将上面代码输出一个完整的代码

当然，这里是完整的Python代码示例，它演示了如何使用GB28181协议从海康威视摄像头获取视频流并应用AI识别功能：

import requests
import cv2
from io import BytesIO

# 替换为实际的摄像头IP和通道ID
camera_ip = 'your_camera_ip'
channel_id = 'your_channel_id'
user_agent = 'YourApp/1.0'

def get_video_stream():
    url = f"http://{camera_ip}/GB28181Streaming/channels/{channel_id}?profile=0&serviceType=1"
    headers = {'User-Agent': user_agent}
    
    try:
        response = requests.get(url, headers=headers, timeout=5)  # 设置超时时间防止阻塞
        response.raise_for_status()  # 检查请求状态是否正常
        return response
    except requests.exceptions.RequestException as e:
        print(f"Error occurred: {e}")
        return None

def run_ai(frame):
    # 这里是一个简单的示例，实际的AI识别应该替换为你的模型和框架
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # 假设你的AI识别模型是预训练好的face_recognition模型
    face_locations = face_recognition.face_locations(gray)
    for (top, right, bottom, left) in face_locations:
        # 对于每个检测到的人脸区域，做进一步处理
        # ... (你的AI识别代码)
    return frame

if __name__ == "__main__":
    stream_response = get_video_stream()

    if stream_response is not None:
        video_capture = cv2.VideoCapture(BytesIO(stream_response.content))

        while True:
            ret, frame = video_capture.read()
            if not ret:
                break

            processed_frame = run_ai(frame)

            # 显示处理后的帧
            cv2.imshow("Processed Frame", processed_frame)

            # 按 'q' 键停止
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        video_capture.release()
        cv2.destroyAllWindows()

**相关问题**:
1. 如果摄像头返回的视频流不是JPEG或H.264格式，该怎样调整代码？
2. 如何优化这段代码以提高性能？
3. 我的AI模型需要实时更新，如何在代码中实现模型更新？