DAY 43 复习日

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers, models, applications
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.models import load_model
import cv2# 设置中文显示
plt.rcParams["font.family"] = ["SimHei", "WenQuanYi Micro Hei", "Heiti TC"]class DataLoader:def __init__(self, data_dir, img_size=(224, 224), batch_size=32):self.data_dir = data_dirself.img_size = img_sizeself.batch_size = batch_sizedef load_data(self):"""加载并预处理图像数据"""# 数据增强配置train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=20,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True,validation_split=0.2)test_datagen = ImageDataGenerator(rescale=1./255)# 生成训练集和验证集train_generator = train_datagen.flow_from_directory(os.path.join(self.data_dir, 'train'),target_size=self.img_size,batch_size=self.batch_size,class_mode='categorical',subset='training')val_generator = train_datagen.flow_from_directory(os.path.join(self.data_dir, 'train'),target_size=self.img_size,batch_size=self.batch_size,class_mode='categorical',subset='validation')# 生成测试集test_generator = test_datagen.flow_from_directory(os.path.join(self.data_dir, 'test'),target_size=self.img_size,batch_size=self.batch_size,class_mode='categorical')return train_generator, val_generator, test_generatorclass CNNModel:def __init__(self, input_shape, num_classes):self.input_shape = input_shapeself.num_classes = num_classesdef build_simple_cnn(self):"""构建简单的CNN模型"""model = models.Sequential([layers.Conv2D(32, (3, 3), activation='relu', input_shape=self.input_shape),layers.MaxPooling2D((2, 2)),layers.Conv2D(64, (3, 3), activation='relu'),layers.MaxPooling2D((2, 2)),layers.Conv2D(128, (3, 3), activation='relu'),layers.MaxPooling2D((2, 2)),layers.Flatten(),layers.Dense(128, activation='relu'),layers.Dropout(0.5),layers.Dense(self.num_classes, activation='softmax')])model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])return modeldef build_pretrained_model(self, model_name='vgg16'):"""构建基于预训练模型的CNN"""if model_name == 'vgg16':base_model = applications.VGG16(weights='imagenet',include_top=False,input_shape=self.input_shape)elif model_name == 'resnet50':base_model = applications.ResNet50(weights='imagenet',include_top=False,input_shape=self.input_shape)else:raise ValueError("不支持的预训练模型")# 冻结预训练层for layer in base_model.layers:layer.trainable = False# 添加自定义层x = base_model.outputx = layers.GlobalAveragePooling2D()(x)x = layers.Dense(256, activation='relu')(x)x = layers.Dropout(0.5)(x)predictions = layers.Dense(self.num_classes, activation='softmax')(x)model = models.Model(inputs=base_model.input, outputs=predictions)model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])return model, base_modelclass ModelTrainer:def __init__(self, model, model_path='best_model.h5'):self.model = modelself.model_path = model_pathdef train(self, train_generator, val_generator, epochs=10):"""训练模型"""callbacks = [EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True),ModelCheckpoint(self.model_path, monitor='val_accuracy', save_best_only=True),ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2, min_lr=0.00001)]history = self.model.fit(train_generator,steps_per_epoch=train_generator.samples // train_generator.batch_size,validation_data=val_generator,validation_steps=val_generator.samples // val_generator.batch_size,epochs=epochs,callbacks=callbacks)return historyclass GradCAM:def __init__(self, model, class_names, layer_name=None):self.model = modelself.class_names = class_names# 如果没有指定层名，尝试自动找到最后一个卷积层if layer_name is None:layer_name = self._find_last_conv_layer()self.layer_name = layer_namedef _find_last_conv_layer(self):"""自动查找模型的最后一个卷积层"""for layer in reversed(self.model.layers):if 'conv' in layer.name:return layer.nameraise ValueError("模型中没有找到卷积层")def generate_heatmap(self, img_array, pred_index=None):"""生成Grad-CAM热力图"""# 创建一个用于获取输出的模型grad_model = tf.keras.models.Model([self.model.inputs], [self.model.get_layer(self.layer_name).output, self.model.output])# 