更新 main.py

.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

@@ -1,328 +0,0 @@
-import torch
-import torch.nn as nn
-import cv2
-import numpy as np
-import os
-import sys
-from torch.autograd import Variable
-from PIL import Image
-
-# 添加父目录到路径，以便导入模型和数据加载器
-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）
-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',
-         '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
-         'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
-         'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
-         'W', 'X', 'Y', 'Z', 'I', 'O', '-']
-
-CHARS_DICT = {char: i for i, char in enumerate(CHARS)}
-
-# 简化的LPRNet模型定义
-class small_basic_block(nn.Module):
-    def __init__(self, ch_in, ch_out):
-        super(small_basic_block, self).__init__()
-        self.block = nn.Sequential(
-            nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
-            nn.ReLU(),
-            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
-            nn.ReLU(),
-            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
-            nn.ReLU(),
-            nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
-        )
-
-    def forward(self, x):
-        return self.block(x)
-
-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
-        self.backbone = nn.Sequential(
-            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1), # 0
-            nn.BatchNorm2d(num_features=64),
-            nn.ReLU(),  # 2
-            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),
-            small_basic_block(ch_in=64, ch_out=128),    # *** 4 ***
-            nn.BatchNorm2d(num_features=128),
-            nn.ReLU(),  # 6
-            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
-            small_basic_block(ch_in=64, ch_out=256),   # 8
-            nn.BatchNorm2d(num_features=256),
-            nn.ReLU(),  # 10
-            small_basic_block(ch_in=256, ch_out=256),   # *** 11 ***
-            nn.BatchNorm2d(num_features=256),
-            nn.ReLU(),  # 13
-            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)),  # 14
-            nn.Dropout(dropout_rate),
-            nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1), # 16
-            nn.BatchNorm2d(num_features=256),
-            nn.ReLU(),  # 18
-            nn.Dropout(dropout_rate),
-            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
-        )
-        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):
-        keep_features = list()
-        for i, layer in enumerate(self.backbone.children()):
-            x = layer(x)
-            if i in [2, 6, 13, 22]: # [2, 4, 8, 11, 22]
-                keep_features.append(x)
-
-        global_context = list()
-        for i, f in enumerate(keep_features):
-            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)
-            f_pow = torch.pow(f, 2)
-            f_mean = torch.mean(f_pow)
-            f = torch.div(f, f_mean)
-            global_context.append(f)
-
-        x = torch.cat(global_context, 1)
-        x = self.container(x)
-        logits = torch.mean(x, dim=2)
-
-        return logits
-
-class LPRNetInference:
-    def __init__(self, model_path=None, img_size=[94, 24], lpr_max_len=8, dropout_rate=0.5):
-        """
-        初始化LPRNet推理类
-        Args:
-            model_path: 训练好的模型权重文件路径
-            img_size: 输入图像尺寸 [width, height]
-            lpr_max_len: 车牌最大长度
-            dropout_rate: dropout率
-        """
-        self.img_size = img_size
-        self.lpr_max_len = lpr_max_len
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        
-        # 设置默认模型路径
-        if model_path is None:
-            current_dir = os.path.dirname(os.path.abspath(__file__))
-            model_path = os.path.join(current_dir, 'LPRNet__iteration_74000.pth')
-        
-        # 初始化模型
-        self.model = LPRNet(lpr_max_len=lpr_max_len, phase=False, class_num=len(CHARS), dropout_rate=dropout_rate)
-        
-        # 加载模型权重
-        if model_path and os.path.exists(model_path):
-            print(f"Loading LPRNet model from {model_path}")
-            try:
-                self.model.load_state_dict(torch.load(model_path, map_location=self.device))
-                print("LPRNet模型权重加载成功")
-            except Exception as e:
-                print(f"Warning: 加载模型权重失败: {e}. 使用随机权重.")
-        else:
-            print(f"Warning: 模型文件不存在或未指定: {model_path}. 使用随机权重.")
-        
-        self.model.to(self.device)
-        self.model.eval()
-        
-        print(f"LPRNet模型加载完成，设备: {self.device}")
-        print(f"模型参数数量: {sum(p.numel() for p in self.model.parameters()):,}")
-    
-    def preprocess_image(self, image_array):
-        """
-        预处理图像数组 - 使用与训练时相同的预处理方式
-        Args:
-            image_array: numpy数组格式的图像 (H, W, C)
-        Returns:
-            preprocessed_image: 预处理后的图像tensor
-        """
-        if image_array is None:
-            raise ValueError("Input image is None")
-        
-        # 确保图像是numpy数组
-        if not isinstance(image_array, np.ndarray):
-            raise ValueError("Input must be numpy array")
