LPR

LPRNET_part/lpr_interface.py

@@ -0,0 +1,284 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import cv2
+from torch.autograd import Variable
+import os
+
+
+# 字符集定义
+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)}
+
+# 全局变量
+lprnet_model = None
+device = None
+
+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),   # 12
+            nn.ReLU(),
+            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
+
+def build_lprnet(lpr_max_len=8, phase=False, class_num=66, dropout_rate=0.5):
+    """构建LPRNet模型"""
+    Net = LPRNet(lpr_max_len, phase, class_num, dropout_rate)
+    
+    if phase == "train":
+        return Net.train()
+    else:
+        return Net.eval()
+
+def preprocess_image(image_array, img_size=(94, 24)):
+    """图像预处理"""
+    # 确保输入是numpy数组
+    if not isinstance(image_array, np.ndarray):
+        raise ValueError("输入必须是numpy数组")
+    
+    # 调整图像尺寸
+    height, width = image_array.shape[:2]
+    if height != img_size[1] or width != img_size[0]:
+        image_array = cv2.resize(image_array, img_size)
+    
+    # 归一化到[0,1]
+    image_array = image_array.astype(np.float32) / 255.0
+    
+    # 转换为CHW格式
+    if len(image_array.shape) == 3:
+        image_array = np.transpose(image_array, (2, 0, 1))
+    
+    # 添加batch维度
+    image_array = np.expand_dims(image_array, axis=0)
+    
+    return image_array
+
+def greedy_decode(prebs):
+    """贪婪解码"""
+    preb_labels = list()
+    for i in range(prebs.shape[0]):
+        preb = prebs[i, :, :]
+        preb_label = list()
+        for j in range(preb.shape[1]):
+            preb_label.append(np.argmax(preb[:, j], axis=0))
+        
+        no_repeat_blank_label = list()
+        pre_c = preb_label[0]
+        if pre_c != len(CHARS) - 1:
+            no_repeat_blank_label.append(pre_c)
+        
+        for c in preb_label:  # 去除重复标签和空白标签
+            if (pre_c == c) or (c == len(CHARS) - 1):
+                if c == len(CHARS) - 1:
+                    pre_c = c
+                continue
+            no_repeat_blank_label.append(c)
+            pre_c = c
+        
+        preb_labels.append(no_repeat_blank_label)
+    
+    return preb_labels
+
+def LPRNinitialize_model(model_path=None):
+    """初始化LPRNet模型"""
+    global lprnet_model, device
+    
+    try:
+        # 设置设备
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        print(f"使用设备: {device}")
+        
+        # 构建模型
+        lprnet_model = build_lprnet(
+            lpr_max_len=8, 
+            phase=False, 
+            class_num=len(CHARS), 
+            dropout_rate=0.5
+        )
+        
+        # 加载预训练权重
+        if model_path is None:
+            model_path = os.path.join(os.path.dirname(__file__), "Final_LPRNet_model.pth")
+        
+        if os.path.exists(model_path):
+            checkpoint = torch.load(model_path, map_location=device)
+            lprnet_model.load_state_dict(checkpoint)
+            print(f"成功加载预训练模型: {model_path}")
+        else:
+            print(f"警告: 未找到预训练模型文件 {model_path}，使用随机初始化权重")
+        
+        lprnet_model.to(device)
+        lprnet_model.eval()
+        
+        print("LPRNet模型初始化完成")
+        
+        # 统计模型参数
+        total_params = sum(p.numel() for p in lprnet_model.parameters())
+        print(f"LPRNet模型参数数量: {total_params:,}")
+        
+        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7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']
+    """
+    global lprnet_model, device
+    
+    if lprnet_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']
+    
+    try:
+        # 预处理图像
+        processed_image = preprocess_image(image_array)
+        
+        # 转换为tensor
+        input_tensor = torch.from_numpy(processed_image).float()
+        input_tensor = input_tensor.to(device)
+        
+        # 模型推理
+        with torch.no_grad():
+            prebs = lprnet_model(input_tensor)
+            prebs = prebs.cpu().detach().numpy()
+        
+        # 贪婪解码
+        preb_labels = greedy_decode(prebs)
+        
+        if len(preb_labels) > 0 and len(preb_labels[0]) > 0:
+            # 将索引转换为字符
+            predicted_chars = [CHARS[idx] for idx in preb_labels[0] if idx < len(CHARS)]
+            
+            print(f"LPRNet识别结果: {''.join(predicted_chars)}")
+            
+            # 确保返回7个字符（车牌标准长度）
+            if len(predicted_chars) < 7:
+                # 如果识别结果少于7个字符，用'0'补齐
+                predicted_chars.extend(['0'] * (7 - len(predicted_chars)))
+            elif len(predicted_chars) > 7:
+                # 如果识别结果多于7个字符，截取前7个
+                predicted_chars = predicted_chars[:7]
+            
+            return predicted_chars
+        else:
+            print("LPRNet识别结果为空")
+            return ['识', '别', '为', '空', '0', '0', '0']
+            
+    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']
+
+# 为了保持与其他模块的一致性，提供一个处理器类
+class LPRProcessor:
+    def __init__(self):
+        self.initialized = False
+    
+    def initialize(self, model_path=None):
+        """初始化模型"""
+        self.initialized = LPRNinitialize_model(model_path)
+        return self.initialized
+    
+    def predict(self, image_array):
+        """预测接口"""
+        if not self.initialized:
+            print("模型未初始化")
+            return ['未', '初', '始', '化', '0', '0', '0']
+        return LPRNmodel_predict(image_array)
+
+# 创建全局处理器实例
+_processor = LPRProcessor()
+
+def get_lpr_processor():
+    """获取LPR处理器实例"""
+    return _processor

README.md

@@ -14,8 +14,12 @@ License_plate_recognition/
 │   └── yolo11s-pose42.pt     # YOLO pose模型文件
 ├── OCR_part/                 # OCR识别模块
 │   └── ocr_interface.py      # OCR接口（占位）
-└── CRNN_part/                # CRNN识别模块
+├── CRNN_part/                # CRNN识别模块
-    └── crnn_interface.py     # CRNN
+│   └── crnn_interface.py     # CRNN接口
+└── LPRNET_part/              # LPRNet识别模块
+    ├── lpr_interface.py      # LPRNet接口
+    ├── Final_LPRNet_model.pth # 预训练模型文件
+    └── will_delete/          # 参考资料（可删除）
 ```
 
 ## 功能特性
@@ -44,8 +48,10 @@ License_plate_recognition/
 
 ### 5. 模块化设计
 - yolopart：负责车牌定位和矫正
-- OCR_part/CRNN_part：负责车牌号识别（接口已预留）
+- OCR_part：基于PaddleOCR的车牌号识别模块
-- 各模块独立，便于维护和扩展
+- CRNN_part：基于CRNN网络的车牌号识别模块
+- LPRNET_part：基于LPRNet网络的车牌号识别模块
+- 各模块独立，便于维护和扩展，可通过修改main.py中的导入语句切换识别模块
 
 ## 安装和使用
 

main.py

@@ -9,9 +9,15 @@ 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
-from OCR_part.ocr_interface import LPRNmodel_predict
+
-from OCR_part.ocr_interface import LPRNinitialize_model
+#选择使用哪个模块
-# 使用CRNN进行车牌字符识别（可选）同时也要修改第395，396行
+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
+# 使用CRNN
 #from CRNN_part.crnn_interface import LPRNmodel_predict
 #from CRNN_part.crnn_interface import LPRNinitialize_model