AttributeError：模块“dlib”没有属性“get_frontal_face_detector”

Posted 2023-03-16

【中文标题】AttributeError：模块“dlib”没有属性“get_frontal_face_detector”

【英文标题】：AttributeError: module 'dlib' has no attribute 'get_frontal_face_detector'

【发布时间】：2021-07-30 22:34:19

【问题描述】：

我正在尝试运行代码以直接从网络摄像头识别人脸，但我遇到了以下错误。

C:\Users\user\python.exe C:/Users/user/PycharmProjects/FaceRecognition/Attendance.py
Traceback (most recent call last):
  File "C:/Users/user/PycharmProjects/FaceRecognition/Attendance.py", line 3, in <module>
    import face_recognition
  File "C:\Users\user\AppData\Roaming\Python\Python38\site-packages\face_recognition\__init__.py", line 7, in <module>
    from .api import load_image_file, face_locations, batch_face_locations, face_landmarks, face_encodings, compare_faces, face_distance
  File "C:\Users\user\AppData\Roaming\Python\Python38\site-packages\face_recognition\api.py", line 17, in <module>
    face_detector = dlib.get_frontal_face_detector()
AttributeError: module 'dlib' has no attribute 'get_frontal_face_detector'`

我已经下载了人脸识别库，并且在与项目文件夹相同的文件夹中也有 dlib 依赖项，但我遇到了这个问题。

这是我的全部代码

import cv2 
import numpy as np 
import face_recognition 
import os 
from datetime import datetime


# from PIL import ImageGrab

path = 'ImagesAttendance' images = [] classNames = [] myList = os.listdir(path) print(myList) for cl in myList:
    curImg = cv2.imread(f'path/cl')
    images.append(curImg)
    classNames.append(os.path.splitext(cl)[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


def markAttendance(name):
    with open('Attendance.csv', 'r+') as f:
        myDataList = f.readlines()
        nameList = []
        for line in myDataList:
            entry = line.split(',')
            nameList.append(entry[0])
            if name not in nameList:
                now = datetime.now()
                dtString = now.strftime('%H:%M:%S')
                f.writelines(f'nname,dtString')

#### FOR CAPTURING SCREEN RATHER THAN WEBCAM
# def captureScreen(bbox=(300,300,690+300,530+300)):
#     capScr = np.array(ImageGrab.grab(bbox))
#     capScr = cv2.cvtColor(capScr, cv2.COLOR_RGB2BGR)
#     return capScr

encodeListKnown = findEncodings(images) print('Encoding Complete')

cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
# img = captureScreen()
    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

    facesCurFrame = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    for encodeFace, faceLoc in zip(encodesCurFrame, 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].upper()
            # 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(1)

【参考方案1】：

Dlib 是一个了不起的库，但它的安装成本很高。我建议你使用 deepface。它包装了 dlib，但它带有几个尖端的面部检测器。除了 dlib 之外，它们都是基于 tensorflow 的。这就是为什么安装和运行这些检测器会比核心 dlib 更容易。

img_path = "img1.jpg"
backends = ['opencv', 'ssd', 'mtcnn', 'dlib', 'retinaface']
img = DeepFace.detectFace(img_path, detector_backend = backend[2])