Python实现CNN（卷积神经网络）图像分类算法

import tkinter as tk from tkinter import filedialog from PIL import Image, ImageTk import numpy as np import cv2 # 加载训练好的模型 from tensorflow.keras.models import load_model model = load_model('my_model.h5') # CIFAR-10的类别 classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] def classify_image(): # 获取图片路径 file_path = filedialog.askopenfilename() if not file_path: return # 显示选中的图片 img = Image.open(file_path) img_resized = img.resize((300, 300)) img_tk = ImageTk.PhotoImage(img_resized) panel.configure(image=img_tk) panel.image = img_tk # 预处理图片 img = img.resize((32, 32)) img = np.array(img) img = img.astype('float32') / 255.0 img = np.expand_dims(img, axis=0) # 预测 prediction = model.predict(img) predicted_class = classes[np.argmax(prediction)] # 显示预测结果 result_label.config(text=f"Prediction: {predicted_class}") # 创建Tkinter窗口 root = tk.Tk() root.title("Image Classification with CNN") root.geometry("400x400") # 创建显示图片的面板 panel = tk.Label(root) panel.pack() # 创建分类按钮 btn_classify = tk.Button(root, text="Classify Image", command=classify_image) btn_classify.pack() # 创建显示结果的标签 result_label = tk.Label(root, text="Prediction: None") result_label.pack() # 运行Tkinter主循环 root.mainloop()