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
1. 安装 Python
请确保您已经安装了 Python 3.x。可以在[Python 官网](https://www.python.org/downloads/)下载并安装。
2. 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
1. 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。
