我正在使用一个Rapberry Pi在网络服务器上进行视频流传输。
视频流工作正常，但一旦我实现了人脸识别，它将是如此滞后。
这是我的代码：

from flask import Flask, render_template, Response
import cv2
import argparse
from imutils.video import VideoStream
import imutils
import subprocess

app = Flask(__name__)

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

vs = VideoStream(usePiCamera=1).start() 

def gen_frames():  # generate frame by frame from camera
    while True:
        # Capture frame-by-frame
        global vs
        frame = vs.read()  # read the camera frame
        frame = imutils.resize(frame, width=800)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # Detect the faces
        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
        #Draw the rectangle around each face
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        
        ret, buffer = cv2.imencode('.jpg', frame)
        frame = buffer.tobytes()
        yield (b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  # concat frame one by one and show result

@app.route('/video_feed')
def video_feed():
    #Video streaming route. Put this in the src attribute of an img tag
    return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

@app.route('/')
def index():
    """Video streaming home page."""
    return render_template('index.html')

def videostreaming():
    # start the flask app
    app.run(host="192.168.0.33",port=8000, debug=True,use_reloader=False)

videostreaming()

有没有办法让视频流更流畅的人脸检测功能？