import sys
import os
import cv2
import numpy as np
import torch
from PyQt5.QtWidgets import QListWidget
from facenet_pytorch import MTCNN, InceptionResnetV1
from PIL import
Image
from PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
QPushButton, QLabel, QFileDialog, QComboBox, QSlider, QMessageBox,
QTextEdit, QGroupBox, QScrollArea, QDialog)
from PyQt5.QtCore import Qt, QTimer
from PyQt5.QtGui import Q
Image, QPixmap, QIcon, QFont
import joblib
import logging
from face_recognition import FaceRecognition
# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class FaceRecognitionSystem(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("寝室人脸识别系统")
self.setGeometry(100, 100, 1200, 800)
# 初始化人脸识别器
self.face_recognition = FaceRecognition()
# 添加反馈按钮
self.add_feedback_button()
def add_feedback_button(self):
"""添加反馈按钮到界面"""
# 创建反馈按钮
self.feedback_button = QPushButton("提供反馈", self)
self.feedback_button.setFixedSize(120, 40) # 设置固定大小
self.feedback_button.setStyleSheet(
"QPushButton {"
" background-color: #4CAF50;"
" color: white;"
" border-radius: 5px;"
" font-weight: bold;"
"}"
"QPushButton:hover {"
" background-color: #45a
049;"
"}"
)
# 连接按钮点击事件
self.feedback_button.clicked.connect(self.open_feedback_dialog)
# 或者添加到工具栏
self.toolbar.addWidget(self.feedback_button)
def handle_feedback(self):
"""处理用户反馈"""
if not hasattr(self, 'last_results') or not self.last_results:
QMessageBox.warning(self, "警告", "没有可反馈的识别结果")
return
# 创建反馈对话框
dialog = QDialog(self)
dialog.setWindowTitle("识别错误反馈")
dialog.setFixedSize(400, 300)
layout = QVBoxLayout(dialog)
# 添加当前识别结果
result_label = QLabel("当前识别结果:")
layout.addWidget(result_label)
self.feedback_list = QListWidget()
for i, result in enumerate(self.last_results, 1):
label = result["label"]
confidence = result["confidence"]
self.feedback_list.addItem(f"人脸 #{i}: {label} (置信度: {confidence:.2f})")
layout.addWidget(self.feedback_list)
# 添加正确身份选择
correct_label = QLabel("正确身份:")
layout.addWidget(correct_label)
self.correct_combo = QComboBox()
self.correct_combo.addItems(
["选择正确身份"] + self.face_recognition.dorm_members + ["陌生人", "不在列表中"])
layout.addWidget(self.correct_combo)
# 添加按钮
btn_layout = QHBoxLayout()
submit_btn = QPushButton("提交反馈")
submit_btn.clicked.connect(lambda: self.submit_feedback(dialog))
btn_layout.addWidget(submit_btn)
cancel_btn = QPushButton("取消")
cancel_btn.clicked.connect(dialog.reject)
btn_layout.addWidget(cancel_btn)
layout.addLayout(btn_layout)
dialog.exec_()
def submit_feedback(self, dialog):
"""提交反馈并更新模型"""
selected_index = self.feedback_list.currentRow()
if selected_index < 0:
QMessageBox.warning(self, "警告", "请选择一个识别结果")
return
result = self.last_results[selected_index]
correct_identity = self.correct_combo.currentText()
if correct_identity == "选择正确身份":
QMessageBox.warning(self, "警告", "请选择正确身份")
return
# 保存反馈数据
self.face_recognition.save_feedback(
self.current_
image.copy(),
result["box"],
result["label"],
correct_identity
)
QMessageBox.information(self, "反馈提交", "感谢您的反馈！数据已保存用于改进模型")
dialog.accept()
def recognize_faces(self,
image):
"""识别人脸并在图像上标注结果"""
# 使用人脸识别器进行识别
self.last_results, display_
image = self.face_recognition.recognize(
image,
threshold=self.threshold_slider.value() / 100
)
# 更新结果文本
self.update_results_text()
# 显示图像
self.display_
image(display_
image)
def update_results_text(self):
"""更新结果文本区域"""
if not self.last_results:
self.results_text.setText("未识别到任何人脸")
return
# 构建结果文本
result_text = "<h3>识别结果：</h3>"
for i, result in enumerate(self.last_results, 1):
