基于深度学习的实时车辆与行人多目标检测与跟踪系统——YOLOv10 + UI界面 + 数据集

 git clone https://github.com/ultralytics/yolov5

    
 cd yolov5
    
 pip install -r requirements.txt
    
    
    
    
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    python train.py --img 640 --batch 16 --epochs 50 --data coco.yaml --weights yolov5s.pt
    
    
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    pip install deep_sort_realtime
    
    
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 from deep_sort_realtime.deepsort_tracker import DeepSort

    
 import cv2
    
 import torch
    
  
    
 # 加载YOLOv10模型
    
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
    
  
    
 # 加载Deep SORT跟踪器
    
 deepsort = DeepSort()
    
  
    
 # 视频读取
    
 cap = cv2.VideoCapture("input_video.mp4")
    
  
    
 while cap.isOpened():
    
     ret, frame = cap.read()
    
     if not ret:
    
     break
    
     
    
     # YOLOv10目标检测
    
     results = model(frame)
    
     detections = results.xywh[0].cpu().numpy()  # 获取检测结果
    
     boxes = detections[:, :4]  # 边界框坐标
    
     confidences = detections[:, 4]  # 置信度
    
     class_ids = detections[:, 5].astype(int)  # 类别ID
    
     
    
     # 跟踪目标
    
     tracks = deepsort.update_tracks(boxes, confidences, class_ids, frame)
    
     
    
     # 绘制检测和跟踪结果
    
     for track in tracks:
    
     x1, y1, x2, y2, track_id = track.to_tlbr()
    
     cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
    
     cv2.putText(frame, f"ID: {track_id}", (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
     
    
     # 显示图像
    
     cv2.imshow("Tracking", frame)
    
     
    
     if cv2.waitKey(1) & 0xFF == ord('q'):
    
     break
    
  
    
 cap.release()
    
 cv2.destroyAllWindows()
    
    
    
    
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 import tkinter as tk

    
 from tkinter import filedialog
    
 import cv2
    
 from PIL import Image, ImageTk
    
  
    
 def open_video():
    
     file_path = filedialog.askopenfilename(filetypes=[("Video Files", "*.mp4;*.avi")])
    
     if file_path:
    
     process_video(file_path)
    
  
    
 def process_video(file_path):
    
     cap = cv2.VideoCapture(file_path)
    
     while cap.isOpened():
    
     ret, frame = cap.read()
    
     if not ret:
    
         break
    
     
    
     # 处理帧：YOLOv10检测 + Deep SORT跟踪
    
     results = model(frame)
    
     detections = results.xywh[0].cpu().numpy()
    
     boxes = detections[:, :4]
    
     confidences = detections[:, 4]
    
     class_ids = detections[:, 5].astype(int)
    
     tracks = deepsort.update_tracks(boxes, confidences, class_ids, frame)
    
     
    
     for track in tracks:
    
         x1, y1, x2, y2, track_id = track.to_tlbr()
    
         cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
    
         cv2.putText(frame, f"ID: {track_id}", (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
     
    
     # 转换为UI显示格式
    
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
     img = Image.fromarray(frame_rgb)
    
     img = img.resize((800, 600))
    
     img_tk = ImageTk.PhotoImage(img)
    
     
    
     label.img_tk = img_tk
    
     label.config(image=img_tk)
    
     
    
     root.update()
    
  
    
     cap.release()
    
  
    
 root = tk.Tk()
    
 root.title("车辆与行人实时检测与跟踪")
    
  
    
 upload_button = tk.Button(root, text="上传视频", command=open_video)
    
 upload_button.pack()
    
  
    
 label = tk.Label(root)
    
 label.pack()
    
  
    
 root.mainloop()
    
    
    
    
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