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7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 7d80c90b47 | |||
| 2b443b7373 | |||
| 74821bb02c | |||
| 88a80c225c | |||
| 8ace9df86a | |||
| 233ef20ce2 | |||
| a688e17e4e |
4
.idea/License_plate_recognition.iml
generated
4
.idea/License_plate_recognition.iml
generated
@ -5,8 +5,4 @@
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<orderEntry type="jdk" jdkName="cnm" jdkType="Python SDK" />
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||||
<orderEntry type="sourceFolder" forTests="false" />
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</component>
|
||||
<component name="PyDocumentationSettings">
|
||||
<option name="format" value="PLAIN" />
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<option name="myDocStringFormat" value="Plain" />
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||||
</component>
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</module>
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Binary file not shown.
@ -1,328 +0,0 @@
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import torch
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import torch.nn as nn
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import cv2
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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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# 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', '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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CHARS_DICT = {char: i for i, char in enumerate(CHARS)}
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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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self.block = nn.Sequential(
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nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
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nn.ReLU(),
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nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
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nn.ReLU(),
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nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
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nn.ReLU(),
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nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
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)
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def forward(self, x):
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return self.block(x)
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class LPRNet(nn.Module):
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def __init__(self, lpr_max_len, phase, class_num, dropout_rate):
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super(LPRNet, self).__init__()
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self.phase = phase
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self.lpr_max_len = lpr_max_len
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self.class_num = class_num
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self.backbone = nn.Sequential(
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nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1), # 0
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nn.BatchNorm2d(num_features=64),
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nn.ReLU(), # 2
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),
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small_basic_block(ch_in=64, ch_out=128), # *** 4 ***
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nn.BatchNorm2d(num_features=128),
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nn.ReLU(), # 6
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
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small_basic_block(ch_in=64, ch_out=256), # 8
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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),
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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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nn.BatchNorm2d(num_features=256),
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nn.ReLU(), # 18
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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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)
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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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)
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def forward(self, x):
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keep_features = list()
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for i, layer in enumerate(self.backbone.children()):
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x = layer(x)
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if i in [2, 6, 13, 22]: # [2, 4, 8, 11, 22]
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keep_features.append(x)
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global_context = list()
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for i, f in enumerate(keep_features):
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if i in [0, 1]:
