#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 主界面窗口 包含视频显示区域、控制按钮和车牌号显示区域 """ import sys import os from PyQt5.QtWidgets import ( QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, QFrame, QTextEdit, QGroupBox, QCheckBox, QSpinBox, QSlider, QGridLayout ) from PyQt5.QtCore import Qt, QTimer, pyqtSignal from PyQt5.QtGui import QFont, QPixmap, QPalette, QImage from .video_widget import VideoWidget from utils.video_capture import VideoCapture from models.yolo_detector import YOLODetector from models.plate_recognizer import PlateRecognizerManager class MainWindow(QMainWindow): """主窗口类""" def __init__(self): super().__init__() self.video_capture = None self.yolo_detector = None self.plate_recognizer = PlateRecognizerManager('mock') # 车牌识别管理器 self.timer = QTimer() self.use_camera = 1 # 1: 摄像头, 0: 视频文件 self.detected_plates = [] # 存储切割后的车牌图像数组 self.current_frame = None # 存储当前帧用于车牌矫正 self.init_ui() self.init_detector() self.init_video_capture() self.connect_signals() def init_ui(self): """初始化用户界面""" self.setWindowTitle("车牌检测系统 - YOLO11s") self.setGeometry(100, 100, 1200, 800) # 创建中央widget central_widget = QWidget() self.setCentralWidget(central_widget) # 主布局 main_layout = QHBoxLayout(central_widget) # 左侧视频显示区域 self.create_video_area(main_layout) # 右侧控制和信息显示区域 self.create_control_area(main_layout) # 设置布局比例 main_layout.setStretch(0, 3) # 视频区域占3/4 main_layout.setStretch(1, 1) # 控制区域占1/4 def create_video_area(self, parent_layout): """创建视频显示区域""" video_frame = QFrame() video_frame.setFrameStyle(QFrame.StyledPanel) video_layout = QVBoxLayout(video_frame) # 视频显示widget self.video_widget = VideoWidget() video_layout.addWidget(self.video_widget) parent_layout.addWidget(video_frame) def create_control_area(self, parent_layout): """创建控制和信息显示区域""" control_frame = QFrame() control_frame.setFrameStyle(QFrame.StyledPanel) control_frame.setMaximumWidth(300) control_layout = QVBoxLayout(control_frame) # 控制按钮组 self.create_control_buttons(control_layout) # 检测信息显示 self.create_detection_info(control_layout) # 车牌号显示区域 self.create_plate_display(control_layout) # 系统状态显示 self.create_status_display(control_layout) parent_layout.addWidget(control_frame) def create_control_buttons(self, parent_layout): """创建控制按钮""" button_group = QGroupBox("控制面板") button_layout = QVBoxLayout(button_group) # 开始/停止按钮 self.start_btn = QPushButton("开始检测") self.start_btn.setMinimumHeight(40) self.start_btn.clicked.connect(self.toggle_detection) button_layout.addWidget(self.start_btn) # 视频源切换 self.camera_checkbox = QCheckBox("使用摄像头") self.camera_checkbox.setChecked(True) self.camera_checkbox.stateChanged.connect(self.toggle_video_source) button_layout.addWidget(self.camera_checkbox) # 检测开关 self.detection_checkbox = QCheckBox("启用检测") self.detection_checkbox.setChecked(True) button_layout.addWidget(self.detection_checkbox) parent_layout.addWidget(button_group) def create_detection_info(self, parent_layout): """创建检测信息显示""" info_group = QGroupBox("检测信息") info_layout = QVBoxLayout(info_group) # FPS显示 self.fps_label = QLabel("FPS: 0") self.fps_label.setFont(QFont("Arial", 12, QFont.Bold)) info_layout.addWidget(self.fps_label) # 检测数量 self.detection_count_label = QLabel("检测到车牌: 0") info_layout.addWidget(self.detection_count_label) # 模型信息 self.model_info_label = QLabel("模型: YOLO11s (ONNX)") info_layout.addWidget(self.model_info_label) parent_layout.addWidget(info_group) def create_plate_display(self, parent_layout): """创建车牌号显示区域""" plate_group = QGroupBox("车牌识别结果") plate_layout = QVBoxLayout(plate_group) # 当前识别的车牌号 self.current_plate_label = QLabel("当前车牌: 未识别") self.current_plate_label.setFont(QFont("Arial", 14, QFont.Bold)) self.current_plate_label.setStyleSheet("color: blue; padding: 10px; border: 1px solid gray;") plate_layout.addWidget(self.current_plate_label) # 矫正后的车牌图像显示 self.plate_image_label = QLabel("矫正后车牌图像") self.plate_image_label.setAlignment(Qt.AlignCenter) self.plate_image_label.setMinimumHeight(100) self.plate_image_label.setMaximumHeight(150) self.plate_image_label.setStyleSheet("border: 1px solid gray; background-color: #f0f0f0;") plate_layout.addWidget(self.plate_image_label) # 历史车牌记录 history_label = QLabel("历史记录:") plate_layout.addWidget(history_label) self.plate_history = QTextEdit() self.plate_history.setMaximumHeight(150) self.plate_history.setReadOnly(True) plate_layout.addWidget(self.plate_history) # 预留接口说明 interface_label = QLabel("注: 车牌识别接口已预留，可接入OCR模型") interface_label.setStyleSheet("color: gray; font-size: 10px;") plate_layout.addWidget(interface_label) parent_layout.addWidget(plate_group) def create_status_display(self, parent_layout): """创建系统状态显示""" status_group = QGroupBox("系统状态") status_layout = QVBoxLayout(status_group) self.status_label = QLabel("状态: 