x1, y1, x2, y2 = result["box"]
label = result["label"]
confidence = result["confidence"]
# 处理中文显示问题
if label in self.face_recognition.dorm_members:
result_text += (
f"<p><b>人脸 #{i}:</b> "
f"<span style='color:green;'>寝室成员 - {label}</span><br>"
f"位置: ({x1}, {y1}), 置信度: {confidence:.2f}</p>"
)
else:
result_text += (
f"<p><b>人脸 #{i}:</b> "
f"<span style='color:red;'>陌生人</span><br>"
f"位置: ({x1}, {y1}), 置信度: {confidence:.2f}</p>"
)
self.results_text.setHtml(result_text)
# 初始化变量
self.model_loaded = False
self.camera_active = False
self.video_capture = None
self.timer = QTimer()
self.current_
image = None
self.last_results = [] # 存储上次识别结果
# 创建主界面
self.main_widget = QWidget()
self.setCentralWidget(self.main_widget)
self.layout = QHBoxLayout(self.main_widget)
# 左侧控制面板 - 占40%宽度
self.control_panel = QWidget()
self.control_layout = QVBoxLayout(self.control_panel)
self.control_layout.setAlignment(Qt.AlignTop)
self.control_panel.setMaximumWidth(400)
self.layout.addWidget(self.control_panel, 40) # 40%宽度
# 右侧图像显示区域 - 占60%宽度
self.
image_panel = QWidget()
self.
image_layout = QVBoxLayout(self.
image_panel)
self.
image_label = QLabel()
self.
image_label.setAlignment(Qt.AlignCenter)
self.
image_label.setMinimumSize(800, 600)
self.
image_label.setStyleSheet("background-color: #333; border: 1px solid #555;")
self.
image_layout.addWidget(self.
image_label)
self.layout.addWidget(self.
image_panel, 60) # 60%宽度
# 状态栏 - 先初始化状态栏
self.status_bar = self.statusBar()
self.status_bar.showMessage("系统初始化中...")
# 初始化UI组件
self.init_ui()
# 初始化模型
self.init_models()
def init_ui(self):
"""初始化用户界面组件"""
# 标题
title_label = QLabel("寝室人脸识别系统")
title_label.setFont(QFont("Arial", 18, QFont.Bold))
title_label.setAlignment(Qt.AlignCenter)
title_label.setStyleSheet("color: #2c3e50; padding: 10px;")
self.control_layout.addWidget(title_label)
# 模型加载
model_group = QGroupBox("模型设置")
model_layout = QVBoxLayout(model_group)
self.load_model_btn = QPushButton("加载模型")
self.load_model_btn.setIcon(QIcon.fromTheme("document-open"))
self.load_model_btn.setStyleSheet("background-color: #3498db;")
self.load_model_btn.clicked.connect(self.load_model)
model_layout.addWidget(self.load_model_btn)
self.model_status = QLabel("模型状态: 未加载")
model_layout.addWidget(self.model_status)
self.control_layout.addWidget(model_group)
# 识别设置
settings_group = QGroupBox("识别设置")
settings_layout = QVBoxLayout(settings_group)
# 置信度阈值
threshold_layout = QHBoxLayout()
threshold_label = QLabel("置信度阈值:")
threshold_layout.addWidget(threshold_label)
self.threshold_slider = QSlider(Qt.Horizontal)
self.threshold_slider.setRange(0, 100)
self.threshold_slider.setValue(70)
self.threshold_slider.valueChanged.connect(self.update_threshold)
threshold_layout.addWidget(self.threshold_slider)
self.threshold_value = QLabel("0.70")
threshold_layout.addWidget(self.threshold_value)
settings_layout.addLayout(threshold_layout)
# 显示选项
display_layout = QHBoxLayout()
display_label = QLabel("显示模式:")
display_layout.addWidget(display_label)
self.display_combo = QComboBox()
self.display_combo.addItems(["原始图像", "检测框", "识别结果"])
self.display_combo.setCurrentIndex(2)
display_layout.addWidget(self.display_combo)
settings_layout.addLayout(display_layout)
self.control_layout.addWidget(settings_group)
# 识别功能
recognition_group = QGroupBox("识别功能")
recognition_layout = QVBoxLayout(recognition_group)
#
图片识别
self.
image_recognition_btn = QPushButton("
图片识别")
self.
image_recognition_btn.setIcon(QIcon.fromTheme("
image-x-generic"))
self.