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f = nn.AvgPool2d(kernel_size=5, stride=5)(f)
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if i in [2]:
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f = nn.AvgPool2d(kernel_size=(4, 10), stride=(4, 2))(f)
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f_pow = torch.pow(f, 2)
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f_mean = torch.mean(f_pow)
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f = torch.div(f, f_mean)
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global_context.append(f)
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x = torch.cat(global_context, 1)
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x = self.container(x)
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logits = torch.mean(x, dim=2)
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return logits
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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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# 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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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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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("Input must be numpy array")
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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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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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# 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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# 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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# 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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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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# 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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# 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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# 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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# 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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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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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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|
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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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# 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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# 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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return True
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|
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except Exception as e:
|
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print(f"LPRNet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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LPRNet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)
|
||||
"""
|
||||
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:
|
||||
# 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']
|
||||
@ -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):
|
||||
@ -15,6 +27,14 @@ class OCRProcessor:
|
||||
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)
|
||||
|
||||
BIN
lightCRNN_part/best_model.pth
Normal file
BIN
lightCRNN_part/best_model.pth
Normal file
Binary file not shown.
781
main.py
781
main.py
@ -1,25 +1,207 @@
|
||||
import sys
|
||||
import os
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PyQt5.QtWidgets import (
|
||||
QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
|
||||
QLabel, QPushButton, QScrollArea, QFrame, QSizePolicy
|
||||
)
|
||||
from collections import defaultdict, deque
|
||||
from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, \
|
||||
QFileDialog, QFrame, QScrollArea, QComboBox
|
||||
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
|
||||
|
||||
#选择使用哪个模块
|
||||
from LPRNET_part.lpr_interface import LPRNmodel_predict
|
||||
from LPRNET_part.lpr_interface import LPRNinitialize_model
|
||||
|
||||
# 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
|
||||
# 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
|
||||
# from CRNN_part.crnn_interface import LPRNmodel_predict
|
||||
# from CRNN_part.crnn_interface import LPRNinitialize_model
|
||||
|
||||
class PlateStabilizer:
|
||||
"""车牌识别结果稳定器"""
|
||||
|
||||
def __init__(self, history_size=10, confidence_threshold=0.6, stability_frames=5):
|
||||
self.history_size = history_size # 历史帧数量
|
||||
self.confidence_threshold = confidence_threshold # 置信度阈值
|
||||
self.stability_frames = stability_frames # 稳定帧数要求
|
||||
|
||||
# 存储每个车牌的历史识别结果
|
||||
self.plate_histories = defaultdict(lambda: deque(maxlen=history_size))
|
||||
# 存储当前稳定的车牌结果
|
||||
self.stable_results = {}
|
||||
# 车牌ID计数器
|
||||
self.plate_id_counter = 0
|
||||
# 车牌位置追踪
|
||||
self.plate_positions = {}
|
||||
|
||||
def calculate_plate_distance(self, pos1, pos2):
