🤔 Всем привет. Делаю прогу для распознавания лиц. Использую библиотеки face_recognition и OpenCV. Код рабочий, но когда происходит обнаружение лица, фпс падает с 24 до 1. Проверял нагрузку на железо — ни проц, ни видеокарта не загружены больше, чем на 10%. Кто может подсказать, почему такая плохая производительность?

Вот код:

import numpy as np
import face_recognition
import cv2
import os
import time
from datetime import datetime
 
path = "KnownFaces"
images = []
classNames = []
myList = os.listdir(path)
print(myList)
 
for cls in myList:
    curImg = cv2.imread(f'{path}/{cls}')
    images.append(curImg)
    classNames.append(os.path.splitext(cls)[0])
 
print(classNames)
 
def findEncodings(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList
 
 
encodeListKnown = findEncodings(images)
print("Обработка завершена")
 
cap = cv2.VideoCapture(0)
 
while True:
    success, img = cap.read()
    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
 
    facesCurFrame = face_recognition.face_locations(imgS)
    encodeCurFrame = face_recognition.face_encodings(imgS, facesCurFrame, num_jitters=1, model='small') #/////
    time.sleep(0.1)
 
    for encodeFace, faceLoc in zip(encodeCurFrame, facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
        print(faceDis)
        matchIndex = np.argmin(faceDis)
 
        if matches[matchIndex]:
            name = classNames[matchIndex]
            print(name)
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            #markAttendance(name)
 
    
    cv2.imshow("WebCam", img)
    cv2.waitKey(27)

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import numpy as np
import face_recognition
import cv2
import os
import time
from datetime import datetime
 
path = "KnownFaces"
images = []
classNames = []
myList = os.listdir(path)
print(myList)
 
for cls in myList:
    curImg = cv2.imread(f'{path}/{cls}')
    images.append(curImg)
    classNames.append(os.path.splitext(cls)[0])
 
print(classNames)
 
def findEncodings(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList
 
 
encodeListKnown = findEncodings(images)
print("Обработка завершена")
 
cap = cv2.VideoCapture(0)
 
while True:
    success, img = cap.read()
    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
 
    facesCurFrame = face_recognition.face_locations(imgS)
    encodeCurFrame = face_recognition.face_encodings(imgS, facesCurFrame, num_jitters=1, model='small') #/////
    time.sleep(0.1)
 
    for encodeFace, faceLoc in zip(encodeCurFrame, facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
        print(faceDis)
        matchIndex = np.argmin(faceDis)
 
        if matches[matchIndex]:
            name = classNames[matchIndex]
            print(name)
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            #markAttendance(name)
 
    
    cv2.imshow("WebCam", img)
    cv2.waitKey(27)