332 lines
11 KiB
Python
332 lines
11 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image
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import cv2
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from torchvision import transforms
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import os
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# 全局变量
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crnn_model = None
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crnn_decoder = None
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crnn_preprocessor = None
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device = None
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class CRNN(nn.Module):
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"""CRNN车牌识别模型"""
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def __init__(self, img_height=32, num_classes=68, hidden_size=256):
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super(CRNN, self).__init__()
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self.img_height = img_height
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self.num_classes = num_classes
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self.hidden_size = hidden_size
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# CNN特征提取部分 - 7层卷积
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self.cnn = nn.Sequential(
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# 第1层：3->64, 3x3卷积
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nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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# 第2层：64->128, 3x3卷积
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nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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# 第3层：128->256, 3x3卷积
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nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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# 第4层：256->256, 3x3卷积
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nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1)),
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# 第5层：256->512, 3x3卷积
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nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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# 第6层：512->512, 3x3卷积
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nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1)),
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# 第7层：512->512, 2x2卷积
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nn.Conv2d(512, 512, kernel_size=2, stride=1, padding=0),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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)
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# RNN序列建模部分 - 2层双向LSTM
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self.rnn = nn.LSTM(
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input_size=512,
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hidden_size=hidden_size,
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num_layers=2,
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batch_first=True,
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bidirectional=True
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)
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# 全连接分类层
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self.fc = nn.Linear(hidden_size * 2, num_classes)
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def forward(self, x):
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batch_size = x.size(0)
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# CNN特征提取
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conv_out = self.cnn(x)
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# 重塑为RNN输入格式
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batch_size, channels, height, width = conv_out.size()
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conv_out = conv_out.permute(0, 3, 1, 2)
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conv_out = conv_out.contiguous().view(batch_size, width, channels * height)
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# RNN序列建模
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rnn_out, _ = self.rnn(conv_out)
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# 全连接分类
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output = self.fc(rnn_out)
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# 转换为CTC需要的格式：(width, batch_size, num_classes)
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output = output.permute(1, 0, 2)
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return output
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class CTCDecoder:
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"""CTC解码器"""
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def __init__(self):
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# 定义中国车牌字符集（68个字符）
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self.chars = [
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# 空白字符（CTC需要）
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'<BLANK>',
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# 中文省份简称
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'京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
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'苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
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'桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁', '新',
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# 字母 A-Z
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'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
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'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
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# 数字 0-9
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'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'
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]
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self.char_to_idx = {char: idx for idx, char in enumerate(self.chars)}
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self.idx_to_char = {idx: char for idx, char in enumerate(self.chars)}
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self.blank_idx = 0
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def decode_greedy(self, predictions):
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"""贪婪解码"""
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# 获取每个时间步的最大概率索引
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indices = torch.argmax(predictions, dim=1)
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# CTC解码：移除重复字符和空白字符
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decoded_chars = []
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prev_idx = -1
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for idx in indices:
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idx = idx.item()
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if idx != prev_idx and idx != self.blank_idx:
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if idx < len(self.chars):
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decoded_chars.append(self.chars[idx])
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prev_idx = idx
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return ''.join(decoded_chars)
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def decode_with_confidence(self, predictions):
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"""解码并返回置信度信息"""
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# 应用softmax获得概率
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probs = torch.softmax(predictions, dim=1)
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# 贪婪解码
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indices = torch.argmax(probs, dim=1)
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max_probs = torch.max(probs, dim=1)[0]
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# CTC解码
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decoded_chars = []
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char_confidences = []
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prev_idx = -1
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for i, idx in enumerate(indices):
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idx = idx.item()
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confidence = max_probs[i].item()
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if idx != prev_idx and idx != self.blank_idx:
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if idx < len(self.chars):
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decoded_chars.append(self.chars[idx])
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char_confidences.append(confidence)
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prev_idx = idx
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text = ''.join(decoded_chars)
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avg_confidence = np.mean(char_confidences) if char_confidences else 0.0
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return text, avg_confidence, char_confidences
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class LicensePlatePreprocessor:
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"""车牌图像预处理器"""
