE);  tft.setTextSize(2);
  tft.setCursor(10, 10);
  tft.println("Initializing...");

  // Check the connection status between Spresense and the SD card.
  while(!theSD.begin()){
    Serial.println("Insert SD card.");
    adjustColor(1,0,0);
    sleep(1);
  }
  Serial.println("SD card is detected successfully!\n");

  // Initialize the camera and print errors, if any.
  /* begin() without parameters means that
   * number of buffers = 1, 30FPS, QVGA, YUV 4:2:2 format */
  Serial.println("Camera initializing...");
  err = theCamera.begin();
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Start video stream and print errors, if any.
  Serial.println("Starting streaming...");
  err = theCamera.startStreaming(true, CamCB);
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Set the Auto white balance parameter and print errors, if any.
  Serial.println("Setting the Auto white balance parameter...");
  err = theCamera.setAutoWhiteBalanceMode(g_wb);
  if(err != CAM_ERR_SUCCESS) printError(err);
 
  // Set the still picture parameters and print errors, if any.
  Serial.println("Setting the still picture parameters...\n");
  err = theCamera.setStillPictureImageFormat(g_width, g_height, g_img_fmt, g_divisor);
  if(err != CAM_ERR_SUCCESS) printError(err);

  adjustColor(0,0,1);
  sleep(2);
}

void loop(){
  // Set the default color.
  adjustColor(1,0,1);
  // Save the recently captured picture to the SD card, named according to the selected class.
  if(!digitalRead(button_A)) takePicture(0);
  if(!digitalRead(button_B)) takePicture(1);
  if(!digitalRead(button_C)) takePicture(2);
}

void CamCB(CamImage img){
  // Check whether the img instance is available or not.
  if (img.isAvailable()){    
    // Convert the image data format to RGB565 so as to display images on the ILI9341 TFT screen.
    img.convertPixFormat(CAM_IMAGE_PIX_FMT_RGB565);
    /* You can use image data directly by using getImgSize() and getImgBuff().
     * for displaying image to a display, etc. */
    tft.drawRGBBitmap(0, 0, (uint16_t *)img.getImgBuff(), 320, 240);
    Serial.print("Image data size => "); Serial.print(img.getImgSize(), DEC); Serial.print(" , ");
    Serial.print("Image buffer address => "); Serial.println((unsigned long)img.getImgBuff(), HEX);
  }else{
    Serial.println("Failed to get video stream image!");
  }
}

void takePicture(int _class){
  char filename[30] = {0};
  // Take a picture with the given still picture settings.
  CamImage img = theCamera.takePicture();
  if(img.isAvailable()){
    // Pause video stream and print errors, if any.
    adjustColor(1,1,0);
    Serial.println("\nPausing streaming...\n");
    err = theCamera.startStreaming(false, CamCB);
    if(err != CAM_ERR_SUCCESS) printError(err);
    // Get the current date and time.
    RtcTime rtc;
    rtc = RTC.getTime();
    // Define the file name. 
    sprintf(filename, "%d_D_%04d.%02d.%02d__%02d.%02d.%02d.%s", _class, rtc.year(), rtc.month(), rtc.day(), rtc.hour(), rtc.minute(), rtc.second(), "JPG");
    // If the same file name exists, remove it in advance to prevent file appending.
    theSD.remove(filename);
    // Save the recently captured picture to the SD card.
    File myFile = theSD.open(filename, FILE_WRITE);
    myFile.write(img.getImgBuff(), img.getImgSize());
    myFile.close();
    Serial.println("Image captured successfully!");
    Serial.print("Selected Class: "); Serial.println(_class);
    Serial.printf("Name: %s\n", filename);
    Serial.printf("Resolution: %dx%d\n", img.getWidth(), img.getHeight());
    Serial.printf("Memory Size: %.2f / %.2f [KB]\n", img.getImgSize() / 1024.0, img.getImgBuffSize() / 1024.0);
    // Display the recently saved image information on the ILI9341 TFT screen.
    int c_x = 10, c_y = 100, r_x = 300, r_y = 120, r = 10, offset = 10, l = 15;
    tft.drawRGBBitmap(10, 10, (uint16_t*)(data_collect.pixel_data), (int16_t)data_collect.width, (int16_t)data_collect.height);
    tft.fillRoundRect(c_x, c_y, r_x, r_y, r, ILI9341_WHITE);
    tft.fillRoundRect(c_x+offset, c_y+offset, r_x-(2*offset), r_y-(2*offset), r, ILI9341_DARKGREEN);
    tft.setTextColor(ILI9341_WHITE); tft.setTextSize(1);
    tft.setCursor(c_x+(2*offset), c_y+(2*offset));
    tft.printf("Name: %s\n", filename);
    tft.setCursor(c_x+(2*offset), c_y+(2*offset)+l);
    tft.printf("Resolution: %dx%d\n", img.getWidth(), img.getHeight());
    tft.setCursor(c_x+(2*offset), c_y+(2*offset)+(2*l));
    tft.printf("Selected Class: %d", _class);
    sleep(5);
    // Resume video stream and print errors, if any.
    adjustColor(0,1,0);
    sleep(2);
    Serial.println("\nResuming streaming...\n");
    err = theCamera.startStreaming(true, CamCB);
    if(err != CAM_ERR_SUCCESS) printError(err);
  }else{
    Serial.println("Failed to take a picture!");
    adjustColor(1,0,0);
    sleep(2);
  }
}

void printError(enum CamErr err){
  adjustColor(1,0,0);
  sleep(2);
  Serial.print("Error: ");
  switch(err){
    case CAM_ERR_NO_DEVICE:             Serial.println("No Device");                      break;
    case CAM_ERR_ILLEGAL_DEVERR:        Serial.println("Illegal device error");           break;
    case CAM_ERR_ALREADY_INITIALIZED:   Serial.println("Already initialized");            break;
    case CAM_ERR_NOT_INITIALIZED:       Serial.println("Not initialized");                break;
    case CAM_ERR_NOT_STILL_INITIALIZED: Serial.println("Still picture not initialized");  break;
    case CAM_ERR_CANT_CREATE_THREAD:    Serial.println("Failed to create thread");        break;
    case CAM_ERR_INVALID_PARAM:         Serial.println("Invalid parameter");              break;
    case CAM_ERR_NO_MEMORY:             Serial.println("No memory");                      break;
    case CAM_ERR_USR_INUSED:            Serial.println("Buffer already in use");          break;
    case CAM_ERR_NOT_PERMITTED:         Serial.println("Operation not permitted");        break;
    default:
      break;
  }
}

void adjustColor(int r, int g, int b){
  digitalWrite(redPin, (1-r));
  digitalWrite(greenPin, (1-g));
  digitalWrite(bluePin, (1-b));
}

dental_model_classifier_run_model.ino

Arduino
         /////////////////////////////////////////////  
        //    AI-driven Dental Model Classifier    //
       //            w/ Edge Impulse              //
      //             ---------------             //
     //             (Sony Spresense)            //           
    //             by Kutluhan Aktar           // 
   //                                         //
  /////////////////////////////////////////////

//
// Via Spresense; collect dental cast images on an SD card to train an object detection model, display video stream, and run the model directly.
//
// For more information:
// https://www.theamplituhedron.com/projects/AI_driven_Dental_Model_Classifier_w_Edge_Impulse
//
//
// Connections
// Sony Spresense (w/ Extension Board) :  
//                                2.8'' 240x320 TFT LCD Touch Screen (ILI9341)
// D7   --------------------------- CS 
// D8   --------------------------- RESET 
// D9   --------------------------- D/C
// MOSI --------------------------- SDI (MOSI)
// SCK  --------------------------- SCK 
// 3.3V --------------------------- LED 
// MISO --------------------------- SDO(MISO) 
//                                Tiny (Embedded) Thermal Printer
// TX   --------------------------- RX
// RX   --------------------------- TX
// GND  --------------------------- GND
//                                Control Button (A)
// D2   --------------------------- +
//                                Control Button (B)
// D4   --------------------------- +
//                                Control Button (C)
// D14  --------------------------- +
//                                Keyes 10mm RGB LED Module (140C05)
// D3   --------------------------- R
// D5   --------------------------- G
// D6   --------------------------- B  


// Include the required libraries:
#include <Camera.h>
#include <Adafruit_GFX.h>
#include <Adafruit_ILI9341.h>
#include "Adafruit_Thermal.h"

// Include the Edge Impulse FOMO model converted to an Arduino library:
#include <Dental_Model_Classifier_inferencing.h>

// Define the required parameters to run an inference with the Edge Impulse model.
#define EI_CAMERA_RAW_FRAME_BUFFER_COLS   1280
#define EI_CAMERA_RAW_FRAME_BUFFER_ROWS   960

#define CAPTURED_IMAGE_BUFFER_COLS        320
#define CAPTURED_IMAGE_BUFFER_ROWS        320

static uint8_t *ei_camera_capture_out = NULL;

// Define the dental model category (class) names and color codes:
const char *classes[] = {"Cast", "Failed", "Implant"};
uint32_t color_codes[] = {ILI9341_GREEN, ILI9341_MAGENTA, ILI9341_ORANGE};

// Include graphics (color bitmaps):
#include "dental.c"

// Include icons for the thermal printer.
#include "dental_logo.h"

// Define camera settings:
int               g_pict_id = 0;
int               g_width   = CAPTURED_IMAGE_BUFFER_COLS;
int               g_height  = CAPTURED_IMAGE_BUFFER_ROWS;
CAM_IMAGE_PIX_FMT g_img_fmt = CAM_IMAGE_PIX_FMT_YUV422;
CAM_WHITE_BALANCE g_wb      = CAM_WHITE_BALANCE_AUTO;
CAM_COLOR_FX      g_cfx     = CAM_COLOR_FX_NONE;
int               g_divisor = 7;

// Define the camera error object.
CamErr err;

// Define the thermal printer object passing commands through Spresense's hardware serial port (Serial2).
Adafruit_Thermal printer(&Serial2);

// Define the required pins for the 240x320 TFT LCD Touch Screen (ILI9341):
#define TFT_CS   7
#define TFT_RST  8
#define TFT_DC   9

// Use hardware SPI (on Spresense, SCK, MISO, MOSI) and the above for DC/CS/RST.
Adafruit_ILI9341 tft = Adafruit_ILI9341(TFT_CS, TFT_DC, TFT_RST);

// Define the control button pins:
#define button_A   2
#define button_B   4
#define button_C   14

// Define the RGB LED pins:
#define redPin     3
#define greenPin   5
#define bluePin    6

// Define the data holders:
#define TFT_ROTATION 1
int predicted_class = -1;
int b_b_x, b_b_y, b_b_w, b_b_h;

void setup(){
  Serial.begin(115200);

  pinMode(button_B, INPUT_PULLUP);
  pinMode(redPin, OUTPUT);
  pinMode(greenPin, OUTPUT);
  pinMode(bluePin, OUTPUT);

  // Initialize the TFT LCD Touch Screen (ILI9341):
  tft.begin();
  tft.setRotation(TFT_ROTATION);
  tft.fillScreen(ILI9341_NAVY);
  tft.setTextColor(ILI9341_WHITE);  tft.setTextSize(2);
  tft.setCursor(10, 10);
  tft.println("Initializing...");

  // Initialize the camera and print errors, if any.
  /* begin() without parameters means that
   * number of buffers = 1, 30FPS, QVGA, YUV 4:2:2 format */
  Serial.println("Camera initializing...");
  err = theCamera.begin();
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Start video stream and print errors, if any.
  Serial.println("Starting streaming...");
  err = theCamera.startStreaming(true, CamCB);
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Set the Auto white balance parameter and print errors, if any.
  Serial.println("Setting the Auto white balance parameter...");
  err = theCamera.setAutoWhiteBalanceMode(g_wb);
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Set the still picture parameters and print errors, if any.
  Serial.println("Setting the still picture parameters...\n");
  err = theCamera.setStillPictureImageFormat(g_width, g_height, g_img_fmt, g_divisor);
  if(err != CAM_ERR_SUCCESS) printError(err);

  // Initialize the hardware serial (Serial2).
  Serial2.begin(9600);
  // Initialize the thermal printer.  
  printer.begin();
  
  adjustColor(0,0,1);
  sleep(2);
}

void loop(){
  // Set the default color.
  adjustColor(1,0,1);
  
  // If the control button (B) is pressed, run the Edge Impulse FOMO model to classify dental casts.
  if(!digitalRead(button_B)) run_inference_to_make_predictions();
}

void run_inference_to_make_predictions(){
  // Summarize the Edge Impulse FOMO model inference settings (from model_metadata.h):
  ei_printf("\nInference settings:\n");
  ei_printf("\tImage resolution: %dx%d\n", EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT);
  ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
  ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0]));
  
  // Take a picture with the given still picture settings.
  CamImage img = theCamera.takePicture();
  
  if(img.isAvailable()){
    // Pause video stream and print errors, if any.
    adjustColor(1,1,0);
    Serial.println("\nPausing streaming...\n");
    err = theCamera.startStreaming(false, CamCB);
    if(err != CAM_ERR_SUCCESS) printError(err);

    // Resize the currently captured image depending on the given FOMO model.
    CamImage res_img;
    img.resizeImageByHW(res_img, EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT);
    Serial.printf("Captured Image Resolution: %d / %d\nResized Image Resolution: %d / %d", img.getWidth(), img.getHeight(), res_img.getWidth(), res_img.getHeight());

    // Convert the resized (sample) image data format to GRAYSCALE so as to run inferences with the model.
    res_img.convertPixFormat(CAM_IMAGE_PIX_FMT_GRAY);
    Serial.print("\nResized Image Format: ");
    Serial.println((res_img.getPixFormat() == CAM_IMAGE_PIX_FMT_GRAY) ? "GRAYSCALE" : "ERROR");

    // Run inference:
    ei::signal_t signal;
    ei_camera_capture_out = res_img.getImgBuff();
    // Create a signal object from the resized and converted sample image.
    signal.total_length = EI_CLASSIFIER_INPUT_WIDTH * EI_CLASSIFIER_INPUT_HEIGHT;
    signal.get_data = &ei_camera_cutout_get_data;
    // Run the classifier:
    ei_impulse_result_t result = { 0 };
    EI_IMPULSE_ERROR _err = run_classifier(&signal, &result, false);
    if(_err != EI_IMPULSE_OK){
      ei_printf("ERR: Failed to run classifier (%d)\n", err);
      return;
    }

    // Print the inference timings on the serial monitor.
    ei_printf("\nPredictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
        result.timing.dsp, result.timing.classification, result.timing.anomaly);

    // Obtain the object detection results and bounding boxes for the detected labels (classes). 
    bool bb_found = result.bounding_boxes[0].value > 0;
    for(size_t ix = 0; ix < EI_CLASSIFIER_OBJECT_DETECTION_COUNT; ix++){
      auto bb = result.bounding_boxes[ix];
      if(bb.value == 0) continue;
      // Print the detected bounding box measurements on the serial monitor.
      ei_printf("    %s (", bb.label);
      ei_printf_float(bb.value);
      ei_printf(") [ x: %u, y: %u, width: %u, height: %u ]\n", bb.x, bb.y, bb.width, bb.height);
      b_b_x = bb.x; b_b_y =  bb.y; b_b_w = bb.width; b_b_h = bb.height;
      // Get the predicted label (class).
      if(bb.label == "cast") predicted_class = 0;
      if(bb.label == "failed") predicted_class = 1;
      if(bb.label == "implant") predicted_class = 2;
      Serial.print("\nPredicted Class: "); Serial.println(predicted_class);
    }
    if(!bb_found) ei_printf("    No objects found!\n");

    // Detect anomalies, if any:
    #if EI_CLASSIFIER_HAS_ANOMALY == 1
      ei_printf("Anomaly: ");
      ei_printf_float(result.anomaly);
      ei_printf("\n");
    #endif    

    // If the Edge Impulse FOMO model predicted a label (class) successfully:
    if(predicted_class != -1){
      // Scale the detected bounding box.
      int box_scale_x = tft.width() / EI_CLASSIFIER_INPUT_WIDTH;
      b_b_x = b_b_x * box_scale_x;
      b_b_w = b_b_w * box_scale_x * 16;
      if((b_b_w + b_b_x) > (tft.width() - 10)) b_b_w = tft.width() - b_b_x - 10;
      int box_scale_y = tft.height() / EI_CLASSIFIER_INPUT_HEIGHT;
      b_b_y = b_b_y * box_scale_y;
      b_b_h = b_b_h * box_scale_y * 16;
      if((b_b_h + b_b_y) > (tft.height() - 10)) b_b_h = tft.height() - b_b_y - 10;
      
      // Display the predicted label (class) and the detected bounding box on the ILI9341 TFT screen.
      for(int i=0; i<5; i++){
        tft.drawRect(b_b_x+i, b_b_y+i, b_b_w-(2*i), b_b_h-(2*i), color_codes[predicted_class]);
      }
      int c_x = 10, c_y = 10, r_x = 120, r_y = 40, r = 3, offset = 6;
      tft.drawRGBBitmap(10, c_y+r_y+10, (uint16_t*)(dental.pixel_data), (int16_t)dental.width, (int16_t)dental.height);
      tft.fillRoundRect(c_x, c_y, r_x, r_y, r, ILI9341_WHITE);
      tft.fillRoundRect(c_x+offset, c_y+offset, r_x-(2*offset), r_y-(2*offset), r, color_codes[predicted_class]);
      tft.setTextColor(ILI9341_WHITE); tft.setTextSize(2);
      tft.setCursor(c_x+(2*offset), c_y+(2*offset));
      tft.printf(classes[predicted_class]);

      // Print the predicted label (class) information via the thermal printer.
      print_thermal(predicted_class);

      // Clear the predicted class (label).
      predicted_class = -1;
    }
     
    sleep(10);
    
    // Resume video stream and print errors, if any.
    adjustColor(0,1,0);
    sleep(2);
    Serial.println("\nResuming streaming...\n");
    err = theCamera.startStreaming(true, CamCB);
    if(err != CAM_ERR_SUCCESS) printError(err);
  }else{
    Serial.println("Failed to take a picture!");
    adjustColor(1,0,0);
    sleep(2);
  }
}

void CamCB(CamImage img){
  // Check whether the img instance is available or not.
  if (img.isAvailable()){    
    // Convert the image data format to RGB565 so as to display images on the ILI9341 TFT screen.
    img.convertPixFormat(CAM_IMAGE_PIX_FMT_RGB565);
    /* You can use image data directly by using getImgSize() and getImgBuff().
     * for displaying image to a display, etc. */
    tft.drawRGBBitmap(0, 0, (uint16_t *)img.getImgBuff(), 320, 240);
    Serial.print("Image data size => "); Serial.print(img.getImgSize(), DEC); Serial.print(" , ");
    Serial.print("Image buffer address => "); Serial.println((unsigned long)img.getImgBuff(), HEX);
  }else{
    Serial.println("Failed to get video stream image!");
  }
}

void print_thermal(int _class){
  printer.printBitmap(80, 80, dental_logo);
  printer.boldOn();
  printer.justify('R');
  printer.setSize('L');
  printer.println(classes[_class]);
  if(_class == 0){
    printer.boldOff();
    printer.justify('L');
    printer.setSize('M');
    printer.println("Dental Casts:\n");
    printer.setSize('S');
    printer.println("Big Central");
    printer.println("Antagonist");
    printer.println("Orthodontic");
    printer.println("Prognathous");
    printer.println("Strange Inf.");
    printer.println("Strange Sup.");
  }
  printer.feed(5);
  printer.setDefault(); // Restore printer to defaults.
}

int ei_camera_cutout_get_data(size_t offset, size_t length, float *out_ptr){
  // Convert the given image data (buffer) to the out_ptr format required by the Edge Impulse FOMO model.
  size_t bytes_left = length;
  size_t out_ptr_ix = 0;
  // read byte for byte
  while(bytes_left != 0){
    // grab the value and convert to r/g/b
    uint8_t pixel = ei_camera_capture_out[offset];
    uint8_t r, g, b;
    mono_to_rgb(pixel, &r, &g, &b);
    // then convert to out_ptr format
    float pixel_f = (r << 16) + (g << 8) + b;
    out_ptr[out_ptr_ix] = pixel_f;
    // and go to the next pixel
    out_ptr_ix++;
    offset++;
    bytes_left--;
  }
  return 0;
}

static inline void mono_to_rgb(uint8_t mono_data, uint8_t *r, uint8_t *g, uint8_t *b){
  uint8_t v = mono_data;
  *r = *g = *b = v;
}

void printError(enum CamErr err){
  adjustColor(1,0,0);
  sleep(2);
  Serial.print("Error: ");
  switch(err){
    case CAM_ERR_NO_DEVICE:             Serial.println("No Device");                      break;
    case CAM_ERR_ILLEGAL_DEVERR:        Serial.println("Illegal device error");           break;
    case CAM_ERR_ALREADY_INITIALIZED:   Serial.println("Already initialized");            break;
    case CAM_ERR_NOT_INITIALIZED:       Serial.println("Not initialized");                break;
    case CAM_ERR_NOT_STILL_INITIALIZED: Serial.println("Still picture not initialized");  break;
    case CAM_ERR_CANT_CREATE_THREAD:    Serial.println("Failed to create thread");        break;
    case CAM_ERR_INVALID_PARAM:         Serial.println("Invalid parameter");              break;
    case CAM_ERR_NO_MEMORY:             Serial.println("No memory");                      break;
    case CAM_ERR_USR_INUSED:            Serial.println("Buffer already in use");          break;
    case CAM_ERR_NOT_PERMITTED:         Serial.println("Operation not permitted");        break;
    default:
      break;
  }
}

void adjustColor(int r, int g, int b){
  digitalWrite(redPin, (1-r));
  digitalWrite(greenPin, (1-g));
  digitalWrite(bluePin, (1-b));
}

Credits

Kutluhan Aktar

Kutluhan Aktar

81 projects • 307 followers
AI & Full-Stack Developer | @EdgeImpulse | @Particle | Maker | Independent Researcher

Comments

Awards
Grand Prize
Spresense Developer Challenge 2022
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