-        
-        # 检查图像维度
-        if len(image_array.shape) != 3:
-            raise ValueError(f"Expected 3D image array, got {len(image_array.shape)}D")
-        
-        height, width, channels = image_array.shape
-        if channels != 3:
-            raise ValueError(f"Expected 3 channels, got {channels}")
-        
-        # 调整图像尺寸到模型要求的尺寸
-        if height != self.img_size[1] or width != self.img_size[0]:
-            image_array = cv2.resize(image_array, tuple(self.img_size))
-        
-        # 使用与训练时相同的预处理方式
-        image_array = image_array.astype('float32')
-        image_array -= 127.5
-        image_array *= 0.0078125
-        image_array = np.transpose(image_array, (2, 0, 1))  # HWC -> CHW
-        
-        # 转换为tensor并添加batch维度
-        image_tensor = torch.from_numpy(image_array).unsqueeze(0)
-        
-        return image_tensor
-    
-    def decode_prediction(self, logits):
-        """
-        解码模型预测结果 - 使用正确的CTC贪婪解码
-        Args:
-            logits: 模型输出的logits [batch_size, num_classes, sequence_length]
-        Returns:
-            predicted_text: 预测的车牌号码
-        """
-        # 转换为numpy进行处理
-        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
-        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))
-        
-        # CTC解码：去除重复字符和空白字符
-        no_repeat_blank_label = []
-        pre_c = preb_label[0]
-        
-        # 处理第一个字符
-        if pre_c != len(CHARS) - 1:  # 不是空白字符
-            no_repeat_blank_label.append(pre_c)
-        
-        # 处理后续字符
-        for c in preb_label:
-            if (pre_c == c) or (c == len(CHARS) - 1):  # 重复字符或空白字符
-                if c == len(CHARS) - 1:
-                    pre_c = c
-                continue
-            no_repeat_blank_label.append(c)
-            pre_c = c
-        
-        # 转换为字符
-        decoded_chars = [CHARS[idx] for idx in no_repeat_blank_label]
-        return ''.join(decoded_chars)
-    
-    def predict(self, image_array):
-        """
-        预测单张图像的车牌号码
-        Args:
-            image_array: numpy数组格式的图像
-        Returns:
-            prediction: 预测的车牌号码
-            confidence: 预测置信度
-        """
-        try:
-            # 预处理图像
-            image = self.preprocess_image(image_array)
-            if image is None:
-                return None, 0.0
-            
-            image = image.to(self.device)
-            
-            # 模型推理
-            with torch.no_grad():
-                logits = self.model(image)
-                # logits shape: [batch_size, class_num, sequence_length]
-                
-                # 计算置信度（使用softmax后的最大概率平均值）
-                probs = torch.softmax(logits, dim=1)
-                max_probs = torch.max(probs, dim=1)[0]
-                confidence = torch.mean(max_probs).item()
-                
-                # 解码预测结果
-                prediction = self.decode_prediction(logits)
-            
-            return prediction, confidence
-            
-        except Exception as e:
-            print(f"预测图像失败: {e}")
-            return None, 0.0
-
-# 全局变量
-lpr_model = None
-
-def LPRNinitialize_model():
-    """
-    初始化LPRNet模型
-    
-    返回:
-        bool: 初始化是否成功
-    """
-    global lpr_model
-    
-    try:
-        # 模型权重文件路径
-        model_path = os.path.join(os.path.dirname(__file__), 'LPRNet__iteration_74000.pth')
-        
-        # 创建推理对象
-        lpr_model = LPRNetInference(model_path)
-        
-        print("LPRNet模型初始化完成")
-        return True
-        
-    except Exception as e:
-        print(f"LPRNet模型初始化失败: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-def LPRNmodel_predict(image_array):
-    """
-    LPRNet车牌号识别接口函数
-    
-    参数:
-        image_array: numpy数组格式的车牌图像，已经过矫正处理
-    
-    返回:
-        list: 包含最多8个字符的列表，代表车牌号的每个字符
-              例如: ['京', 'A', '1', '2', '3', '4', '5'] (蓝牌7位)
-                   ['京', 'A', 'D', '1', '2', '3', '4', '5'] (绿牌8位)
-    """
-    global lpr_model
-    
-    if lpr_model is None:
-        print("LPRNet模型未初始化，请先调用LPRNinitialize_model()")
-        return ['待', '识', '别', '0', '0', '0', '0', '0']
-    
-    try:
-        # 预测车牌号
-        predicted_text, confidence = lpr_model.predict(image_array)
-        
-        if predicted_text is None:
-            print("LPRNet识别失败")
-            return ['识', '别', '失', '败', '0', '0', '0', '0']
-        
-        print(f"LPRNet识别结果: {predicted_text}, 置信度: {confidence:.3f}")
-        
-        # 将字符串转换为字符列表
-        char_list = list(predicted_text)
-        
-        # 确保返回至少7个字符，最多8个字符
-        if len(char_list) < 7:
-            # 如果识别结果少于7个字符，用'0'补齐到7位
-            char_list.extend(['0'] * (7 - len(char_list)))
-        elif len(char_list) > 8:
-            # 如果识别结果多于8个字符，截取前8个
-            char_list = char_list[:8]
-        
-        # 如果是7位，补齐到8位以保持接口一致性（第8位用空字符或占位符）
-        if len(char_list) == 7:
-            char_list.append('')  # 添加空字符作为第8位占位符
-        
-        return char_list
-        
-    except Exception as e:
-        print(f"LPRNet识别失败: {e}")
-        import traceback
-        traceback.print_exc()
-        return ['识', '别', '失', '败', '0', '0', '0', '0']

