TensorFlow 2.0 简单车牌识别
TensorFlow 2.0 简单车牌识别
说明：使用tensorflow2 训练模型，然后冻结模型，使用opencv 的 dnn 模块进行推断。这只是个简单的demo,本质上就是个分类模型。主要工作还是在车牌字符的分割。在推断部分只要主意好图片的格式就好。
GitHub
1、tensorflow 训练模型
（1）数据集准备
使用 百度 ai studio 上提供的车牌数据集
部分数据集图片（图片命名还是比较乱的）：
（2）数据集处理
data_path = 'E:\\code\\tf\\proj\\car_num\\data'
character_folders = os.listdir(data_path)
label = 0
LABEL_temp = {
}
if(os.path.exists('./train_data.list')):
os.remove('./train_data.list')
if(os.path.exists('./test_data.list')):
os.remove('./test_data.list')
for character_folder in character_folders:
with open('./train_data.list', 'a') as f_train:
with open('./test_data.list', 'a') as f_test:
if character_folder == '.DS_Store' or character_folder == '.ipynb_checkpoints' or character_folder == 'data23617':
continue
print(character_folder + " " + str(label))
LABEL_temp[str(label)] = character_folder #存储一下标签的对应关系
character_imgs = os.listdir(os.path.join(data_path, character_folder))
for i in range(len(character_imgs)):
if i%10 == 0:
f_test.write(os.path.join(os.path.join(data_path, character_folder), character_imgs[i]) + "\t" + str(label) + '\n')
else:
f_train.write(os.path.join(os.path.join(data_path, character_folder), character_imgs[i]) + "\t" + str(label) + '\n')
label = label + 1
print('图像列表已生成')
all_image_paths = []
all_image_labels = []
test_image_paths = []
test_image_labels = []
with open('./train_data.list', 'r') as f:
lines = f.readlines()
for line in lines:
img, label = line.split('\t')
all_image_paths.append(img)
all_image_labels.append(int(label))
with open('./test_data.list', 'r') as f:
lines = f.readlines()
for line in lines:
img, label = line.split('\t')
test_image_paths.append(img)
test_image_labels.append(int(label))
def preprocess_image(image):
image = tf.image.decode_jpeg(image, channels=3)
image = tf.cast(image,dtype=tf.float32)
image = tf.image.resize(image, [20, 20])
image /= 255.0 # normalize to [0,1] range
return image
def load_and_preprocess_image(path,label):
image = tf.io.read_file(path)
return preprocess_image(image), label
ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))
train_data = ds.map(load_and_preprocess_image).batch(64)
db = tf.data.Dataset.from_tensor_slices((test_image_paths, test_image_labels))
test_data = db.map(load_and_preprocess_image).batch(64)
（3）搭建网络训练模型
def train_model(train_data,test_data):
#构建模型
network = keras.Sequential([
keras.layers.Conv2D(32, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
keras.layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
keras.layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
keras.layers.BatchNormalization(),
keras.layers.Flatten(),
keras.layers.Dense(512, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(65)])
network.build(input_shape=(None, 20, 20, 3))
network.summary()
reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')
network.compile(optimizer=optimizers.SGD(lr=0.001),
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
network.fit(train_data, epochs=100, validation_data=test_data, callbacks=[reduce_lr])
network.evaluate(test_data)
tf.saved_model.save(network, 'E:\\code\\tf\proj\\car_num\\model\\')
train_model(train_data,test_data)
模型测试集上的准确率大概 97%，忘了截图了。
2、模型转换
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
import matplotlib.pyplot as plt
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
print('tf版本：',tf.__version__)
DEFAULT_FUNCTION_KEY = "serving_default"
loaded = tf.saved_model.load('.\\model\\')
network = loaded.signatures[DEFAULT_FUNCTION_KEY]
print(list(loaded.signatures.keys()))
print('加载 weights 成功')
# Convert Keras model to ConcreteFunction
full_model = tf.function(lambda x: network(x))
full_model = full_model.get_concrete_function(
tf.TensorSpec(network.inputs[0].shape, network.inputs[0].dtype))
# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
layers = [op.name for op in frozen_func.graph.get_operations()]
print("-" * 50)
print("Frozen model layers: ")
for layer in layers:
print(layer)
print("Frozen model inputs: ")
print(frozen_func.inputs)
print("Frozen model outputs: ")
print(frozen_func.outputs)
# Save frozen graph from frozen ConcreteFunction to hard drive
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
logdir="./frozen_models",
name="frozen_graph.pb",
as_text=False)
3、OpenCV C++ 推断。
#include
#include
#include
#define DEBUG
using namespace std;
using namespace cv;
using namespace cv::dnn;
string label_list[] = {"0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B",
"C", "川",
"D", "E", "鄂", "F",
"G", "赣", "甘", "贵", "桂",
"H", "黑", "沪",
"J", "冀", "津", "京", "吉",
"K", "L", "辽", "鲁", "M", "蒙", "闽",
"N", "宁",
"P", "Q", "青", "琼",
"R", "S", "陕", "苏", "晋",
"T", "U", "V", "W", "皖",
"X", "湘", "新",
"Y", "豫", "渝", "粤", "云",
"Z", "藏", "浙"};
int main()
{
string model_path = "../infer/frozen_graph.pb";
Net net = readNetFromTensorflow(model_path);
Mat license_plate = imread("E:\\code\\tf\\proj\\car_num\\zf.png",1);
int image_h = license_plate.rows;
// cout< pix(binary_plate.cols,0);
for(int i = 0; i < binary_plate.cols;i++)
{
pix.push_back(0);
for(int j = 0;j(j,i);
}
// printf("%d ",pix[i]);
// cout< index_range;
while(i < pix.size())
{
if (pix[i] == 0)
{
i +=1 ;
}
else
{
uint index = i + 1;
while(pix[index] != 0)
{
index += 1;
}
index_range.push_back(Point(i,index-1));
num += 1;
i = index;
}
}
// cout< seg_img;
for(uint i = 0,num = 0;i 