更新接口
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@ -282,14 +282,15 @@ def LPRNmodel_predict(image_array):
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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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list: 包含最多8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', ''] (蓝牌7位+占位符)
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['京', 'A', 'D', '1', '2', '3', '4', '5'] (绿牌8位)
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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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return ['待', '识', '别', '0', '0', '0', '0', '0']
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try:
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# 预处理图像
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@ -314,13 +315,17 @@ def LPRNmodel_predict(image_array):
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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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# 确保返回至少7个字符，最多8个字符
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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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# 如果识别结果少于7个字符，用'0'补齐到7位
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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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elif len(char_list) > 8:
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# 如果识别结果多于8个字符，截取前8个
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char_list = char_list[:8]
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# 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）
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if len(char_list) == 7:
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char_list.append('') # 添加空字符作为第8位占位符
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return char_list
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@ -328,4 +333,4 @@ def LPRNmodel_predict(image_array):
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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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return ['识', '别', '失', '败', '0', '0', '0', '0']
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@ -1,28 +1,27 @@
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import torch
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import torch.nn as nn
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import numpy as np
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import cv2
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from torch.autograd import Variable
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import numpy as np
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import os
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import sys
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from torch.autograd import Variable
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from PIL import Image
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# 添加父目录到路径，以便导入模型和数据加载器
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# 字符集定义
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# LPRNet字符集定义（与训练时保持一致）
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CHARS = ['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',
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'新',
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'桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁', '新',
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'0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
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'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
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'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
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'W', 'X', 'Y', 'Z', 'I', 'O', '-'
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]
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'W', 'X', 'Y', 'Z', 'I', 'O', '-']
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CHARS_DICT = {char: i for i, char in enumerate(CHARS)}
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# 全局变量
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lprnet_model = None
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device = None
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# 简化的LPRNet模型定义
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class small_basic_block(nn.Module):
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def __init__(self, ch_in, ch_out):
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super(small_basic_block, self).__init__()
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@ -58,8 +57,8 @@ class LPRNet(nn.Module):
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nn.BatchNorm2d(num_features=256),
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nn.ReLU(), # 10
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small_basic_block(ch_in=256, ch_out=256), # *** 11 ***
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nn.BatchNorm2d(num_features=256), # 12
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nn.ReLU(),
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nn.BatchNorm2d(num_features=256),
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nn.ReLU(), # 13
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)), # 14
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nn.Dropout(dropout_rate),
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nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1), # 16
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@ -68,7 +67,7 @@ class LPRNet(nn.Module):
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nn.Dropout(dropout_rate),
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nn.Conv2d(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1), # 20
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nn.BatchNorm2d(num_features=class_num),
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nn.ReLU(), # *** 22 ***
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nn.ReLU(), # 22
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)
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self.container = nn.Sequential(
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nn.Conv2d(in_channels=448+self.class_num, out_channels=self.class_num, kernel_size=(1,1), stride=(1,1)),
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@ -98,101 +97,177 @@ class LPRNet(nn.Module):
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return logits
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def build_lprnet(lpr_max_len=8, phase=False, class_num=66, dropout_rate=0.5):
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"""构建LPRNet模型"""
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Net = LPRNet(lpr_max_len, phase, class_num, dropout_rate)
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class LPRNetInference:
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def __init__(self, model_path=None, img_size=[94, 24], lpr_max_len=8, dropout_rate=0.5):
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"""
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初始化LPRNet推理类
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Args:
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model_path: 训练好的模型权重文件路径
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img_size: 输入图像尺寸 [width, height]
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lpr_max_len: 车牌最大长度
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dropout_rate: dropout率
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"""
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self.img_size = img_size
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self.lpr_max_len = lpr_max_len
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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if phase == "train":
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return Net.train()
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# 设置默认模型路径
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if model_path is None:
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'LPRNet__iteration_74000.pth')
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# 初始化模型
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self.model = LPRNet(lpr_max_len=lpr_max_len, phase=False, class_num=len(CHARS), dropout_rate=dropout_rate)
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# 加载模型权重
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if model_path and os.path.exists(model_path):
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print(f"Loading LPRNet model from {model_path}")
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try:
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self.model.load_state_dict(torch.load(model_path, map_location=self.device))
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print("LPRNet模型权重加载成功")
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except Exception as e:
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print(f"Warning: 加载模型权重失败: {e}. 使用随机权重.")
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else:
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return Net.eval()
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print(f"Warning: 模型文件不存在或未指定: {model_path}. 使用随机权重.")
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def preprocess_image(image_array, img_size=(94, 24)):
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"""图像预处理"""
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# 确保输入是numpy数组
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self.model.to(self.device)
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self.model.eval()
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print(f"LPRNet模型加载完成，设备: {self.device}")
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print(f"模型参数数量: {sum(p.numel() for p in self.model.parameters()):,}")
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def preprocess_image(self, image_array):
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"""
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预处理图像数组 - 使用与训练时相同的预处理方式
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Args:
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image_array: numpy数组格式的图像 (H, W, C)
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Returns:
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preprocessed_image: 预处理后的图像tensor
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"""
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if image_array is None:
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raise ValueError("Input image is None")
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# 确保图像是numpy数组
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if not isinstance(image_array, np.ndarray):
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raise ValueError("输入必须是numpy数组")
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raise ValueError("Input must be numpy array")
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# 调整图像尺寸
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height, width = image_array.shape[:2]
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if height != img_size[1] or width != img_size[0]:
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image_array = cv2.resize(image_array, img_size)
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# 检查图像维度
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if len(image_array.shape) != 3:
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raise ValueError(f"Expected 3D image array, got {len(image_array.shape)}D")
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# 归一化到[0,1]
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image_array = image_array.astype(np.float32) / 255.0
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height, width, channels = image_array.shape
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if channels != 3:
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raise ValueError(f"Expected 3 channels, got {channels}")
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# 转换为CHW格式
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if len(image_array.shape) == 3:
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image_array = np.transpose(image_array, (2, 0, 1))
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# 调整图像尺寸到模型要求的尺寸
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if height != self.img_size[1] or width != self.img_size[0]:
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image_array = cv2.resize(image_array, tuple(self.img_size))
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# 添加batch维度
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image_array = np.expand_dims(image_array, axis=0)
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# 使用与训练时相同的预处理方式
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image_array = image_array.astype('float32')
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image_array -= 127.5
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image_array *= 0.0078125
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image_array = np.transpose(image_array, (2, 0, 1)) # HWC -> CHW
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return image_array
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# 转换为tensor并添加batch维度
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image_tensor = torch.from_numpy(image_array).unsqueeze(0)
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def greedy_decode(prebs):
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"""贪婪解码"""
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preb_labels = list()
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for i in range(prebs.shape[0]):
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preb = prebs[i, :, :]
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preb_label = list()
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for j in range(preb.shape[1]):
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return image_tensor
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def decode_prediction(self, logits):
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"""
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解码模型预测结果 - 使用正确的CTC贪婪解码
