将yolov5和deepsort结合起来，给出python代码

以下是将YOLOv5和DeepSORT结合的Python代码：

import cv2
import numpy as np
from deep_sort import preprocessing
from deep_sort import nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from yolov5 import YOLOv5

# Initialize YOLOv5 model
model = YOLOv5(weights='yolov5s.pt')

# Initialize DeepSORT
max_cosine_distance = 0.5
nn_budget = None
model_filename = 'mars-small128.pb'
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)

# Load video
video_path = 'path/to/video.mp4'
cap = cv2.VideoCapture(video_path)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # Detect objects with YOLOv5
    detections = model.detect(frame)
    
    # Preprocess detections for DeepSORT
    features = np.array([detection.feature for detection in detections])
    boxes = np.array([detection.box for detection in detections])
    detections = [Detection(bbox, feature) for bbox, feature in zip(boxes, features)]
    
    # Update tracker with detections
    tracker.predict()
    tracker.update(detections)
    
    # Draw bounding boxes on frame
    for track in tracker.tracks:
        if not track.is_confirmed() or track.time_since_update > 1:
            continue
        bbox = track.to_tlbr()
        cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 0), 2)
        cv2.putText(frame, str(track.track_id), (int(bbox[0]), int(bbox[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
    
    # Show frame
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

这段代码使用YOLOv5检测视频中的对象，并将检测结果传递给DeepSORT进行目标跟踪。最终将跟踪结果绘制在视频帧上。请注意，此代码仅为示例，您需要将其中的一些参数和文件路径调整为您自己的数据。