Add lyh部分

lyh部分

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+import tensorflow as tf
+from tensorflow.keras.models import load_model, Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+def create_model():
+    model = Sequential([
+        Conv2D(32, (3, 3), activation='relu', input_shape=(640, 640, 3)),
+        MaxPooling2D((2, 2)),
+        Conv2D(64, (3, 3), activation='relu'),
+        MaxPooling2D((2, 2)),
+        Flatten(),
+        Dense(128, activation='relu'),
+        Dropout(0.5),
+        Dense(10, activation='softmax')  # 假设有10个交通标志类别
+    ])
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+def train_model(model, train_data_dir, validation_data_dir):
+    train_datagen = ImageDataGenerator(rescale=1./255)
+    val_datagen = ImageDataGenerator(rescale=1./255)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(640, 640),
+        batch_size=32,
+        class_mode='sparse'
+    )
+
+    validation_generator = val_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(640, 640),
+        batch_size=32,
+        class_mode='sparse'
+    )
+
+    model.fit(train_generator, epochs=10, validation_data=validation_generator)
+
+# 示例
+model = create_model()
+train_data_dir = 'path/to/train/data'
+validation_data_dir = 'path/to/validation/data'
+train_model(model, train_data_dir, validation_data_dir)
+
+# 保存模型
+model.save('path/to/your/model.h5')
+
+# 加载模型并预测
+model = load_model('path/to/your/model.h5')
+
+def predict_traffic_sign(image):
+    # 扩展维度以适配模型输入
+    image = np.expand_dims(image, axis=0)
+    # 模型预测
+    predictions = model.predict(image)
+    return predictions
+
+# 示例
+predictions = predict_traffic_sign(preprocessed_image)
+predicted_class = np.argmax(predictions, axis=1)