Flask应用程序在opencv中运行非常慢

Question

我有一个烧瓶应用程序，它从相机读取帧并将其流到网站

照相机.py

from threading import Thread
from copy import deepcopy

import queue
import cv2

class Camera(Thread):
    def __init__(self, cam, normalQue, detectedQue):
        Thread.__init__(self)
        self.__cam = cam
        self.__normalQue = normalQue
        self.__detectedQue = detectedQue
        self.__shouldStop = False
        
    def __del__(self):
        self.__cam.release()
        print('Camera released')
        
    def run(self):
        while True:
            rval, frame = self.__cam.read()

            if rval:
                frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
                _, jpeg = cv2.imencode('.jpg', frame)

                self.__normalQue.put(jpeg.tobytes())
                self.__detectedQue.put(deepcopy(jpeg.tobytes()))

            if self.__shouldStop:
                break

    def stopCamera(self):
        self.__shouldStop = True

从你所看到的，我只是阅读框架，调整它的大小，并存储在两个不同的问题。没什么太复杂的。 我还有两个班负责mjpeg流：

NormalVideoStream.py

from threading import Thread

import traceback
import cv2

class NormalVideoStream(Thread):
    def __init__(self, framesQue):
        Thread.__init__(self)
        self.__frames = framesQue
        self.__img = None

    def run(self):
        while True:
            if self.__frames.empty():
                continue

            self.__img = self.__frames.get()

    def gen(self):
        while True:
            try:
                if self.__img is None:
                    print('Normal stream frame is none')
                    continue

                yield (b'--frame\r\n'
                    b'Content-Type: image/jpeg\r\n\r\n' + self.__img + b'\r\n')
            except:
                traceback.print_exc()
                print('Normal video stream genenation exception')

及

DetectionVideoStream.py

from threading import Thread

import cv2
import traceback

class DetectionVideoStream(Thread):
    def __init__(self, framesQue):
        Thread.__init__(self)
        
        self.__frames = framesQue
        self.__img = None
        self.__faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    def run(self):
        while True:
            if self.__frames.empty():
                continue
            
            self.__img = self.__detectFace()

    def gen(self):
        while True:
            try:
                if self.__img is None:
                    print('Detected stream frame is none')

                yield (b'--frame\r\n'
                    b'Content-Type: image/jpeg\r\n\r\n' + self.__img + b'\r\n')
            except:
                traceback.print_exc()
                print('Detection video stream genenation exception')
    
    def __detectFace(self):
        retImg = None

        try:
            img = self.__frames.get()

            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

            faces = self.__faceCascade.detectMultiScale(gray, 1.1, 4)

            for (x, y, w, h) in faces:
                cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

            (_, encodedImage) = cv2.imencode('.jpg', img)

            retImg = encodedImage.tobytes()
        except:
            traceback.print_exc()
            print('Face detection exception')

        return retImg

从这两条流中你们可以看到，我在无限循环中读取来自ques的相机帧。这两个类都有gen（）方法，该方法生成站点本身的框架。唯一的区别是，在检测流中，我也在做人脸识别

现在在我的主文件中：

main.py

from flask import Blueprint, render_template, Response, abort, redirect, url_for
from flask_login import login_required, current_user
from queue import Queue
from . import db
from .Camera import Camera
from .NormalVideoStream import NormalVideoStream
from .DetectionVideoStream import DetectionVideoStream
from .models import User

import cv2

main = Blueprint('main', __name__)

# Queues for both streams
framesNormalQue = Queue(maxsize=0)
framesDetectionQue = Queue(maxsize=0)
print('Queues created')

# RPi camera instance
camera = Camera(cv2.VideoCapture(0), framesNormalQue, framesDetectionQue)
camera.start()
print('Camera thread started')

# Streams
normalStream = NormalVideoStream(framesNormalQue)
detectionStream = DetectionVideoStream(framesDetectionQue)
print('Streams created')

normalStream.start()
print('Normal stream thread started')
detectionStream.start()
print('Detection stream thread started')

@main.route('/')
def index():
    return render_template('index.html')

@main.route('/profile', methods=["POST", "GET"])
def profile():
    if not current_user.is_authenticated:
        abort(403)

    return render_template('profile.html', name=current_user.name, id=current_user.id, detectionState=current_user.detectionState)

@main.route('/video_stream/<int:stream_id>')
def video_stream(stream_id):
    if not current_user.is_authenticated:
        abort(403)

    print(f'Current user detection: {current_user.detectionState}')

    global detectionStream
    global normalStream

    stream = None

    if current_user.detectionState:
        stream = detectionStream
        print('Stream set to detection one')
    else:
        stream = normalStream
        print('Stream set to normal one')

    return Response(stream.gen(), mimetype='multipart/x-mixed-replace; boundary=frame')

@main.route('/detection')
def detection():
    if not current_user.is_authenticated:
        abort(403)

    if current_user.detectionState:
        current_user.detectionState = False
    else:
        current_user.detectionState = True

    user = User.query.filter_by(id=current_user.id)
    user.detectionState = current_user.detectionState

    db.session.commit()

    return redirect(url_for('main.profile', id=current_user.id, user_name=current_user.name))

@main.errorhandler(404)
def page_not_found(e):
    return render_template('404.html'), 404

@main.errorhandler(403)
def page_forbidden(e):
    return render_template('403.html'), 403

我正在全局创建摄影机、队列和流对象。此外，当用户登录网站时，他将能够看到实时视频流。还有一个按钮，用于更改当前显示的流

整个项目运行良好，只有一个例外：当我将流更改为检测流时，它将有巨大的延迟（大约10/15秒），这使得整个项目无法正常运行。试图在我自己的搜索中搜索bug/优化，但找不到任何东西。我故意在不同的线程上运行所有东西来卸载应用程序，但看起来这还不够。延迟1-2秒是可以接受的，但不超过10秒。伙计们，也许你们能看到一些虫子？或者知道如何优化它

还需要提到的是，整个应用程序都在RPI4B4GB上运行，我正在访问我桌面上的网站。默认服务器更改为Nginx和Gunicorn。从我可以看到Pi的CPU使用率是100%，当应用程序工作。在默认服务器上进行测试时，behaviout是相同的。估计1.5GHz的CPU有足够的功率使其运行更平稳

Answer 1

其中一个问题，甚至我有重新编码和解码。就像Opencv的编码器太慢一样，请尝试使用simplejpeg的编码器。使用pip3安装simplejpeg并且关于使用cv2.imencode（）使用simplejpeg.encode\u jpeg（）