---
# System prepended metadata

title: arduino

---

# 在MQTT 下的影像傳輸與物件檢測
## 本次實驗使用 ESP32-CAM 來做影像傳輸與 YOLOv5 模型對保存的圖片進行物件檢測


YOLOv5 是一個物件偵測方法，可直接從輸入圖像生成邊界框和分類結果，適合對速度有高要求的應用，如即時檢測。

![image](https://hackmd.io/_uploads/H1r-qBsXJx.png)


![image](https://hackmd.io/_uploads/HkKb2ziQke.png)

![image](https://hackmd.io/_uploads/HyUOUbiXyx.png)
![image](https://hackmd.io/_uploads/SyFR8WomJx.png)




arduino(使用C)
```c
#include <esp_camera.h>   // 引入ESP32相機功能庫
#include <WiFi.h>         // 引入WiFi功能庫
#include <PubSubClient.h> // 引入MQTT功能庫
// ------ WiFi帳號密碼 ------
const char* ssid = "HPC_1";      // 定義WiFi名稱
const char* password = "HPC200-1"; // 定義WiFi密碼

// ------ MQTT設定 ------
const char* mqtt_server = "mqtt.eclipseprojects.io";          // 定義MQTT伺服器位址
const unsigned int mqtt_port = 1883;                          // 定義MQTT伺服器的通訊埠
#define MQTT_PUBLISH_Monitor    "ESP32CAM_hpcLab/esp32cam/pic" // 定義發布圖片的Topic名稱

// ------ OV2640相機設定 ------------
#define CAMERA_MODEL_AI_THINKER // 指定相機模組為AI-THINKER
#define PWDN_GPIO_NUM     32    // 定義相機的電源控制引腳
#define RESET_GPIO_NUM    -1    // 定義相機的重置引腳 (未使用)
#define XCLK_GPIO_NUM      0    // 定義相機的時鐘信號引腳
#define SIOD_GPIO_NUM     26    // 定義SCCB的資料引腳
#define SIOC_GPIO_NUM     27    // 定義SCCB的時鐘引腳
#define Y9_GPIO_NUM       35    // 定義影像數據引腳Y9
#define Y8_GPIO_NUM       34    // 定義影像數據引腳Y8
#define Y7_GPIO_NUM       39    // 定義影像數據引腳Y7
#define Y6_GPIO_NUM       36    // 定義影像數據引腳Y6
#define Y5_GPIO_NUM       21    // 定義影像數據引腳Y5
#define Y4_GPIO_NUM       19    // 定義影像數據引腳Y4
#define Y3_GPIO_NUM       18    // 定義影像數據引腳Y3
#define Y2_GPIO_NUM        5    // 定義影像數據引腳Y2
#define VSYNC_GPIO_NUM    25    // 定義垂直同步引腳
#define HREF_GPIO_NUM     23    // 定義水平同步引腳
#define PCLK_GPIO_NUM     22    // 定義像素時鐘引腳

char clientId[50];     // 定義MQTT客戶端ID字串緩衝區
WiFiClient wifiClient; // 定義WiFi客戶端 
PubSubClient mqttClient(mqtt_server, mqtt_port, mqtt_callback, wifiClient); // 定義MQTT客戶端

// 啟動WiFi連線
void setup_wifi() {
  Serial.printf("\nConnecting to %s", ssid); // 顯示連接的WiFi名稱
  WiFi.begin(ssid, password); // 開始連接WiFi
  while (WiFi.status() != WL_CONNECTED) { // 持續檢查連線狀態
    delay(500);
    Serial.print("."); // 顯示連接過程的提示
  }
  Serial.print("\nWiFi Connected.  IP Address: "); // 顯示WiFi連線成功訊息
  Serial.println(WiFi.localIP()); // 顯示取得的IP位址
}


// 重新連線到MQTT伺服器
boolean mqtt_nonblock_reconnect() {
  boolean doConn = false; // 定義連線狀態標誌
  if (! mqttClient.connected()) { // 檢查是否已連線
    boolean isConn = mqttClient.connect(clientId); // 嘗試連線到MQTT伺服器
    char logConnected[100]; // 定義記錄訊息的字串緩衝區
    sprintf(logConnected, "MQTT Client [%s] Connect %s !", clientId, (isConn ? "Successful" : "Failed")); // 格式化連線訊息
    Serial.println(logConnected); // 顯示連線結果
  }
  return doConn; // 返回連線狀態
}

// MQTT傳遞照片
void MQTT_picture() {
  camera_fb_t * fb = NULL; // 定義相機緩衝區
  fb = esp_camera_fb_get(); // 從相機取得圖片
  if (!fb) { // 如果獲取圖片失敗
    delay(100);
    Serial.println("Camera capture failed, Reset"); // 顯示錯誤訊息
    ESP.restart(); // 重新啟動ESP32
  }

  char* logIsPublished; // 定義傳輸結果訊息的指標

  if (! mqttClient.connected()) { // 如果MQTT連線中斷
    Serial.printf("MQTT Client [%s] Connection LOST !\n", clientId); // 顯示錯誤訊息
    mqtt_nonblock_reconnect(); // 嘗試重新連線
  }

  if (! mqttClient.connected())
    logIsPublished = "  No MQTT Connection, Photo NOT Published !"; // 顯示傳輸失敗訊息
  else {
    int imgSize = fb->len; // 獲取圖片大小
    int ps = MQTT_MAX_PACKET_SIZE; // 定義MQTT封包大小
    mqttClient.beginPublish(MQTT_PUBLISH_Monitor, imgSize, false); // 開始傳輸圖片
    for (int i = 0; i < imgSize; i += ps) { // 將圖片分段傳輸
      int s = (imgSize - i < s) ? (imgSize - i) : ps; // 計算每段大小
      mqttClient.write((uint8_t *)(fb->buf) + i, s); // 傳輸圖片數據
    }

    boolean isPublished = mqttClient.endPublish(); // 結束圖片傳輸
    if (isPublished)
      logIsPublished = "  Publishing Photo to MQTT Successfully !"; // 顯示成功訊息
    else
      logIsPublished = "  Publishing Photo to MQTT Failed !"; // 顯示失敗訊息
  }
  Serial.println(logIsPublished); // 顯示傳輸結果
  esp_camera_fb_return(fb); // 清空相機緩衝區
}

// 初始化函數
void setup() {
  Serial.begin(115200); // 初始化序列埠
  // 相機設定
  camera_config_t config; // 定義相機配置結構體
  config.ledc_channel = LEDC_CHANNEL_0; // 設定LED控制通道
  config.ledc_timer = LEDC_TIMER_0; // 設定LED控制計時器
  config.pin_d0 = Y2_GPIO_NUM; // 設定數據引腳
  config.pin_d1 = Y3_GPIO_NUM;
  config.pin_d2 = Y4_GPIO_NUM;
  config.pin_d3 = Y5_GPIO_NUM;
  config.pin_d4 = Y6_GPIO_NUM;
  config.pin_d5 = Y7_GPIO_NUM;
  config.pin_d6 = Y8_GPIO_NUM;
  config.pin_d7 = Y9_GPIO_NUM;
  config.pin_xclk = XCLK_GPIO_NUM; // 設定時鐘信號引腳
  config.pin_pclk = PCLK_GPIO_NUM; // 設定像素時鐘引腳
  config.pin_vsync = VSYNC_GPIO_NUM; // 設定垂直同步引腳
  config.pin_href = HREF_GPIO_NUM; // 設定水平同步引腳
  config.pin_sscb_sda = SIOD_GPIO_NUM; // 設定SCCB資料引腳
  config.pin_sscb_scl = SIOC_GPIO_NUM; // 設定SCCB時鐘引腳
  config.pin_pwdn = PWDN_GPIO_NUM; // 設定電源控制引腳
  config.pin_reset = RESET_GPIO_NUM; // 設定重置引腳
  config.xclk_freq_hz = 20000000; // 設定時鐘頻率
  config.pixel_format = PIXFORMAT_JPEG; // 設定像素格式為JPEG
  config.jpeg_quality = 10; // 設定JPEG品質 (數字越小品質越高)
  config.fb_count = 2; // 設定幀緩衝數量
  // 設定照片解析度
  config.frame_size = FRAMESIZE_QVGA; // 設定照片大小為QVGA
  esp_err_t err = esp_camera_init(&config); // 初始化相機
  delay(500); // 延遲500ms
  // 啟動WiFi連線
  setup_wifi();
  sprintf(clientId, "ESP32CAM_shiaw87"); // 設定MQTT客戶端ID
  // 啟動MQTT連線
  mqtt_nonblock_reconnect();
}

// 主循環函數
void loop() {
  mqtt_nonblock_reconnect(); // 確保MQTT連線
  MQTT_picture(); // 透過MQTT傳輸照片
  delay(500); // 延遲300ms
}

```

detect.py
```python
# YOLOv5  by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
                                                             img.jpg        # image
                                                             vid.mp4        # video
                                                             path/          # directory
                                                             path/*.jpg     # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # OpenVINO
                                         yolov5s.engine             # TensorRT
                                         yolov5s.mlmodel            # CoreML (macOS-only)
                                         yolov5s_saved_model        # TensorFlow SavedModel
                                         yolov5s.pb                 # TensorFlow GraphDef
                                         yolov5s.tflite             # TensorFlow Lite
                                         yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
"""

import argparse
import os
from re import S
import sys
from pathlib import Path
import requests
import time
import torch
import torch.backends.cudnn as cudnn

import logging
logging.getLogger().setLevel(logging.ERROR)  # 設定全局日誌層級為 ERROR，只顯示錯誤訊息

import warnings

warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=FutureWarning)

import paho.mqtt.client as mqtt
# MQTT 設定
MQTT_BROKER = "mqtt.eclipseprojects.io"  # 替換為你的 MQTT Broker 地址
MQTT_PORT = 1883                         # MQTT 埠號（默認為 1883）
MQTT_TOPIC_SUB = "ESP32CAM_hpcLab/esp32cam/pic"   # 要訂閱的主題
MQTT_TOPIC_PUB = "ESP32CAM_hpcLab/esp32cam/detect"  # 發佈檢測結果的主題

mqtt_client = mqtt.Client()

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync


@torch.no_grad()
def run(
        weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download
     

    # Directories
    #save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    #(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
    # Fixed Directory
    save_dir = Path(project) / name  # 固定輸出到同一資料夾
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # 建立資料夾

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    t=0
    for path, im, im0s, vid_cap, s in dataset:
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
        im /= 255  # 0 - 255 to 0.0 - 1.0
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

        # Process predictions
        
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
               #s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            #save_path = str(save_dir / p.name)  # im.jpg
            save_path = str(save_dir / "detect.jpg")  # 統一命名為 detect.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            #s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    s += f"{names[int(c)]} "  # add to string
                    mqtt_message=f"{names[int(c)]}"
                    if (mqtt_message != "backgrounds"):
                        mqtt_client.publish(MQTT_TOPIC_PUB, mqtt_message)  # 發送訊息到 MQTT
                    print("辨識結果 :",mqtt_message)
                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(f'{txt_path}.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
            
            # Stream results
            im0 = annotator.result()
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

    
      
    
        #LOGGER.info(f'{s}')#印出 重製s
        

    # Print results
    #t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
    #LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
    #if save_txt or save_img:
    #    s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
    #    LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    #    #print(s)
    #    #r = requests.post('http://192.168.1.143/test?yolo='+s)
    #if update:
    #    strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
    


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'D:/test/yolo-detect/last.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'D:/test/yolo-detect/img/received_image.jpg', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--data', type=str, default=ROOT / 'D:/test/yolo-detect/mydata.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default=ROOT / 'img', help='save results to project/name')
    parser.add_argument('--name', default='', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    # print_args(vars(opt)) 啟動參數
    return opt

# MQTT 回調函數
def on_connect(client, userdata, flags, rc):
    if rc == 0:
        print("MQTT 成功連線")
        mqtt_client.subscribe(MQTT_TOPIC_SUB)
        print(f"訂閱主題: {MQTT_TOPIC_SUB}")
    else:
        print(f"連線失敗，錯誤碼: {rc}")

def on_message(client, userdata, msg):
    print(f"接收到 MQTT 訊息，主題: {msg.topic}")
    try:
        # 假設接收到的是圖片二進位數據
        output_file = "D:/test/yolo-detect/img/received_image.jpg"  # 儲存圖片的檔案名稱
        with open(output_file, "wb") as file:
            file.write(msg.payload)
        print(f"圖片已保存為 {output_file}")
        
        # 執行 YOLOv5 檢測
        print(f"開始執行 YOLOv5 檢測，圖片路徑: {output_file}")
        run(**vars(opt))
    except Exception as e:
        print(f"處理訊息時發生錯誤: {e}")

def main(opt):
    mqtt_client.on_connect = on_connect
    mqtt_client.on_message = on_message
    mqtt_client.connect(MQTT_BROKER, MQTT_PORT, 60)
    print("啟動 MQTT 客戶端...")
    mqtt_client.loop_forever()
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)



```



![image](https://hackmd.io/_uploads/SkBkbbzmJl.png)







![image](https://hackmd.io/_uploads/HkAjjqaf1g.png)