计算梯度with tf.GradientTape() as tape:conv_outputs, predictions = grad_model(img_array)if pred_index is None:pred_index = tf.argmax(predictions[0])class_channel = predictions[:, pred_index]# 获取梯度grads = tape.gradient(class_channel, conv_outputs)# 平均梯度pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))# 权重激活映射conv_outputs = conv_outputs[0]heatmap = tf.reduce_mean(tf.multiply(pooled_grads, conv_outputs), axis=-1)# 归一化热力图heatmap = np.maximum(heatmap, 0) / np.max(heatmap)return heatmapdef overlay_heatmap(self, heatmap, img, alpha=0.4):"""将热力图叠加到原图上"""# 调整热力图大小以匹配原图heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))# 将热力图转换为RGBheatmap = np.uint8(255 * heatmap)heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)# 将热力图叠加到原图上superimposed_img = heatmap * alpha + imgsuperimposed_img = np.uint8(255 * superimposed_img / np.max(superimposed_img))return superimposed_imgdef visualize(self, img_path, img_size=(224, 224), alpha=0.4, top_n=3):"""可视化Grad-CAM结果"""# 加载和预处理图像img = tf.keras.preprocessing.image.load_img(img_path, target_size=img_size)img_array = tf.keras.preprocessing.image.img_to_array(img)img_array = np.expand_dims(img_array, axis=0)img_array = img_array / 255.0# 预测类别predictions = self.model.predict(img_array)top_indices = np.argsort(predictions[0])[::-1][:top_n]# 生成热力图heatmap = self.generate_heatmap(img_array)# 加载原图用于显示original_img = cv2.imread(img_path)original_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB)original_img = cv2.resize(original_img, img_size)# 叠加热力图superimposed_img = self.overlay_heatmap(heatmap, original_img, alpha)# 显示结果plt.figure(figsize=(15, 5))plt.subplot(131)plt.title('原始图像')plt.imshow(original_img)plt.axis('off')plt.subplot(132)plt.title('Grad-CAM热力图')plt.imshow(heatmap, cmap='jet')plt.axis('off')plt.subplot(133)plt.title('叠加结果')plt.imshow(superimposed_img)plt.axis('off')# 显示预测结果plt.figtext(0.5, 0.01, f"预测结果:\n" + "\n".join([f"{self.class_names[idx]}: {predictions[0][idx]:.2%}" for idx in top_indices]),ha="center", fontsize=12)plt.tight_layout()plt.show()def download_kaggle_dataset(api_command):"""使用Kaggle API下载数据集"""print(f"正在下载数据集: {api_command}")os.system(f"kaggle datasets download {api_command} -p ./data --unzip")print("数据集下载完成")def main():# 设置参数data_dir = './data/your_dataset'  # 数据集路径img_size = (224, 224)batch_size = 32epochs = 10model_path = 'best_model.h5'use_pretrained = True  # 是否使用预训练模型# 下载Kaggle数据集（取消注释并提供正确的API命令）# download_kaggle_dataset('username/dataset-name')# 加载数据data_loader = DataLoader(data_dir, img_size, batch_size)train_generator, val_generator, test_generator = data_loader.load_data()num_classes = len(train_generator.class_indices)class_names = list(train_generator.class_indices.keys())# 构建模型cnn_model = CNNModel(input_shape=(*img_size, 3), num_classes=num_classes)if use_pretrained:model, base_model = cnn_model.build_pretrained_model()last_conv_layer = base_model.get_layer('block5_conv3').name  # VGG16最后一个卷积层else:model = cnn_model.build_simple_cnn()last_conv_layer = None  # 自动查找最后一个卷积层# 训练模型trainer = ModelTrainer(model, model_path)history = trainer.train(train_generator, val_generator, epochs)# 评估模型test_loss, test_acc = model.evaluate(test_generator)print(f"测试准确率: {test_acc:.2%}")# 可视化训练历史plt.figure(figsize=(12, 4))plt.subplot(121)plt.plot(history.history['accuracy'])plt.plot(history.history['val_accuracy'])plt.title('模型准确率')plt.ylabel('准确率')plt.xlabel('训练轮次')plt.legend(['训练', '验证'], loc='upper left')plt.subplot(122)plt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('模型损失')plt.ylabel('损失')plt.xlabel('训练轮次')plt.legend(['训练', '验证'], loc='upper left')plt.tight_layout()plt.show()# 加载最佳模型用于Grad-CAMbest_model = load_model(model_path)# 选择一个测试图像进行Grad-CAM可视化test_image_path = os.path.join(data_dir, 'test', class_names[0], os.listdir(os.path.join(data_dir, 'test', class_names[0]))[0])# 创建Grad-CAM对象并可视化grad_cam = GradCAM(best_model, class_names, last_conv_layer)grad_cam.visualize(test_image_path)if __name__ == "__main__":main()