|
||||
"""计算两个车牌位置的距离"""
|
||||
if pos1 is None or pos2 is None:
|
||||
return float('inf')
|
||||
|
||||
# 计算中心点距离
|
||||
center1 = ((pos1[0] + pos1[2]) / 2, (pos1[1] + pos1[3]) / 2)
|
||||
center2 = ((pos2[0] + pos2[2]) / 2, (pos2[1] + pos2[3]) / 2)
|
||||
|
||||
return np.sqrt((center1[0] - center2[0])**2 + (center1[1] - center2[1])**2)
|
||||
|
||||
def match_plates_to_history(self, current_detections):
|
||||
"""将当前检测结果匹配到历史记录"""
|
||||
matched_plates = {}
|
||||
used_ids = set()
|
||||
|
||||
for detection in current_detections:
|
||||
bbox = detection.get('bbox', [0, 0, 0, 0])
|
||||
best_match_id = None
|
||||
min_distance = float('inf')
|
||||
|
||||
# 寻找最佳匹配的历史车牌
|
||||
for plate_id, last_pos in self.plate_positions.items():
|
||||
if plate_id in used_ids:
|
||||
continue
|
||||
|
||||
distance = self.calculate_plate_distance(bbox, last_pos)
|
||||
if distance < min_distance and distance < 100: # 距离阈值
|
||||
min_distance = distance
|
||||
best_match_id = plate_id
|
||||
|
||||
if best_match_id is not None:
|
||||
matched_plates[best_match_id] = detection
|
||||
used_ids.add(best_match_id)
|
||||
self.plate_positions[best_match_id] = bbox
|
||||
else:
|
||||
# 创建新的车牌ID
|
||||
new_id = f"plate_{self.plate_id_counter}"
|
||||
self.plate_id_counter += 1
|
||||
matched_plates[new_id] = detection
|
||||
self.plate_positions[new_id] = bbox
|
||||
|
||||
return matched_plates
|
||||
|
||||
def calculate_confidence(self, plate_text, detection_quality=1.0):
|
||||
"""计算识别结果的置信度"""
|
||||
if not plate_text or plate_text == "识别失败":
|
||||
return 0.0
|
||||
|
||||
# 基础置信度基于文本长度和字符类型
|
||||
base_confidence = 0.5
|
||||
|
||||
# 长度合理性检查
|
||||
if 7 <= len(plate_text) <= 8:
|
||||
base_confidence += 0.2
|
||||
|
||||
# 字符类型检查（中文+字母+数字的组合）
|
||||
has_chinese = any('\u4e00' <= char <= '\u9fff' for char in plate_text)
|
||||
has_letter = any(char.isalpha() for char in plate_text)
|
||||
has_digit = any(char.isdigit() for char in plate_text)
|
||||
|
||||
if has_chinese and has_letter and has_digit:
|
||||
base_confidence += 0.2
|
||||
|
||||
# 检测质量影响
|
||||
confidence = base_confidence * detection_quality
|
||||
|
||||
return min(confidence, 1.0)
|
||||
|
||||
def update_and_get_stable_result(self, current_detections, corrected_images, plate_texts):
|
||||
"""更新历史记录并返回稳定的识别结果"""
|
||||
if not current_detections:
|
||||
return []
|
||||
|
||||
# 匹配当前检测到历史记录
|
||||
matched_plates = self.match_plates_to_history(current_detections)
|
||||
|
||||
stable_results = []
|
||||
|
||||
for plate_id, detection in matched_plates.items():
|
||||
# 获取对应的矫正图像和识别文本
|
||||
detection_idx = current_detections.index(detection)
|
||||
corrected_image = corrected_images[detection_idx] if detection_idx < len(corrected_images) else None
|
||||
plate_text = plate_texts[detection_idx] if detection_idx < len(plate_texts) else "识别失败"
|
||||
|
||||
# 计算置信度
|
||||
confidence = self.calculate_confidence(plate_text)
|
||||
|
||||
# 添加到历史记录
|
||||
history_entry = {
|
||||
'text': plate_text,
|
||||
'confidence': confidence,
|
||||
'detection': detection,
|
||||
'corrected_image': corrected_image
|
||||
}
|
||||
self.plate_histories[plate_id].append(history_entry)
|
||||
|
||||
# 计算稳定结果
|
||||
stable_text = self.get_stable_text(plate_id)
|
||||
|
||||
if stable_text and stable_text != "识别失败":
|
||||
stable_results.append({
|
||||
'id': plate_id,
|
||||
'class_name': detection['class_name'],
|
||||
'corrected_image': corrected_image,
|
||||
'plate_number': stable_text,
|
||||
'detection': detection
|
||||
})
|
||||
|
||||
return stable_results
|
||||
|
||||
def get_stable_text(self, plate_id):
|
||||
"""获取指定车牌的稳定识别结果"""
|
||||
history = self.plate_histories[plate_id]
|
||||
|
||||
if len(history) < 3: # 历史记录太少，返回最新结果
|
||||
return history[-1]['text'] if history else "识别失败"
|
||||
|
||||
# 统计各种识别结果的加权投票
|
||||
text_votes = defaultdict(float)
|
||||
total_confidence = 0
|
||||
|
||||
for entry in history:
|
||||
text = entry['text']
|
||||
confidence = entry['confidence']
|
||||
|
||||
if text != "识别失败" and confidence > 0.3:
|
||||
text_votes[text] += confidence
|
||||
total_confidence += confidence
|
||||
|
||||
if not text_votes:
|
||||
return "识别失败"
|
||||
|
||||
# 找到得票最高的结果
|
||||
best_text = max(text_votes.items(), key=lambda x: x[1])
|
||||
|
||||
# 检查是否足够稳定（得票率超过阈值）
|
||||
vote_ratio = best_text[1] / total_confidence if total_confidence > 0 else 0
|
||||
|
||||
if vote_ratio >= self.confidence_threshold:
|
||||
return best_text[0]
|
||||
else:
|
||||
# 不够稳定，返回最近的高置信度结果
|
||||
recent_high_conf = [entry for entry in list(history)[-5:]
|
||||
if entry['confidence'] > 0.5 and entry['text'] != "识别失败"]
|
||||
|
||||
if recent_high_conf:
|
||||
return recent_high_conf[-1]['text']
|
||||
else:
|
||||
return history[-1]['text']
|
||||
|
||||
def clear_old_plates(self, current_plate_ids):
|
||||
"""清理不再出现的车牌历史记录"""
|
||||
# 移除超过一定时间未更新的车牌
|
||||
plates_to_remove = []
|
||||
for plate_id in self.plate_histories.keys():
|
||||
if plate_id not in current_plate_ids:
|
||||
plates_to_remove.append(plate_id)
|
||||
|
||||
for plate_id in plates_to_remove:
|
||||
if plate_id in self.plate_histories:
|
||||
del self.plate_histories[plate_id]
|
||||
if plate_id in self.plate_positions:
|
||||
del self.plate_positions[plate_id]
|
||||
if plate_id in self.stable_results:
|
||||
del self.stable_results[plate_id]
|
||||
|
||||
class CameraThread(QThread):
|
||||
"""摄像头线程类"""
|
||||
@ -56,6 +238,60 @@ class CameraThread(QThread):
|
||||
self.frame_ready.emit(frame)
|
||||
self.msleep(30) # 约30fps
|
||||
|
||||
class VideoThread(QThread):
|
||||
"""视频处理线程类"""
|
||||
frame_ready = pyqtSignal(np.ndarray)
|
||||
video_finished = pyqtSignal()
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.video_path = None
|
||||
self.cap = None
|
||||
self.running = False
|
||||
self.paused = False
|
||||
|
||||
def load_video(self, video_path):
|
||||
"""加载视频文件"""
|
||||
self.video_path = video_path
|
||||
self.cap = cv2.VideoCapture(video_path)
|
||||
return self.cap.isOpened()
|
||||
|
||||
def start_video(self):
|
||||
"""开始播放视频"""
|
||||
if self.cap and self.cap.isOpened():
|
||||
self.running = True
|
||||
self.paused = False
|
||||
self.start()
|
||||
return True
|
||||
return False
|
||||
|
||||
def pause_video(self):
|
||||
"""暂停/继续视频"""
|
||||
self.paused = not self.paused
|
||||
return self.paused
|
||||
|
||||
def stop_video(self):
|
||||
"""停止视频"""
|
||||
self.running = False
|
||||
if self.cap:
|
||||
self.cap.release()
|
||||
self.quit()
|
||||
self.wait()
|
||||
|
||||
def run(self):
|
||||
"""线程运行函数"""
|
||||
while self.running:
|
||||
if not self.paused and self.cap and self.cap.isOpened():
|
||||
ret, frame = self.cap.read()
|
||||
if ret:
|
||||
self.frame_ready.emit(frame)
|
||||
else:
|
||||
# 视频播放结束
|
||||
self.video_finished.emit()
|
||||
self.running = False
|
||||
break
|
||||
self.msleep(30) # 约30fps
|
||||
|
||||
class LicensePlateWidget(QWidget):
|
||||
"""单个车牌结果显示组件"""
|
||||
|
||||
@ -67,6 +303,7 @@ class LicensePlateWidget(QWidget):
|
||||
def init_ui(self, class_name, corrected_image, plate_number):
|
||||
layout = QHBoxLayout()
|
||||
layout.setContentsMargins(10, 5, 10, 5)
|
||||
layout.setSpacing(8) # 设置组件间距
|
||||
|
||||
# 车牌类型标签
|
||||
type_label = QLabel(class_name)
|
||||
@ -104,7 +341,6 @@ class LicensePlateWidget(QWidget):
|
||||
|
||||
# 矫正后的车牌图像
|
||||
image_label = QLabel()
|
||||
image_label.setFixedSize(120, 40)
|
||||
image_label.setStyleSheet("border: 1px solid #ddd; background-color: white;")
|
||||
|
||||
if corrected_image is not None:
|
||||
@ -118,16 +354,44 @@ class LicensePlateWidget(QWidget):
|
||||
q_image = QImage(corrected_image.data, w, h, bytes_per_line, QImage.Format_Grayscale8)
|
||||
|
||||
pixmap = QPixmap.fromImage(q_image)
|
||||
scaled_pixmap = pixmap.scaled(120, 40, Qt.KeepAspectRatio, Qt.SmoothTransformation)
|
||||
|
||||
# 动态计算显示尺寸，保持车牌的宽高比
|
||||
original_width = pixmap.width()
|
||||
original_height = pixmap.height()
|
||||
|
||||
# 设置最大显示尺寸限制
|
||||
max_width = 150
|
||||
max_height = 60
|
||||
|
||||
# 计算缩放比例，确保图像完整显示
|
||||
width_ratio = max_width / original_width if original_width > 0 else 1
|
||||
height_ratio = max_height / original_height if original_height > 0 else 1
|
||||
scale_ratio = min(width_ratio, height_ratio, 1.0) # 不放大，只缩小
|
||||
|
||||
# 计算实际显示尺寸
|
||||
display_width = int(original_width * scale_ratio)
|
||||
display_height = int(original_height * scale_ratio)
|
||||
|
||||
# 确保最小显示尺寸
|
||||
display_width = max(display_width, 80)
|
||||
display_height = max(display_height, 25)
|
||||
|
||||
# 设置标签尺寸并缩放图像
|
||||
image_label.setFixedSize(display_width, display_height)
|
||||
scaled_pixmap = pixmap.scaled(display_width, display_height, Qt.KeepAspectRatio, Qt.SmoothTransformation)
|
||||
image_label.setPixmap(scaled_pixmap)
|
||||
image_label.setAlignment(Qt.AlignCenter)
|
||||
else:
|
||||
# 当没有图像时，设置固定尺寸显示提示信息
|
||||
image_label.setFixedSize(120, 40)
|
||||
image_label.setText("车牌未完全\n进入摄像头")
|
||||
image_label.setAlignment(Qt.AlignCenter)
|
||||
image_label.setStyleSheet("border: 1px solid #ddd; background-color: #f5f5f5; color: #666;")
|
||||
|
||||
# 车牌号标签
|
||||
# 车牌号标签 - 使用自适应宽度
|
||||
number_label = QLabel(plate_number)
|
||||
number_label.setFixedWidth(150)
|
||||
number_label.setMinimumWidth(120) # 设置最小宽度
|
||||
number_label.setMaximumWidth(200) # 设置最大宽度
|
||||
number_label.setAlignment(Qt.AlignCenter)
|
||||
number_label.setStyleSheet(
|
||||
"QLabel { "
|
||||
@ -139,6 +403,11 @@ class LicensePlateWidget(QWidget):
|
||||
"font-weight: bold; "
|
||||
"}"
|
||||
)
|
||||
# 根据文本长度调整宽度
|
||||
font_metrics = number_label.fontMetrics()
|
||||
text_width = font_metrics.boundingRect(plate_number).width()
|
||||
optimal_width = max(120, min(200, text_width + 20)) # 加20像素的边距
|
||||
number_label.setFixedWidth(optimal_width)
|
||||
|
||||
layout.addWidget(type_label)
|
||||
layout.addWidget(image_label)
|
||||
@ -146,6 +415,9 @@ class LicensePlateWidget(QWidget):
|
||||
layout.addStretch()
|
||||
|
||||
self.setLayout(layout)
|
||||
# 调整整体组件的最小高度以适应动态图像尺寸
|
||||
min_height = max(60, image_label.height() + 20) # 至少60像素高度
|
||||
self.setMinimumHeight(min_height)
|
||||
self.setStyleSheet(
|
||||
"QWidget { "
|
||||
"background-color: white; "
|
||||
@ -162,15 +434,28 @@ class MainWindow(QMainWindow):
|
||||
super().__init__()
|
||||
self.detector = None
|
||||
self.camera_thread = None
|
||||
self.video_thread = None
|
||||
self.current_frame = None
|
||||
self.detections = []
|
||||
self.current_mode = "camera" # 当前模式：camera, video, image
|
||||
self.is_processing = False # 标志位，表示是否正在处理识别任务
|
||||
self.last_plate_results = [] # 存储上一次的车牌识别结果
|
||||
self.current_recognition_method = "CRNN" # 当前识别方法
|
||||
|
||||
# 添加车牌稳定器
|
||||
self.plate_stabilizer = PlateStabilizer(
|
||||
history_size=15, # 保存15帧历史
|
||||
confidence_threshold=0.7, # 70%置信度阈值
|
||||
stability_frames=5 # 需要5帧稳定
|
||||
)
|
||||
|
||||
self.init_ui()
|
||||
self.init_detector()
|
||||
self.init_camera()
|
||||
self.init_video()
|
||||
|
||||
# 初始化OCR/CRNN模型（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）
|
||||
LPRNinitialize_model()
|
||||
# 初始化默认识别方法（CRNN）的模型
|
||||
self.change_recognition_method(self.current_recognition_method)
|
||||
|
||||
|
||||
def init_ui(self):
|
||||
@ -197,7 +482,7 @@ class MainWindow(QMainWindow):
|
||||
self.camera_label.setStyleSheet("QLabel { background-color: black; border: 1px solid #ccc; }")
|
||||
self.camera_label.setAlignment(Qt.AlignCenter)
|
||||
self.camera_label.setText("摄像头未启动")
|
||||
self.camera_label.setScaledContents(True)
|
||||
self.camera_label.setScaledContents(False)
|
||||
|
||||
# 控制按钮
|
||||
button_layout = QHBoxLayout()
|
||||
@ -207,8 +492,26 @@ class MainWindow(QMainWindow):
|
||||
self.stop_button.clicked.connect(self.stop_camera)
|
||||
self.stop_button.setEnabled(False)
|
||||
|
||||
# 视频控制按钮
|
||||
self.open_video_button = QPushButton("打开视频")
|
||||
self.stop_video_button = QPushButton("停止视频")
|
||||
self.pause_video_button = QPushButton("暂停视频")
|
||||
self.open_video_button.clicked.connect(self.open_video_file)
|
||||
self.stop_video_button.clicked.connect(self.stop_video)
|
||||
self.pause_video_button.clicked.connect(self.pause_video)
|
||||
self.stop_video_button.setEnabled(False)
|
||||
self.pause_video_button.setEnabled(False)
|
||||
|
||||
# 图片控制按钮
|
||||
self.open_image_button = QPushButton("打开图片")
|
||||
self.open_image_button.clicked.connect(self.open_image_file)
|
||||
|
||||
button_layout.addWidget(self.start_button)
|
||||
button_layout.addWidget(self.stop_button)
|
||||
button_layout.addWidget(self.open_video_button)
|
||||
button_layout.addWidget(self.stop_video_button)
|
||||
button_layout.addWidget(self.pause_video_button)
|
||||
button_layout.addWidget(self.open_image_button)
|
||||
button_layout.addStretch()
|
||||
|
||||
left_layout.addWidget(self.camera_label)
|
||||
@ -217,7 +520,7 @@ class MainWindow(QMainWindow):
|
||||
# 右侧结果显示区域
|
||||
right_frame = QFrame()
|
||||
right_frame.setFrameStyle(QFrame.StyledPanel)
|
||||
right_frame.setFixedWidth(400)
|
||||
right_frame.setFixedWidth(460)
|
||||
right_frame.setStyleSheet("QFrame { background-color: #fafafa; border: 2px solid #ddd; }")
|
||||
right_layout = QVBoxLayout(right_frame)
|
||||
|
||||
@ -227,6 +530,20 @@ class MainWindow(QMainWindow):
|
||||
title_label.setFont(QFont("Arial", 16, QFont.Bold))
|
||||
title_label.setStyleSheet("QLabel { color: #333; padding: 10px; }")
|
||||
|
||||
# 识别方法选择
|
||||
method_layout = QHBoxLayout()
|
||||
method_label = QLabel("识别方法:")
|
||||
method_label.setFont(QFont("Arial", 10))
|
||||
|
||||
self.method_combo = QComboBox()
|
||||
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)
|
||||
|
||||
method_layout.addWidget(method_label)
|
||||
method_layout.addWidget(self.method_combo)
|
||||
method_layout.addStretch()
|
||||
|
||||
# 车牌数量显示
|
||||
self.count_label = QLabel("识别到的车牌数量: 0")
|
||||
self.count_label.setAlignment(Qt.AlignCenter)
|
||||
@ -253,9 +570,17 @@ class MainWindow(QMainWindow):
|
||||
|
||||
scroll_area.setWidget(self.results_widget)
|
||||
|
||||
# 当前识别任务显示
|
||||
self.current_method_label = QLabel("当前识别方法: CRNN")
|
||||
self.current_method_label.setAlignment(Qt.AlignRight)
|
||||
self.current_method_label.setFont(QFont("Arial", 9))
|
||||
self.current_method_label.setStyleSheet("QLabel { color: #666; padding: 5px; }")
|
||||
|
||||
right_layout.addWidget(title_label)
|
||||
right_layout.addLayout(method_layout)
|
||||
right_layout.addWidget(self.count_label)
|
||||
right_layout.addWidget(scroll_area)
|
||||
right_layout.addWidget(self.current_method_label)
|
||||
|
||||
# 添加到主布局
|
||||
main_layout.addWidget(left_frame, 2)
|
||||
@ -291,14 +616,50 @@ class MainWindow(QMainWindow):
|
||||
model_path = os.path.join(os.path.dirname(__file__), "yolopart", "yolo11s-pose42.pt")
|
||||
self.detector = LicensePlateYOLO(model_path)
|
||||
|
||||
def reset_processing_state(self):
|
||||
"""重置处理状态和清理界面"""
|
||||
# 重置处理标志
|
||||
self.is_processing = False
|
||||
|
||||
# 清空当前帧和检测结果
|
||||
self.current_frame = None
|
||||
self.detections = []
|
||||
|
||||
# 重置车牌稳定器
|
||||
self.plate_stabilizer = PlateStabilizer(
|
||||
history_size=15,
|
||||
confidence_threshold=0.7,
|
||||
stability_frames=5
|
||||
)
|
||||
|
||||
# 清空右侧结果显示
|
||||
self.count_label.setText("识别到的车牌数量: 0")
|
||||
for i in reversed(range(self.results_layout.count())):
|
||||
child = self.results_layout.itemAt(i).widget()
|
||||
if child:
|
||||
child.setParent(None)
|
||||
self.last_plate_results = []
|
||||
|
||||
print("处理状态已重置，界面已清理")
|
||||
|
||||
def init_camera(self):
|
||||
"""初始化摄像头线程"""
|
||||
self.camera_thread = CameraThread()
|
||||
self.camera_thread.frame_ready.connect(self.process_frame)
|
||||
|
||||
def init_video(self):
|
||||
"""初始化视频线程"""
|
||||
self.video_thread = VideoThread()
|
||||
self.video_thread.frame_ready.connect(self.process_frame)
|
||||
self.video_thread.video_finished.connect(self.on_video_finished)
|
||||
|
||||
def start_camera(self):
|
||||
"""启动摄像头"""
|
||||
# 重置处理状态和清理界面
|
||||
self.reset_processing_state()
|
||||
|
||||
if self.camera_thread.start_camera():
|
||||
self.current_mode = "camera"
|
||||
self.start_button.setEnabled(False)
|
||||
self.stop_button.setEnabled(True)
|
||||
self.camera_label.setText("摄像头启动中...")
