为基于cnn实现书法字体风格识别的python代码设计编写pyqt的gui界面，该程序包含以下几个模块（class MainWindow(QMainWindow)，def load_model(self)，def open_image(self)，def preprocess_images(self, image_paths)，def predict_images(self)，def display_images(self, image_paths)，def classify_image(self)）要求按一次“选择图片”按钮可以直接输入五张图片，并且按一次识别图片按钮之后一次性输出五个结果：class LeNet5(nn.Module): def init(self, num_class=10): super(LeNet5, self).init() self.conv1 = nn.Conv2d(3, 8, 5) self.pool1 = nn.AvgPool2d((2, 2)) self.conv2 = nn.Conv2d(8, 16, 5) self.pool2 = nn.AvgPool2d((2, 2)) self.conv3 = nn.Conv2d(16, 32, 5) self.relu = nn.ReLU() self.fc1 = nn.Linear(28800, 1024) self.fc2 = nn.Linear(1024, num_class) # 实现LeNet5模型的前向传播过程。输入通过卷积层、激活函数和池化层进行特征提取，然后通过全连接层进行分类。 def forward(self, x): # x: torch.Size([32, 3, 150, 150]) x = self.conv1(x) # torch.Size([32, 8, 146, 146]) x = self.relu(x) x = self.pool1(x) # torch.Size([32, 8, 73, 73]) x = self.conv2(x) # torch.Size([32, 16, 69, 69]) x = self.relu(x) x = self.pool2(x) # torch.Size([32, 16, 34, 34]) x = self.conv3(x) # torch.Size([32, 32, 30, 30]) x = self.relu(x) x = x.flatten(start_dim=1) # torch.Size([32, 28800]) x = self.fc1(x) # torch.Size([32, 2024]) x = self.relu(x) x = self.fc2(x) # torch.Size([32, 4]) return x

很高兴为您解答这个问题！以下是一个基于PyQt的GUI界面的代码示例，其中包含了您提供的函数和LeNet5模型的代码实现： ```python import sys from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QPushButton, QFileDialog from PyQt5.QtGui import QPixmap import torch import torch.nn as nn from torchvision import transforms class LeNet5(nn.Module): def __init__(self, num_class=10): super(LeNet5, self).__init__() self.conv1 = nn.Conv2d(3, 8, 5) self.pool1 = nn.AvgPool2d((2, 2)) self.conv2 = nn.Conv2d(8, 16, 5) self.pool2 = nn.AvgPool2d((2, 2)) self.conv3 = nn.Conv2d(16, 32, 5) self.relu = nn.ReLU() self.fc1 = nn.Linear(28800, 1024) self.fc2 = nn.Linear(1024, num_class) def forward(self, x): x = self.conv1(x) x = self.relu(x) x = self.pool1(x) x = self.conv2(x) x = self.relu(x) x = self.pool2(x) x = self.conv3(x) x = self.relu(x) x = x.flatten(start_dim=1) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) return x class MainWindow(QMainWindow): def __init__(self): super().__init__() self.title = '书法字体风格识别' self.left = 100 self.top = 100 self.width = 600 self.height = 400 self.initUI() self.model = None self.transform = transforms.Compose([ transforms.Resize((150, 150)), # 将所有图像缩放到150x150 transforms.ToTensor(), # 将图像转换为张量 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 标准化图像张量 ]) self.image_paths = [] def initUI(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height) # 创建标签和按钮 self.label = QLabel(self) self.label.move(50, 50) self.label.resize(150, 150) self.button_load_model = QPushButton('加载模型', self) self.button_load_model.move(50, 250) self.button_load_model.clicked.connect(self.load_model) self.button_open_image = QPushButton('选择图片', self) self.button_open_image.move(200, 250) self.button_open_image.clicked.connect(self.open_image) self.button_predict_images = QPushButton('识别图片', self) self.button_predict_images.move(350, 250) self.button_predict_images.clicked.connect(self.predict_images) def load_model(self): # 加载预训练的模型 self.model = LeNet5() self.model.load_state_dict(torch.load('lenet5.pth', map_location=torch.device('cpu'))) self.model.eval() def open_image(self): # 打开文件对话框选择图像文件 options = QFileDialog.Options() options |= QFileDialog.DontUseNativeDialog files, _ = QFileDialog.getOpenFileNames(self, "选择图像文件", "", "Image Files (*.jpg *.jpeg *.png *.bmp)", options=options) self.image_paths = files[:5] # 最多选择5张图片 self.display_images(self.image_paths) def preprocess_images(self, image_paths): # 预处理图像：将所有图像缩放、转换为张量并标准化 images = [] for path in image_paths: image = Image.open(path) image = self.transform(image) images.append(image) images = torch.stack(images) return images def predict_images(self): if not self.model: return if not self.image_paths: return images = self.preprocess_images(self.image_paths) outputs = self.model(images) _, predicted = torch.max(outputs, 1) results = predicted.tolist() self.display_results(results) def display_images(self, image_paths): # 在标签上显示图像 pixmap = QPixmap(image_paths[0]) pixmap = pixmap.scaled(150, 150) self.label.setPixmap(pixmap) for i in range(1, len(image_paths)): label = QLabel(self) label.move(50 + i * 150, 50) label.resize(150, 150) pixmap = QPixmap(image_paths[i]) pixmap = pixmap.scaled(150, 150) label.setPixmap(pixmap) def display_results(self, results): # 在标签上显示结果 for i in range(len(results)): label = QLabel(self) label.move(50 + i * 150, 230) label.resize(150, 20) text = '预测结果：{}'.format(results[i]) label.setText(text) if __name__ == '__main__': app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec_()) ``` 上述代码中，`MainWindow`是主窗口类，包括了`load_model()`、`open_image()`、`preprocess_images()`、`predict_images()`和`display_images()`等方法，用于加载预训练模型、打开图像文件、预处理图像、预测图像并在GUI界面上显示图像和结果。`LeNet5`是一个PyTorch模型类，用于实现LeNet5模型的前向传播过程。在`MainWindow`类中，我们使用了PyQt的各种组件和布局方式，实现了一个简单的GUI界面，包括了标签、按钮等。 在`open_image()`方法中，我们使用了`QFileDialog`类打开文件对话框，让用户选择图像文件。在`preprocess_images()`方法中，我们使用了`PIL`库对图像进行预处理，包括将图像缩放到150x150、转换为张量并标准化。在`predict_images()`方法中，我们使用预训练的LeNet5模型对图像进行预测，并在GUI界面上显示预测结果。 总体来说，这个代码示例实现了一个基于PyQt的GUI界面，可以识别书法字体风格。您可以根据自己的需求进行修改和优化。