更新接口

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):
@@ -14,6 +26,14 @@ class OCRProcessor:
         results = output[0]["rec_text"]
         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)

lightCRNN_part/lightcrnn_interface.py

@@ -0,0 +1,546 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+import cv2
+from torchvision import transforms
+import os
+import math
+
+# 全局变量
+lightcrnn_model = None
+lightcrnn_decoder = None
+lightcrnn_preprocessor = None
+device = None
+
+class DepthwiseSeparableConv(nn.Module):
+    """深度可分离卷积"""
+    
+    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
+        super(DepthwiseSeparableConv, self).__init__()
+        # 深度卷积
+        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, 
+                                 stride=stride, padding=padding, groups=in_channels, bias=False)
+        # 逐点卷积
+        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU6(inplace=True)
+    
+    def forward(self, x):
+        x = self.depthwise(x)
+        x = self.pointwise(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+class ChannelAttention(nn.Module):
+    """通道注意力机制"""
+    
+    def __init__(self, in_channels, reduction=16):
+        super(ChannelAttention, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.max_pool = nn.AdaptiveMaxPool2d(1)
+        
+        self.fc = nn.Sequential(
+            nn.Conv2d(in_channels, in_channels // reduction, 1, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels // reduction, in_channels, 1, bias=False)
+        )
+        self.sigmoid = nn.Sigmoid()
+    
+    def forward(self, x):
+        avg_out = self.fc(self.avg_pool(x))
+        max_out = self.fc(self.max_pool(x))
+        out = avg_out + max_out
+        return x * self.sigmoid(out)
+
+class InvertedResidual(nn.Module):
+    """MobileNetV2的倒残差块"""
+    
+    def __init__(self, in_channels, out_channels, stride=1, expand_ratio=6):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        self.use_residual = stride == 1 and in_channels == out_channels
+        
+        hidden_dim = int(round(in_channels * expand_ratio))
+        
+        layers = []
+        if expand_ratio != 1:
+            # 扩展层
+            layers.extend([
+                nn.Conv2d(in_channels, hidden_dim, 1, bias=False),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True)
+            ])
+        
+        # 深度卷积
+        layers.extend([
+            nn.Conv2d(hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim, bias=False),
+            nn.BatchNorm2d(hidden_dim),
+            nn.ReLU6(inplace=True),
+            # 线性瓶颈
+            nn.Conv2d(hidden_dim, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels)
+        ])
+        
+        self.conv = nn.Sequential(*layers)
+    
+    def forward(self, x):
+        if self.use_residual:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+class LightweightCNN(nn.Module):
+    """增强版轻量化CNN特征提取器"""
+    
+    def __init__(self, num_channels=3):
+        super(LightweightCNN, self).__init__()
+        
+        # 初始卷积层 - 适当增加通道数
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(num_channels, 48, kernel_size=3, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(48),
+            nn.ReLU6(inplace=True)
+        )
+        
+        # 增强版MobileNet风格的特征提取
+        self.features = nn.Sequential(
+            # 第一组：48 -> 32
+            InvertedResidual(48, 32, stride=1, expand_ratio=2),
+            InvertedResidual(32, 32, stride=1, expand_ratio=2),  # 增加一层
+            nn.MaxPool2d(kernel_size=2, stride=2),  # 32x128 -> 16x64
+            
+            # 第二组：32 -> 48
+            InvertedResidual(32, 48, stride=1, expand_ratio=4),
+            InvertedResidual(48, 48, stride=1, expand_ratio=4),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # 16x64 -> 8x32
+            
+            # 第三组：48 -> 64
+            InvertedResidual(48, 64, stride=1, expand_ratio=4),
+            InvertedResidual(64, 64, stride=1, expand_ratio=4),
+            
+            # 第四组：64 -> 96
+            InvertedResidual(64, 96, stride=1, expand_ratio=4),
+            InvertedResidual(96, 96, stride=1, expand_ratio=4),
+            nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1)),  # 8x32 -> 4x32
+            
+            # 第五组：96 -> 128
+            InvertedResidual(96, 128, stride=1, expand_ratio=4),