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

@@ -1,19 +1,17 @@
 import sys
+import os
 import cv2
 import numpy as np
-from PyQt5.QtWidgets import (
-    QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
-    QLabel, QPushButton, QScrollArea, QFrame, QSizePolicy
-)
+from collections import defaultdict, deque
+from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, \
+    QFileDialog, QFrame, QScrollArea, QComboBox
 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 LPRNinitialize_model
-
+# from LPRNET_part.lpr_interface import LPRNmodel_predict
+# 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 LPRNinitialize_model
@@ -21,6 +19,190 @@ from LPRNET_part.lpr_interface import LPRNinitialize_model
 # from CRNN_part.crnn_interface import LPRNmodel_predict
 # from CRNN_part.crnn_interface import LPRNinitialize_model
 
+class PlateStabilizer:
+    """车牌识别结果稳定器"""
+    
+    def __init__(self, history_size=10, confidence_threshold=0.6, stability_frames=5):
+        self.history_size = history_size  # 历史帧数量
+        self.confidence_threshold = confidence_threshold  # 置信度阈值
+        self.stability_frames = stability_frames  # 稳定帧数要求
+        
+        # 存储每个车牌的历史识别结果
+        self.plate_histories = defaultdict(lambda: deque(maxlen=history_size))
+        # 存储当前稳定的车牌结果
+        self.stable_results = {}
+        # 车牌ID计数器
+        self.plate_id_counter = 0
+        # 车牌位置追踪
+        self.plate_positions = {}
+        
+    def calculate_plate_distance(self, pos1, pos2):
+        """计算两个车牌位置的距离"""
+        if pos1 is None or pos2 is None:
+            return float('inf')
+        
+        # 计算中心点距离
+        center1 = ((pos1[0] + pos1[2]) / 2, (pos1[1] + pos1[3]) / 2)
+        center2 = ((pos2[0] + pos2[2]) / 2, (pos2[1] + pos2[3]) / 2)
+        
+        return np.sqrt((center1[0] - center2[0])**2 + (center1[1] - center2[1])**2)
+    
+    def match_plates_to_history(self, current_detections):
+        """将当前检测结果匹配到历史记录"""
+        matched_plates = {}
+        used_ids = set()
+        
+        for detection in current_detections:
+            bbox = detection.get('bbox', [0, 0, 0, 0])
+            best_match_id = None
+            min_distance = float('inf')
+            
+            # 寻找最佳匹配的历史车牌
+            for plate_id, last_pos in self.plate_positions.items():
+                if plate_id in used_ids:
+                    continue
+                    
+                distance = self.calculate_plate_distance(bbox, last_pos)
+                if distance < min_distance and distance < 100:  # 距离阈值
+                    min_distance = distance
+                    best_match_id = plate_id
+            
+            if best_match_id is not None:
+                matched_plates[best_match_id] = detection
+                used_ids.add(best_match_id)
+                self.plate_positions[best_match_id] = bbox
+            else:
+                # 创建新的车牌ID
+                new_id = f"plate_{self.plate_id_counter}"
+                self.plate_id_counter += 1
+                matched_plates[new_id] = detection
+                self.plate_positions[new_id] = bbox
+        
+        return matched_plates
+    
+    def calculate_confidence(self, plate_text, detection_quality=1.0):
+        """计算识别结果的置信度"""
+        if not plate_text or plate_text == "识别失败":
+            return 0.0
+        
+        # 基础置信度基于文本长度和字符类型
+        base_confidence = 0.5
+        
+        # 长度合理性检查
+        if 7 <= len(plate_text) <= 8:
+            base_confidence += 0.2
+        
+        # 字符类型检查（中文+字母+数字的组合）
+        has_chinese = any('\u4e00' <= char <= '\u9fff' for char in plate_text)
+        has_letter = any(char.isalpha() for char in plate_text)
+        has_digit = any(char.isdigit() for char in plate_text)
+        
+        if has_chinese and has_letter and has_digit:
+            base_confidence += 0.2
+        
+        # 检测质量影响
+        confidence = base_confidence * detection_quality
