Delete 'main'

main

@@ -1,61 +0,0 @@
-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)