|
||||
@ -311,68 +672,371 @@ class MainWindow(QMainWindow):
|
||||
self.start_button.setEnabled(True)
|
||||
self.stop_button.setEnabled(False)
|
||||
self.camera_label.setText("摄像头已停止")
|
||||
# 只在摄像头模式下清除标签内容
|
||||
if self.current_mode == "camera":
|
||||
self.camera_label.clear()
|
||||
|
||||
def on_video_finished(self):
|
||||
"""视频播放结束时的处理"""
|
||||
self.video_thread.stop_video()
|
||||
self.open_video_button.setEnabled(True)
|
||||
self.stop_video_button.setEnabled(False)
|
||||
self.pause_video_button.setEnabled(False)
|
||||
self.camera_label.setText("视频播放结束")
|
||||
self.current_mode = "camera"
|
||||
|
||||
def open_video_file(self):
|
||||
"""打开视频文件"""
|
||||
# 停止当前模式
|
||||
if self.current_mode == "camera" and self.camera_thread and self.camera_thread.running:
|
||||
self.stop_camera()
|
||||
elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
|
||||
self.stop_video()
|
||||
|
||||
# 重置处理状态和清理界面
|
||||
self.reset_processing_state()
|
||||
|
||||
# 选择视频文件
|
||||
video_path, _ = QFileDialog.getOpenFileName(self, "选择视频文件", "", "视频文件 (*.mp4 *.avi *.mov *.mkv)")
|
||||
|
||||
if video_path:
|
||||
if self.video_thread.load_video(video_path):
|
||||
self.current_mode = "video"
|
||||
self.start_video()
|
||||
self.camera_label.setText(f"正在播放视频: {os.path.basename(video_path)}")
|
||||
else:
|
||||
self.camera_label.setText("视频加载失败")
|
||||
|
||||
def start_video(self):
|
||||
"""开始播放视频"""
|
||||
if self.video_thread.start_video():
|
||||
self.open_video_button.setEnabled(False)
|
||||
self.stop_video_button.setEnabled(True)
|
||||
self.pause_video_button.setEnabled(True)
|
||||
self.pause_video_button.setText("暂停")
|
||||
else:
|
||||
self.camera_label.setText("视频播放失败")
|
||||
|
||||
def pause_video(self):
|
||||
"""暂停/继续视频"""
|
||||
if self.video_thread.pause_video():
|
||||
self.pause_video_button.setText("继续")
|
||||
else:
|
||||
self.pause_video_button.setText("暂停")
|
||||
|
||||
def stop_video(self):
|
||||
"""停止视频"""
|
||||
self.video_thread.stop_video()
|
||||
self.open_video_button.setEnabled(True)
|
||||
self.stop_video_button.setEnabled(False)
|
||||
self.pause_video_button.setEnabled(False)
|
||||
self.camera_label.setText("视频已停止")
|
||||
# 只在视频模式下清除标签内容
|
||||
if self.current_mode == "video":
|
||||
self.camera_label.clear()
|
||||
self.current_mode = "camera"
|
||||
|
||||
def open_image_file(self):
|
||||
"""打开图片文件"""
|
||||
# 停止当前模式
|
||||
if self.current_mode == "camera" and self.camera_thread and self.camera_thread.running:
|
||||
self.stop_camera()
|
||||
elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
|
||||
self.stop_video()
|
||||
|
||||
# 重置处理状态和清理界面
|
||||
self.reset_processing_state()
|
||||
|
||||
# 选择图片文件
|
||||
image_path, _ = QFileDialog.getOpenFileName(self, "选择图片文件", "", "图片文件 (*.jpg *.jpeg *.png *.bmp)")
|
||||
|
||||
if image_path:
|
||||
self.current_mode = "image"
|
||||
try:
|
||||
# 读取图片 - 方法1: 使用cv2.imdecode处理中文路径
|
||||
image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
|
||||
|
||||
# 如果方法1失败，尝试方法2: 直接使用cv2.imread
|
||||
if image is None:
|
||||
image = cv2.imread(image_path)
|
||||
|
||||
if image is not None:
|
||||
print(f"成功加载图片: {image_path}, 尺寸: {image.shape}")
|
||||
self.process_image(image)
|
||||
# 不在这里设置文本，避免覆盖图片
|
||||
# self.camera_label.setText(f"正在显示图片: {os.path.basename(image_path)}")
|
||||
else:
|
||||
print(f"图片加载失败: {image_path}")
|
||||
self.camera_label.setText("图片加载失败")
|
||||
except Exception as e:
|
||||
print(f"图片处理异常: {str(e)}")
|
||||
self.camera_label.setText(f"图片处理错误: {str(e)}")
|
||||
|
||||
def process_image(self, image):
|
||||
"""处理图片"""
|
||||
try:
|
||||
print(f"开始处理图片，图片尺寸: {image.shape}")
|
||||
self.current_frame = image.copy()
|
||||
|
||||
# 进行车牌检测
|
||||
print("正在进行车牌检测...")
|
||||
self.detections = self.detector.detect_license_plates(image)
|
||||
print(f"检测到 {len(self.detections)} 个车牌")
|
||||
|
||||
# 在图像上绘制检测结果
|
||||
print("正在绘制检测结果...")
|
||||
display_frame = self.draw_detections(image.copy())
|
||||
|
||||
# 转换为Qt格式并显示
|
||||
print("正在显示图片...")
|
||||
self.display_frame(display_frame)
|
||||
|
||||
# 更新右侧结果显示
|
||||
print("正在更新结果显示...")
