影像辨識技術結合機器手臂Q1ARM進行物品分類的實作

#include <Servo.h>
Servo __myservo16;
Servo __myservo11;
Servo __myservo9;
Servo __myservo8;
Servo __myservo7;

if (Serial.available()) {
    // 讀取傳入的字串直到"\n"結尾
    str = Serial.readStringUntil('\n');

    String_to_Int(str, 2);
    Serial.println(SA[0]);
    Serial.println(A[1]);
  }

  if (SA[0] == "數值") {
    int PRE_angle = __myservo16.read();
    if (A[1] >= PRE_angle) {
      for (int angle=PRE_angle;angle<=A[1];angle++){
         __myservo16.write(angle);
         delay(20);
      }
      Serial.println("Servo_OK");
    }
    if (A[1] < PRE_angle) {
      for (int angle=PRE_angle;angle>=A[1];angle--){
         __myservo16.write(angle);
         delay(20);
      }
      Serial.println("Servo_OK");
    }    
  }

void String_to_Int(String temp, int count)
{
  int index;

  index = temp.indexOf(',');
  SA[0] = temp.substring(0, index);

  for (int i = 1; i < count; i++) {
    temp = temp.substring(index + 1, temp.length());
    index = temp.indexOf(',');
    SA[i] = temp.substring(0, index);
    A[i] = SA[i].toInt();
  }
}

python3 PHOTO_ARM.py --video 攝影機編號 --com UART連接埠編號
#例如
python3 PHOTO_ARM.py --video 0 --com 5

python PHOTO_ARM.py --video 攝影機編號 --com UART連接埠編號
#例如
python PHOTO_ARM.py --video 0 --com 5

Condition_1 = 'OBJ_1'
Condition_2 = 'OBJ_2'
Condition_3 = 'OBJ_3'
Condition_4 = 'OBJ_4'
Condition_Other = 'Other'

def get_prediction(image, interpreter, signature):
    影像推論的程式片段(略)
    return outputs
#image是即時影像資料
#interpreter是神經網路模型
#signature是模型參數檔內容

def process_image(image, input_shape):
    #影像前處理的程式片段(略)
    return image.reshape(input_shape).astype(np.float32)
#image是即時影像資料
#input_shape是輸入影像的解析度資訊

    COM_PORT = args.com  #COM_PORT變數設定UART通訊連接埠的編號
    BAUD_RATES = 9600  #BAUD_RATES變數設定RAUD為9600BPS
    ser = serial.Serial(COM_PORT, BAUD_RATES)  # 建立 ser 物件啟動序列通訊

if (times==1) and (key_flag == 0) and (Motion_Busy ==0):
    prediction = get_prediction(image, interpreter, signature)

if (classification_flag == 1) and (Label_name == Condition_1) and (prob > 0.5):
    Condition_1_count = Condition_1_count + 1
    Condition_2_count = 0
    Condition_3_count = 0
    Condition_4_count = 0

if (Condition_1_count == 50):
    Motion_Busy = 1
    伺服馬達動作角度設定片段程式(略)
     Motion_Busy = 0

read_dir_key = cv2.waitKeyEx(1)
if (read_dir_key != -1):
    print(read_dir_key)
    #進行各種按鍵偵測的動作程式片段(略)
    # 按 q鍵 或 Esc鍵 結束程式    
elif ((read_dir_key == 113) or (read_dir_key == 27)):
    key_detect = 1

if (key_flag == 1):
    cv2.rectangle(image_src,
        (box_left,box_top),(box_left+scale,box_top+scale),
        (0,255,0),2)
elif (key_flag == 0):
    cv2.rectangle(image_src,
         (box_left,box_top),(box_left+scale,box_top+scale),
        (0,0,255),2)

box_center_x = box_left + int(scale / 2)
box_center_y = box_top + int(scale / 2)

dw = int(frame_width / 2) - box_center_x
dh = frame_height - box_center_y
angle = (math.atan(dw / dh)) / math.pi * 180

cv2.line(image_src,
     (int(frame_width / 2),frame_height),
     (box_center_x,box_center_y),
     (0,255,255),2)

 cv2.putText(image_src, str(round(angle,3)),
     (int(frame_width * 0.6),30), cv2.FONT_HERSHEY_SIMPLEX, 1,
     (0,255,255), 6, cv2.LINE_AA)
cv2.putText(image_src, str(round(angle,3)),
    (int(frame_width * 0.6),30), cv2.FONT_HERSHEY_SIMPLEX, 1,
    (0,0,0), 2, cv2.LINE_AA)

cv2.imshow('Detecting_1 ....',image_src)