代码收藏家 Python深度学习实现人脸识别与管理系统详解
Python-基于深度学习的人脸识别与管理系统（UI界面整强版Python代码）Python版本3.8
功能：
系统实现了集识别人脸、录入人脸、管理人脸在内的多项功能：包括通过选择人脸图片、视频、摄像头进行已录入人脸的实时识别；可通过图片和摄像头检测人脸并录入新的人脸；通过系统管理和更新人脸数据等功能，检测速度快、识别精度较高。
①选择人脸照片识别。
②选择人脸视频识别。
③摄像头检测人脸识别。
④录入人脸功能。
⑤管理人脸功能。
为了实现一个基于深度学习的人脸识别与管理系统，我们可以使用Python结合一些流行的库，如OpenCV用于图像处理、dlib或face_recognition用于人脸识别、以及Pandas用于数据管理。下面是一个简化版本的代码示例，展示了如何实现你提到的功能。
安装依赖
首先，确保安装了必要的Python包：
pip install opencv-python dlib face-recognition pandas numpy
代码实现
1. 初始化和辅助函数
import cv2
import face_recognition
import os
import numpy as np
import pandas as pd
# 加载已知人脸编码和名字
def load_known_faces():
known_face_encodings = []
known_face_names = []
for filename in os.listdir('known_faces'):
if filename.endswith('.jpg') or filename.endswith('.png'):
image = face_recognition.load_image_file(f'known_faces/{filename}')
encoding = face_recognition.face_encodings(image)[0]
known_face_encodings.append(encoding)
known_face_names.append(filename.split('.')[0])
return known_face_encodings, known_face_names
# 将新面孔保存到磁盘
def save_face(image, name):
filename = f'known_faces/{name}.jpg'
cv2.imwrite(filename, image)
print(f'Saved {filename}')
# 更新数据库
def update_database(name):
df = pd.read_csv('faces.csv')
if name not in df['Name'].values:
df = df.append({'Name': name}, ignore_index=True)
df.to_csv('faces.csv', index=False)
print(f'Updated database with new name: {name}')
2. 选择照片进行识别
def recognize_from_image(image_path):
image = face_recognition.load_image_file(image_path)
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, 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]
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(image, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow("Image", image)
cv2.waitKey(0)
3. 视频流中的人脸识别
def recognize_from_video(video_path=0): # 使用摄像头时video_path设为0
video_capture = cv2.VideoCapture(video_path)
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]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, 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]
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
4. 录入新的人脸
def capture_and_save_face(name):
video_capture = cv2.VideoCapture(0)
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]
face_locations = face_recognition.face_locations(rgb_small_frame)
if len(face_locations) > 0:
top, right, bottom, left = face_locations[0]
face_image = frame[top*4:bottom*4, left*4:right*4]
save_face(face_image, name)
update_database(name)
break
cv2.imshow('Capture Face', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
5. 管理人脸功能
这可以通过一个简单的界面或者命令行工具来完成，例如查看当前已录入的所有人脸名称列表，并提供删除某个特定人脸的功能等。这部分可以根据具体需求进一步开发。
总结
以上代码片段提供了一个基本框架，你可以根据自己的需要进一步扩展和完善。比如添加更多错误处理逻辑、优化性能、增加用户界面等。此外，考虑到隐私和安全问题，在实际部署这样的系统之前，请确保遵守相关的法律法规。
根据你提供的截图和需求，我们可以构建一个基于Python的人脸识别与管理系统。这个系统将包括以下几个主要功能：
- 选择人脸照片识别。
- 选择人脸视频识别。
- 摄像头检测人脸识别。
- 录入人脸功能。
- 管理人脸功能。
我们将使用face_recognition库进行人脸识别，并使用PyQt5来构建图形用户界面（GUI）。以下是完整的代码实现。
安装依赖
首先，确保安装了必要的Python包：
pip install opencv-python face-recognition numpy pandas PyQt5
代码实现
1. 初始化和辅助函数
import cv2
import face_recognition
import os
import numpy as np
import pandas as pd
from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QMessageBox
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import QTimer
from FaceRecognition_Ui import Ui_MainWindow
class FaceRecognitionSystem(QMainWindow, Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.known_face_encodings = []
self.known_face_names = []
self.load_known_faces()
self.init_ui()
def init_ui(self):
self.pushButton_load.clicked.connect(self.load_image)
self.pushButton_camera.clicked.connect(self.start_camera)
self.pushButton_add.clicked.connect(self.add_face)
self.pushButton_manage.clicked.connect(self.manage_faces)
def load_known_faces(self):
for filename in os.listdir('known_faces'):
if filename.endswith('.jpg') or filename.endswith('.png'):
image = face_recognition.load_image_file(f'known_faces/{filename}')
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
self.known_face_names.append(filename.split('.')[0])
def load_image(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
self.recognize_from_image(file_name)
def recognize_from_image(self, image_path):
image = face_recognition.load_image_file(image_path)
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(image, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def start_camera(self):
self.cap = cv2.VideoCapture(0)
self.timer = QTimer()
self.timer.timeout.connect(self.update_frame)
self.timer.start(10)
def update_frame(self):
ret, frame = self.cap.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(frame.data, frame.shape[1], frame.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def add_face(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
image = face_recognition.load_image_file(file_name)
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
name = os.path.basename(file_name).split('.')[0]
self.known_face_names.append(name)
cv2.imwrite(f'known_faces/{name}.jpg', image)
QMessageBox.information(self, "Success", f"Face {name} added successfully!")
def manage_faces(self):
# Implement management functionality here
pass
if __name__ == "__main__":
app = QApplication([])
window = FaceRecognitionSystem()
window.show()
app.exec_()
解释
-
初始化和辅助函数：
load_known_faces：加载已知的人脸图像并提取特征编码。recognize_from_image：从图片中识别已知人脸。start_camera和update_frame：从摄像头实时捕获帧并识别已知人脸。add_face：添加新的人脸到数据库。-
UI部分：
- 使用
PyQt5构建了一个简单的GUI，包括按钮和标签。 - 按钮分别用于加载图片、启动摄像头、添加新的人脸和管理人脸。
-
运行：
- 运行脚本后，会显示一个窗口，可以通过点击按钮执行相应的功能。
注意事项
known_faces目录下有已知的人脸图像。希望这能帮助你构建一个人脸识别与管理系统！
，我们可以构建一个基于Python的人脸识别与管理系统。这个系统将包括以下几个主要功能：
- 选择人脸照片识别。
- 选择人脸视频识别。
- 摄像头检测人脸识别。
- 录入人脸功能。
- 管理人脸功能。
我们将使用face_recognition库进行人脸识别，并使用PyQt5来构建图形用户界面（GUI）。以下是完整的代码实现。
安装依赖
首先，确保安装了必要的Python包：
pip install opencv-python face-recognition numpy pandas PyQt5
代码实现
1. 初始化和辅助函数
import cv2
import face_recognition
import os
import numpy as np
import pandas as pd
from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QMessageBox
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import QTimer
from FaceRecognition_Ui import Ui_MainWindow
class FaceRecognitionSystem(QMainWindow, Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.known_face_encodings = []
self.known_face_names = []
self.load_known_faces()
self.init_ui()
def init_ui(self):
self.pushButton_load.clicked.connect(self.load_image)
self.pushButton_camera.clicked.connect(self.start_camera)
self.pushButton_add.clicked.connect(self.add_face)
self.pushButton_manage.clicked.connect(self.manage_faces)
def load_known_faces(self):
for filename in os.listdir('known_faces'):
if filename.endswith('.jpg') or filename.endswith('.png'):
image = face_recognition.load_image_file(f'known_faces/{filename}')
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
self.known_face_names.append(filename.split('.')[0])
def load_image(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
self.recognize_from_image(file_name)
def recognize_from_image(self, image_path):
image = face_recognition.load_image_file(image_path)
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(image, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def start_camera(self):
self.cap = cv2.VideoCapture(0)
self.timer = QTimer()
self.timer.timeout.connect(self.update_frame)
self.timer.start(10)
def update_frame(self):
ret, frame = self.cap.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(frame.data, frame.shape[1], frame.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def add_face(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
image = face_recognition.load_image_file(file_name)
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
name = os.path.basename(file_name).split('.')[0]
self.known_face_names.append(name)
cv2.imwrite(f'known_faces/{name}.jpg', image)
QMessageBox.information(self, "Success", f"Face {name} added successfully!")
def manage_faces(self):
# Implement management functionality here
pass
if __name__ == "__main__":
app = QApplication([])
window = FaceRecognitionSystem()
window.show()
app.exec_()
解释
-
初始化和辅助函数：
load_known_faces：加载已知的人脸图像并提取特征编码。recognize_from_image：从图片中识别已知人脸。start_camera和update_frame：从摄像头实时捕获帧并识别已知人脸。add_face：添加新的人脸到数据库。-
UI部分：
- 使用
PyQt5构建了一个简单的GUI，包括按钮和标签。 - 按钮分别用于加载图片、启动摄像头、添加新的人脸和管理人脸。
-
运行：
- 运行脚本后，会显示一个窗口，可以通过点击按钮执行相应的功能。
注意事项
known_faces目录下有已知的人脸图像。
这个项目使用了PyQt5来构建图形用户界面（GUI），并且集成了人脸识别功能。为了帮助你更好地理解代码结构和实现细节，我们可以基于截图中的目录结构和功能需求来编写相应的Python代码。
目录结构
根据截图中的目录结构，我们可以看到以下文件和目录：
FaceRecognition
data
images
test_imgplatech.txtFaceRecognition_Ui.pyFaceRecognition.pyrunMain.py功能需求
- 选择人脸照片识别。
- 选择人脸视频识别。
- 摄像头检测人脸识别。
- 录入人脸功能。
- 管理人脸功能。
代码实现
我们将分步骤实现这些功能，并确保代码结构清晰、易于维护。
1. 初始化和辅助函数
import cv2
import face_recognition
import os
import numpy as np
import pandas as pd
from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QMessageBox
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import QTimer
from FaceRecognition_Ui import Ui_MainWindow
class FaceRecognitionSystem(QMainWindow, Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.known_face_encodings = []
self.known_face_names = []
self.load_known_faces()
self.init_ui()
def init_ui(self):
self.pushButton_load.clicked.connect(self.load_image)
self.pushButton_camera.clicked.connect(self.start_camera)
self.pushButton_add.clicked.connect(self.add_face)
self.pushButton_manage.clicked.connect(self.manage_faces)
def load_known_faces(self):
for filename in os.listdir('known_faces'):
if filename.endswith('.jpg') or filename.endswith('.png'):
image = face_recognition.load_image_file(f'known_faces/{filename}')
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
self.known_face_names.append(filename.split('.')[0])
def load_image(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
self.recognize_from_image(file_name)
def recognize_from_image(self, image_path):
image = face_recognition.load_image_file(image_path)
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(image, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def start_camera(self):
self.cap = cv2.VideoCapture(0)
self.timer = QTimer()
self.timer.timeout.connect(self.update_frame)
self.timer.start(10)
def update_frame(self):
ret, frame = self.cap.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
name = "Unknown"
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]
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
qimage = QImage(frame.data, frame.shape[1], frame.shape[0], QImage.Format_RGB888).rgbSwapped()
pixmap = QPixmap.fromImage(qimage)
self.label.setPixmap(pixmap)
def add_face(self):
options = QFileDialog.Options()
file_name, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.jpg *.jpeg *.png)", options=options)
if file_name:
image = face_recognition.load_image_file(file_name)
encoding = face_recognition.face_encodings(image)[0]
self.known_face_encodings.append(encoding)
name = os.path.basename(file_name).split('.')[0]
self.known_face_names.append(name)
cv2.imwrite(f'known_faces/{name}.jpg', image)
QMessageBox.information(self, "Success", f"Face {name} added successfully!")
def manage_faces(self):
# Implement management functionality here
pass
if __name__ == "__main__":
app = QApplication([])
window = FaceRecognitionSystem()
window.show()
app.exec_()
解释
-
初始化和辅助函数：
load_known_faces：加载已知的人脸图像并提取特征编码。recognize_from_image：从图片中识别已知人脸。start_camera和update_frame：从摄像头实时捕获帧并识别已知人脸。add_face：添加新的人脸到数据库。-
UI部分：
- 使用
PyQt5构建了一个简单的GUI，包括按钮和标签。 - 按钮分别用于加载图片、启动摄像头、添加新的人脸和管理人脸。
-
运行：
- 运行脚本后，会显示一个窗口，可以通过点击按钮执行相应的功能。
注意事项
known_faces目录下有已知的人脸图像。希望这能帮助你构建一个人脸识别与管理系统！
作者：资深码侬