+        
+        return min(confidence, 1.0)
+    
+    def update_and_get_stable_result(self, current_detections, corrected_images, plate_texts):
+        """更新历史记录并返回稳定的识别结果"""
+        if not current_detections:
+            return []
+        
+        # 匹配当前检测到历史记录
+        matched_plates = self.match_plates_to_history(current_detections)
+        
+        stable_results = []
+        
+        for plate_id, detection in matched_plates.items():
+            # 获取对应的矫正图像和识别文本
+            detection_idx = current_detections.index(detection)
+            corrected_image = corrected_images[detection_idx] if detection_idx < len(corrected_images) else None
+            plate_text = plate_texts[detection_idx] if detection_idx < len(plate_texts) else "识别失败"
+            
+            # 计算置信度
+            confidence = self.calculate_confidence(plate_text)
+            
+            # 添加到历史记录
+            history_entry = {
+                'text': plate_text,
+                'confidence': confidence,
+                'detection': detection,
+                'corrected_image': corrected_image
+            }
+            self.plate_histories[plate_id].append(history_entry)
+            
+            # 计算稳定结果
+            stable_text = self.get_stable_text(plate_id)
+            
+            if stable_text and stable_text != "识别失败":
+                stable_results.append({
+                    'id': plate_id,
+                    'class_name': detection['class_name'],
+                    'corrected_image': corrected_image,
+                    'plate_number': stable_text,
+                    'detection': detection
+                })
+        
+        return stable_results
+    
+    def get_stable_text(self, plate_id):
+        """获取指定车牌的稳定识别结果"""
+        history = self.plate_histories[plate_id]
+        
+        if len(history) < 3:  # 历史记录太少，返回最新结果
+            return history[-1]['text'] if history else "识别失败"
+        
+        # 统计各种识别结果的加权投票
+        text_votes = defaultdict(float)
+        total_confidence = 0
+        
+        for entry in history:
+            text = entry['text']
+            confidence = entry['confidence']
+            
+            if text != "识别失败" and confidence > 0.3:
+                text_votes[text] += confidence
+                total_confidence += confidence
+        
+        if not text_votes:
+            return "识别失败"
+        
+        # 找到得票最高的结果
+        best_text = max(text_votes.items(), key=lambda x: x[1])
+        
+        # 检查是否足够稳定（得票率超过阈值）
+        vote_ratio = best_text[1] / total_confidence if total_confidence > 0 else 0
+        
+        if vote_ratio >= self.confidence_threshold:
+            return best_text[0]
+        else:
+            # 不够稳定，返回最近的高置信度结果
+            recent_high_conf = [entry for entry in list(history)[-5:] 
+                              if entry['confidence'] > 0.5 and entry['text'] != "识别失败"]
+            
+            if recent_high_conf:
+                return recent_high_conf[-1]['text']
+            else:
+                return history[-1]['text']
+    
+    def clear_old_plates(self, current_plate_ids):
+        """清理不再出现的车牌历史记录"""
+        # 移除超过一定时间未更新的车牌
+        plates_to_remove = []
+        for plate_id in self.plate_histories.keys():
+            if plate_id not in current_plate_ids:
+                plates_to_remove.append(plate_id)
+        
+        for plate_id in plates_to_remove:
+            if plate_id in self.plate_histories:
+                del self.plate_histories[plate_id]
+            if plate_id in self.plate_positions:
+                del self.plate_positions[plate_id]
+            if plate_id in self.stable_results:
+                del self.stable_results[plate_id]
+
 class CameraThread(QThread):
     """摄像头线程类"""
     frame_ready = pyqtSignal(np.ndarray)
@@ -56,6 +238,60 @@ 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"""
     