+            InvertedResidual(128, 128, stride=1, expand_ratio=4),
+            nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1)),  # 4x32 -> 2x32
+            
+            # 最后的卷积层 - 增加通道数
+            nn.Conv2d(128, 160, kernel_size=2, stride=1, padding=0, bias=False),  # 2x32 -> 1x31
+            nn.BatchNorm2d(160),
+            nn.ReLU6(inplace=True)
+        )
+        
+        # 通道注意力
+        self.channel_attention = ChannelAttention(160)
+    
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.features(x)
+        x = self.channel_attention(x)
+        return x
+
+class LightweightGRU(nn.Module):
+    """增强版轻量化GRU层"""
+    
+    def __init__(self, input_size, hidden_size, num_layers=2):  # 默认增加到2层
+        super(LightweightGRU, self).__init__()
+        self.gru = nn.GRU(input_size, hidden_size, num_layers=num_layers, 
+                         bidirectional=True, batch_first=True, dropout=0.2 if num_layers > 1 else 0)
+        # 增加一个额外的线性层
+        self.linear1 = nn.Linear(hidden_size * 2, hidden_size * 2)
+        self.linear2 = nn.Linear(hidden_size * 2, hidden_size)
+        self.dropout = nn.Dropout(0.2)  # 增加dropout率
+        self.norm = nn.LayerNorm(hidden_size)  # 添加层归一化
+    
+    def forward(self, x):
+        gru_out, _ = self.gru(x)
+        output = self.linear1(gru_out)
+        output = F.relu(output)  # 添加激活函数
+        output = self.dropout(output)
+        output = self.linear2(output)
+        output = self.norm(output)  # 应用层归一化
+        output = self.dropout(output)
+        return output
+
+class LightweightCRNN(nn.Module):
+    """增强版轻量化CRNN模型"""
+    
+    def __init__(self, img_height, num_classes, num_channels=3, hidden_size=160):  # 调整隐藏层大小
+        super(LightweightCRNN, self).__init__()
+        
+        self.img_height = img_height
+        self.num_classes = num_classes
+        self.hidden_size = hidden_size
+        
+        # 增强版轻量化CNN特征提取器
+        self.cnn = LightweightCNN(num_channels)
+        
+        # 增强版轻量化RNN序列建模器
+        self.rnn = LightweightGRU(160, hidden_size, num_layers=2)  # 使用更大的输入尺寸和2层GRU
+        
+        # 输出层 - 添加额外的全连接层
+        self.fc = nn.Linear(hidden_size, hidden_size // 2)
+        self.dropout = nn.Dropout(0.2)
+        self.classifier = nn.Linear(hidden_size // 2, num_classes)
+        
+        # 初始化权重
+        self._initialize_weights()
+    
+    def _initialize_weights(self):
+        """初始化模型权重"""
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+    
+    def forward(self, input):
+        """
+        input: [batch_size, channels, height, width]
+        output: [seq_len, batch_size, num_classes]
+        """
+        # CNN特征提取
+        conv_features = self.cnn(input)  # [batch_size, 160, 1, seq_len]
+        
+        # 重塑为RNN输入格式
+        batch_size, channels, height, width = conv_features.size()
+        assert height == 1, f"Height should be 1, got {height}"
+        
+        # [batch_size, 160, 1, seq_len] -> [batch_size, seq_len, 160]
+        conv_features = conv_features.squeeze(2)  # [batch_size, 160, seq_len]
+        conv_features = conv_features.permute(0, 2, 1)  # [batch_size, seq_len, 160]
+        
+        # RNN序列建模
+        rnn_output = self.rnn(conv_features)  # [batch_size, seq_len, hidden_size]
+        
+        # 全连接层处理
+        fc_output = self.fc(rnn_output)  # [batch_size, seq_len, hidden_size//2]
+        fc_output = F.relu(fc_output)
+        fc_output = self.dropout(fc_output)
+        
+        # 分类
+        output = self.classifier(fc_output)  # [batch_size, seq_len, num_classes]
+        
+        # 转换为CTC期望的格式: [seq_len, batch_size, num_classes]
+        output = output.permute(1, 0, 2)
+        
+        return output
+
+class LightCTCDecoder:
+    """轻量化CTC解码器"""
+    def __init__(self):
+        # 中国车牌字符集
+        # 省份简称
+        provinces = ['京', '津', '沪', '渝', '冀', '豫', '云', '辽', '黑', '湘', '皖', '鲁',
+                    '新', '苏', '浙', '赣', '鄂', '桂', '甘', '晋', '蒙', '陕', '吉', '闽',
+                    '贵', '粤', '青', '藏', '川', '宁', '琼']
+        
+        # 字母（包含I和O）
+        letters = ['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
+        digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+        
+        # 组合所有字符
+        self.character = provinces + letters + digits
+        
+        # 添加空白字符用于CTC
+        self.character = ['[blank]'] + self.character
+        
+        # 创建字符到索引的映射
