更新CRNN,lightCRNN模型，修正pyqt的问号，增加识别框上方的车牌号，更新模型选择列表

LPRNET_part/lpr_interface.py

@@ -1,372 +0,0 @@
-# 导入必要的库
-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）
-# 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',
-         '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', '-']
-
-# 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 - 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):
-        """
-        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
-        # 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
-        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.")
-        
-        # 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()
-        
-        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
-        # 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
-        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，用于存储模型实例
-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:
-        # 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)
-        
-        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']

main.py

@@ -308,7 +308,7 @@ class MainWindow(QMainWindow):
         method_label.setFont(QFont("Arial", 10))
         
         self.method_combo = QComboBox()
-        self.method_combo.addItems(["CRNN", "LPRNET", "OCR"])
+        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)
         
@@ -578,7 +578,19 @@ class MainWindow(QMainWindow):
     
     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"""
@@ -760,7 +772,7 @@ class MainWindow(QMainWindow):
              # 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":
+             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
@@ -798,7 +810,7 @@ class MainWindow(QMainWindow):
          if method == "CRNN":
              from CRNN_part.crnn_interface import LPRNinitialize_model
              LPRNinitialize_model()
-         elif method == "LPRNET":
+         elif method == "LightCRNN":
              from lightCRNN_part.lightcrnn_interface import LPRNinitialize_model
              LPRNinitialize_model()
          elif method == "OCR":

test_lpr_real_images.py

@@ -1,100 +0,0 @@
-#!/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()

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: