引言
人脸识别技术在许多领域都有广泛的应用，如安全监控、门禁系统、智能设备等。本文将介绍如何使用Python实现一个人脸识别系统，并将其封装为一个类库。我们将逐步扩展和完善这个类库，增加代码优化、人脸照片存储到数据库、对特殊场景（如戴口罩、眼镜）的优化，以及灵活的识别距离设置。
1. 基本人脸识别实现
1.1 安装依赖
首先，我们需要安装一些必要的库：
pip install face_recognition opencv-python psycopg2-binary dlib
1.2 基本类库实现
我们创建一个 FaceRecognition 类，实现基本的人脸识别功能。
import cv2
import face_recognition
import numpy as np
import psycopg2
from psycopg2 import sql
from PIL import Image, ImageDraw
import configparser
class FaceRecognition:
def __init__(self, db_config, config_file='config.ini'):
"""
初始化FaceRecognition类。
:param db_config: 数据库配置
:param config_file: 配置文件路径
"""
self.db_config = db_config
self.config = configparser.ConfigParser()
self.config.read(config_file)
self.tolerance = float(self.config.get('FaceRecognition', 'tolerance'))
self.model = self.config.get('FaceRecognition', 'model')
self.known_face_encodings, self.known_face_names, self.tolerances = self.load_faces_from_db()
def load_faces_from_db(self):
"""
从数据库加载已知人脸信息。
:return: 已知人脸的编码、名称和识别距离
"""
known_face_encodings = []
known_face_names = []
tolerances = []
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
cursor.execute("SELECT name, encoding, tolerance FROM faces")
rows = cursor.fetchall()
for row in rows:
name, encoding_str, tolerance = row
encoding = np.fromstring(encoding_str, dtype=float, sep=' ')
known_face_encodings.append(encoding)
known_face_names.append(name)
tolerances.append(tolerance)
cursor.close()
conn.close()
return known_face_encodings, known_face_names, tolerances
def add_face_to_db(self, image_path, name, tolerance=None):
"""
将新的人脸信息添加到数据库。
:param image_path: 人脸图像的路径
:param name: 人脸的名称
:param tolerance: 识别距离
"""
if tolerance is None:
tolerance = self.tolerance
# 加载图片
image = face_recognition.load_image_file(image_path)
# 编码人脸
face_encoding = face_recognition.face_encodings(image)[0]
encoding_str = ' '.join(map(str, face_encoding))
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
query = sql.SQL("INSERT INTO faces (name, encoding, image_path, tolerance) VALUES (%s, %s, %s, %s)")
cursor.execute(query, (name, encoding_str, image_path, tolerance))
conn.commit()
cursor.close()
conn.close()
def real_time_face_recognition(self, camera_index=0):
"""
实现实时人脸识别。
:param camera_index: 摄像头索引
"""
# 打开摄像头
video_capture = cv2.VideoCapture(camera_index)
while True:
# 读取一帧
ret, frame = video_capture.read()
if not ret:
continue
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
continue
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
name = "Unknown"
min_distance = float('inf')
best_match_index = -1
for i, (known_encoding, known_name, known_tolerance) in enumerate(zip(self.known_face_encodings, self.known_face_names, self.tolerances)):
# 计算欧氏距离
distance = face_recognition.face_distance([known_encoding], face_encoding)[0]
if distance < min_distance and distance <= known_tolerance:
min_distance = distance
name = known_name
best_match_index = i
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
video_capture.release()
cv2.destroyAllWindows()
def train_model(self, training_data):
"""
训练人脸识别模型。
:param training_data: 训练数据，格式为 [(image_path, name, tolerance)]
"""
for image_path, name, tolerance in training_data:
self.add_face_to_db(image_path, name, tolerance)
# 示例用法
if __name__ == "__main__":
# 数据库配置
db_config = {
'dbname': 'your_dbname',
'user': 'your_user',
'password': 'your_password',
'host': 'localhost',
'port': '5432'
}
# 初始化FaceRecognition类
face_recognition = FaceRecognition(db_config)
# 添加新人脸
face_recognition.add_face_to_db('path/to/new_face.jpg', 'New Face', tolerance=0.6)
