所以，我想创建一个结构，其中人脸识别将在后台线程上完成，并且流将通过正在进行的前台线程显示。

我仍然不明白为什么我的电脑以 20fps 而不是 30fps 显示流。

更新了代码（添加了 perf_counter 并添加了用户定义的函数）

请参阅下面的链接旧代码。

import cv2
from simple_facerec import SimpleFacerec
from datetime import datetime
from time import perf_counter
prev = 0
count = 0
vid_cod = ""
cap = ""
path =""
vid_name = ""
output = ""
sfr = ""
def vidCapture():
    global cap,prev,count,sfr
    while (cap.isOpened()):
        ret, frame = cap.read()
        
        # Detect Faces
        startDetect = perf_counter()
        face_locations, face_names = sfr.detect_known_faces(frame)
        for face_loc, name in zip(face_locations, face_names):
            y1, x2, y2, x1 = face_loc[0], face_loc[1], face_loc[2], face_loc[3]
            #if the face is recognised then 
            if name != "Unknown" :
                print("known Person detected :) => " + name)
                stopDetect = perf_counter()
                print(f"Time taken to identify face : {stopDetect-startDetect}")
            #else if the face is unknown then 
            else :
                stopDetect = perf_counter()
                print(f"Time taken to identify face : {stopDetect-startDetect}")
                print("Unknown Person detected :( => Frame captured...")
        #sw the frames on the display
        cv2.imsw("Frame", frame)
        #write frames to output object so it can save the video
        output.write(frame)
        #to terminate the process press "x"
        if cv2.waitKey(1) & 0XFF == ord('x'):
            break
    cap.release()
    output.release()
    cv2.destroyAllWindows()
def __init__():
    global prev,count,vid_cod,vid_name,cap,path,output,sfr
    startSfr = perf_counter()
    # Encode faces from a folder
    sfr = SimpleFacerec()
    sfr.load_encoding_images("images/")
    stopSfr = perf_counter()
    loadSfr = stopSfr-startSfr
    print(f"Time taken to load sfr : {loadSfr}")
    startCam = perf_counter()
    # Load Camera
    cap = cv2.VideoCapture('https://192...:8080/videofeed')
    #cap = cv2.VideoCapture(0)
    stopCam = perf_counter()
    loadCam = stopCam-startCam
    print(f"Time taken to load cam : {loadCam}")
    startCodec = perf_counter()
    #Use MPEG video codec
    vid_cod = cv2.VideoWriter_fourcc(*'MPEG')
    stopCodec = perf_counter()
    loadCodec = stopCodec-startCodec
    print(f"Time taken to load codec : {loadCodec}")
    startInit = perf_counter()
    prev = 0
    count = 0
    #defining the path where video will be saved
    path = 'C:/Users/Username/Doents/Recorded/'
    #Encode video name with date and time
    vid_name = str("recording_"+datetime.now().strftime("%b-%d-%Y_%H:%M").replace(":","_")+".avi")
    #initialize video saving process as a "output" object
    output = cv2.VideoWriter( str(path+vid_name) , vid_cod, 20.0 ,(640,480))
    stopInit = perf_counter()
    loadInit = stopInit - startInit
    print(f"Time taken to load rest : {loadInit}")
    vidCapture()
__init__()

下面的文件是在上面的代码中导入的 simplefacerec。

当我将output中的 fps 从 20 更改为 25 时，保存的视频长度会减少，当我将 fps 从 20 更改为 15 时，视频长度会增加。

在放入perf_counter()后，我发现单独的人脸识别大约需要 0.5 秒。整个代码运行了大约 7 到 8 秒，其中只显示了 5 帧。

Time taken to load sfr : 2.3252885000001697
Time taken to load cam : 0.3193597999998019
Time taken to load codec : 1.3199998647905886e-05
Time taken to load rest : 0.0018557999974291306
known Person detected :) => Anirudhdhsinh Jadeja
Time taken to identify face : 0.4715902999996615
known Person detected :) => Anirudhdhsinh Jadeja
Time taken to identify face : 0.5326913999997487
known Person detected :) => Anirudhdhsinh Jadeja
Time taken to identify face : 0.4969602000019222
known Person detected :) => Anirudhdhsinh Jadeja
Time taken to identify face : 0.4868558000016492
known Person detected :) => Anirudhdhsinh Jadeja
Time taken to identify face : 0.4679383000002417