就绪") status_layout.addWidget(self.status_label) self.gpu_status_label = QLabel("GPU: 检测中...") status_layout.addWidget(self.gpu_status_label) parent_layout.addWidget(status_group) # 添加弹性空间 parent_layout.addStretch() def init_detector(self): """初始化YOLO检测器""" try: model_path = os.path.join(os.path.dirname(__file__), "..", "yolo11sth50.onnx") self.yolo_detector = YOLODetector(model_path) self.model_info_label.setText(f"模型: YOLO11s (ONNX) - GPU: {self.yolo_detector.use_gpu}") self.gpu_status_label.setText(f"GPU: {'启用' if self.yolo_detector.use_gpu else '禁用'}") except Exception as e: self.status_label.setText(f"模型加载失败: {str(e)}") def init_video_capture(self): """初始化视频捕获""" try: self.video_capture = VideoCapture() self.status_label.setText("视频捕获初始化成功") except Exception as e: self.status_label.setText(f"视频捕获初始化失败: {str(e)}") def connect_signals(self): """连接信号和槽""" self.timer.timeout.connect(self.update_frame) def toggle_detection(self): """切换检测状态""" if self.timer.isActive(): self.stop_detection() else: self.start_detection() def start_detection(self): """开始检测""" if self.video_capture and self.video_capture.start_capture(self.use_camera): # 根据视频源类型设置定时器间隔 video_fps = self.video_capture.get_video_fps() timer_interval = int(1000 / video_fps) # 转换为毫秒 self.timer.start(timer_interval) self.start_btn.setText("停止检测") source_type = "摄像头" if self.use_camera else f"视频文件({video_fps:.1f}FPS)" self.status_label.setText(f"检测中... - {source_type}") else: self.status_label.setText("启动失败") def stop_detection(self): """停止检测""" self.timer.stop() if self.video_capture: self.video_capture.stop_capture() self.start_btn.setText("开始检测") self.status_label.setText("已停止") def toggle_video_source(self, state): """切换视频源""" self.use_camera = 1 if state == Qt.Checked else 0 if self.timer.isActive(): self.stop_detection() self.start_detection() def update_frame(self): """更新帧""" if not self.video_capture: return frame, fps = self.video_capture.get_frame() if frame is None: return # 保存当前帧用于车牌矫正 self.current_frame = frame.copy() # 更新FPS显示 self.fps_label.setText(f"FPS: {fps:.1f}") # 进行检测 if self.detection_checkbox.isChecked() and self.yolo_detector: detections = self.yolo_detector.detect(frame) frame = self.yolo_detector.draw_detections(frame, detections) # 切割车牌图像 if detections: self.detected_plates = self.yolo_detector.crop_plates(frame, detections) # 统计不同类型车牌数量 blue_count = sum(1 for d in detections if d['class_id'] == 0) green_count = sum(1 for d in detections if d['class_id'] == 1) total_count = len(detections) self.detection_count_label.setText(f"检测到车牌: {total_count} (蓝牌:{blue_count}, 绿牌:{green_count})") # 调用车牌识别接口（预留） self.recognize_plates(self.detected_plates, detections) else: self.detection_count_label.setText("检测到车牌: 0") # 显示帧 self.video_widget.update_frame(frame) def recognize_plates(self, plate_images, detections): """车牌识别接口（预留）""" # 这里是预留的车牌识别接口 # 可以接入OCR模型进行车牌号识别 if plate_images and detections and self.current_frame is not None: # 获取最新检测到的车牌信息 latest_detection = detections[-1] # 取最后一个检测结果 plate_type = "Blue Plate" if latest_detection['class_id'] == 0 else "Green Plate" confidence = latest_detection['confidence'] # 处理蓝色车牌的矫正 corrected_image = None if latest_detection['class_id'] == 0: # 蓝色车牌 try: bbox = latest_detection['bbox'] corrected_image = self.plate_recognizer.preprocess_blue_plate( plate_images[-1], self.current_frame, bbox ) self._display_plate_image(corrected_image) except Exception as e: print(f"蓝色车牌矫正失败: {e}") self.plate_image_label.setText("蓝色车牌矫正失败") elif latest_detection['class_id'] == 1: # 绿色车牌 # 绿色车牌处理预留 self.plate_image_label.setText("绿色车牌处理\n(待实现)") # 模拟识别结果 plate_text = f"Mock {plate_type}-{len(plate_images)}" self.current_plate_label.setText(f"Current Plate: {plate_text} (Confidence: {confidence:.2f})") # 添加到历史记录 import datetime timestamp = datetime.datetime.now().strftime("%H:%M:%S") self.plate_history.append(f"[{timestamp}] {plate_text} (Confidence: {confidence:.2f})") def _display_plate_image(self, image): """在界面上显示车牌图像""" try: # 将OpenCV图像转换为QPixmap if len(image.shape) == 3: height, width, channel = image.shape bytes_per_line = 3 * width q_image = QImage(image.data, width, height, bytes_per_line, QImage.Format_RGB888).rgbSwapped() else: height, width = image.shape bytes_per_line = width q_image = QImage(image.data, width, height, bytes_per_line, QImage.Format_Grayscale8) # 缩放图像以适应标签大小 pixmap = QPixmap.fromImage(q_image) scaled_pixmap = pixmap.scaled(self.plate_image_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation) self.plate_image_label.setPixmap(scaled_pixmap) except Exception as e: print(f"显示车牌图像失败: {e}") self.plate_image_label.setText(f"图像显示失败: {str(e)}") def closeEvent(self, event): """窗口关闭事件""" self.stop_detection() event.accept()