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Args:
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logits: 模型输出的logits [batch_size, num_classes, sequence_length]
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Returns:
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predicted_text: 预测的车牌号码
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"""
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# 转换为numpy进行处理
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prebs = logits.cpu().detach().numpy()
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preb = prebs[0, :, :] # 取第一个batch [num_classes, sequence_length]
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# 贪婪解码：对每个时间步选择最大概率的字符
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preb_label = []
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for j in range(preb.shape[1]): # 遍历每个时间步
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preb_label.append(np.argmax(preb[:, j], axis=0))
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no_repeat_blank_label = list()
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# CTC解码：去除重复字符和空白字符
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no_repeat_blank_label = []
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pre_c = preb_label[0]
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if pre_c != len(CHARS) - 1:
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# 处理第一个字符
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if pre_c != len(CHARS) - 1: # 不是空白字符
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no_repeat_blank_label.append(pre_c)
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for c in preb_label: # 去除重复标签和空白标签
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if (pre_c == c) or (c == len(CHARS) - 1):
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# 处理后续字符
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for c in preb_label:
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if (pre_c == c) or (c == len(CHARS) - 1): # 重复字符或空白字符
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if c == len(CHARS) - 1:
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pre_c = c
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continue
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no_repeat_blank_label.append(c)
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pre_c = c
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preb_labels.append(no_repeat_blank_label)
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# 转换为字符
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decoded_chars = [CHARS[idx] for idx in no_repeat_blank_label]
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return ''.join(decoded_chars)
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return preb_labels
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def predict(self, image_array):
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"""
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预测单张图像的车牌号码
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Args:
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image_array: numpy数组格式的图像
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Returns:
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prediction: 预测的车牌号码
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confidence: 预测置信度
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"""
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try:
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# 预处理图像
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image = self.preprocess_image(image_array)
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if image is None:
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return None, 0.0
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def LPRNinitialize_model(model_path=None):
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"""初始化LPRNet模型"""
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global lprnet_model, device
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image = image.to(self.device)
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# 模型推理
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with torch.no_grad():
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logits = self.model(image)
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# logits shape: [batch_size, class_num, sequence_length]
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# 计算置信度（使用softmax后的最大概率平均值）
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probs = torch.softmax(logits, dim=1)
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max_probs = torch.max(probs, dim=1)[0]
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confidence = torch.mean(max_probs).item()
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# 解码预测结果
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prediction = self.decode_prediction(logits)
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return prediction, confidence
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except Exception as e:
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print(f"预测图像失败: {e}")
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return None, 0.0
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# 全局变量
