帮我写一个demo，实现yolov5双向追踪计数的功能。

以下是一个使用YOLOv5实现双向追踪计数的示例demo：

import cv2
import torch
from torchvision.models import detection
from torchvision.transforms import functional as F
from deep_sort import build_tracker
from utils import draw_boxes, draw_counts

# 加载YOLOv5模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model = model.autoshape()  # 自动调整输入大小
model.eval()

# 初始化DeepSORT追踪器
tracker = build_tracker()

# 设定计数线坐标和计数器
count_line = [(0, 300), (800, 300)]
count_up = 0
count_down = 0

def detect_and_track(frame):
    global count_up, count_down

    # YOLOv5目标检测
    results = model(frame)

    # 解析检测结果并提取边界框和类别
    boxes = results.xyxy[0][:, :4].tolist()
    scores = results.xyxy[0][:, 4].tolist()
    labels = results.xyxy[0][:, 5].tolist()

    # 运行DeepSORT追踪器
    tracker.update(boxes, labels)

    # 获取追踪结果
    tracked_objects = tracker.get_tracked_objects()

    # 绘制边界框和计数线
    frame = draw_boxes(frame, tracked_objects)
    frame = draw_counts(frame, count_line, count_up, count_down)

    # 计算车辆通过计数线的数量
    for obj in tracked_objects:
        cx, cy = obj['center']
        if cy > count_line[0][1] and cy < count_line[1][1]:
            if obj['direction'] == 'up':
                count_up += 1
            elif obj['direction'] == 'down':
                count_down += 1

    return frame

# 打开视频文件
cap = cv2.VideoCapture('path/to/video.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # 调整帧大小和颜色通道顺序
    frame = cv2.resize(frame, (800, 600))
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # 转换为PyTorch张量
    frame_tensor = F.to_tensor(frame)

    # 双向追踪计数
    output_frame = detect_and_track(frame_tensor)

    # 显示结果帧
    cv2.imshow('Double Tracking and Counting', output_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

请确保在运行代码之前已经安装了YOLOv5、DeepSORT和相关的依赖项，并将视频文件的路径替换为实际的视频文件路径。此外，你可能还需要根据你的项目需求进行适当的调整和修改。