垃圾分类的Python代码

垃圾分类的Python代码通常涉及到图像识别和分类算法，例如使用深度学习库（如TensorFlow或PyTorch）结合卷积神经网络（CNN）。以下是一个简单的概念性的代码示例，用于说明如何用Python进行垃圾分类：

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

# 加载预训练模型ResNet50
base_model = ResNet50(weights='imagenet', include_top=False)

# 定义新的顶部分类层
num_classes = 4  # 假设我们有4种垃圾类别
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation='softmax')(x)

# 创建新模型
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结预训练模型的权重
for layer in base_model.layers:
    layer.trainable = False

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 数据预处理，这里使用ImageDataGenerator处理图片数据增强
train_datagen = ImageDataGenerator(rescale=1./255)  # 预处理
train_generator = train_datagen.flow_from_directory(
    'path_to_train_data',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical')

# 训练模型
model.fit(train_generator, epochs=10)

# 使用模型进行预测
def classify_image(image_path):
    img = preprocess_input(tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224)))
    img = tf.keras.preprocessing.image.img_to_array(img)
    img = tf.expand_dims(img, axis=0)
    predictions = model.predict(img)
    return np.argmax(predictions)

# 示例
image_path = 'path_to_test_image'
predicted_class = classify_image(image_path)
print(f"Predicted class: {predicted_class}")