使用Python基于卷积神经网络实现是否佩戴安全帽的二分类问题带详细注释，并绘出损失和准确率的曲线图，并显示分类结果

在Python中，我们可以使用深度学习库如TensorFlow或PyTorch结合Keras库来实现基于卷积神经网络（Convolutional Neural Network, CNN）的二分类任务，比如识别行人是否佩戴了安全帽。以下是使用Keras的一个简单示例：

# 导入所需库
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical
import matplotlib.pyplot as plt

# 数据预处理
data_dir = 'your_data_directory'  # 替换为实际数据路径
img_width, img_height = 64, 64  # 图像尺寸
train_datagen = ImageDataGenerator(rescale=1./255)  # 对图像做归一化
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    data_dir + '/train',
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='binary')  # 二分类问题
validation_generator = validation_datagen.flow_from_directory(
    data_dir + '/validation',
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='binary')

# 构建CNN模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型并绘制训练过程
history = model.fit(train_generator,
                    epochs=10,
                    validation_data=validation_generator)

# 绘制损失和准确率曲线图
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss Curves')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Accuracy Curves')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

# 预测和展示分类结果
test_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(
    data_dir + '/test',
    target_size=(img_width, img_height),
    batch_size=1,
    class_mode=None)  # 这里不需要class_mode，因为我们只关心结果
predictions = model.predict(test_generator)
results = np.argmax(predictions, axis=1)
for i, img in enumerate(test_generator.filenames):
    print(f"Image: {img}\nPredicted: {'wearing hat' if results[i] else 'not wearing hat'}\n")

注意：这个例子假设你已经有了标注好的训练集、验证集和测试集，它们分别放在'data_dir/train'、'data_dir/validation'和'data_dir/test'下，每个目录下还应有对应的子目录，分别表示不同的类别。实际操作中你需要替换`your_data_directory`为实际的数据存储位置。