yolov5源码:车辆跟踪源码 +车牌识别+车辆检测+行人检测+违规停车

 pip install opencv-python

    
 pip install numpy
    
 pip install paddlepaddle
    
 pip install torch
    
 pip install torchvision
    
 pip install filterpy # 用于实现SORT跟踪算法
    
    
    
    
    代码解读

 import cv2

    
 import numpy as np
    
 import paddle
    
 from paddle.vision.models import yolo
    
 from oc_sort import OCSort
    
  
    
 # 加载PP-YOLOE模型
    
 def load_yolo_model(model_type='s'):  # 's' for PP-YOLOE S, 'l' for PP-YOLOE L
    
     model = yolo.YOLO('PP-YOLOE', model_type)
    
     model = paddle.Model(model)
    
     model.prepare()
    
     model.load('path/to/your/PP-YOLOE')
    
     return model
    
  
    
 yolo_model = load_yolo_model()
    
  
    
 # 初始化OC-SORT跟踪器
    
 tracker = OCSort()
    
  
    
 # 车辆检测与跟踪
    
 def detect_and_track_vehicles(frame):
    
     # 使用PP-YOLOE进行车辆检测
    
     detections = yolo_model(frame)
    
  
    
     # 过滤出车辆目标
    
     vehicles = [d for d indetections if d['class'] in ['car', 'truck', 'bus', 'motorbike']]
    
  
    
     # 使用OC-SORT进行跟踪
    
     tracked_objects = tracker.update(np.array([v['bbox'] for v in vehicles]))
    
  
    
     # 在原图上绘制检测框和跟踪ID
    
     for idx, obj in enumerate(tracked_objects):
    
     x1, y1, x2, y2, track_id = obj
    
     cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
    
     cv2.putText(frame, 'ID: {}'.format(int(track_id)), (int(x1), int(y1) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
    
  
    
     return frame
    
  
    
 # 读取视频并进行车辆检测与跟踪
    
 video_capture = cv2.VideoCapture('path/to/your/video')
    
  
    
 while True:
    
     ret, frame = video_capture.read()
    
     if not ret:
    
     break
    
  
    
     result_frame = detect_and_track_vehicles(frame)
    
     cv2.imshow('Vehicle Tracking', result_frame)
    
  
    
     if cv2.waitKey(1) & 0xFF == ord('q'):
    
     break
    
  
    
 video_capture.release()
    
 cv2.destroyAllWindows()
    
    
    
    
    代码解读