使用Python实现深度学习模型：智能垃圾分类与回收系统

pip install pandas numpy scikit-learn tensorflow keras matplotlib seaborn opencv-python

import pandas as pd
import numpy as np
import os
import cv2
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

# 数据路径
data_dir = 'path_to_garbage_dataset'

# 类别标签
categories = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']

# 读取数据
data = []
labels = []

for category in categories:
    path = os.path.join(data_dir, category)
    class_num = categories.index(category)
    for img in os.listdir(path):
        try:
            img_array = cv2.imread(os.path.join(path, img))
            img_array = cv2.resize(img_array, (128, 128))
            data.append(img_array)
            labels.append(class_num)
        except Exception as e:
            pass

# 转换为NumPy数组
data = np.array(data)
labels = np.array(labels)

# 查看数据形状
print(f"数据形状: {data.shape}, 标签形状: {labels.shape}")

# 标准化图像数据
data = data.astype('float32') / 255.0

# 将标签转换为独热编码
labels = to_categorical(labels, num_classes=len(categories))

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

# 创建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(128, 128, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(categories), activation='softmax'))

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

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

# 训练模型
history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

# 评估模型
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Loss: {loss}')
print(f'Accuracy: {accuracy}')

# 预测
y_pred = model.predict(X_test)
y_pred_classes = y_pred.argmax(axis=-1)
y_true = y_test.argmax(axis=-1)

# 打印预测结果
print(y_pred_classes[:10])
print(y_true[:10])

# 可视化训练过程
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.title('Training and Validation Loss')

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

plt.show()

# 混淆矩阵
from sklearn.metrics import confusion_matrix
import seaborn as sns

cm = confusion_matrix(y_true, y_pred_classes)
plt.figure(figsize=(10, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=categories, yticklabels=categories)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()