@@ -67,6 +303,7 @@ class LicensePlateWidget(QWidget):
     def init_ui(self, class_name, corrected_image, plate_number):
         layout = QHBoxLayout()
         layout.setContentsMargins(10, 5, 10, 5)
+        layout.setSpacing(8)  # 设置组件间距
         
         # 车牌类型标签
         type_label = QLabel(class_name)
@@ -104,7 +341,6 @@ class LicensePlateWidget(QWidget):
         
         # 矫正后的车牌图像
         image_label = QLabel()
-        image_label.setFixedSize(120, 40)
         image_label.setStyleSheet("border: 1px solid #ddd; background-color: white;")
         
         if corrected_image is not None:
@@ -118,16 +354,44 @@ class LicensePlateWidget(QWidget):
                 q_image = QImage(corrected_image.data, w, h, bytes_per_line, QImage.Format_Grayscale8)
             
             pixmap = QPixmap.fromImage(q_image)
-            scaled_pixmap = pixmap.scaled(120, 40, Qt.KeepAspectRatio, Qt.SmoothTransformation)
+            
+            # 动态计算显示尺寸，保持车牌的宽高比
+            original_width = pixmap.width()
+            original_height = pixmap.height()
+            
+            # 设置最大显示尺寸限制
+            max_width = 150
+            max_height = 60
+            
+            # 计算缩放比例，确保图像完整显示
+            width_ratio = max_width / original_width if original_width > 0 else 1
+            height_ratio = max_height / original_height if original_height > 0 else 1
+            scale_ratio = min(width_ratio, height_ratio, 1.0)  # 不放大，只缩小
+            
+            # 计算实际显示尺寸
+            display_width = int(original_width * scale_ratio)
+            display_height = int(original_height * scale_ratio)
+            
+            # 确保最小显示尺寸
+            display_width = max(display_width, 80)
+            display_height = max(display_height, 25)
+            
+            # 设置标签尺寸并缩放图像
+            image_label.setFixedSize(display_width, display_height)
+            scaled_pixmap = pixmap.scaled(display_width, display_height, Qt.KeepAspectRatio, Qt.SmoothTransformation)
             image_label.setPixmap(scaled_pixmap)
+            image_label.setAlignment(Qt.AlignCenter)
         else:
+            # 当没有图像时，设置固定尺寸显示提示信息
+            image_label.setFixedSize(120, 40)
             image_label.setText("车牌未完全\n进入摄像头")
             image_label.setAlignment(Qt.AlignCenter)
             image_label.setStyleSheet("border: 1px solid #ddd; background-color: #f5f5f5; color: #666;")
         
-        # 车牌号标签
+        # 车牌号标签 - 使用自适应宽度
         number_label = QLabel(plate_number)
-        number_label.setFixedWidth(150)
+        number_label.setMinimumWidth(120)  # 设置最小宽度
+        number_label.setMaximumWidth(200)  # 设置最大宽度
         number_label.setAlignment(Qt.AlignCenter)
         number_label.setStyleSheet(
             "QLabel { "
@@ -139,6 +403,11 @@ class LicensePlateWidget(QWidget):
             "font-weight: bold; "
             "}"
         )
+        # 根据文本长度调整宽度
+        font_metrics = number_label.fontMetrics()
+        text_width = font_metrics.boundingRect(plate_number).width()
+        optimal_width = max(120, min(200, text_width + 20))  # 加20像素的边距
+        number_label.setFixedWidth(optimal_width)
         
         layout.addWidget(type_label)
         layout.addWidget(image_label)
@@ -146,6 +415,9 @@ class LicensePlateWidget(QWidget):
         layout.addStretch()
         
         self.setLayout(layout)
+        # 调整整体组件的最小高度以适应动态图像尺寸
+        min_height = max(60, image_label.height() + 20)  # 至少60像素高度
+        self.setMinimumHeight(min_height)
         self.setStyleSheet(
             "QWidget { "
             "background-color: white; "
@@ -162,15 +434,28 @@ 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
+        
+        # 添加车牌稳定器
+        self.plate_stabilizer = PlateStabilizer(
+            history_size=15,  # 保存15帧历史
+            confidence_threshold=0.7,  # 70%置信度阈值
+            stability_frames=5  # 需要5帧稳定
+        )
         
         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）
-        LPRNinitialize_model()
+        # 初始化默认识别方法（CRNN）的模型
+        self.change_recognition_method(self.current_recognition_method)
 
     
     def init_ui(self):
@@ -197,7 +482,7 @@ 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 +492,26 @@ 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)
@@ -217,7 +520,7 @@ class MainWindow(QMainWindow):
         # 右侧结果显示区域
         right_frame = QFrame()
         right_frame.setFrameStyle(QFrame.StyledPanel)
-        right_frame.setFixedWidth(400)
+        right_frame.setFixedWidth(460)
         right_frame.setStyleSheet("QFrame { background-color: #fafafa; border: 2px solid #ddd; }")
         right_layout = QVBoxLayout(right_frame)
         
@@ -227,6 +530,20 @@ 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", "LightCRNN", "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 +570,17 @@ 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)
@@ -291,14 +616,50 @@ class MainWindow(QMainWindow):
         model_path = os.path.join(os.path.dirname(__file__), "yolopart", "yolo11s-pose42.pt")
         self.detector = LicensePlateYOLO(model_path)
     
+    def reset_processing_state(self):
+        """重置处理状态和清理界面"""
+        # 重置处理标志
+        self.is_processing = False
+        
+        # 清空当前帧和检测结果
+        self.current_frame = None
+        self.detections = []
+        
+        # 重置车牌稳定器
+        self.plate_stabilizer = PlateStabilizer(
+            history_size=15,
+            confidence_threshold=0.7,
+            stability_frames=5
+        )
+        
+        # 清空右侧结果显示
+        self.count_label.setText("识别到的车牌数量: 0")
+        for i in reversed(range(self.results_layout.count())):
+            child = self.results_layout.itemAt(i).widget()
+            if child:
+                child.setParent(None)
+        self.last_plate_results = []
+        
+        print("处理状态已重置，界面已清理")
+    
     def init_camera(self):
         """初始化摄像头线程"""
         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"""
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
         if self.camera_thread.start_camera():
+            self.current_mode = "camera"
             self.start_button.setEnabled(False)
             self.stop_button.setEnabled(True)
             self.camera_label.setText("摄像头启动中...")
@@ -311,68 +672,371 @@ 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()
+        
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
+        # 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()
+        
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
+        # 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"""
+        if frame is None:
+            return
+            
         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"""
-        return self.detector.draw_detections(frame, self.detections)
+        # 获取车牌号列表
+        plate_numbers = []
+        for detection in self.detections:
+            # 矫正车牌图像
+            corrected_image = self.correct_license_plate(detection)
+            # 获取车牌号
+            if corrected_image is not None:
+                plate_number = self.recognize_plate_number(corrected_image, detection['class_name'])
+                plate_numbers.append(plate_number)
+            else:
+                plate_numbers.append("识别失败")
+        
+        return self.detector.draw_detections(frame, self.detections, plate_numbers)
     
     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"""
-        # 更新车牌数量
-        count = len(self.detections)
-        self.count_label.setText(f"识别到的车牌数量: {count}")
+        """更新右侧结果显示（使用稳定化结果）"""
+        print(f"开始更新结果显示，当前模式: {self.current_mode}, 检测数量: {len(self.detections) if self.detections else 0}")
         
+        if not self.detections:
+            self.count_label.setText("识别到的车牌数量: 0")
+            # 清除显示
+            for i in reversed(range(self.results_layout.count())):
+                child = self.results_layout.itemAt(i).widget()
+                if child:
+                    child.setParent(None)
+            self.last_plate_results = []
+            print("无检测结果，已清空界面")
+            return
+        
+        # 获取矫正图像和识别文本
+        corrected_images = []
+        plate_texts = []
+        
+        for detection in self.detections:
+            corrected_image = self.correct_license_plate(detection)
+            corrected_images.append(corrected_image)
+            
+            if corrected_image is not None:
+                plate_text = self.recognize_plate_number(corrected_image, detection['class_name'])
+            else:
+                plate_text = "识别失败"
+            plate_texts.append(plate_text)
+        
+        # 使用稳定器获取稳定的识别结果
+        stable_results = self.plate_stabilizer.update_and_get_stable_result(
+            self.detections, corrected_images, plate_texts
+        )
+        
+        # 更新车牌数量显示
+        self.count_label.setText(f"识别到的车牌数量: {len(stable_results)}")
+        print(f"稳定结果数量: {len(stable_results)}")
+        
+        # 检查结果是否发生变化
+        results_changed = self.check_results_changed(stable_results)
+        print(f"结果是否变化: {results_changed}")
+        
+        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 i, detection in enumerate(self.detections):
-            # 矫正车牌图像
-            corrected_image = self.correct_license_plate(detection)
-            
-            # 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
+            # 添加新的稳定结果
+            for i, result in enumerate(stable_results):
                 plate_widget = LicensePlateWidget(
-                i + 1,
-                detection['class_name'],
-                corrected_image,
-                plate_number
+                    i + 1,  # 显示序号
+                    result['class_name'],
+                    result['corrected_image'],
+                    result['plate_number']
                 )
-            
                 self.results_layout.addWidget(plate_widget)
+                print(f"添加车牌widget: {result['plate_number']}")
+            
+            # 更新存储的结果
+            self.last_plate_results = stable_results
+        
+        # 清理旧的车牌记录
+        current_plate_ids = [result['id'] for result in stable_results]
+        self.plate_stabilizer.clear_old_plates(current_plate_ids)
+        print("结果显示更新完成")
+    
+    def check_results_changed(self, new_results):
+        """检查识别结果是否发生变化"""
+        if len(self.last_plate_results) != len(new_results):
+            return True
+        
+        for i, new_result in enumerate(new_results):
+            if i >= len(self.last_plate_results):
+                return True
+            
+            old_result = self.last_plate_results[i]
+            
+            # 比较关键字段
+            if (old_result.get('class_name') != new_result.get('class_name') or 
+                old_result.get('plate_number') != new_result.get('plate_number')):
+                return True
+        
+        return False
     
     def correct_license_plate(self, detection):
         """矫正车牌图像"""
@@ -395,8 +1059,15 @@ 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 == "LightCRNN":
+                 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 +1091,32 @@ 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 == "LightCRNN":
+             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():

yolopart/detector.py

@@ -2,6 +2,7 @@ import cv2
 import numpy as np
 from ultralytics import YOLO
 import os
+from PIL import Image, ImageDraw, ImageFont
 
 class LicensePlateYOLO:
     """
@@ -113,19 +114,38 @@ class LicensePlateYOLO:
             print(f"检测过程中出错: {e}")
             return []
     
-    def draw_detections(self, image, detections):
+    def draw_detections(self, image, detections, plate_numbers=None):
         """
         在图像上绘制检测结果
         
         参数:
             image: 输入图像
             detections: 检测结果列表
+            plate_numbers: 车牌号列表，与detections对应
         