|
||||
self.update_results_display()
|
||||
print("图片处理完成")
|
||||
except Exception as e:
|
||||
print(f"图片处理过程中出错: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
def process_frame(self, frame):
|
||||
"""处理摄像头帧"""
|
||||
if frame is None:
|
||||
return
|
||||
|
||||
self.current_frame = frame.copy()
|
||||
|
||||
# 先显示原始帧，保证视频流畅播放
|
||||
self.display_frame(frame)
|
||||
|
||||
# 如果当前没有在处理识别任务，则开始新的识别任务
|
||||
if not self.is_processing:
|
||||
self.is_processing = True
|
||||
# 异步进行车牌检测和识别
|
||||
QTimer.singleShot(0, self.async_detect_and_update)
|
||||
|
||||
def async_detect_and_update(self):
|
||||
"""异步进行车牌检测和识别"""
|
||||
if self.current_frame is None:
|
||||
self.is_processing = False # 重置标志位
|
||||
return
|
||||
|
||||
try:
|
||||
# 进行车牌检测
|
||||
self.detections = self.detector.detect_license_plates(frame)
|
||||
self.detections = self.detector.detect_license_plates(self.current_frame)
|
||||
|
||||
# 在图像上绘制检测结果
|
||||
display_frame = self.draw_detections(frame.copy())
|
||||
display_frame = self.draw_detections(self.current_frame.copy())
|
||||
|
||||
# 转换为Qt格式并显示
|
||||
# 更新显示帧（显示带检测结果的帧）
|
||||
# 无论是摄像头模式还是视频模式，都显示检测框
|
||||
self.display_frame(display_frame)
|
||||
|
||||
# 更新右侧结果显示
|
||||
self.update_results_display()
|
||||
except Exception as e:
|
||||
print(f"异步检测和更新失败: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
# 无论成功或失败，都要重置标志位
|
||||
self.is_processing = False
|
||||
|
||||
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"""
|
||||
try:
|
||||
print(f"开始显示帧，帧尺寸: {frame.shape}")
|
||||
|
||||
# 方法1: 标准方法
|
||||
try:
|
||||
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
h, w, ch = rgb_frame.shape
|
||||
bytes_per_line = ch * w
|
||||
qt_image = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
|
||||
|
||||
print(f"方法1: 创建QImage，尺寸: {qt_image.width()}x{qt_image.height()}")
|
||||
if qt_image.isNull():
|
||||
print("方法1: QImage为空，尝试方法2")
|
||||
raise Exception("QImage为空")
|
||||
|
||||
pixmap = QPixmap.fromImage(qt_image)
|
||||
if pixmap.isNull():
|
||||
print("方法1: QPixmap为空，尝试方法2")
|
||||
raise Exception("QPixmap为空")
|
||||
|
||||
# 手动缩放图片以适应标签大小，保持宽高比
|
||||
scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
|
||||
self.camera_label.setPixmap(scaled_pixmap)
|
||||
print("方法1: 帧显示完成")
|
||||
return
|
||||
except Exception as e1:
|
||||
print(f"方法1失败: {str(e1)}")
|
||||
|
||||
# 方法2: 使用imencode和imdecode
|
||||
try:
|
||||
print("尝试方法2: 使用imencode和imdecode")
|
||||
_, buffer = cv2.imencode('.jpg', frame)
|
||||
rgb_frame = cv2.imdecode(buffer, cv2.IMREAD_COLOR)
|
||||
rgb_frame = cv2.cvtColor(rgb_frame, cv2.COLOR_BGR2RGB)
|
||||
h, w, ch = rgb_frame.shape
|
||||
bytes_per_line = ch * w
|
||||
qt_image = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
|
||||
|
||||
print(f"方法2: 创建QImage，尺寸: {qt_image.width()}x{qt_image.height()}")
|
||||
if qt_image.isNull():
|
||||
print("方法2: QImage为空")
|
||||
raise Exception("QImage为空")
|
||||
|
||||
pixmap = QPixmap.fromImage(qt_image)
|
||||
if pixmap.isNull():
|
||||
print("方法2: QPixmap为空")
|
||||
raise Exception("QPixmap为空")
|
||||
|
||||
# 手动缩放图片以适应标签大小，保持宽高比
|
||||
scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
|
||||
self.camera_label.setPixmap(scaled_pixmap)
|
||||
print("方法2: 帧显示完成")
|
||||
return
|
||||
except Exception as e2:
|
||||
print(f"方法2失败: {str(e2)}")
|
||||
|
||||
# 方法3: 直接使用QImage的构造函数
|
||||
try:
|
||||
print("尝试方法3: 直接使用QImage的构造函数")
|
||||
height, width, channel = frame.shape
|
||||
bytes_per_line = 3 * width
|
||||
q_image = QImage(frame.data, width, height, bytes_per_line, QImage.Format_BGR888)
|
||||
|
||||
print(f"方法3: 创建QImage，尺寸: {q_image.width()}x{q_image.height()}")
|
||||
if q_image.isNull():
|
||||
print("方法3: QImage为空")
|
||||
raise Exception("QImage为空")
|
||||
|
||||
pixmap = QPixmap.fromImage(q_image)
|
||||
if pixmap.isNull():
|
||||
print("方法3: QPixmap为空")
|
||||
raise Exception("QPixmap为空")
|
||||
|
||||
# 手动缩放图片以适应标签大小，保持宽高比
|
||||
scaled_pixmap = pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
|
||||
self.camera_label.setPixmap(scaled_pixmap)
|
||||
print("方法3: 帧显示完成")
|
||||
return
|
||||
except Exception as e3:
|
||||
print(f"方法3失败: {str(e3)}")
|
||||
|
||||
# 所有方法都失败
|
||||
print("所有显示方法都失败")
|
||||
self.camera_label.setText("图片显示失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"显示帧过程中出错: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
self.camera_label.setText(f"显示错误: {str(e)}")
|
||||
|
||||
def update_results_display(self):
|
||||
"""更新右侧结果显示"""
|
||||
# 更新车牌数量
|
||||
count = len(self.detections)
|
||||
self.count_label.setText(f"识别到的车牌数量: {count}")
|
||||
"""更新右侧结果显示（使用稳定化结果）"""
|
||||
print(f"开始更新结果显示，当前模式: {self.current_mode}, 检测数量: {len(self.detections) if self.detections else 0}")
|
||||
|
||||