+        self.dict = {char: i for i, char in enumerate(self.character)}
+        self.dict_reverse = {i: char for i, char in enumerate(self.character)}
+        
+        self.num_classes = len(self.character)
+        self.blank_idx = 0
+    
+    def decode_greedy(self, predictions):
+        """贪婪解码"""
+        # 获取每个时间步的最大概率索引
+        indices = torch.argmax(predictions, dim=1)
+        
+        # CTC解码：移除重复字符和空白字符
+        decoded_chars = []
+        prev_idx = -1
+        
+        for idx in indices:
+            idx = idx.item()
+            if idx != prev_idx and idx != self.blank_idx:
+                if idx < len(self.character):
+                    decoded_chars.append(self.character[idx])
+            prev_idx = idx
+        
+        return ''.join(decoded_chars)
+    
+    def decode_with_confidence(self, predictions):
+        """解码并返回置信度信息"""
+        # 应用softmax获得概率
+        probs = torch.softmax(predictions, dim=1)
+        
+        # 贪婪解码
+        indices = torch.argmax(probs, dim=1)
+        max_probs = torch.max(probs, dim=1)[0]
+        
+        # CTC解码
+        decoded_chars = []
+        char_confidences = []
+        prev_idx = -1
+        
+        for i, idx in enumerate(indices):
+            idx = idx.item()
+            confidence = max_probs[i].item()
+            
+            if idx != prev_idx and idx != self.blank_idx:
+                if idx < len(self.character):
+                    decoded_chars.append(self.character[idx])
+                    char_confidences.append(confidence)
+            prev_idx = idx
+        
+        text = ''.join(decoded_chars)
+        avg_confidence = np.mean(char_confidences) if char_confidences else 0.0
+        
+        return text, avg_confidence, char_confidences
+
+class LightLicensePlatePreprocessor:
+    """轻量化车牌图像预处理器"""
+    def __init__(self, target_height=32, target_width=128):
+        self.target_height = target_height
+        self.target_width = target_width
+        
+        # 定义图像变换
+        self.transform = transforms.Compose([
+            transforms.Resize((target_height, target_width)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                               std=[0.229, 0.224, 0.225])
+        ])
+    
+    def preprocess_numpy_array(self, image_array):
+        """预处理numpy数组格式的图像"""
+        try:
+            # 确保图像是RGB格式
+            if len(image_array.shape) == 3 and image_array.shape[2] == 3:
+                # 如果是BGR格式，转换为RGB
+                if image_array.dtype == np.uint8:
+                    image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB)
+            
+            # 转换为PIL图像
+            if image_array.dtype != np.uint8:
+                image_array = (image_array * 255).astype(np.uint8)
+            
+            image = Image.fromarray(image_array)
+            
+            # 应用变换
+            tensor = self.transform(image)
+            
+            # 添加batch维度
+            tensor = tensor.unsqueeze(0)
+            
+            return tensor
+            
+        except Exception as e:
+            print(f"图像预处理失败: {e}")
+            return None
+
+def LPRNinitialize_model():
+    """
+    初始化轻量化CRNN模型
+    
+    返回:
+        bool: 初始化是否成功
+    """
+    global lightcrnn_model, lightcrnn_decoder, lightcrnn_preprocessor, device
+    
+    try:
+        # 设置设备
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        print(f"LightCRNN使用设备: {device}")
+        
+        # 初始化组件
+        lightcrnn_decoder = LightCTCDecoder()
+        lightcrnn_preprocessor = LightLicensePlatePreprocessor(target_height=32, target_width=128)
+        
+        # 创建模型实例
+        lightcrnn_model = LightweightCRNN(
+            img_height=32, 
+            num_classes=lightcrnn_decoder.num_classes, 
+            hidden_size=160
+        )
+        
+        # 加载模型权重
+        model_path = os.path.join(os.path.dirname(__file__), 'best_model.pth')
+        
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"模型文件不存在: {model_path}")
+        
+        print(f"正在加载LightCRNN模型: {model_path}")
+        
+        # 加载检查点，处理可能的模块依赖问题
+        try:
+            checkpoint = torch.load(model_path, map_location=device, weights_only=False)
+        except (ModuleNotFoundError, AttributeError) as e:
+            if 'config' in str(e) or 'Config' in str(e):
+                print("检测到模型文件包含config依赖，尝试使用weights_only模式加载...")
