python+OpenCV人脸识别考勤系统实现的详细代码

一、环境配置

• 安装 Python

请确保您已经安装了 Python 3.x。可以在Python 官网下载并安装。

• 安装所需库

在命令提示符或终端中运行以下命令来安装所需的库：

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

二、创建数据集

• 创建文件夹结构

在项目目录下创建如下文件夹结构：

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、实现人脸识别算法

在 src 文件夹下创建一个名为 face_recognition.py 的文件，并添加以下代码：

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、实现考勤系统

在 src 文件夹下创建一个名为 attendance.py 的文件，并添加以下代码：

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、运行考勤系统

运行 attendance.py 文件，系统将开始识别并记录考勤信息。考勤记录将保存在 attendance.csv 文件中。

python src/attendance.py

现在，您的基于人脸识别的考勤系统已经实现。请注意，这是一个基本示例，您可能需要根据实际需求对其进行优化和扩展。例如，您可以考虑添加更多的人脸识别算法、考勤规则等。

来源：https://juejin.cn/post/7235458133505867837