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作为一个基于人脸辨认算法的考勤体系的设计与完成教程，以下内容将供给具体的步骤和代码示例。本教程将使用 Python 语言和 OpenCV 库进行完成。

一、环境装备

1. 装置 Python

请确保您现已装置了 Python 3.x。能够在Python 官网下载并装置。

2. 装置所需库

在指令提示符或终端中运转以下指令来装置所需的库：

pip install opencv-python
pip install opencv-contrib-python
pip install numpy
pip install face-recognition

二、创立数据集

1. 创立文件夹结构

在项目目录下创立如下文件夹结构：

attendance-system/
├── dataset/
│   ├── person1/
│   ├── person2/
│   └── ...
└── src/

将每个人的照片放入对应的文件夹中，例如：

attendance-system/
├── dataset/
│   ├── person1/
│   │   ├── 01.jpg
│   │   ├── 02.jpg
│   │   └── ...
│   ├── person2/
│   │   ├── 01.jpg
│   │   ├── 02.jpg
│   │   └── ...
│   └── ...
└── src/

三、完成人脸辨认算法

在 src 文件夹下创立一个名为 face_recognition.py 的文件，并增加以下代码：

import os
import cv2
import face_recognition
import numpy as np
def load_images_from_folder(folder):
    images = []
    for filename in os.listdir(folder):
        img = cv2.imread(os.path.join(folder, filename))
        if img is not None:
            images.append(img)
    return images
def create_known_face_encodings(root_folder):
    known_face_encodings = []
    known_face_names = []
    for person_name in os.listdir(root_folder):
        person_folder = os.path.join(root_folder, person_name)
        images = load_images_from_folder(person_folder)
        for image in images:
            face_encoding = face_recognition.face_encodings(image)[0]
            known_face_encodings.append(face_encoding)
            known_face_names.append(person_name)
    return known_face_encodings, known_face_names
def recognize_faces_in_video(known_face_encodings, known_face_names):
    video_capture = cv2.VideoCapture(0)
    face_locations = []
    face_encodings = []
    face_names = []
    process_this_frame = True
    while True:
        ret, frame = video_capture.read()
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        if process_this_frame:
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
            face_names = []
            for face_encoding in face_encodings:
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"
                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = known_face_names[best_match_index]
                face_names.append(name)
        process_this_frame = not process_this_frame
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)
        cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    video_capture.release()
    cv2.destroyAllWindows()
if __name__ == "__main__":
    dataset_folder = "../dataset/"
    known_face_encodings, known_face_names = create_known_face_encodings(dataset_folder)
    recognize_faces_in_video(known_face_encodings, known_face_names)

四、完成考勤体系

在 src 文件夹下创立一个名为 attendance.py 的文件，并增加以下代码：

import os
import datetime
import csv
from face_recognition import create_known_face_encodings, recognize_faces_in_video
def save_attendance(name):
    attendance_file = "../attendance/attendance.csv"
    now = datetime.datetime.now()
    date_string = now.strftime("%Y-%m-%d")
    time_string = now.strftime("%H:%M:%S")
    if not os.path.exists(attendance_file):
        with open(attendance_file, "w", newline="") as csvfile:
            csv_writer = csv.writer(csvfile)
            csv_writer.writerow(["Name", "Date", "Time"])
    with open(attendance_file, "r+", newline="") as csvfile:
        csv_reader = csv.reader(csvfile)
        rows = [row for row in csv_reader]
        for row in rows:
            if row[0] == name and row[1] == date_string:
                return
        csv_writer = csv.writer(csvfile)
        csv_writer.writerow([name, date_string, time_string])
def custom_recognize_faces_in_video(known_face_encodings, known_face_names):
    video_capture = cv2.VideoCapture(0)
    face_locations = []
    face_encodings = []
    face_names = []
    process_this_frame = True
    while True:
        ret, frame = video_capture.read()
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        if process_this_frame:
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
            face_names = []
            for face_encoding in face_encodings:
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"
                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = known_face_names[best_match_index]
                    save_attendance(name)
                face_names.append(name)
        process_this_frame = not process_this_frame
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)
        cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    video_capture.release()
    cv2.destroyAllWindows()
if __name__ == "__main__":
    dataset_folder = "../dataset/"
    known_face_encodings, known_face_names = create_known_face_encodings(dataset_folder)
    custom_recognize_faces_in_video(known_face_encodings, known_face_names)

五、运转考勤体系

运转 attendance.py 文件，体系将开端辨认并记录考勤信息。考勤记录将保存在 attendance.csv 文件中。

python src/attendance.py

现在，您的基于人脸辨认的考勤体系现已完成。请注意，这是一个根本示例，您可能需求依据实践需求对其进行优化和扩展。例如，您能够考虑增加更多的人脸辨认算法、考勤规矩等。

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