# 启动实时人脸识别
face_recognition.real_time_face_recognition(camera_index=0)
# 训练模型
training_data = [
('path/to/training_face_1.jpg', 'Training Face 1', 0.6),
('path/to/training_face_2.jpg', 'Training Face 2', 0.5),
# 添加更多训练数据
]
face_recognition.train_model(training_data)
2. 代码优化
2.1 优化加载和编码已知人脸
我们将优化 load_and_encode_faces 方法，使其更高效且更易维护。
def load_and_encode_faces(self, image_paths):
"""
加载并编码已知人脸图像。
:param image_paths: 已知人脸图像的路径列表
:return: 已知人脸的编码和名称
"""
known_face_encodings = []
known_face_names = []
for image_path in image_paths:
try:
# 加载图片
image = face_recognition.load_image_file(image_path)
# 编码人脸
face_encoding = face_recognition.face_encodings(image)[0]
# 获取文件名作为名字
name = image_path.split('/')[-1].split('.')[0]
# 添加到已知人脸列表
known_face_encodings.append(face_encoding)
known_face_names.append(name)
except Exception as e:
print(f"Error processing image {image_path}: {e}")
return known_face_encodings, known_face_names
2.2 优化实时人脸识别
我们将优化 real_time_face_recognition 方法，减少不必要的计算，提高性能。
def real_time_face_recognition(self, camera_index=0):
"""
实现实时人脸识别。
:param camera_index: 摄像头索引
"""
# 打开摄像头
video_capture = cv2.VideoCapture(camera_index)
while True:
# 读取一帧
ret, frame = video_capture.read()
if not ret:
continue
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
continue
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# 匹配已知人脸
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding, tolerance=self.tolerance)
name = "Unknown"
# 计算欧氏距离
face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = self.known_face_names[best_match_index]
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
video_capture.release()
cv2.destroyAllWindows()
3. 人脸照片存储数据库
我们将使用PostgreSQL数据库来存储人脸照片及其编码。首先，确保你已经安装了 psycopg2 库：
pip install psycopg2-binary
3.1 数据库表结构
创建一个名为 faces 的表，用于存储人脸信息：
CREATE TABLE faces (
id SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
encoding TEXT NOT NULL,
image_path VARCHAR(255) NOT NULL,
tolerance FLOAT DEFAULT 0.6
);
3.2 修改类库以支持数据库操作
import cv2
import face_recognition
import numpy as np
import psycopg2
from psycopg2 import sql
from PIL import Image, ImageDraw
import configparser
class FaceRecognition:
def __init__(self, db_config, config_file='config.ini'):
"""
初始化FaceRecognition类。
:param db_config: 数据库配置
:param config_file: 配置文件路径
"""
self.db_config = db_config
self.config = configparser.ConfigParser()
self.config.read(config_file)
self.tolerance = float(self.config.get('FaceRecognition', 'tolerance'))
self.model = self.config.get('FaceRecognition', 'model')
self.known_face_encodings, self.known_face_names, self.tolerances = self.load_faces_from_db()
def load_faces_from_db(self):
"""
从数据库加载已知人脸信息。
:return: 已知人脸的编码、名称和识别距离
"""
known_face_encodings = []
known_face_names = []
tolerances = []
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
cursor.execute("SELECT name, encoding, tolerance FROM faces")
rows = cursor.fetchall()
for row in rows:
name, encoding_str, tolerance = row
encoding = np.fromstring(encoding_str, dtype=float, sep=' ')
known_face_encodings.append(encoding)
known_face_names.append(name)
tolerances.append(tolerance)
cursor.close()
conn.close()
return known_face_encodings, known_face_names, tolerances
def add_face_to_db(self, image_path, name, tolerance=None):
"""
将新的人脸信息添加到数据库。
:param image_path: 人脸图像的路径
:param name: 人脸的名称
:param tolerance: 识别距离
"""
if tolerance is None:
tolerance = self.tolerance
# 加载图片
image = face_recognition.load_image_file(image_path)
# 编码人脸
face_encoding = face_recognition.face_encodings(image)[0]
encoding_str = ' '.join(map(str, face_encoding))