+                try:
+                    # 尝试使用weights_only=True来避免pickle问题
+                    checkpoint = torch.load(model_path, map_location=device, weights_only=True)
+                except Exception:
+                    # 如果还是失败，创建一个更完整的mock config
+                    import sys
+                    import types
+                    
+                    # 创建mock config模块
+                    mock_config = types.ModuleType('config')
+                    
+                    # 添加可能需要的Config类
+                    class Config:
+                        def __init__(self):
+                            pass
+                    
+                    mock_config.Config = Config
+                    sys.modules['config'] = mock_config
+                    
+                    try:
+                        checkpoint = torch.load(model_path, map_location=device, weights_only=False)
+                    finally:
+                        # 清理临时模块
+                        if 'config' in sys.modules:
+                            del sys.modules['config']
+            else:
+                raise e
+        
+        # 处理不同的模型保存格式
+        if isinstance(checkpoint, dict):
+            if 'model_state_dict' in checkpoint:
+                # 完整检查点格式
+                state_dict = checkpoint['model_state_dict']
+                print(f"检查点信息:")
+                print(f"  - 训练轮次: {checkpoint.get('epoch', 'N/A')}")
+                print(f"  - 最佳验证损失: {checkpoint.get('best_val_loss', 'N/A')}")
+            else:
+                # 精简模型格式（只包含权重）
+                print("加载精简模型（仅权重）")
+                state_dict = checkpoint
+        else:
+            # 直接是状态字典
+            state_dict = checkpoint
+        
+        # 加载权重
+        lightcrnn_model.load_state_dict(state_dict)
+        lightcrnn_model.to(device)
+        lightcrnn_model.eval()
+        
+        print("LightCRNN模型初始化完成")
+        
+        # 统计模型参数
+        total_params = sum(p.numel() for p in lightcrnn_model.parameters())
+        print(f"LightCRNN模型参数数量: {total_params:,}")
+        
+        return True
+        
+    except Exception as e:
+        print(f"LightCRNN模型初始化失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def LPRNmodel_predict(image_array):
+    """
+    轻量化CRNN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+占位符)
+                   ['京', 'A', 'D', '1', '2', '3', '4', '5'] (绿牌8位)
+    """
+    global lightcrnn_model, lightcrnn_decoder, lightcrnn_preprocessor, device
+    
+    if lightcrnn_model is None or lightcrnn_decoder is None or lightcrnn_preprocessor is None:
+        print("LightCRNN模型未初始化，请先调用LPRNinitialize_model()")
+        return ['待', '识', '别', '0', '0', '0', '0', '0']
+    
+    try:
+        # 预处理图像
+        input_tensor = lightcrnn_preprocessor.preprocess_numpy_array(image_array)
+        if input_tensor is None:
+            raise ValueError("图像预处理失败")
+        
+        input_tensor = input_tensor.to(device)
+        
+        # 模型推理
+        with torch.no_grad():
+            outputs = lightcrnn_model(input_tensor)  # (seq_len, batch_size, num_classes)
+            
+            # 移除batch维度
+            outputs = outputs.squeeze(1)  # (seq_len, num_classes)
+            
+            # CTC解码
+            predicted_text, confidence, char_confidences = lightcrnn_decoder.decode_with_confidence(outputs)
+            
+            print(f"LightCRNN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"LightCRNN识别失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return ['识', '别', '失', '败', '0', '0', '0', '0']
+
+def create_lightweight_model(model_type='lightweight_crnn', img_height=32, num_classes=66, hidden_size=160):
+    """创建增强版轻量化模型"""
+    if model_type == 'lightweight_crnn':
+        return LightweightCRNN(img_height, num_classes, hidden_size=hidden_size)
+    else:
+        raise ValueError(f"Unknown lightweight model type: {model_type}")
+
+if __name__ == "__main__":
+    # 测试轻量化模型
+    print("测试LightCRNN模型...")
+    
+    # 初始化模型
+    success = LPRNinitialize_model()
+    if success:
+        print("模型初始化成功")
+        
+        # 创建测试输入
+        test_input = np.random.randint(0, 255, (32, 128, 3), dtype=np.uint8)
+        
+        # 测试预测
+        result = LPRNmodel_predict(test_input)
+        print(f"测试预测结果: {result}")
+    else:
+        print("模型初始化失败")

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)
+            
+            # 构建标签文本
+            if plate_number:
+                label = f"{class_name} {plate_number} {confidence:.2f}"
+            else:
+                label = f"{class_name} {confidence:.2f}"
             
-            # 绘制标签
-            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: