作为一个基于人脸识别算法的考勤系统的设计与实现教程，以下内容将提供详细的步骤和代码示例。本教程将使用 Python 语言和 OpenCV 库进行实现。
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。