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lpr_model = None
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def LPRNinitialize_model():
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"""
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初始化LPRNet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 lpr_model
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try:
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# 设置设备
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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print(f"使用设备: {device}")
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# 模型权重文件路径
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model_path = os.path.join(os.path.dirname(__file__), 'LPRNet__iteration_74000.pth')
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# 构建模型
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lprnet_model = build_lprnet(
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lpr_max_len=8,
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phase=False,
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class_num=len(CHARS),
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dropout_rate=0.5
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)
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# 加载预训练权重
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if model_path is None:
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model_path = os.path.join(os.path.dirname(__file__), "Final_LPRNet_model.pth")
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if os.path.exists(model_path):
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checkpoint = torch.load(model_path, map_location=device)
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lprnet_model.load_state_dict(checkpoint)
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print(f"成功加载预训练模型: {model_path}")
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else:
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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")
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lprnet_model.to(device)
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lprnet_model.eval()
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# 创建推理对象
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lpr_model = LPRNetInference(model_path)
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print("LPRNet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 lprnet_model.parameters())
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print(f"LPRNet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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@ -209,76 +284,45 @@ def LPRNmodel_predict(image_array):
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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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list: 包含最多8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'] (蓝牌7位)
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['京', 'A', 'D', '1', '2', '3', '4', '5'] (绿牌8位)
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"""
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global lprnet_model, device
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global lpr_model
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if lprnet_model is None:
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if lpr_model is None:
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print("LPRNet模型未初始化，请先调用LPRNinitialize_model()")
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return ['待', '识', '别', '0', '0', '0', '0']
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return ['待', '识', '别', '0', '0', '0', '0', '0']
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try:
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# 预处理图像
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processed_image = preprocess_image(image_array)
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# 预测车牌号
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predicted_text, confidence = lpr_model.predict(image_array)
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# 转换为tensor
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input_tensor = torch.from_numpy(processed_image).float()
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input_tensor = input_tensor.to(device)
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if predicted_text is None:
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print("LPRNet识别失败")
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return ['识', '别', '失', '败', '0', '0', '0', '0']
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# 模型推理
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with torch.no_grad():
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prebs = lprnet_model(input_tensor)
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prebs = prebs.cpu().detach().numpy()
|
||||
print(f"LPRNet识别结果: {predicted_text}, 置信度: {confidence:.3f}")
|
||||
|
||||
# 贪婪解码
|
||||
preb_labels = greedy_decode(prebs)
|
||||
# 将字符串转换为字符列表
|
||||
char_list = list(predicted_text)
|
||||
|
||||
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)]
|
||||
# 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]
|
||||
|
||||
print(f"LPRNet识别结果: {''.join(predicted_chars)}")
|
||||
# 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
|
||||
|
||||
# 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']
|
||||
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']
|
||||
|
||||
# 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
|
||||
return ['识', '别', '失', '败', '0', '0', '0', '0']
|
||||
@ -22,6 +22,17 @@ def LPRNinitialize_model():
|
||||
return _processor
|
||||
|
||||
def LPRNmodel_predict(image_array):
|
||||
"""
|
||||
OCR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)
|
||||
"""
|
||||
# 获取原始预测结果
|
||||
raw_result = _processor.predict(image_array)
|
||||
|
||||
@ -37,13 +48,19 @@ def LPRNmodel_predict(image_array):
|
||||
# 转换为字符列表
|
||||
char_list = list(filtered_str)
|
||||
|
||||
# 确保返回长度为7的列表
|
||||
if len(char_list) >= 7:
|
||||
# 如果长度大于等于7，取前7个字符
|
||||
return char_list[:7]
|
||||
else:
|
||||
# 如果长度小于7，用空字符串补齐到7位
|
||||
return char_list + [''] * (7 - len(char_list))
|
||||
# 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
|
||||
|
||||
|
||||
|
||||
|
||||
53
README.md
53
README.md
@ -15,11 +15,10 @@ License_plate_recognition/
|
||||
├── OCR_part/ # OCR识别模块
|
||||
│ └── ocr_interface.py # OCR接口（占位）
|
||||
├── CRNN_part/ # CRNN识别模块
|
||||
│ └── crnn_interface.py # CRNN接口
|
||||
│ └── crnn_interface.py # CRNN接口（占位）
|
||||
└── 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）
|
||||
├── lpr_interface.py # LPRNet接口（已完成）
|
||||
└── LPRNet__iteration_74000.pth # LPRNet模型权重文件
|
||||
```
|
||||
|
||||
## 功能特性
|
||||
@ -39,19 +38,22 @@ License_plate_recognition/
|
||||
- 将倾斜的车牌矫正为标准矩形
|
||||
- 输出标准尺寸的车牌图像供识别使用
|
||||
|
||||
### 4. PyQt界面
|
||||
### 4. 多种识别方案
|
||||
- 支持OCR、CRNN和LPRNet三种车牌识别方法
|
||||
- LPRNet模型准确率高达98%
|
||||
- 模块化接口设计，便于切换不同识别算法
|
||||
|
||||
### 5. PyQt界面
|
||||
- 左侧：实时摄像头画面显示
|
||||
- 右侧：检测结果展示区域
|
||||
- 顶部显示识别到的车牌数量
|
||||
- 每行显示：车牌类型、矫正后图像、车牌号
|
||||
- 美观的现代化界面设计
|
||||
|
||||
### 5. 模块化设计
|
||||
### 6. 模块化设计
|
||||
- yolopart：负责车牌定位和矫正
|
||||
- OCR_part：基于PaddleOCR的车牌号识别模块
|
||||
- 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
|
||||
- OCR_part/CRNN_part/LPRNET_part：负责车牌号识别
|
||||
- 各模块独立，便于维护和扩展
|
||||
|
||||
## 安装和使用
|
||||
|
||||
@ -73,7 +75,21 @@ pip install -r requirements.txt
|
||||
python main.py
|
||||
```
|
||||
|
||||
### 5. 使用说明
|
||||
### 5. 选择识别模块
|
||||
在 `main.py` 中修改导入语句来选择不同的识别方案：
|
||||
|
||||
```python
|
||||
# 使用LPRNet（推荐，准确率98%）
|
||||
from LPRNET_part.lpr_interface import LPRNmodel_predict, LPRNinitialize_model
|
||||
|
||||
# 使用OCR
|
||||
from OCR_part.ocr_interface import LPRNmodel_predict, LPRNinitialize_model
|
||||
|
||||
# 使用CRNN
|
||||
from CRNN_part.crnn_interface import LPRNmodel_predict, LPRNinitialize_model
|
||||
```
|
||||
|
||||
### 6. 使用说明
|
||||
1. 点击"启动摄像头"按钮开始检测
|
||||
2. 将车牌对准摄像头
|
||||
3. 系统会自动检测车牌并显示：
|
||||
@ -95,8 +111,9 @@ YOLO Pose模型输出包含：
|
||||
|
||||
## 接口说明
|
||||
|
||||
### OCR/CRNN接口
|
||||
车牌号识别部分使用统一接口：
|
||||
### 车牌识别接口
|
||||
|
||||
项目为OCR、CRNN和LPRNet识别模块提供了标准接口：
|
||||
|
||||
```python
|
||||
# 接口函数名（导入所需模块，每个模块统一函数名）
|
||||
@ -108,7 +125,7 @@ LPRNinitialize_model()
|
||||
# 预测主函数
|
||||
from 对应模块 import LPRNmodel_predict
|
||||
result = LPRNmodel_predict(corrected_image) # 返回7个字符的列表
|
||||
|
||||
```
|
||||
|
||||
### 输入参数
|
||||
- `corrected_image`：numpy数组格式的矫正后车牌图像
|
||||
@ -117,6 +134,14 @@ result = LPRNmodel_predict(corrected_image) # 返回7个字符的列表
|
||||
- 长度为7的字符列表，包含车牌号的每个字符
|
||||
- 例如：`['京', 'A', '1', '2', '3', '4', '5']`
|
||||
|
||||
### LPRNet模块特性
|
||||
|
||||
- **高准确率**: 模型准确率高达98%
|
||||
- **快速推理**: 基于深度学习的端到端识别
|
||||
- **CTC解码**: 使用CTC（Connectionist Temporal Classification）解码算法
|
||||
- **支持中文**: 完整支持中文省份简称和字母数字组合
|
||||
- **模型权重**: 使用预训练的LPRNet__iteration_74000.pth权重文件
|
||||
|
||||
## 开发说明
|
||||
|
||||
### 添加新的识别算法
|
||||
|
||||
22
main.py
22
main.py
@ -361,8 +361,8 @@ class MainWindow(QMainWindow):
|
||||
# 矫正车牌图像
|
||||
corrected_image = self.correct_license_plate(detection)
|
||||
|
||||
# 获取车牌号（占位）
|
||||
plate_number = self.recognize_plate_number(corrected_image)
|
||||
# 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
|
||||
plate_widget = LicensePlateWidget(
|
||||
@ -389,7 +389,7 @@ class MainWindow(QMainWindow):
|
||||
detection['keypoints']
|
||||
)
|
||||
|
||||
def recognize_plate_number(self, corrected_image):
|
||||
def recognize_plate_number(self, corrected_image, class_name):
|
||||
"""识别车牌号"""
|
||||
if corrected_image is None:
|
||||
return "识别失败"
|
||||
@ -399,9 +399,21 @@ class MainWindow(QMainWindow):
|
||||
# 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
|
||||
# 将字符列表转换为字符串，支持8位车牌号
|
||||
if isinstance(result, list) and len(result) >= 7:
|
||||
return ''.join(result[:7])
|
||||
# 根据车牌类型决定显示位数
|
||||
if class_name == '绿牌' and len(result) >= 8:
|
||||
# 绿牌显示8位，过滤掉空字符占位符
|
||||
plate_chars = [char for char in result[:8] if char != '']
|
||||
# 如果过滤后确实有8位，显示8位；否则显示7位
|
||||
if len(plate_chars) == 8:
|
||||
return ''.join(plate_chars)
|
||||
else:
|
||||
return ''.join(plate_chars[:7])
|
||||
else:
|
||||
# 蓝牌或其他类型显示前7位，过滤掉空字符
|
||||
plate_chars = [char for char in result[:7] if char != '']
|
||||
return ''.join(plate_chars)
|
||||
else:
|
||||
return "识别失败"
|
||||
except Exception as e:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user