养生网站源码吉林seo基础
一、项目背景⭐：
本项目是“从零开始学习深度学习”系列中的第二个实战项目，旨在实现第一个简易App(图像分类任务——水果分类)，进一步地落地AI模型应用，帮助初学者初步了解模型落地。
基于PyQt5图形界面的水果图像分类系统，用户可以通过加载模型、选择图像并一键完成图像识别。
二、项目目标🚀：
基于PyQt5图形界面实现以下功能：
加载本地
.pth训练好的模型；加载本地图像进行展示；
自动完成图像预处理（Resize、ToTensor、Normalize）；
使用模型完成预测并展示结果；
界面美观，交互友好。
三、适合人群🫵：
- 深度学习零基础或刚入门的学习者
- 希望通过项目实战学习BP神经网络、卷积神经网络模型搭建的开发者
- 对图像识别、分类应用感兴趣的童鞋
- 适用于想学习通过界面实现AI模型推理，
四、项目实战✊：
1.主界面构建
def initUI(self):# 主窗口设置self.setWindowTitle("水果分类应用")self.setGeometry(100, 100, 800, 600)# 创建主窗口部件central_widget = QWidget()self.setCentralWidget(central_widget)# 创建主布局main_layout = QVBoxLayout()# 模型选择部分model_layout = QHBoxLayout()model_label = QLabel("模型路径：")self.model_path_edit = QtWidgets.QLineEdit()model_button = QPushButton("选择模型")model_button.clicked.connect(self.select_model_path)self.load_model_button = QPushButton("加载模型")self.load_model_button.clicked.connect(self.load_model)self.load_model_button.setEnabled(False)model_layout.addWidget(model_label)model_layout.addWidget(self.model_path_edit)model_layout.addWidget(model_button)model_layout.addWidget(self.load_model_button)main_layout.addLayout(model_layout)# 图像显示部分self.image_label = QLabel()self.image_label.setAlignment(QtCore.Qt.AlignCenter)self.image_label.setMinimumSize(600, 400)main_layout.addWidget(self.image_label)# 图像选择部分image_layout = QHBoxLayout()image_path_label = QLabel("图像路径：")self.image_path_edit = QtWidgets.QLineEdit()image_select_button = QPushButton("选择图像")image_select_button.clicked.connect(self.select_image_path)self.predict_button = QPushButton("分类预测")self.predict_button.clicked.connect(self.predict_image)self.predict_button.setEnabled(False)image_layout.addWidget(image_path_label)image_layout.addWidget(self.image_path_edit)image_layout.addWidget(image_select_button)image_layout.addWidget(self.predict_button)main_layout.addLayout(image_layout)# 结果显示部分self.result_label = QLabel("请先加载模型并选择图像")self.result_label.setAlignment(QtCore.Qt.AlignCenter)self.result_label.setStyleSheet("font-size: 20px")main_layout.addWidget(self.result_label)central_widget.setLayout(main_layout)2.功能辅助函数
def select_model_path(self):file_path, _ = QFileDialog.getOpenFileName(self,"选择模型文件","","Pytorch模型 (*.pth);;所有文件(*)")if file_path:self.model_path_edit.setText(file_path)self.load_model_button.setEnabled(True)def load_model(self):model_path = self.model_path_edit.text()if not model_path:returntry:# 模型类型（根据你的模型的时间需求进行修改）self.model = FruitClassificationModelResnet18(4)self.model.load_state_dict(torch.load(model_path, map_location=self.device, weights_only=False))self.model = self.model.to(self.device)self.model.eval()self.result_label.setText("模型加载成功!请选择图像进行预测.")self.predict_button.setEnabled(True)except Exception as e:self.result_label.setText(f"模型加载失败: {str(e)}")self.model = Noneself.predict_button.setEnabled(False)def select_image_path(self):file_path, _ = QFileDialog.getOpenFileName(self,"选择图像文件","","图像文件 (*bmp *.png *.jpg *.jpeg);;所有文件(*)")if file_path:self.image_path_edit.setText(file_path)self.display_image(file_path)def display_image(self, file_path):pixmap = QtGui.QPixmap(file_path)if not pixmap.isNull():scaled_pixmap = pixmap.scaled(self.image_label.size(),QtCore.Qt.KeepAspectRatio,QtCore.Qt.SmoothTransformation)self.image_label.setPixmap(scaled_pixmap)else:self.image_label.setText("无法加载图像")def preprocess_image(self, image_path):try:# 定义图像预处理流程transform = transforms.Compose([transforms.Resize((224, 224)), # 调整图像大小为224x224transforms.ToTensor(), # 转换为Tensor格式transforms.Normalize([0.485, 0.456, 0.406], # 标准化均值(ImageNet数据集)[0.229, 0.224, 0.225]) # 标准化标准差])# 打开图像文件image = Image.open(image_path)# 如果图像不是RGB模式，转换为RGBif image.mode != "RGB":image = image.convert("RGB")# 应用预处理变换并添加batch维度(unsqueeze(0))，然后移动到指定设备image = transform(image).unsqueeze(0).to(self.device)return imageexcept Exception as e:self.result_label.setText(f"图像预处理失败: {str(e)}")return None3.加载模型
def load_model(self):model_path = self.model_path_edit.text()if not model_path:returntry:# 模型类型（根据你的模型的时间需求进行修改）self.model = FruitClassificationModelResnet18(4)self.model.load_state_dict(torch.load(model_path, map_location=self.device, weights_only=False))self.model = self.model.to(self.device)self.model.eval()self.result_label.setText("模型加载成功!请选择图像进行预测.")self.predict_button.setEnabled(True)except Exception as e:self.result_label.setText(f"模型加载失败: {str(e)}")self.model = Noneself.predict_button.setEnabled(False)4.预测函数
def predict_image(self):if not self.model:self.result_label.setText("请先加载模型")returnimage_path = self.image_path_edit.text()if not image_path:self.result_label.setText("请选择图像")returninput_tensor = self.preprocess_image(image_path)if input_tensor is None:return# 预测with torch.no_grad():input_tensor = input_tensor.to(self.device)outputs = self.model(input_tensor)_, predicted = torch.max(outputs.data, 1)class_id = predicted.item()# 显示结果class_names = ['Apple', 'Banana', 'Orange', 'Pinenapple'] # 示例类别 根据你的模型进行修改if class_id < len(class_names):self.result_label.setText(f"预测结果: {class_names[class_id]}")else:self.result_label.setText(f"预测结果: 未知类别 ({class_id})")QtWidgets.QApplication.processEvents()6.完整实现代码
import cv2
import sys
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from PyQt5 import QtWidgets, QtCore, QtGui
from PyQt5.QtWidgets import QFileDialog, QLabel, QPushButton, QVBoxLayout, QWidget, QHBoxLayout
from model import FruitClassificationModelResnet18class FruitClassificationApp(QtWidgets.QMainWindow):def __init__(self):super().__init__()self.model = Noneself.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")self.initUI()def initUI(self):# 主窗口设置self.setWindowTitle("水果分类应用")self.setGeometry(100, 100, 800, 600)# 创建主窗口部件central_widget = QWidget()self.setCentralWidget(central_widget)# 创建主布局main_layout = QVBoxLayout()# 模型选择部分model_layout = QHBoxLayout()model_label = QLabel("模型路径：")self.model_path_edit = QtWidgets.QLineEdit()model_button = QPushButton("选择模型")model_button.clicked.connect(self.select_model_path)self.load_model_button = QPushButton("加载模型")self.load_model_button.clicked.connect(self.load_model)self.load_model_button.setEnabled(False)model_layout.addWidget(model_label)model_layout.addWidget(self.model_path_edit)model_layout.addWidget(model_button)model_layout.addWidget(self.load_model_button)main_layout.addLayout(model_layout)# 图像显示部分self.image_label = QLabel()self.image_label.setAlignment(QtCore.Qt.AlignCenter)self.image_label.setMinimumSize(600, 400)main_layout.addWidget(self.image_label)# 图像选择部分image_layout = QHBoxLayout()image_path_label = QLabel("图像路径：")self.image_path_edit = QtWidgets.QLineEdit()image_select_button = QPushButton("选择图像")image_select_button.clicked.connect(self.select_image_path)self.predict_button = QPushButton("分类预测")self.predict_button.clicked.connect(self.predict_image)self.predict_button.setEnabled(False)image_layout.addWidget(image_path_label)image_layout.addWidget(self.image_path_edit)image_layout.addWidget(image_select_button)image_layout.addWidget(self.predict_button)main_layout.addLayout(image_layout)# 结果显示部分self.result_label = QLabel("请先加载模型并选择图像")self.result_label.setAlignment(QtCore.Qt.AlignCenter)self.result_label.setStyleSheet("font-size: 20px")main_layout.addWidget(self.result_label)central_widget.setLayout(main_layout)def select_model_path(self):file_path, _ = QFileDialog.getOpenFileName(self,"选择模型文件","","Pytorch模型 (*.pth);;所有文件(*)")if file_path:self.model_path_edit.setText(file_path)self.load_model_button.setEnabled(True)def load_model(self):model_path = self.model_path_edit.text()if not model_path:returntry:# 模型类型（根据你的模型的时间需求进行修改）self.model = FruitClassificationModelResnet18(4)self.model.load_state_dict(torch.load(model_path, map_location=self.device, weights_only=False))self.model = self.model.to(self.device)self.model.eval()self.result_label.setText("模型加载成功!请选择图像进行预测.")self.predict_button.setEnabled(True)except Exception as e:self.result_label.setText(f"模型加载失败: {str(e)}")self.model = Noneself.predict_button.setEnabled(False)def select_image_path(self):file_path, _ = QFileDialog.getOpenFileName(self,"选择图像文件","","图像文件 (*bmp *.png *.jpg *.jpeg);;所有文件(*)")if file_path:self.image_path_edit.setText(file_path)self.display_image(file_path)def display_image(self, file_path):pixmap = QtGui.QPixmap(file_path)if not pixmap.isNull():scaled_pixmap = pixmap.scaled(self.image_label.size(),QtCore.Qt.KeepAspectRatio,QtCore.Qt.SmoothTransformation)self.image_label.setPixmap(scaled_pixmap)else:self.image_label.setText("无法加载图像")def preprocess_image(self, image_path):try:# 定义图像预处理流程transform = transforms.Compose([transforms.Resize((224, 224)), # 调整图像大小为224x224transforms.ToTensor(), # 转换为Tensor格式transforms.Normalize([0.485, 0.456, 0.406], # 标准化均值(ImageNet数据集)[0.229, 0.224, 0.225]) # 标准化标准差])# 打开图像文件image = Image.open(image_path)# 如果图像不是RGB模式，转换为RGBif image.mode != "RGB":image = image.convert("RGB")# 应用预处理变换并添加batch维度(unsqueeze(0))，然后移动到指定设备image = transform(image).unsqueeze(0).to(self.device)return imageexcept Exception as e:self.result_label.setText(f"图像预处理失败: {str(e)}")return Nonedef predict_image(self):if not self.model:self.result_label.setText("请先加载模型")returnimage_path = self.image_path_edit.text()if not image_path:self.result_label.setText("请选择图像")returninput_tensor = self.preprocess_image(image_path)if input_tensor is None:return# 预测with torch.no_grad():input_tensor = input_tensor.to(self.device)outputs = self.model(input_tensor)_, predicted = torch.max(outputs.data, 1)class_id = predicted.item()# 显示结果class_names = ['Apple', 'Banana', 'Orange', 'Pinenapple'] # 示例类别 根据你的模型进行修改if class_id < len(class_names):self.result_label.setText(f"预测结果: {class_names[class_id]}")else:self.result_label.setText(f"预测结果: 未知类别 ({class_id})")QtWidgets.QApplication.processEvents()if __name__ == '__main__':app = QtWidgets.QApplication(sys.argv)window = FruitClassificationApp()window.show()sys.exit(app.exec_())五、学习收获🎁：
通过本次 PyTorch 与 PyQt5 的项目实战，我不仅巩固了深度学习模型的使用方法，也系统地学习了如何将模型部署到图形界面中。以下是我的一些具体收获：
1️⃣ 深度学习模型部署实践
学会了如何将
.pth格式的模型加载到推理环境；熟悉了图像的预处理流程（如Resize、ToTensor、Normalize）；
掌握了
torch.no_grad()推理模式下的使用，避免梯度计算加速推理。
2️⃣ PyQt5 图形界面开发
掌握了 PyQt5 中常用的控件如
QLabel、QPushButton、QLineEdit等；学会了如何使用
QFileDialog实现文件选择；了解了如何通过
QPixmap加载并展示图像；熟悉了
QVBoxLayout和QHBoxLayout进行界面布局。
3️⃣ 端到端流程整合
实现了从模型加载 → 图像读取 → 图像预处理 → 推理 → 展示结果 的完整流程；
初步理解了如何将 AI 模型变成一个用户可交互的软件；
为后续构建更复杂的推理系统（如视频流识别、多模型切换）打下了基础。
注：完整代码，请私聊，免费获取。