使用VGG实现识别静态和动态车牌的代码

以下是使用VGG实现识别静态和动态车牌的代码：

import cv2
import numpy as np
import os
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Flatten, Dense, Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K

# 设置输入图像的维度
img_width, img_height = 64, 64

# 训练集和验证集的路径
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'

# 设置训练集和验证集的样本数量
nb_train_samples = 800
nb_validation_samples = 200

# 设置训练的批次大小和训练的轮次数
epochs = 10
batch_size = 16

# 检测通道顺序
if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

# 创建VGG模型
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# 数据增强
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

# 训练模型
model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

# 保存模型
model.save_weights('models/car_plate_vgg.h5')

上述代码中，使用了VGG模型来进行车牌的识别。训练集和验证集的路径可以根据实际情况进行修改。通过数据增强，可以使得模型更加鲁棒。最后，训练好的模型可以保存在本地，并用于后续的车牌检测。