         返回:
             numpy.ndarray: 绘制了检测结果的图像
         """
         draw_image = image.copy()
         
+        # 转换为PIL图像以支持中文字符
+        pil_image = Image.fromarray(cv2.cvtColor(draw_image, cv2.COLOR_BGR2RGB))
+        draw = ImageDraw.Draw(pil_image)
+        
+        # 尝试加载中文字体
+        try:
+            # Windows系统常见的中文字体
+            font_path = "C:/Windows/Fonts/simhei.ttf"  # 黑体
+            if not os.path.exists(font_path):
+                font_path = "C:/Windows/Fonts/msyh.ttc"  # 微软雅黑
+            if not os.path.exists(font_path):
+                font_path = "C:/Windows/Fonts/simsun.ttc"  # 宋体
+            
+            font = ImageFont.truetype(font_path, 20)
+        except:
+            # 如果无法加载字体，使用默认字体
+            font = ImageFont.load_default()
+        
         for i, detection in enumerate(detections):
             box = detection['box']
             keypoints = detection['keypoints']
@@ -133,6 +153,11 @@ class LicensePlateYOLO:
             confidence = detection['confidence']
             incomplete = detection.get('incomplete', False)
             
+            # 获取对应的车牌号
+            plate_number = ""
+            if plate_numbers and i < len(plate_numbers):
+                plate_number = plate_numbers[i]
+            
             # 绘制边界框
             x1, y1, x2, y2 = map(int, box)
             
@@ -140,30 +165,53 @@ class LicensePlateYOLO:
             if class_name == '绿牌':
                 box_color = (0, 255, 0)  # 绿色
             elif class_name == '蓝牌':
-                box_color = (255, 0, 0)  # 蓝色
+                box_color = (0, 0, 255)  # 蓝色
             else:
                 box_color = (128, 128, 128)  # 灰色
             
-            cv2.rectangle(draw_image, (x1, y1), (x2, y2), box_color, 2)
+            # 在PIL图像上绘制边界框
+            draw.rectangle([(x1, y1), (x2, y2)], outline=box_color, width=2)
             
-            # 绘制标签
+            # 构建标签文本
+            if plate_number:
+                label = f"{class_name} {plate_number} {confidence:.2f}"
+            else:
                 label = f"{class_name} {confidence:.2f}"
+            
             if incomplete:
                 label += " (不完整)"
             
-            # 计算文本大小和位置
-            font = cv2.FONT_HERSHEY_SIMPLEX
-            font_scale = 0.6
-            thickness = 2
-            (text_width, text_height), _ = cv2.getTextSize(label, font, font_scale, thickness)
+            # 计算文本大小
+            bbox = draw.textbbox((0, 0), label, font=font)
+            text_width = bbox[2] - bbox[0]
+            text_height = bbox[3] - bbox[1]
             
             # 绘制文本背景
-            cv2.rectangle(draw_image, (x1, y1 - text_height - 10), 
-                         (x1 + text_width, y1), box_color, -1)
+            draw.rectangle([(x1, y1 - text_height - 10), (x1 + text_width, y1)], 
+                         fill=box_color)
             
             # 绘制文本
-            cv2.putText(draw_image, label, (x1, y1 - 5), 
-                       font, font_scale, (255, 255, 255), thickness)
+            draw.text((x1, y1 - text_height - 5), label, fill=(255, 255, 255), font=font)
+        
+        # 转换回OpenCV格式
+        draw_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+        
+        # 绘制关键点和连线（使用OpenCV）
+        for i, detection in enumerate(detections):
+            box = detection['box']
+            keypoints = detection['keypoints']
+            incomplete = detection.get('incomplete', False)
+            
+            x1, y1, x2, y2 = map(int, box)
+            
+            # 根据车牌类型选择颜色
+            class_name = detection['class_name']
+            if class_name == '绿牌':
+                box_color = (0, 255, 0)  # 绿色
+            elif class_name == '蓝牌':
+                box_color = (0, 0, 255)  # 蓝色
+            else:
+                box_color = (128, 128, 128)  # 灰色
             
             # 绘制关键点和连线
             if len(keypoints) >= 4 and not incomplete: