.idea/License_plate_recognition.iml

@@ -5,8 +5,4 @@
     <orderEntry type="jdk" jdkName="cnm" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="PLAIN" />
-    <option name="myDocStringFormat" value="Plain" />
-  </component>
 </module>

LPRNET_part/lpr_interface.py

@@ -1,3 +1,4 @@
+# 导入必要的库
 import torch
 import torch.nn as nn
 import cv2
@@ -11,6 +12,7 @@ from PIL import Image
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 # LPRNet字符集定义（与训练时保持一致）
+# 包含中国省份简称、数字、字母和特殊字符
 CHARS = ['京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
          '苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
          '桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁', '新',
@@ -19,84 +21,115 @@ CHARS = ['京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
          'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
          'W', 'X', 'Y', 'Z', 'I', 'O', '-']
 
+# 创建字符到索引的映射字典
 CHARS_DICT = {char: i for i, char in enumerate(CHARS)}
 
-# 简化的LPRNet模型定义
+# 简化的LPRNet模型定义 - 基础卷积块
 class small_basic_block(nn.Module):
     def __init__(self, ch_in, ch_out):
         super(small_basic_block, self).__init__()
+        # 定义一个小的基本卷积块，包含四个卷积层
         self.block = nn.Sequential(
+            # 1x1卷积，降低通道数
             nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
             nn.ReLU(),
+            # 3x1卷积，处理水平特征
             nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
             nn.ReLU(),
+            # 1x3卷积，处理垂直特征
             nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
             nn.ReLU(),
+            # 1x1卷积，恢复通道数
             nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
         )
 
     def forward(self, x):
         return self.block(x)
 
+# LPRNet模型定义 - 车牌识别网络
 class LPRNet(nn.Module):
     def __init__(self, lpr_max_len, phase, class_num, dropout_rate):
         super(LPRNet, self).__init__()
         self.phase = phase
         self.lpr_max_len = lpr_max_len
         self.class_num = class_num
+        
+        # 定义主干网络
         self.backbone = nn.Sequential(
+            # 初始卷积层
             nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1), # 0
             nn.BatchNorm2d(num_features=64),
             nn.ReLU(),  # 2
+            # 最大池化层
             nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),
+            # 第一个基本块
             small_basic_block(ch_in=64, ch_out=128),    # *** 4 ***
             nn.BatchNorm2d(num_features=128),
             nn.ReLU(),  # 6
+            # 第二个池化层
             nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
+            # 第二个基本块
             small_basic_block(ch_in=64, ch_out=256),   # 8
             nn.BatchNorm2d(num_features=256),
             nn.ReLU(),  # 10
+            # 第三个基本块
             small_basic_block(ch_in=256, ch_out=256),   # *** 11 ***
             nn.BatchNorm2d(num_features=256),
             nn.ReLU(),  # 13
+            # 第三个池化层
             nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)),  # 14
+            # Dropout层，防止过拟合
             nn.Dropout(dropout_rate),
+            # 特征提取卷积层
             nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1), # 16
             nn.BatchNorm2d(num_features=256),
             nn.ReLU(),  # 18
+            # 第二个Dropout层
             nn.Dropout(dropout_rate),
+            # 分类卷积层
             nn.Conv2d(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1), # 20
             nn.BatchNorm2d(num_features=class_num),
             nn.ReLU(),  # 22
         )
+        
+        # 定义容器层，用于融合全局上下文信息
         self.container = nn.Sequential(
             nn.Conv2d(in_channels=448+self.class_num, out_channels=self.class_num, kernel_size=(1,1), stride=(1,1)),
         )
 
     def forward(self, x):
+        # 保存中间特征
         keep_features = list()
         for i, layer in enumerate(self.backbone.children()):
             x = layer(x)
+            # 保存特定层的输出特征
             if i in [2, 6, 13, 22]: # [2, 4, 8, 11, 22]
                 keep_features.append(x)
 
+        # 处理全局上下文信息
         global_context = list()
         for i, f in enumerate(keep_features):
+            # 对不同层的特征进行不同尺度的平均池化
             if i in [0, 1]:
                 f = nn.AvgPool2d(kernel_size=5, stride=5)(f)
             if i in [2]:
                 f = nn.AvgPool2d(kernel_size=(4, 10), stride=(4, 2))(f)
+            # 对特征进行归一化处理
             f_pow = torch.pow(f, 2)
             f_mean = torch.mean(f_pow)
             f = torch.div(f, f_mean)
             global_context.append(f)
 
