经过7日的飞桨深度学习，让我对PaddlePaddle有了更深的了解。课程在老师的引导教学下，通过理论与实践相结合，让我们了解掌握了很多前沿的深度学习神经网络。本来对Python并不熟悉的我，经过这7日的摸索，也掌握了部分Python编程规则。此外，在与同行的各个大佬的交流中，也让自己提升了很多下面分享一下我在课程中所学习到的一些实用代码。这个程序采用了LeNet经典神经网络，通过对大量数据的学习，来实现车牌识别。程序中也用到了图像增强技术。

#%%
#导入需要的包

import numpy as np
import paddle as paddle
import paddle.fluid as fluid
from PIL import Image
import cv2
import matplotlib.pyplot as plt
import os
from multiprocessing import cpu_count
from paddle.fluid.dygraph import Pool2D,Conv2D
# from paddle.fluid.dygraph import FC
from paddle.fluid.dygraph import Linear

# %%
# 生成车牌字符图像列表
data_path = 'e:/chepai/chepai/home/aistudio/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')

# %%
# 用上一步生成的图像列表定义车牌字符训练集和测试集的reader
def data_mapper(sample):
img, label = sample
img = paddle.dataset.image.load_image(file=img, is_color=False)
img = img.flatten().astype('float32') / 255.0
return img, label
def data_reader(data_list_path):
def reader():
with open(data_list_path, 'r') as f:
lines = f.readlines()
for line in lines:
img, label = line.split('\t')
yield img, int(label)
return paddle.reader.xmap_readers(data_mapper, reader, cpu_count(), 1024)

# %%
# 用于训练的数据提供器
train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=data_reader('./train_data.list'), buf_size=512), batch_size=128)
# 用于测试的数据提供器
test_reader = paddle.batch(reader=data_reader('./test_data.list'), batch_size=128)

# %%
#定义网络
class MyLeNet(fluid.dygraph.Layer):
def __init__(self):
super(MyLeNet,self).__init__()
self.hidden1_1 = Conv2D(1,28,5,1) #通道数、卷积核个数、卷积核大小
self.hidden1_2 = Pool2D(pool_size=2,pool_type='max',pool_stride=1)
self.hidden2_1 = Conv2D(28,32,3,1)
self.hidden2_2 = Pool2D(pool_size=2,pool_type='max',pool_stride=1)
self.hidden3 = Conv2D(32,32,3,1)
self.hidden4 = Linear(32*10*10,65,act='softmax')
def forward(self,input):
x = self.hidden1_1(input)
x = self.hidden1_2(x)
x = self.hidden2_1(x)
x = self.hidden2_2(x)
x = self.hidden3(x)
x = fluid.layers.reshape(x,shape=[-1,32*10*10])
y = self.hidden4(x)
return y
#%%#定义网络
class MyLeNet(fluid.dygraph.Layer):
def __init__(self):
super(MyLeNet,self).__init__()
self.conv1 = Conv2D(num_channels=1, num_filters=12, filter_size=3, stride=1, padding=1, act='relu')
self.conv2 = Conv2D(num_channels=12, num_filters=12, filter_size=3, stride=1, padding=1, act='relu')
self.conv3 = Conv2D(num_channels=12, num_filters=12, filter_size=3, stride=1, padding=1, act='relu')
self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.conv4 = Conv2D(num_channels=12, num_filters=24, filter_size=3, stride=1, padding=1, act='relu')
self.conv5 = Conv2D(num_channels=24, num_filters=24, filter_size=3, stride=1, padding=1, act='relu')
self.conv6 = Conv2D(num_channels=24, num_filters=24, filter_size=3, stride=1, padding=1, act='relu')
self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.conv7 = Conv2D(num_channels=24, num_filters=36, filter_size=3, stride=1, padding=1, act='relu')
self.conv8 = Conv2D(num_channels=36, num_filters=36, filter_size=3, stride=1, padding=1, act='relu')
self.fc1 = Linear(input_dim=36*5*5, output_dim=120, act='relu')
self.fc2 = Linear(input_dim=120, output_dim=84, act='relu')
self.fc3 = Linear(input_dim=84, output_dim=65, act='softmax')
def forward(self,input):
out = self.conv1(input)
out = self.conv2(out)
out = self.conv3(out)
out = fluid.layers.dropout(out, dropout_prob=0.1,is_test=label)
out = self.pool1(out)
out = self.conv4(out)
out = self.conv5(out)
out = self.conv6(out)
out = fluid.layers.dropout(out, dropout_prob=0.1,is_test=label)
out = self.pool2(out)
out = self.conv7(out)
out = self.conv8(out)
out = fluid.layers.dropout(out, dropout_prob=0.1,is_test=label)
out = fluid.layers.reshape(out, [out.shape[0], 36*5*5])
out = self.fc1(out)
out = self.fc2(out)
y = self.fc3(out)
return y
# %%
#画图
Iter=0
Iters=[]
all_train_loss=[]
all_train_accs=[]
def draw_train_process(iters,train_loss,train_accs):
title="training loss/training accs"
plt.title(title ,fontsize=24)
plt.xlabel("iter",fontsize=14)
plt.ylabel("loss/accs",fontsize=14)
plt.plot(iters,train_loss,colo='red',label='training loss')
plt.plot(iters,train_accs,colo='green',label='training accs')
plt.legend()
plt.grid()
plt.show()

