带有opencv和dlib face_recognition库的人脸识别考勤系统给出不正确的识别

Posted 2023-03-16

【中文标题】带有opencv和dlib face_recognition库的人脸识别考勤系统给出不正确的识别

【英文标题】：face recognition attendance system with opencv and dlib face_recognition libraries giving incorrect recognitions

【发布时间】：2020-10-23 12:02:53

【问题描述】：

所以我使用 opencv dlib 和 face_recognition 制作了一个人脸识别考勤系统，但由于某种原因，模型无法正确识别，例如当我使用网络摄像头在一帧中识别多个人时，它会不断更改边界框标签，这样一来，超过一个人的出勤率就会被标记，因为盒子的标签会不断变化。我尝试使用不止一张图片，比如每个人 30 张图片，但仍然是同样的问题，你能帮我理解为什么吗？

这是我的代码：

encodings.py

import cv2
import face_recognition
import numpy as np
import os
from datetime import datetime
from imutils import paths
import pickle

path='./imageAttendance'
imagePaths= list(paths.list_images(path))

knownEncodings=[]
knownNames=[]

for (i, imagePath) in enumerate(imagePaths):
    print("[INFO] processing image /".format(i + 1,
                                                 len(imagePaths)))
    name= imagePath.split(os.path.sep)[-2]
    image= cv2.imread(imagePath)
    rgb= cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    boxes= face_recognition.face_locations(rgb, model='cnn')
    encodings= face_recognition.face_encodings(rgb, boxes)

    for encoding in encodings:
        knownEncodings.append(encoding)
        knownNames.append(name)

print("[INFO] serializing encodings...")
data='encodings': knownEncodings, 'names': knownNames
f= open('encodings.pickle', 'wb')
f.write(pickle.dumps(data))
f.close()

网络摄像头识别.py

import face_recognition
import argparse
import pickle
import cv2
from datetime import datetime

print("[INFO] loading encodings...")
data= pickle.loads(open('encodings.pickle', 'rb').read())

def mark_attendance(n):
    with open('attendance.csv', 'r+') as f:
        myDataList= f.readlines()
        print(myDataList)
        nameList=[]
        for line in myDataList:
            name= line.split(',')[0]
            nameList.append(name)

        if n not in nameList:
            now= datetime.now()
            dtString= now.strftime('%H:%M:%S')
            f.writelines(f'\nn,dtString')

cap= cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    # img = captureScreen()
    image = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    print("[INFO] recognizing faces...")
    boxes = face_recognition.face_locations(rgb,
                                            model='cnn')
    encodings = face_recognition.face_encodings(rgb, boxes)
    names = []

    for encoding in encodings:
        matches = face_recognition.compare_faces(data['encodings'], encoding)
        name = 'Unknown'

        if True in matches:
            matchedIdxs = [i for (i, b) in enumerate(matches) if b]
            counts = 

            for i in matchedIdxs:
                name = data['names'][i]
                counts[name] = counts.get(name, 0) + 1

            name = max(counts, key=counts.get)

        names.append(name)
    for ((top, right, bottom, left), name) in zip(boxes, names):
        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
        y = top - 15 if top - 15 > 15 else top + 15
        cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
                    0.75, (0, 255, 0), 2)
        mark_attendance(name)

    cv2.imshow('Webcam', image)
    cv2.waitKey(1)

【参考方案1】：

文献中有几种最先进的模型，dlib resnet 模型是其中之一，但不是唯一的。您可以实时处理您的网络摄像头流，并通过几行代码在 deepface 中应用人脸识别。

#!pip install deepface
from deepface import DeepFace

models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
DeepFace.stream(db_path = 'C:/my_db', model_name = models[0], enable_face_analysis = False)

它将在 my_db 文件夹中的网络摄像头上查找检测到的面孔。您应该将您的面部数据库存储在此文件夹中。此外，您可以使用 model_name 变量更改人脸识别模型。我添加了所有候选模型。 VGG-Face、FaceNet 和 Dlib 的表现优于其他。