+        # 拼接全局上下文特征
         x = torch.cat(global_context, 1)
+        # 通过容器层处理
         x = self.container(x)
+        # 对序列维度进行平均，得到最终输出
         logits = torch.mean(x, dim=2)
 
         return logits
 
+# LPRNet推理类
 class LPRNetInference:
     def __init__(self, model_path=None, img_size=[94, 24], lpr_max_len=8, dropout_rate=0.5):
         """
@@ -109,6 +142,7 @@ class LPRNetInference:
         """
         self.img_size = img_size
         self.lpr_max_len = lpr_max_len
+        # 检测是否有可用的CUDA设备
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         
         # 设置默认模型路径
@@ -130,6 +164,7 @@ class LPRNetInference:
         else:
             print(f"Warning: 模型文件不存在或未指定: {model_path}. 使用随机权重.")
         
+        # 将模型移动到指定设备并设置为评估模式
         self.model.to(self.device)
         self.model.eval()
         
@@ -164,9 +199,11 @@ class LPRNetInference:
             image_array = cv2.resize(image_array, tuple(self.img_size))
         
         # 使用与训练时相同的预处理方式
+        # 归一化处理：减去127.5并乘以0.0078125，将像素值从[0,255]映射到[-1,1]
         image_array = image_array.astype('float32')
         image_array -= 127.5
         image_array *= 0.0078125
+        # 调整维度顺序从HWC到CHW
         image_array = np.transpose(image_array, (2, 0, 1))  # HWC -> CHW
         
         # 转换为tensor并添加batch维度
@@ -186,7 +223,7 @@ class LPRNetInference:
         prebs = logits.cpu().detach().numpy()
         preb = prebs[0, :, :]  # 取第一个batch [num_classes, sequence_length]
         
-        # 贪婪解码：对每个时间步选择最大概率的字符
+        # 贪婪解码： 对每个时间步选择最大概率的字符
         preb_label = []
         for j in range(preb.shape[1]):  # 遍历每个时间步
             preb_label.append(np.argmax(preb[:, j], axis=0))
@@ -248,7 +285,7 @@ class LPRNetInference:
             print(f"预测图像失败: {e}")
             return None, 0.0
 
-# 全局变量
+# 全局变量，用于存储模型实例
 lpr_model = None
 
 def LPRNinitialize_model():
@@ -295,6 +332,13 @@ def LPRNmodel_predict(image_array):
         return ['待', '识', '别', '0', '0', '0', '0', '0']
     
     try:
+        # 使用OpenCV调整图像大小到模型要求的尺寸
+        image_array = cv2.resize(image_array, (94, 24))
+        print(f"666999图片尺寸: {image_array.shape}")
+        
+        # 显示修正后的图像
+        cv2.imshow('Resized License Plate Image (94x24)', image_array)
+        cv2.waitKey(1)  # 非阻塞显示，允许程序继续执行
         # 预测车牌号
         predicted_text, confidence = lpr_model.predict(image_array)
         

OCR_part/ocr_interface.py

@@ -5,6 +5,18 @@ import cv2
 class OCRProcessor:
     def __init__(self):
         self.model = TextRecognition(model_name="PP-OCRv5_server_rec")
+        # 定义允许的字符集合（不包含空白字符）
+        self.allowed_chars = [
+            # 中文省份简称
+            '京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
+            '苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
+            '桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁', '新',
+            # 字母 A-Z
+            'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 
+            'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
+            # 数字 0-9
+            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9'
+        ]
         print("OCR模型初始化完成（占位）")
 
     def predict(self, image_array):
@@ -15,6 +27,14 @@ class OCRProcessor:
         placeholder_result = results.split(',')
         return placeholder_result
     
+    def filter_allowed_chars(self, text):
+        """只保留允许的字符"""
+        filtered_text = ""
+        for char in text:
+            if char in self.allowed_chars:
+                filtered_text += char
+        return filtered_text
+
 # 保留原有函数接口
 _processor = OCRProcessor()
 
@@ -42,8 +62,12 @@ def LPRNmodel_predict(image_array):
     else:
         result_str = str(raw_result)
     
-    # 过滤掉'·'字符
+    # 过滤掉'·'和'-'字符
     filtered_str = result_str.replace('·', '')
+    filtered_str = filtered_str.replace('-', '')
+    
+    # 只保留允许的字符
+    filtered_str = _processor.filter_allowed_chars(filtered_str)
     
     # 转换为字符列表
     char_list = list(filtered_str)

main.py

@@ -1,25 +1,22 @@
 import sys
+import os
 import cv2
 import numpy as np
-from PyQt5.QtWidgets import (
+from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, \
-    QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
+    QFileDialog, QFrame, QScrollArea, QComboBox
-    QLabel, QPushButton, QScrollArea, QFrame, QSizePolicy
-)
 from PyQt5.QtCore import QTimer, Qt, pyqtSignal, QThread
 from PyQt5.QtGui import QImage, QPixmap, QFont, QPainter, QPen, QColor
-import os
 from yolopart.detector import LicensePlateYOLO
 
 #选择使用哪个模块
-from LPRNET_part.lpr_interface import LPRNmodel_predict
+# from LPRNET_part.lpr_interface import LPRNmodel_predict
-from LPRNET_part.lpr_interface import LPRNinitialize_model
+# from LPRNET_part.lpr_interface import LPRNinitialize_model
-
 #使用OCR
-#from OCR_part.ocr_interface import LPRNmodel_predict
+# from OCR_part.ocr_interface import LPRNmodel_predict
-#from OCR_part.ocr_interface import LPRNinitialize_model
+# from OCR_part.ocr_interface import LPRNinitialize_model
 # 使用CRNN
-#from CRNN_part.crnn_interface import LPRNmodel_predict
+# from CRNN_part.crnn_interface import LPRNmodel_predict
-#from CRNN_part.crnn_interface import LPRNinitialize_model
+# from CRNN_part.crnn_interface import LPRNinitialize_model
 
 class CameraThread(QThread):
     """摄像头线程类"""
@@ -56,6 +53,60 @@ class CameraThread(QThread):
                     self.frame_ready.emit(frame)
             self.msleep(30)  # 约30fps
 
+class VideoThread(QThread):
+    """视频处理线程类"""
+    frame_ready = pyqtSignal(np.ndarray)
+    video_finished = pyqtSignal()
+    
+    def __init__(self):
+        super().__init__()
+        self.video_path = None
+        self.cap = None
+        self.running = False
+        self.paused = False
+    
+    def load_video(self, video_path):
+        """加载视频文件"""
+        self.video_path = video_path
+        self.cap = cv2.VideoCapture(video_path)
+        return self.cap.isOpened()
+    
+    def start_video(self):
+        """开始播放视频"""
+        if self.cap and self.cap.isOpened():
+            self.running = True
+            self.paused = False
+            self.start()
+            return True
+        return False
+    
+    def pause_video(self):
+        """暂停/继续视频"""
+        self.paused = not self.paused
+        return self.paused
+    
+    def stop_video(self):
+        """停止视频"""
+        self.running = False
+        if self.cap:
+            self.cap.release()
+        self.quit()
+        self.wait()
+    
+    def run(self):
+        """线程运行函数"""
+        while self.running:
+            if not self.paused and self.cap and self.cap.isOpened():
+                ret, frame = self.cap.read()
+                if ret:
+                    self.frame_ready.emit(frame)
+                else:
+                    # 视频播放结束
+                    self.video_finished.emit()
+                    self.running = False
+                    break
+            self.msleep(30)  # 约30fps
+
 class LicensePlateWidget(QWidget):
     """单个车牌结果显示组件"""
     
@@ -162,15 +213,21 @@ class MainWindow(QMainWindow):
         super().__init__()
         self.detector = None
         self.camera_thread = None
+        self.video_thread = None
         self.current_frame = None
         self.detections = []
+        self.current_mode = "camera"  # 当前模式：camera, video, image
+        self.is_processing = False  # 标志位，表示是否正在处理识别任务
+        self.last_plate_results = []  # 存储上一次的车牌识别结果
+        self.current_recognition_method = "CRNN"  # 当前识别方法
         
         self.init_ui()
         self.init_detector()
         self.init_camera()
+        self.init_video()
 
-        # 初始化OCR/CRNN模型（函数名改成一样的了，所以不要修改这里了，想用哪个模块直接导入）
+        # 初始化默认识别方法（CRNN）的模型
-        LPRNinitialize_model()
+        self.change_recognition_method(self.current_recognition_method)
 
     
     def init_ui(self):
@@ -197,7 +254,7 @@ class MainWindow(QMainWindow):
         self.camera_label.setStyleSheet("QLabel { background-color: black; border: 1px solid #ccc; }")
         self.camera_label.setAlignment(Qt.AlignCenter)
         self.camera_label.setText("摄像头未启动")
-        self.camera_label.setScaledContents(True)
+        self.camera_label.setScaledContents(False)
         
         # 控制按钮
         button_layout = QHBoxLayout()
@@ -207,8 +264,26 @@ class MainWindow(QMainWindow):
         self.stop_button.clicked.connect(self.stop_camera)
         self.stop_button.setEnabled(False)
         
+        # 视频控制按钮
+        self.open_video_button = QPushButton("打开视频")
+        self.stop_video_button = QPushButton("停止视频")
+        self.pause_video_button = QPushButton("暂停视频")
+        self.open_video_button.clicked.connect(self.open_video_file)
+        self.stop_video_button.clicked.connect(self.stop_video)
+        self.pause_video_button.clicked.connect(self.pause_video)
+        self.stop_video_button.setEnabled(False)
+        self.pause_video_button.setEnabled(False)
+        
+        # 图片控制按钮
+        self.open_image_button = QPushButton("打开图片")
+        self.open_image_button.clicked.connect(self.open_image_file)
+        
         button_layout.addWidget(self.start_button)
         button_layout.addWidget(self.stop_button)
+        button_layout.addWidget(self.open_video_button)
+        button_layout.addWidget(self.stop_video_button)
+        button_layout.addWidget(self.pause_video_button)
+        button_layout.addWidget(self.open_image_button)
         button_layout.addStretch()
         
         left_layout.addWidget(self.camera_label)
@@ -227,6 +302,20 @@ class MainWindow(QMainWindow):
         title_label.setFont(QFont("Arial", 16, QFont.Bold))
         title_label.setStyleSheet("QLabel { color: #333; padding: 10px; }")
         
+        # 识别方法选择
+        method_layout = QHBoxLayout()
+        method_label = QLabel("识别方法:")
+        method_label.setFont(QFont("Arial", 10))
+        
+        self.method_combo = QComboBox()
+        self.method_combo.addItems(["CRNN", "LPRNET", "OCR"])
+        self.method_combo.setCurrentText("CRNN")  # 默认选择CRNN
+        self.method_combo.currentTextChanged.connect(self.change_recognition_method)
+        
+        method_layout.addWidget(method_label)
+        method_layout.addWidget(self.method_combo)
+        method_layout.addStretch()
+        
         # 车牌数量显示
         self.count_label = QLabel("识别到的车牌数量: 0")
         self.count_label.setAlignment(Qt.AlignCenter)
@@ -253,9 +342,17 @@ class MainWindow(QMainWindow):
         
         scroll_area.setWidget(self.results_widget)
         
+        # 当前识别任务显示
+        self.current_method_label = QLabel("当前识别方法: CRNN")
+        self.current_method_label.setAlignment(Qt.AlignRight)
+        self.current_method_label.setFont(QFont("Arial", 9))
+        self.current_method_label.setStyleSheet("QLabel { color: #666; padding: 5px; }")
+        
         right_layout.addWidget(title_label)
+        right_layout.addLayout(method_layout)
         right_layout.addWidget(self.count_label)
         right_layout.addWidget(scroll_area)
+        right_layout.addWidget(self.current_method_label)
         
         # 添加到主布局
         main_layout.addWidget(left_frame, 2)
@@ -296,6 +393,12 @@ class MainWindow(QMainWindow):
         self.camera_thread = CameraThread()
         self.camera_thread.frame_ready.connect(self.process_frame)
     
+    def init_video(self):
+        """初始化视频线程"""
+        self.video_thread = VideoThread()
+        self.video_thread.frame_ready.connect(self.process_frame)
+        self.video_thread.video_finished.connect(self.on_video_finished)
+    
     def start_camera(self):
         """启动摄像头"""
         if self.camera_thread.start_camera():
@@ -311,23 +414,167 @@ class MainWindow(QMainWindow):
         self.start_button.setEnabled(True)
         self.stop_button.setEnabled(False)
         self.camera_label.setText("摄像头已停止")
+        # 只在摄像头模式下清除标签内容
+        if self.current_mode == "camera":
             self.camera_label.clear()
     
+    def on_video_finished(self):
+        """视频播放结束时的处理"""
+        self.video_thread.stop_video()
+        self.open_video_button.setEnabled(True)
+        self.stop_video_button.setEnabled(False)
+        self.pause_video_button.setEnabled(False)
+        self.camera_label.setText("视频播放结束")
+        self.current_mode = "camera"
+    
+    def open_video_file(self):
+        """打开视频文件"""
+        # 停止当前模式
+        if self.current_mode == "camera" and self.camera_thread and self.camera_thread.running:
+            self.stop_camera()
+        elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
+            self.stop_video()
+        
+        # 选择视频文件
+        video_path, _ = QFileDialog.getOpenFileName(self, "选择视频文件", "", "视频文件 (*.mp4 *.avi *.mov *.mkv)")
+        
+        if video_path:
+            if self.video_thread.load_video(video_path):
+                self.current_mode = "video"
+                self.start_video()
+                self.camera_label.setText(f"正在播放视频: {os.path.basename(video_path)}")
+            else:
+                self.camera_label.setText("视频加载失败")
+    
+    def start_video(self):
+        """开始播放视频"""
+        if self.video_thread.start_video():
+            self.open_video_button.setEnabled(False)
+            self.stop_video_button.setEnabled(True)
+            self.pause_video_button.setEnabled(True)
+            self.pause_video_button.setText("暂停")
+        else:
+            self.camera_label.setText("视频播放失败")
+    
+    def pause_video(self):
+        """暂停/继续视频"""
+        if self.video_thread.pause_video():
+            self.pause_video_button.setText("继续")
+        else:
+            self.pause_video_button.setText("暂停")
+    
+    def stop_video(self):
+        """停止视频"""
+        self.video_thread.stop_video()
+        self.open_video_button.setEnabled(True)
+        self.stop_video_button.setEnabled(False)
+        self.pause_video_button.setEnabled(False)
+        self.camera_label.setText("视频已停止")
+        # 只在视频模式下清除标签内容
+        if self.current_mode == "video":
+            self.camera_label.clear()
+        self.current_mode = "camera"
+    
+    def open_image_file(self):
+        """打开图片文件"""
+        # 停止当前模式
+        if self.current_mode == "camera" and self.camera_thread and self.camera_thread.running:
+            self.stop_camera()
+        elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
+            self.stop_video()
+        
+        # 选择图片文件
+        image_path, _ = QFileDialog.getOpenFileName(self, "选择图片文件", "", "图片文件 (*.jpg *.jpeg *.png *.bmp)")
+        
+        if image_path:
+            self.current_mode = "image"
+            try:
+                # 读取图片 - 方法1: 使用cv2.imdecode处理中文路径
+                image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+                
+                # 如果方法1失败，尝试方法2: 直接使用cv2.imread
+                if image is None:
+                    image = cv2.imread(image_path)
+                    
+                if image is not None:
+                    print(f"成功加载图片: {image_path}, 尺寸: {image.shape}")
+                    self.process_image(image)
+                    # 不在这里设置文本，避免覆盖图片
+                    # self.camera_label.setText(f"正在显示图片: {os.path.basename(image_path)}")
+                else:
+                    print(f"图片加载失败: {image_path}")
+                    self.camera_label.setText("图片加载失败")
+            except Exception as e:
+                print(f"图片处理异常: {str(e)}")
+                self.camera_label.setText(f"图片处理错误: {str(e)}")
+    
+    def process_image(self, image):
+        """处理图片"""
+        try:
+            print(f"开始处理图片，图片尺寸: {image.shape}")
+            self.current_frame = image.copy()
+            
+            # 进行车牌检测
+            print("正在进行车牌检测...")
+            self.detections = self.detector.detect_license_plates(image)
+            print(f"检测到 {len(self.detections)} 个车牌")
+            
+            # 在图像上绘制检测结果
+            print("正在绘制检测结果...")
+            display_frame = self.draw_detections(image.copy())
+            
+            # 转换为Qt格式并显示
+            print("正在显示图片...")
+            self.display_frame(display_frame)
+            
+            # 更新右侧结果显示
+            print("正在更新结果显示...")
+            self.update_results_display()
+            print("图片处理完成")
+        except Exception as e:
+            print(f"图片处理过程中出错: {str(e)}")
+            import traceback
+            traceback.print_exc()
+    
     def process_frame(self, frame):
         """处理摄像头帧"""
         self.current_frame = frame.copy()
         
+        # 先显示原始帧，保证视频流畅播放
+        self.display_frame(frame)
+        
+        # 如果当前没有在处理识别任务，则开始新的识别任务
+        if not self.is_processing:
+            self.is_processing = True
+            # 异步进行车牌检测和识别
+            QTimer.singleShot(0, self.async_detect_and_update)
+    
+    def async_detect_and_update(self):
+        """异步进行车牌检测和识别"""
+        if self.current_frame is None:
+            self.is_processing = False  # 重置标志位
+            return
+            
+        try:
             # 进行车牌检测
-        self.detections = self.detector.detect_license_plates(frame)
+            self.detections = self.detector.detect_license_plates(self.current_frame)
             
             # 在图像上绘制检测结果
-        display_frame = self.draw_detections(frame.copy())
+            display_frame = self.draw_detections(self.current_frame.copy())
             
-        # 转换为Qt格式并显示
+            # 更新显示帧（显示带检测结果的帧）
+            # 无论是摄像头模式还是视频模式，都显示检测框
             self.display_frame(display_frame)
             
             # 更新右侧结果显示
             self.update_results_display()
+        except Exception as e:
+            print(f"异步检测和更新失败: {str(e)}")
+            import traceback
+            traceback.print_exc()
+        finally:
+            # 无论成功或失败，都要重置标志位
+            self.is_processing = False
     
     def draw_detections(self, frame):
         """在图像上绘制检测结果"""
@@ -335,14 +582,96 @@ class MainWindow(QMainWindow):
     
     def display_frame(self, frame):
         """显示帧到界面"""
+        try:
+            print(f"开始显示帧，帧尺寸: {frame.shape}")
+            
+            # 方法1: 标准方法
+            try:
                 rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                 h, w, ch = rgb_frame.shape
                 bytes_per_line = ch * w
                 qt_image = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
                 
+                print(f"方法1: 创建QImage，尺寸: {qt_image.width()}x{qt_image.height()}")
+                if qt_image.isNull():
+                    print("方法1: QImage为空，尝试方法2")
+                    raise Exception("QImage为空")
+                    
                 pixmap = QPixmap.fromImage(qt_image)
+                if pixmap.isNull():
+                    print("方法1: QPixmap为空，尝试方法2")
+                    raise Exception("QPixmap为空")
+                    
+                # 手动缩放图片以适应标签大小，保持宽高比
                 scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
                 self.camera_label.setPixmap(scaled_pixmap)
+                print("方法1: 帧显示完成")
+                return
+            except Exception as e1:
+                print(f"方法1失败: {str(e1)}")
+            
+            # 方法2: 使用imencode和imdecode
+            try:
+                print("尝试方法2: 使用imencode和imdecode")
+                _, buffer = cv2.imencode('.jpg', frame)
+                rgb_frame = cv2.imdecode(buffer, cv2.IMREAD_COLOR)
+                rgb_frame = cv2.cvtColor(rgb_frame, cv2.COLOR_BGR2RGB)
+                h, w, ch = rgb_frame.shape
+                bytes_per_line = ch * w
+                qt_image = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
+                
+                print(f"方法2: 创建QImage，尺寸: {qt_image.width()}x{qt_image.height()}")
+                if qt_image.isNull():
+                    print("方法2: QImage为空")
+                    raise Exception("QImage为空")
+                    
+                pixmap = QPixmap.fromImage(qt_image)
+                if pixmap.isNull():
+                    print("方法2: QPixmap为空")
+                    raise Exception("QPixmap为空")
+                    
+                # 手动缩放图片以适应标签大小，保持宽高比
+                scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
+                self.camera_label.setPixmap(scaled_pixmap)
+                print("方法2: 帧显示完成")
+                return
+            except Exception as e2:
+                print(f"方法2失败: {str(e2)}")
+            
+            # 方法3: 直接使用QImage的构造函数
+            try:
+                print("尝试方法3: 直接使用QImage的构造函数")
+                height, width, channel = frame.shape
+                bytes_per_line = 3 * width
+                q_image = QImage(frame.data, width, height, bytes_per_line, QImage.Format_BGR888)
+                
+                print(f"方法3: 创建QImage，尺寸: {q_image.width()}x{q_image.height()}")
+                if q_image.isNull():
+                    print("方法3: QImage为空")
+                    raise Exception("QImage为空")
+                    
+                pixmap = QPixmap.fromImage(q_image)
+                if pixmap.isNull():
+                    print("方法3: QPixmap为空")
+                    raise Exception("QPixmap为空")
+                    
+                # 手动缩放图片以适应标签大小，保持宽高比
+                scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
+                self.camera_label.setPixmap(scaled_pixmap)
+                print("方法3: 帧显示完成")
+                return
+            except Exception as e3:
+                print(f"方法3失败: {str(e3)}")
+                
+            # 所有方法都失败
+            print("所有显示方法都失败")
+            self.camera_label.setText("图片显示失败")
+                
+        except Exception as e:
+            print(f"显示帧过程中出错: {str(e)}")
+            import traceback
+            traceback.print_exc()
+            self.camera_label.setText(f"显示错误: {str(e)}")
     
     def update_results_display(self):
         """更新右侧结果显示"""
@@ -350,13 +679,8 @@ class MainWindow(QMainWindow):
         count = len(self.detections)
         self.count_label.setText(f"识别到的车牌数量: {count}")
         
-        # 清除之前的结果
+        # 准备新的车牌结果列表
-        for i in reversed(range(self.results_layout.count())):
+        new_plate_results = []
-            child = self.results_layout.itemAt(i).widget()
-            if child:
-                child.setParent(None)
-        
-        # 添加新的结果
         for i, detection in enumerate(self.detections):
             # 矫正车牌图像
             corrected_image = self.correct_license_plate(detection)
@@ -364,16 +688,54 @@ class MainWindow(QMainWindow):
             # 获取车牌号，传入车牌类型信息
             plate_number = self.recognize_plate_number(corrected_image, detection['class_name'])
             
-            # 创建车牌显示组件
+            # 添加到新结果列表
-            plate_widget = LicensePlateWidget(
+            new_plate_results.append({
-                i + 1,
+                'id': i + 1,
-                detection['class_name'],
+                'class_name': detection['class_name'],
-                corrected_image,
+                'corrected_image': corrected_image,
-                plate_number
+                'plate_number': plate_number
-            )
+            })
         
+        # 比较新旧结果是否相同
+        results_changed = False
+        if len(self.last_plate_results) != len(new_plate_results):
+            results_changed = True
+        else:
+            for i in range(len(new_plate_results)):
+                if i >= len(self.last_plate_results):
+                    results_changed = True
+                    break
+                
+                last_result = self.last_plate_results[i]
+                new_result = new_plate_results[i]
+                
+                # 比较车牌类型和车牌号
+                if (last_result['class_name'] != new_result['class_name'] or 
+                    last_result['plate_number'] != new_result['plate_number']):
+                    results_changed = True
+                    break
+        
+        # 只有当结果发生变化时才更新显示
+        if results_changed:
+            # 清除之前的结果
+            for i in reversed(range(self.results_layout.count())):
+                child = self.results_layout.itemAt(i).widget()
+                if child:
+                    child.setParent(None)
+            
+            # 添加新的结果
+            for result in new_plate_results:
+                plate_widget = LicensePlateWidget(
+                    result['id'],
+                    result['class_name'],
+                    result['corrected_image'],
+                    result['plate_number']
+                )
                 self.results_layout.addWidget(plate_widget)
             
+            # 更新存储的上一次结果
+            self.last_plate_results = new_plate_results
+    
     def correct_license_plate(self, detection):
         """矫正车牌图像"""
         if self.current_frame is None:
@@ -395,8 +757,15 @@ class MainWindow(QMainWindow):
              return "识别失败"
          
          try:
+             # 根据当前选择的识别方法调用相应的函数
+             if self.current_recognition_method == "CRNN":
+                 from CRNN_part.crnn_interface import LPRNmodel_predict
+             elif self.current_recognition_method == "LPRNET":
+                 from lightCRNN_part.lightcrnn_interface import LPRNmodel_predict
+             elif self.current_recognition_method == "OCR":
+                 from OCR_part.ocr_interface import LPRNmodel_predict
+             
              # 预测函数（来自模块）
-            # 函数名改成一样的了，所以不要修改这里了，想用哪个模块直接导入
              result = LPRNmodel_predict(corrected_image)
              
              # 将字符列表转换为字符串，支持8位车牌号
@@ -420,10 +789,32 @@ class MainWindow(QMainWindow):
              print(f"车牌号识别失败: {e}")
              return "识别失败"
     
+    def change_recognition_method(self, method):
+         """切换识别方法"""
+         self.current_recognition_method = method
+         self.current_method_label.setText(f"当前识别方法: {method}")
+         
+         # 初始化对应的模型
+         if method == "CRNN":
+             from CRNN_part.crnn_interface import LPRNinitialize_model
+             LPRNinitialize_model()
+         elif method == "LPRNET":
+             from lightCRNN_part.lightcrnn_interface import LPRNinitialize_model
+             LPRNinitialize_model()
+         elif method == "OCR":
+             from OCR_part.ocr_interface import LPRNinitialize_model
+             LPRNinitialize_model()
+         
+         # 如果当前有显示的帧，重新处理以更新识别结果
+         if self.current_frame is not None:
+             self.process_frame(self.current_frame)
+    
     def closeEvent(self, event):
         """窗口关闭事件"""
-        if self.camera_thread:
+        if self.camera_thread and self.camera_thread.running:
             self.camera_thread.stop_camera()
+        if self.video_thread and self.video_thread.running:
+            self.video_thread.stop_video()
         event.accept()
 
 def main():

test_lpr_real_images.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+LPRNet接口真实图片测试脚本
+测试LPRNET_part目录下的真实车牌图片
+"""
+
+import cv2
+import numpy as np
+import os
+from LPRNET_part.lpr_interface import LPRNinitialize_model, LPRNmodel_predict
+
+def test_real_images():
+    """
+    测试LPRNET_part目录下的真实车牌图片
+    """
+    print("=== LPRNet真实图片测试 ===")
+    
+    # 初始化模型
+    print("1. 初始化LPRNet模型...")
+    success = LPRNinitialize_model()
+    if not success:
+        print("模型初始化失败！")
+        return
+    
+    # 获取LPRNET_part目录下的图片文件
+    lprnet_dir = "LPRNET_part"
+    image_files = []
+    
+    if os.path.exists(lprnet_dir):
+        for file in os.listdir(lprnet_dir):
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
+                image_files.append(os.path.join(lprnet_dir, file))
+    
+    if not image_files:
+        print("未找到图片文件！")
+        return
+    
+    print(f"2. 找到 {len(image_files)} 个图片文件")
+    
+    # 测试每个图片
+    for i, image_path in enumerate(image_files, 1):
+        print(f"\n--- 测试图片 {i}: {os.path.basename(image_path)} ---")
+        
+        try:
+            # 使用支持中文路径的方式读取图片
+            image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+            
+            if image is None:
+                print(f"无法读取图片: {image_path}")
+                continue
+            
+            print(f"图片尺寸: {image.shape}")
+            
+            # 进行预测
+            result = LPRNmodel_predict(image)
+            print(f"识别结果: {result}")
+            print(f"识别车牌号: {''.join(result)}")
+            
+        except Exception as e:
+            print(f"处理图片 {image_path} 时出错: {e}")
+            import traceback
+            traceback.print_exc()
+    
+    print("\n=== 测试完成 ===")
+
+def test_image_loading():
+    """
+    测试图片加载方式
+    """
+    print("\n=== 图片加载测试 ===")
+    
+    lprnet_dir = "LPRNET_part"
+    
+    if os.path.exists(lprnet_dir):
+        for file in os.listdir(lprnet_dir):
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
+                image_path = os.path.join(lprnet_dir, file)
+                print(f"\n测试文件: {file}")
+                
+                # 方法1: 普通cv2.imread
+                img1 = cv2.imread(image_path)
+                print(f"cv2.imread结果: {img1 is not None}")
+                
+                # 方法2: 支持中文路径的方式
+                try:
+                    img2 = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+                    # img2 = cv2.resize(img2,(128,48))
+                    print(f"cv2.imdecode结果: {img2 is not None}")
+                    if img2 is not None:
+                        print(f"图片尺寸: {img2.shape}")
+                except Exception as e:
+                    print(f"cv2.imdecode失败: {e}")
+
+if __name__ == "__main__":
+    # 首先测试图片加载
+    test_image_loading()
+    
+    # 然后测试完整的识别流程
+    test_real_images()