# %%
#模型训练
with fluid.dygraph.guard():
model=MyLeNet() #模型实例化
model.train() #训练模式
opt=fluid.optimizer.SGDOptimizer(learning_rate=0.001, parameter_list=model.parameters())#优化器选用SGD随机梯度下降，学习率为0.001.
epochs_num=400 #迭代次数为2

for pass_num in range(epochs_num):

for batch_id,data in enumerate(train_reader()):
images=np.array([x[0].reshape(1,20,20) for x in data],np.float32)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]
image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)

predict=model(image)#预测

loss=fluid.layers.cross_entropy(predict,label)
avg_loss=fluid.layers.mean(loss)#获取loss值

acc=fluid.layers.accuracy(predict,label)#计算精度

#Iter = Iter + 32
#Iters.append(Iter)
#all_train_loss.append(loss.numpy([0])
#all_train_accs.append(acc.numpy()[0])

if batch_id!=0 and batch_id%50==0:
print("train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(pass_num,batch_id,avg_loss.numpy(),acc.numpy()))

avg_loss.backward()
opt.minimize(avg_loss)
model.clear_gradients()

fluid.save_dygraph(model.state_dict(),'MyLeNet')#保存模型

# %%
#模型校验
with fluid.dygraph.guard():
accs = []
model=MyLeNet()#模型实例化
model_dict,_=fluid.load_dygraph('MyLeNet')
model.load_dict(model_dict)#加载模型参数
model.eval()#评估模式
for batch_id,data in enumerate(test_reader()):#测试集
images=np.array([x[0].reshape(1,20,20) for x in data],np.float32)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]

image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)

predict=model(image)#预测
acc=fluid.layers.accuracy(predict,label)
accs.append(acc.numpy()[0])
avg_acc = np.mean(accs)
print(avg_acc)
#%%
# 对车牌图片进行处理，分割出车牌中的每一个字符并保存
license_plate = cv2.imread('./车牌.png')
gray_plate = cv2.cvtColor(license_plate, cv2.COLOR_RGB2GRAY)
ret, binary_plate = cv2.threshold(gray_plate, 175, 255, cv2.THRESH_BINARY)
result = []
for col in range(binary_plate.shape[1]):
result.append(0)
for row in range(binary_plate.shape[0]):
result[col] = result[col] + binary_plate[row][col]/255
character_dict = {}
num = 0
i = 0
while i < len(result):
if result[i] == 0:
i += 1
else:
index = i + 1
while result[index] != 0:
index += 1
character_dict[num] = [i, index-1]
num += 1
i = index

for i in range(8):
if i==2:
continue
padding = (170 - (character_dict[i][1] - character_dict[i][0])) / 2
ndarray = np.pad(binary_plate[:,character_dict[i][0]:character_dict[i][1]], ((0,0), (int(padding), int(padding))), 'constant', constant_values=(0,0))
ndarray = cv2.resize(ndarray, (20,20))
cv2.imwrite('./' + str(i) + '.png', ndarray)

def load_image(path):
img = paddle.dataset.image.load_image(file=path, is_color=False)
img = img.astype('float32')
img = img[np.newaxis, ] / 255.0
return img
#%%
#将标签进行转换
print('Label:',LABEL_temp)
match = {'A':'A','B':'B','C':'C','D':'D','E':'E','F':'F','G':'G','H':'H','I':'I','J':'J','K':'K','L':'L','M':'M','N':'N',
'O':'O','P':'P','Q':'Q','R':'R','S':'S','T':'T','U':'U','V':'V','W':'W','X':'X','Y':'Y','Z':'Z',
'yun':'云','cuan':'川','hei':'黑','zhe':'浙','ning':'宁','jin':'津','gan':'赣','hu':'沪','liao':'辽','jl':'吉','qing':'青','zang':'藏',
'e1':'鄂','meng':'蒙','gan1':'甘','qiong':'琼','shan':'陕','min':'闽','su':'苏','xin':'新','wan':'皖','jing':'京','xiang':'湘','gui':'贵',
'yu1':'渝','yu':'豫','ji':'冀','yue':'粤','gui1':'桂','sx':'晋','lu':'鲁',
'0':'0','1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9'}
L = 0
LABEL ={}

for V in LABEL_temp.values():
LABEL[str(L)] = match[V]
L += 1
print(LABEL)
#%%
#构建预测动态图过程
with fluid.dygraph.guard():
model=MyLeNet()#模型实例化
model_dict,_=fluid.load_dygraph('MyLeNet')
model.load_dict(model_dict)#加载模型参数
model.eval()#评估模式
lab=[]
for i in range(8):
if i==2:
continue
infer_imgs = []
infer_imgs.append(load_image('./' + str(i) + '.png'))
infer_imgs = np.array(infer_imgs)
infer_imgs = fluid.dygraph.to_variable(infer_imgs)
result=model(infer_imgs)
lab.append(np.argmax(result.numpy()))
# print(lab)

display(Image.open('./车牌.png'))
print('\n车牌识别结果为：',end='')
for i in range(len(lab)):
print(LABEL2[str(lab[i])],end='')
#print(str(lab[i]),end='')