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def __init__(self, target_height=32, target_width=128):
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self.target_height = target_height
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self.target_width = target_width
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# 定义图像变换
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self.transform = transforms.Compose([
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transforms.Resize((target_height, target_width)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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])
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def preprocess_numpy_array(self, image_array):
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"""预处理numpy数组格式的图像"""
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try:
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# 确保图像是RGB格式
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if len(image_array.shape) == 3 and image_array.shape[2] == 3:
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# 如果是BGR格式，转换为RGB
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if image_array.dtype == np.uint8:
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image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB)
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# 转换为PIL图像
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if image_array.dtype != np.uint8:
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image_array = (image_array * 255).astype(np.uint8)
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image = Image.fromarray(image_array)
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# 应用变换
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tensor = self.transform(image)
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# 添加batch维度
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tensor = tensor.unsqueeze(0)
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return tensor
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except Exception as e:
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print(f"图像预处理失败: {e}")
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return None
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def LPRNinitialize_model():
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"""
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初始化CRNN模型
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返回:
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bool: 初始化是否成功
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"""
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global crnn_model, crnn_decoder, crnn_preprocessor, device
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try:
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# 设置设备
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f"CRNN使用设备: {device}")
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# 初始化组件
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crnn_decoder = CTCDecoder()
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crnn_preprocessor = LicensePlatePreprocessor(target_height=32, target_width=128)
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# 创建模型实例
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crnn_model = CRNN(num_classes=len(crnn_decoder.chars), hidden_size=256)
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# 加载模型权重
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model_path = os.path.join(os.path.dirname(__file__), 'best_model.pth')
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"模型文件不存在: {model_path}")
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print(f"正在加载CRNN模型: {model_path}")
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# 加载检查点
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checkpoint = torch.load(model_path, map_location=device, weights_only=False)
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# 处理不同的模型保存格式
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if isinstance(checkpoint, dict):
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if 'model_state_dict' in checkpoint:
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# 完整检查点格式
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state_dict = checkpoint['model_state_dict']
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print(f"检查点信息:")
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print(f" - 训练轮次: {checkpoint.get('epoch', 'N/A')}")
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print(f" - 最佳验证损失: {checkpoint.get('best_val_loss', 'N/A')}")
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else:
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# 精简模型格式（只包含权重）
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print("加载精简模型（仅权重）")
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state_dict = checkpoint
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else:
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# 直接是状态字典
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state_dict = checkpoint
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# 加载权重
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crnn_model.load_state_dict(state_dict)
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crnn_model.to(device)
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crnn_model.eval()
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print("CRNN模型初始化完成")
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# 统计模型参数
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total_params = sum(p.numel() for p in crnn_model.parameters())
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print(f"CRNN模型参数数量: {total_params:,}")
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return True
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except Exception as e:
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print(f"CRNN模型初始化失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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def LPRNmodel_predict(image_array):
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"""
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CRNN车牌号识别接口函数
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参数:
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image_array: numpy数组格式的车牌图像，已经过矫正处理
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返回:
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list: 包含7个字符的列表，代表车牌号的每个字符
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例如: ['京', 'A', '1', '2', '3', '4', '5']
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"""
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global crnn_model, crnn_decoder, crnn_preprocessor, device
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if crnn_model is None or crnn_decoder is None or crnn_preprocessor is None:
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print("CRNN模型未初始化，请先调用initialize_crnn_model()")
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return ['待', '识', '别', '0', '0', '0', '0']
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try:
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# 预处理图像
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input_tensor = crnn_preprocessor.preprocess_numpy_array(image_array)
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if input_tensor is None:
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raise ValueError("图像预处理失败")
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input_tensor = input_tensor.to(device)
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# 模型推理
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with torch.no_grad():
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outputs = crnn_model(input_tensor) # (seq_len, batch_size, num_classes)
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# 移除batch维度
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outputs = outputs.squeeze(1) # (seq_len, num_classes)
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# CTC解码
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predicted_text, confidence, char_confidences = crnn_decoder.decode_with_confidence(outputs)
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print(f"CRNN识别结果: {predicted_text}, 置信度: {confidence:.3f}")
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# 将字符串转换为字符列表
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char_list = list(predicted_text)
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# 确保返回7个字符（车牌标准长度）
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if len(char_list) < 7:
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# 如果识别结果少于7个字符，用'0'补齐
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char_list.extend(['0'] * (7 - len(char_list)))
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elif len(char_list) > 7:
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# 如果识别结果多于7个字符，截取前7个
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char_list = char_list[:7]
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return char_list
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except Exception as e:
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print(f"CRNN识别失败: {e}")
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import traceback
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traceback.print_exc()
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return ['识', '别', '失', '败', '0', '0', '0']
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