AMB82-mini 範例程式介紹
Arduino 範例程式
微控制器界面簡介
GPIO
Examples> 01.Basics > Blink
Examples> 02.Digitial > GPIO > Button
程式碼修改：
const int buttonPin = 1; // the number of the pushbutton pin
const int ledPin = LED_BUILTIN; // the number of the LED pin
Examples> 01.Basic > AnalogReadSerial
程式碼修改：Serial.begin(115200);
WiFi
Examples> WiFi > SimpleTCPServer
WiFi - Simple TCP Server
Examples> WiFi > SimpleHttpWeb > ReceiveData
WiFi - Simple Http Server to Receive Data
Examples> WiFi > SimpleHttpWeb > ControlLED
WiFi - Simple Http Server to Control LED
Sketchbook> AMB82-mini > WebServer_ControlLED
Sketchbook> WebServer_ControlLED
BLE
Exmples> AmebaBLE > BLEV7RC_CAR_VIDEO
MQTT
MQTT is an OASIS standard messaging protocol for the Internet of Things (IoT)
How MQTT Works -Beginners Guide
Examples> AmebaMQTTClient > MQTT_basic
MQTT - Set up MQTT Client to Communicate with Broker
pip install paho.mqtt
publish messages to AMB82-mini
import paho.mqtt.publish as publish
host = "test.mosquitto.org"
publish.single("ntou/edgeai/robot1", "go to the kitchen", hostname=host)
subsribe messages from AMB82-mini
import paho.mqtt.subscribe as subscribe
host = "test.mosquitto.org"
msg = subscribe.simple("ntou/edgeai/robot1", hostname=host)
print("%s %s" % (msg.topic, msg.payload.decode("utf-8")))
MQTT IOT App
Download extension : UrsAI2PahoMqtt
GeminiMQTT App
Download to PC, and import to ai2.appinventor2.mit.edu
edit [Block] global API_key (get API key from https://aistudio.google.com/apikey)
感測器與週邊裝置
紅外線測距模組
Datasheet: VL53L0X - Time-of-Flight ranging sensor
Sketchbook> AMB82-mini > IR_VL53L0X
慣性感測
Sketchbook > AMB82-mini > MPU6050-DMP6v12
PWM
Examples> AmebaAnalog > PWM_ServoControl
myservo.attach(8);
myservo.write(pos);
H-bridge 全橋式馬達驅動
TB6612
DRV8833
ILI9341 TFT-LCD
Interface signal names:
- MOSI: Standard SPI Pin
- MISO: Standard SPI Pin
- SLK: Standard SPI Pin
- CS: Standard SPI Pin
- RESET: Used to reboot LCD.
- D/C: Data/Command. When it is at LOW, the signal transmitted are commands, otherwise the data transmitted are data.
- LED (or BL): Adapt the screen backlight. Can be controlled by PWM or connected to VCC for 100% backlight.
- VCC: Connected to 3V or 5V, depends on its spec.
- GND: Connected to GND.
AMB82 MINI and QVGA TFT LCD Wiring Diagram:
Sketchbook> AMB82-mini > HTTP_Post_ImageText_TFTLCD
Exmples> AmebaSPI > Camera_2_lcd
Camera output , then Jpeg Decoder to TFT-LCD
compilation error: need to modify Libraries/TJpg_Decoder/src/User_Config.h
//#define TJPGD_LOAD_SD_LIBRARY
Exmples > AmebaSPI > Camera_2_Lcd_JPEGDEC
Camera output, saved to SDcard, then Jpeg Decoder to read to TFT-LCD
Exmples > AmebaSPI > LCD_Screen_ILI9341_TFT
LCD Draw Tests
影像串流
Examples> AmebaMultimedia > StreamRTSP > RTSP_VideoOnly
Sketchbook> RTSP_VideoOnly
動作偵測 (motion detection)
Examples> AmebaMultimedia > MotionDetection > LoopPostProcessing
- 修改ssid, passwd, 後燒錄到AMB82-mini,
- 按reset後程式即開始運行, 用serial-monitor 查看顯示串流網址
- 啟動手機或電腦上之VLC player, 設定RTSP串流網址
Motion Detection Google Line Notify
Examples> AmebaMultimedia > MotionDetection > MotionDetectGoogleLineNotify
音頻環回測試
Examples> AmebaMultimedia > Audio >LoopbackTest
MP3 播放
AMB82-mini + PAM8403 + 4ohm 3W speaker
Sketchbook> AMB82-mini > SDcardPlayMP3
- .mp3 files stored under mp3 directory
Sketchbook> AMB82-mini > SDcardPlayMP3_All
音頻串流範例
Examples> AmebaMultimedia > Audio > RTSPAudioStream
MP4錄音範例
Examples> AmebaMultimedia > RecordMP4 > AudioOnly
音頻分類範例
Examples> AmebaNN > AudioClassification
人臉辨識與識別介紹
人臉檢測範例
Examples> AmebaNN > RTSPFaceDetection
人臉識別範例
Examples> AmebaNN > RTSPFaceRecognition
Serial_monitor: REG=RKUO
- Enter the command REG=Name to give the targeted face a name.
- Enter the command DEL=Name to delete a certain registered face. For example,
DEL=SAM - Enter the command BACKUP to save a copy of registered faces to flash.
- If a backup exists, enter the command RESTORE to load registered faces from flash.
- Enter the command RESET to forget all previously registered faces.
影像分類 (Image Classification)
回收物分類
RTSP_GarbageClassification.ino
Garbage模型訓練與檔案轉換
模型訓練： kaggle.com/rkuo2000/garbage-cnn
required in kaggle for AmebaPro2 1) pip install tensorflow==2.14.1 2) model.save(‘garbage_cnn.h5’, include_optimizer=False)
模型轉換：AMB82 AI Model Conversion
- Download garbage_cnn.h5 from kaggle.com/rkuo2000/garbage-cnn
Output - Compress garbage_cnn.h5 to garbage_cnn.zip
- Go to Amebapro2 AI convert model, fill up your E-mail
- Upload garbage_cnn.zip
- Upload one (.jpg) test picture (EX. glass100.jpg from Garbage dataset)
- Email will be sent to you for the link of
network_binary.nb
程式範例：RTSP_GarbageClassification.ino
- click the recieved Email link to download
network_binary.nb - create NN_MDL folder in SDcard, save network_binary.nb under NN_MDL folder, and rename it to
imgclassification.nb - plugin SDcard back to AMB82-MINI
- modify Sketch RTSP_GarbageClassification.ino 1) modify SSID and PASSWD 2) modify imgclass.modelSelect (change DEFAULT_IMGCLASS to CUSTOMIZED_IMGCLASS)
- burn code into board AMB82-MINI, and run it with VLC player streaming
物件偵測 (Object Detection)
Public Dataset
物件檢測介紹
Kaggle範例:
- YOLOv7 Facemask detection
- YOLOv7 Pothole detection
- YOLOv7 Sushi detection
- YOLOv7 refrigeratoryfood
- YOLOv7 reparm
Pothole模型訓練與檔案轉換
模型訓練： kaggle.com/rkuo2000/yolov7-pothole
1) repro https://github.com/WongKinYiu/yolov7
2) create pothole.yaml
%%writefile data/pothole.yaml
train: ./Datasets/pothole/train/images
val: ./Datasets/pothole/valid/images
test: ./Datasets/pothole/test/images
# Classes
nc: 1 # number of classes
names: ['pothole'] # class names
3) YOLOv7-Tiny Fixed Resolution Training
!sed -i "s/nc: 80/nc: 1/" cfg/training/yolov7-tiny.yaml
!sed -i "s/IDetect/Detect/" cfg/training/yolov7-tiny.yaml
模型轉換：AMB82 AI Model Conversion
- Download
best.ptfrom kaggle.com/rkuo2000/yolov7-pothole - Compress best.pt to
best.zip - Go to Amebapro2 AI convert model, fill up your E-mail
- Upload best.zip
- Upload one (.jpg) test picture (EX. pothole_test.jpg from Pothole dataset)
- Email will be sent to you for the link of
network_binary.nb
程式範例：RTSP_YOLOv7_Pothole_Detection.ino
- click the recieved Email link to download
network_binary.nb - create NN_MDL folder in SDcard, save network_binary.nb under NN_MDL folder, and rename it to
yolov7_tiny.nb - plugin SDcard back to AMB82-MINI
- modify Sketch RTSP_YOLOv7_Pothole_Detection.ino 1) modify SSID and PASSWD 2) modify ObjDet.modelSelect(OBJECT_DETECTION, CUSTOMIZED_YOLOV7TINY, NA_MODEL, NA_MODEL);
- burn code into board AMB82-MINI, and run it with VLC player streaming
AMB82 Mini 物件偵測範例
RTSP_YOLOv7_Pothole
RTSP_YOLOv7_Sushi
AMB82 Mini SD卡加載模型範例
RTPS_ObjectDetection_AudioClassification.ino
Online NN Conversion Tool (客製化模型轉換工具)
大型語言模型範例 (LLM)
語音辨識範例
ffmpeg.exe is needed for Windows to run Whisper!
語音交談範例
ffmpeg.exe is needed for Windows to run Whisper!
Download ffmpeg-master-latest-win64-gpl.zip, extract & put ffmpeg.exe into where you run Whisper server.
AmebaPro2 Whisper LLM_server
AmebaPro2_Whisper_Gemini_server
- 到 https://aistudio.google.com/app/apikey 取得API_Key 填入GOOGLE_API_KEY, 再執行AmebaPro2_Whisper_Gemini_server.py
HTTP_Post_Audio.ino
- 修改server IP位址 in RecordMP4_HTTP_Post_Audio.ino server IP位址, then 燒錄到 AMB82-MINI
- reset AMB82-MINI 來啟動, 按鍵兩秒後即可錄音詢問 LLM/Gemini
視覺語言模型 (VLM)
影像+語音交談範例
生成式AI API
Examples: AmebaNN > MultimediaAI > GenAIVision
Examples: AmebaNN > MultimediaAI > GenAISpeech_Gemini
Examples: AmebaNN > MultimediaAI > TextToSpeech
專題實作
Vaccum Robot
(trash classification, IR collision detection)
RoboCar
(voice-control, BLE remote-control, line-notify, object-detection)