if not self.detections:
|
||||
self.count_label.setText("识别到的车牌数量: 0")
|
||||
# 清除显示
|
||||
for i in reversed(range(self.results_layout.count())):
|
||||
child = self.results_layout.itemAt(i).widget()
|
||||
if child:
|
||||
child.setParent(None)
|
||||
self.last_plate_results = []
|
||||
print("无检测结果，已清空界面")
|
||||
return
|
||||
|
||||
# 获取矫正图像和识别文本
|
||||
corrected_images = []
|
||||
plate_texts = []
|
||||
|
||||
for detection in self.detections:
|
||||
corrected_image = self.correct_license_plate(detection)
|
||||
corrected_images.append(corrected_image)
|
||||
|
||||
if corrected_image is not None:
|
||||
plate_text = self.recognize_plate_number(corrected_image, detection['class_name'])
|
||||
else:
|
||||
plate_text = "识别失败"
|
||||
plate_texts.append(plate_text)
|
||||
|
||||
# 使用稳定器获取稳定的识别结果
|
||||
stable_results = self.plate_stabilizer.update_and_get_stable_result(
|
||||
self.detections, corrected_images, plate_texts
|
||||
)
|
||||
|
||||
# 更新车牌数量显示
|
||||
self.count_label.setText(f"识别到的车牌数量: {len(stable_results)}")
|
||||
print(f"稳定结果数量: {len(stable_results)}")
|
||||
|
||||
# 检查结果是否发生变化
|
||||
results_changed = self.check_results_changed(stable_results)
|
||||
print(f"结果是否变化: {results_changed}")
|
||||
|
||||
if results_changed:
|
||||
# 清除之前的结果
|
||||
for i in reversed(range(self.results_layout.count())):
|
||||
child = self.results_layout.itemAt(i).widget()
|
||||
if child:
|
||||
child.setParent(None)
|
||||
|
||||
# 添加新的结果
|
||||
for i, detection in enumerate(self.detections):
|
||||
# 矫正车牌图像
|
||||
corrected_image = self.correct_license_plate(detection)
|
||||
|
||||
# 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
|
||||
# 添加新的稳定结果
|
||||
for i, result in enumerate(stable_results):
|
||||
plate_widget = LicensePlateWidget(
|
||||
i + 1,
|
||||
detection['class_name'],
|
||||
corrected_image,
|
||||
plate_number
|
||||
i + 1, # 显示序号
|
||||
result['class_name'],
|
||||
result['corrected_image'],
|
||||
result['plate_number']
|
||||
)
|
||||
|
||||
self.results_layout.addWidget(plate_widget)
|
||||
print(f"添加车牌widget: {result['plate_number']}")
|
||||
|
||||
# 更新存储的结果
|
||||
self.last_plate_results = stable_results
|
||||
|
||||
# 清理旧的车牌记录
|
||||
current_plate_ids = [result['id'] for result in stable_results]
|
||||
self.plate_stabilizer.clear_old_plates(current_plate_ids)
|
||||
print("结果显示更新完成")
|
||||
|
||||
def check_results_changed(self, new_results):
|
||||
"""检查识别结果是否发生变化"""
|
||||
if len(self.last_plate_results) != len(new_results):
|
||||
return True
|
||||
|
||||
for i, new_result in enumerate(new_results):
|
||||
if i >= len(self.last_plate_results):
|
||||
return True
|
||||
|
||||
old_result = self.last_plate_results[i]
|
||||
|
||||
# 比较关键字段
|
||||
if (old_result.get('class_name') != new_result.get('class_name') or
|
||||
old_result.get('plate_number') != new_result.get('plate_number')):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def correct_license_plate(self, detection):
|
||||
"""矫正车牌图像"""
|
||||
@ -395,8 +1059,15 @@ class MainWindow(QMainWindow):
|
||||
return "识别失败"
|
||||
|
||||
try:
|
||||
# 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 == "LightCRNN":
|
||||
from lightCRNN_part.lightcrnn_interface import LPRNmodel_predict
|
||||
elif self.current_recognition_method == "OCR":
|
||||
from OCR_part.ocr_interface import LPRNmodel_predict
|
||||
|
||||
# 预测函数（来自模块）
|
||||
# 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，支持8位车牌号
|
||||
@ -420,10 +1091,32 @@ class MainWindow(QMainWindow):
|
||||
print(f"车牌号识别失败: {e}")
|
||||
return "识别失败"
|
||||
|
||||
def change_recognition_method(self, method):
|
||||
"""切换识别方法"""
|
||||
self.current_recognition_method = method
|
||||
self.current_method_label.setText(f"当前识别方法: {method}")
|
||||
|
||||
# 初始化对应的模型
|
||||
if method == "CRNN":
|
||||
from CRNN_part.crnn_interface import LPRNinitialize_model
|
||||
LPRNinitialize_model()
|
||||
elif method == "LightCRNN":
|
||||
from lightCRNN_part.lightcrnn_interface import LPRNinitialize_model
|
||||
LPRNinitialize_model()
|
||||
elif method == "OCR":
|
||||
from OCR_part.ocr_interface import LPRNinitialize_model
|
||||
LPRNinitialize_model()
|
||||
|
||||
# 如果当前有显示的帧，重新处理以更新识别结果
|
||||
if self.current_frame is not None:
|
||||
self.process_frame(self.current_frame)
|
||||
|
||||
def closeEvent(self, event):
|
||||
"""窗口关闭事件"""
|
||||
if self.camera_thread:
|
||||
if self.camera_thread and self.camera_thread.running:
|
||||
self.camera_thread.stop_camera()
|
||||
if self.video_thread and self.video_thread.running:
|
||||
self.video_thread.stop_video()
|
||||
event.accept()
|
||||
|
||||
def main():
|
||||
|
||||
@ -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:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user