image_recognition_btn.setStyleSheet("background-color: #9b59b6;")
self.
image_recognition_btn.clicked.connect(self.open_
image)
self.
image_recognition_btn.setEnabled(False)
recognition_layout.addWidget(self.
image_recognition_btn)
# 摄像头识别
self.camera_recognition_btn = QPushButton("启动摄像头识别")
self.camera_recognition_btn.setIcon(QIcon.fromTheme("camera-web"))
self.camera_recognition_btn.setStyleSheet("background-color: #e74c3c;")
self.camera_recognition_btn.clicked.connect(self.toggle_camera)
self.camera_recognition_btn.setEnabled(False)
recognition_layout.addWidget(self.camera_recognition_btn)
self.control_layout.addWidget(recognition_group)
# 结果展示区域 - 使用QTextEdit替代QLabel
results_group = QGroupBox("识别结果")
results_layout = QVBoxLayout(results_group)
self.results_text = QTextEdit()
self.results_text.setReadOnly(True)
self.results_text.setFont(QFont("Microsoft YaHei", 12)) # 使用支持中文的字体
self.results_text.setStyleSheet("background-color: #f8f9fa; border: 1px solid #ddd; padding: 10px;")
self.results_text.setPlaceholderText("识别结果将显示在这里")
# 添加滚动区域
scroll_area = QScrollArea()
scroll_area.setWidgetResizable(True)
scroll_area.setWidget(self.results_text)
results_layout.addWidget(scroll_area)
self.control_layout.addWidget(results_group, 1) # 占据剩余空间
# 系统信息
info_group = QGroupBox("系统信息")
info_layout = QVBoxLayout(info_group)
self.device_label = QLabel(f"计算设备: {'GPU' if torch.cuda.is_available() else 'CPU'}")
info_layout.addWidget(self.device_label)
self.model_info = QLabel("加载模型以显示信息")
info_layout.addWidget(self.model_info)
self.control_layout.addWidget(info_group)
# 退出按钮
exit_btn = QPushButton("退出系统")
exit_btn.setIcon(QIcon.fromTheme("application-exit"))
exit_btn.clicked.connect(self.close)
exit_btn.setStyleSheet("background-color: #ff6b6b; color: white;")
self.control_layout.addWidget(exit_btn)
def open_feedback_dialog(self):
"""打开反馈对话框"""
# 实现对话框创建逻辑
dialog = FeedbackDialog(self)
dialog.exec_()
def init_models(self):
"""初始化模型组件"""
# 设置设备
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.device_label.setText(f"计算设备: {'GPU' if torch.cuda.is_available() else 'CPU'}")
# 初始化人脸检测器
try:
self.detector = MTCNN(
keep_all=True,
post_process=False,
device=self.device
)
self.status_bar.showMessage("MTCNN 检测器初始化完成")
logger.info("MTCNN 检测器初始化完成")
except Exception as e:
self.status_bar.showMessage(f"MTCNN 初始化失败: {str(e)}")
logger.error(f"MTCNN 初始化失败: {str(e)}")
return
# 初始化人脸特征提取器
try:
self.embedder = InceptionResnetV1(
pretrained='vggface2',
classify=False,
device=self.device
).eval()
self.status_bar.showMessage("FaceNet 特征提取器初始化完成")
logger.info("FaceNet 特征提取器初始化完成")
except Exception as e:
self.status_bar.showMessage(f"FaceNet 初始化失败: {str(e)}")
logger.error(f"FaceNet 初始化失败: {str(e)}")
def load_model(self):
"""加载预训练的SVM分类器"""
options = QFileDialog.Options()
file_path, _ = QFileDialog.getOpenFileName(
self, "选择模型文件", "", "模型文件 (*.pkl);;所有文件 (*)", options=options
)
if file_path:
try:
# 加载模型
model_data = joblib.load(file_path)
self.classifier = model_data['classifier']
self.label_encoder = model_data['label_encoder']
self.dorm_members = model_data['dorm_members']
# 更新UI状态
self.model_loaded = True
self.model_status.setText("模型状态: 已加载")
self.model_info.setText(f"寝室成员: {', '.join(self.dorm_members)}")
self.
image_recognition_btn.setEnabled(True)
self.camera_recognition_btn.setEnabled(True)
# 状态栏消息
self.status_bar.showMessage(f"模型加载成功: {os.path.basename(file_path)}")
# 显示成功消息
QMessageBox.information(
self, "模型加载",
f"模型加载成功！\n识别成员: {len(self.dorm_members)}人\n置信度阈值: {self.threshold_slider.value() / 100:.2f}"
)
except Exception as e:
QMessageBox.critical(self, "加载错误", f"模型加载失败: {str(e)}")
self.status_bar.showMessage(f"模型加载失败: {str(e)}")
def update_threshold(self, value):
"""更新置信度阈值"""
threshold = value / 100
self.threshold_value.setText(f"{threshold:.2f}")
self.status_bar.showMessage(f"置信度阈值更新为: {threshold:.2f}")
def open_
image(self):
"""打开
图片文件进行识别"""
if not self.model_loaded:
QMessageBox.warning(self, "警告", "请先加载模型！")
return
options = QFileDialog.Options()
file_path, _ = QFileDialog.getOpenFileName(
self, "选择识别
图片", "",
"
图片文件 (*.jpg *.jpeg *.png);;所有文件 (*)", options=options
)
if file_path:
# 读取
图片
image = cv2.imread(file_path)
if
image is None:
QMessageBox.critical(self, "错误", "无法读取
图片文件！")
return
# 保存当前
图片
self.current_
image =
image.copy()
# 进行识别
self.recognize_faces(
image)
def toggle_camera(self):
"""切换摄像头状态"""
if not self.model_loaded:
QMessageBox.warning(self, "警告", "请先加载模型！")
return
if not self.camera_active:
# 尝试打开摄像头
self.video_capture = cv2.VideoCapture(0)
if not self.video_capture.isOpened():
QMessageBox.critical(self, "错误", "无法打开摄像头！")
return
# 启动摄像头
self.camera_active = True
self.camera_recognition_btn.setText("停止摄像头识别")
self.camera_recognition_btn.setIcon(QIcon.fromTheme("media-playback-stop"))
self.timer.timeout.connect(self.process_camera_frame)
self.timer.start(30) # 约33 FPS
self.status_bar.showMessage("摄像头已启动")
else:
# 停止摄像头
self.camera_active = False
self.camera_recognition_btn.setText("启动摄像头识别")
self.camera_recognition_btn.setIcon(QIcon.fromTheme("camera-web"))
self.timer.stop()
if self.video_capture:
self.video_capture.release()
self.status_bar.showMessage("摄像头已停止")
def process_camera_frame(self):
"""处理摄像头帧"""
ret, frame = self.video_capture.read()
if ret:
# 保存当前帧
self.current_
image = frame.copy()
# 进行识别
self.recognize_faces(frame)
def recognize_faces(self,
image):
"""识别人脸并在图像上标注结果"""
# 清空上次结果
self.last_results = []
# 转换为 PIL 图像
pil_
image =
Image.fromarray(cv2.cvtColor(
image, cv2.COLOR_BGR2RGB))
# 检测人脸
boxes, probs, _ = self.detector.detect(pil_
image, landmarks=True)
# 获取显示选项
display_mode = self.display_combo.currentIndex()
# 准备显示图像
display_
image =
image.copy()
# 如果没有检测到人脸
if boxes is None:
if display_mode == 2: # 识别结果模式
cv2.putText(display_
image, "未检测到人脸", (20, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
self.results_text.setText("未检测到人脸")
else:
# 提取每个人脸
faces = []
for box in boxes:
x1, y1, x2, y2 = box
face = pil_
image.crop((x1, y1, x2, y2))
faces.append(face)
# 提取特征
embeddings = []
if faces and self.model_loaded:
# 批量处理所有人脸
face_tensors = [self.preprocess_face(face) for face in faces]
if face_tensors:
face_tensors = torch.stack(face_tensors).to(self.device)
with torch.no_grad():
embeddings = self.embedder(face_tensors).cpu().numpy()
# 处理每个人脸
for i, (box, prob) in enumerate(zip(boxes, probs)):
x1, y1, x2, y2 = box
w, h = x2 - x1, y2 - y1
# 在图像上绘制结果
if display_mode == 0: # 原始图像
# 不绘制任何内容
pass
elif display_mode == 1: # 检测框
# 绘制人脸框
cv2.rectangle(display_
image, (int(x1), int(y1)),
(int(x2), int(y2)), (0, 255, 0), 2)
elif display_mode == 2: # 识别结果
# 绘制人脸框
color = (0, 255, 0) # 绿色
# 如果有嵌入向量，则进行识别
if i < len(embeddings):
# 预测
probabilities = self.classifier.predict_proba([embeddings[i]])[0]
max_prob = np.max(probabilities)
pred_class = self.classifier.predict([embeddings[i]])[0]
pred_label = self.label_encoder.inverse_transform([pred_class])[0]
# 获取置信度阈值
threshold = self.threshold_slider.value() / 100
# 判断是否为陌生人
if max_prob < threshold or pred_label == 'stranger':
label = "陌生人"
color = (0, 0, 255) # 红色
else:
label = pred_label
color = (0, 255, 0) # 绿色
# 保存结果用于文本显示
result = {
"position": (int(x1), int(y1)),
"label": label,
"confidence": max_prob
}
self.last_results.append(result)
# 绘制标签
cv2.rectangle(display_
image, (int(x1), int(y1)),
(int(x2), int(y2)), color, 2)
cv2.putText(display_
image, f"{label} ({max_prob:.2f})",
(int(x1), int(y1) - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
else:
# 无法识别的处理
cv2.rectangle(display_
image, (int(x1), int(y1)),
(int(x2), int(y2)), (0, 165, 255), 2)
cv2.putText(display_
image, "处理中...",
(int(x1), int(y1) - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 165, 255), 2)
# 更新结果文本
self.update_results_text()
# 在图像上显示FPS（摄像头模式下）
if self.camera_active:
fps = self.timer.interval()
if fps > 0:
cv2.putText(display_
image, f"FPS: {1000 / fps:.1f}",
(20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# 显示图像
self.display_
image(display_
image)
def update_results_text(self):
"""更新结果文本区域"""
if not self.last_results:
self.results_text.setText("未识别到任何人脸")
return
# 构建结果文本
result_text = "<h3>识别结果：</h3>"
for i, result in enumerate(self.last_results, 1):
x, y = result["position"]
label = result["label"]
confidence = result["confidence"]
# 处理中文显示问题
if label in self.dorm_members:
result_text += (
f"<p><b>人脸 #{i}:</b> "
f"<span style='color:green;'>寝室成员 - {label}</span><br>"
f"位置: ({x}, {y}), 置信度: {confidence:.2f}</p>"
)
else:
result_text += (
f"<p><b>人脸 #{i}:</b> "
f"<span style='color:red;'>陌生人</span><br>"
f"位置: ({x}, {y}), 置信度: {confidence:.2f}</p>"
)
self.results_text.setHtml(result_text)
def preprocess_face(self, face_img):
"""预处理人脸图像"""
# 调整大小
face_img = face_img.resize((160, 160))
# 转换为张量并归一化
face_img = np.array(face_img).astype(np.float32) / 255.0
face_img = (face_img - 0.5) / 0.5 # 归一化到[-1, 1]
face_img = torch.tensor(face_img).permute(2, 0, 1) # HWC to CHW
return face_img
def display_
image(self,
image):
"""在QLabel中显示图像"""
# 将OpenCV图像转换为Qt格式
height, width, channel =
image.shape
bytes_per_line = 3 * width
q_img = Q
Image(
image.data, width, height, bytes_per_line, Q
Image.Format_RGB888).rgbSwapped()
# 缩放图像以适应标签
pixmap = QPixmap.from
Image(q_img)
self.
image_label.setPixmap(pixmap.scaled(
self.
image_label.width(),
self.
image_label.height(),
Qt.KeepAspectRatio,
Qt.SmoothTransformation
))
def closeEvent(self, event):
"""关闭事件处理"""
if self.camera_active:
self.timer.stop()
if self.video_capture:
self.video_capture.release()
# 确认退出
reply = QMessageBox.question(
self, '确认退出',
"确定要退出系统吗？",
QMessageBox.Yes | QMessageBox.No,
QMessageBox.No
)
if reply == QMessageBox.Yes:
event.accept()
else:
event.ignore()
帮我改吧，已经不知道改哪里了
本文详细介绍了Element UI的Image组件，包括其特性、属性、事件和插槽的使用，并通过创建多个示例（基础用法、占位内容、加载失败、懒加载和大图预览）展示了其在实际项目中的应用。这些示例涵盖了图片适应容器、自定义占位符、错误处理、懒加载和预览功能。
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