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
query = sql.SQL("INSERT INTO faces (name, encoding, image_path, tolerance) VALUES (%s, %s, %s, %s)")
cursor.execute(query, (name, encoding_str, image_path, tolerance))
conn.commit()
cursor.close()
conn.close()
def real_time_face_recognition(self, camera_index=0):
"""
实现实时人脸识别。
:param camera_index: 摄像头索引
"""
# 打开摄像头
video_capture = cv2.VideoCapture(camera_index)
while True:
# 读取一帧
ret, frame = video_capture.read()
if not ret:
continue
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
continue
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
name = "Unknown"
min_distance = float('inf')
best_match_index = -1
for i, (known_encoding, known_name, known_tolerance) in enumerate(zip(self.known_face_encodings, self.known_face_names, self.tolerances)):
# 计算欧氏距离
distance = face_recognition.face_distance([known_encoding], face_encoding)[0]
if distance < min_distance and distance <= known_tolerance:
min_distance = distance
name = known_name
best_match_index = i
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
video_capture.release()
cv2.destroyAllWindows()
def train_model(self, training_data):
"""
训练人脸识别模型。
:param training_data: 训练数据，格式为 [(image_path, name, tolerance)]
"""
for image_path, name, tolerance in training_data:
self.add_face_to_db(image_path, name, tolerance)
# 示例用法
if __name__ == "__main__":
# 数据库配置
db_config = {
'dbname': 'your_dbname',
'user': 'your_user',
'password': 'your_password',
'host': 'localhost',
'port': '5432'
}
# 初始化FaceRecognition类
face_recognition = FaceRecognition(db_config)
# 添加新人脸
face_recognition.add_face_to_db('path/to/new_face.jpg', 'New Face', tolerance=0.6)
# 启动实时人脸识别
face_recognition.real_time_face_recognition(camera_index=0)
# 训练模型
training_data = [
('path/to/training_face_1.jpg', 'Training Face 1', 0.6),
('path/to/training_face_2.jpg', 'Training Face 2', 0.5),
# 添加更多训练数据
]
face_recognition.train_model(training_data)
4. 特殊场景优化
4.1 戴口罩优化
对于戴口罩的情况，我们可以使用面部关键点检测来辅助识别。OpenCV和dlib提供了面部关键点检测的功能。
def detect_face_keypoints(self, image):
"""
检测面部关键点。
:param image: 输入图像
:return: 面部关键点
"""
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
landmarks = predictor(gray, face)
keypoints = [(landmarks.part(i).x, landmarks.part(i).y) for i in range(68)]
return keypoints
return None
4.2 戴眼镜优化
对于戴眼镜的情况，我们可以使用眼镜区域的关键点来辅助识别。
def is_wearing_glasses(self, keypoints):
"""
判断是否戴眼镜。
:param keypoints: 面部关键点
:return: 是否戴眼镜
"""
if keypoints:
# 检查眼镜区域的关键点
left_eye = keypoints[36:42]
right_eye = keypoints[42:48]
for point in left_eye + right_eye:
x, y = point
if image[y, x][0] > 100 and image[y, x][1] > 100 and image[y, x][2] > 100:
return True
return False
5. 灵活的识别距离设置
我们将识别距离作为系统参数来调整，并通过配置文件来管理。
5.1 配置文件
创建一个名为 config.ini 的配置文件，内容如下：
[FaceRecognition]
tolerance = 0.6
model = hog
5.2 修改类库以支持配置文件
import cv2
import face_recognition
import numpy as np
import psycopg2
from psycopg2 import sql
from PIL import Image, ImageDraw
import configparser
class FaceRecognition:
def __init__(self, db_config, config_file='config.ini'):
"""
初始化FaceRecognition类。
:param db_config: 数据库配置
:param config_file: 配置文件路径
"""
self.db_config = db_config
self.config = configparser.ConfigParser()
self.config.read(config_file)
self.tolerance = float(self.config.get('FaceRecognition', 'tolerance'))
self.model = self.config.get('FaceRecognition', 'model')
self.known_face_encodings, self.known_face_names, self.tolerances = self.load_faces_from_db()
def load_faces_from_db(self):
"""
从数据库加载已知人脸信息。
:return: 已知人脸的编码、名称和识别距离
"""
known_face_encodings = []
known_face_names = []
tolerances = []
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
cursor.execute("SELECT name, encoding, tolerance FROM faces")
rows = cursor.fetchall()
for row in rows:
name, encoding_str, tolerance = row
encoding = np.fromstring(encoding_str, dtype=float, sep=' ')
known_face_encodings.append(encoding)
known_face_names.append(name)
tolerances.append(tolerance)
cursor.close()
conn.close()
return known_face_encodings, known_face_names, tolerances
def add_face_to_db(self, image_path, name, tolerance=None):
"""
将新的人脸信息添加到数据库。
:param image_path: 人脸图像的路径
:param name: 人脸的名称
:param tolerance: 识别距离
"""
if tolerance is None:
tolerance = self.tolerance
# 加载图片
image = face_recognition.load_image_file(image_path)
# 编码人脸
face_encoding = face_recognition.face_encodings(image)[0]
encoding_str = ' '.join(map(str, face_encoding))
conn = psycopg2.connect(**self.db_config)
cursor = conn.cursor()
query = sql.SQL("INSERT INTO faces (name, encoding, image_path, tolerance) VALUES (%s, %s, %s, %s)")
cursor.execute(query, (name, encoding_str, image_path, tolerance))
conn.commit()
cursor.close()
conn.close()
def real_time_face_recognition(self, camera_index=0):
"""
实现实时人脸识别。
:param camera_index: 摄像头索引
"""
# 打开摄像头
video_capture = cv2.VideoCapture(camera_index)
while True:
# 读取一帧
ret, frame = video_capture.read()
if not ret:
continue
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
continue
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
name = "Unknown"
min_distance = float('inf')
best_match_index = -1
for i, (known_encoding, known_name, known_tolerance) in enumerate(zip(self.known_face_encodings, self.known_face_names, self.tolerances)):
# 计算欧氏距离
distance = face_recognition.face_distance([known_encoding], face_encoding)[0]
if distance < min_distance and distance <= known_tolerance:
min_distance = distance
name = known_name
best_match_index = i
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
video_capture.release()
cv2.destroyAllWindows()
def train_model(self, training_data):
"""
训练人脸识别模型。
:param training_data: 训练数据，格式为 [(image_path, name, tolerance)]
"""
for image_path, name, tolerance in training_data:
self.add_face_to_db(image_path, name, tolerance)
# 示例用法
if __name__ == "__main__":
# 数据库配置
db_config = {
'dbname': 'your_dbname',
'user': 'your_user',
'password': 'your_password',
'host': 'localhost',
'port': '5432'
}
# 初始化FaceRecognition类
face_recognition = FaceRecognition(db_config)
# 添加新人脸
face_recognition.add_face_to_db('path/to/new_face.jpg', 'New Face', tolerance=0.6)
# 启动实时人脸识别
face_recognition.real_time_face_recognition(camera_index=0)
# 训练模型
training_data = [
('path/to/training_face_1.jpg', 'Training Face 1', 0.6),
('path/to/training_face_2.jpg', 'Training Face 2', 0.5),
# 添加更多训练数据
]
face_recognition.train_model(training_data)
6. 未来工作
6.1 多摄像头支持
扩展类库以支持多个摄像头。可以通过传递多个摄像头索引来实现。
def real_time_face_recognition_multi_camera(self, camera_indices):
"""
实现多摄像头实时人脸识别。
:param camera_indices: 摄像头索引列表
"""
video_captures = [cv2.VideoCapture(index) for index in camera_indices]
while True:
frames = []
for capture in video_captures:
ret, frame = capture.read()
if not ret:
frames.append(None)
continue
frames.append(frame)
for frame in frames:
if frame is None:
continue
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
continue
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
name = "Unknown"
min_distance = float('inf')
best_match_index = -1
for i, (known_encoding, known_name, known_tolerance) in enumerate(zip(self.known_face_encodings, self.known_face_names, self.tolerances)):
# 计算欧氏距离
distance = face_recognition.face_distance([known_encoding], face_encoding)[0]
if distance < min_distance and distance <= known_tolerance:
min_distance = distance
name = known_name
best_match_index = i
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
for capture in video_captures:
capture.release()
cv2.destroyAllWindows()
6.2 性能优化
优化人脸检测和识别的性能，特别是在高分辨率视频流中。可以考虑使用多线程或多进程来加速处理。
from concurrent.futures import ThreadPoolExecutor
def process_frame(self, frame):
"""
处理单帧图像。
:param frame: 输入图像
:return: 处理后的图像
"""
# 将帧转换为RGB
rgb_frame = frame[:, :, ::-1]
# 检测人脸位置
face_locations = face_recognition.face_locations(rgb_frame, model=self.model)
if not face_locations:
return frame
# 编码人脸
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历检测到的人脸
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
name = "Unknown"
min_distance = float('inf')
best_match_index = -1
for i, (known_encoding, known_name, known_tolerance) in enumerate(zip(self.known_face_encodings, self.known_face_names, self.tolerances)):
# 计算欧氏距离
distance = face_recognition.face_distance([known_encoding], face_encoding)[0]
if distance < min_distance and distance <= known_tolerance:
min_distance = distance
name = known_name
best_match_index = i
# 在帧上绘制矩形和标签
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, 1.0, (255, 255, 255), 1)
return frame
def real_time_face_recognition_multi_camera(self, camera_indices):
"""
实现多摄像头实时人脸识别。
:param camera_indices: 摄像头索引列表
"""
video_captures = [cv2.VideoCapture(index) for index in camera_indices]
executor = ThreadPoolExecutor(max_workers=len(camera_indices))
while True:
frames = []
for capture in video_captures:
ret, frame = capture.read()
if not ret:
frames.append(None)
continue
frames.append(frame)
processed_frames = list(executor.map(self.process_frame, frames))
for frame in processed_frames:
if frame is None:
continue
# 显示结果
cv2.imshow('Video', frame)
# 按q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
for capture in video_captures:
capture.release()
cv2.destroyAllWindows()
6.3 多平台支持
确保类库在不同操作系统和硬件平台上都能正常运行。可以通过使用跨平台的库和工具来实现。
6.4 用户界面
开发一个图形用户界面（GUI），使用户更容易使用和配置人脸识别系统。可以使用 tkinter 或 PyQt 等库来实现。
import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageTk
class FaceRecognitionGUI:
def __init__(self, master, face_recognition):
self.master = master
self.face_recognition = face_recognition
self.master.title("Face Recognition System")
self.label = tk.Label(master, text="Face Recognition System")
self.label.pack()
self.add_face_button = tk.Button(master, text="Add New Face", command=self.add_new_face)
self.add_face_button.pack()
self.start_recognition_button = tk.Button(master, text="Start Real-Time Recognition", command=self.start_real_time_recognition)
self.start_recognition_button.pack()
def add_new_face(self):
file_path = filedialog.askopenfilename()
if file_path:
name = filedialog.askstring("Input", "Enter the name of the person:")
if name:
self.face_recognition.add_face_to_db(file_path, name, tolerance=0.6)
self.label.config(text=f"Added new face: {name}")
def start_real_time_recognition(self):
self.face_recognition.real_time_face_recognition(camera_index=0)
if __name__ == "__main__":
# 数据库配置
db_config = {
'dbname': 'your_dbname',
'user': 'your_user',
'password': 'your_password',
'host': 'localhost',
'port': '5432'
}
# 初始化FaceRecognition类
face_recognition = FaceRecognition(db_config)
# 初始化GUI
root = tk.Tk()
app = FaceRecognitionGUI(root, face_recognition)
root.mainloop()
结论
通过上述步骤，我们不仅优化了代码，还增加了人脸照片存储到数据库、对特殊场景的优化，以及灵活的识别距离设置。这些改进使得人脸识别系统更加精确和健壮。希望这篇文章对你有帮助！
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