百度paddle学习日记（三)-----车牌识别
百度paddle学习日记(三）------车牌识别
今天的任务也就开门见山了
车牌识别
意思和题目一样，让计算机能看懂 车牌！
先介绍一下
本次使用CNN 也就是卷积神经网络。
通过过滤器和池化层对图像进行处理来达到学习的目的！
训练数据和paddle教程可分别在以下两个网址获取：
https://aistudio.baidu.com
https://www.paddlepaddle.org.cn
废话不多说，直接上代码：
#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
2.生成图像列表有利于之后的批处理：
# 生成车牌字符图像列表
data_path = '/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)
`
```python
class DensenNet(fluid.dygraph.Layer):
def __init__(self,training=True):
super(DensenNet,self).__init__()
self.conv1 = Conv2D(num_channels=1,num_filters=32,filter_size=5,act='relu')
self.pool1 = Pool2D(pool_size=2,pool_stride=2,pool_type='max')
self.conv2 = Conv2D(num_channels=32, num_filters=64, filter_size=5, act='relu')
self.pool2 = Pool2D(pool_size=2, pool_stride=2,pool_type='max')
self.conv3 = Conv2D(num_channels=64, num_filters=128, filter_size=1, act='relu')
#self.pool3 = Pool2D(pool_size=2, pool_stride=2,pool_type='max')
#self.conv4 = Conv2D(num_channels=128,num_filters=256,filter_size=1,act='relu')
self.fc1 = Linear(input_dim=128*4,output_dim=256,act='relu')
self.drop_ratiol = 0.5 if training else 0.0
self.fc2 = Linear(input_dim=256,output_dim=65,act='softmax')
def forward(self,input1):
conv1 = self.conv1(input1)
pool1 = self.pool1(conv1)
#
conv2 = self.conv2(pool1)
pool2 = self.pool2(conv2)
#
conv3 = self.conv3(pool2)
#pool3 = self.pool3(conv3)
#
#conv4 = self.conv4(conv3)
rs_1 = fluid.layers.reshape(conv3,[conv3.shape[0],-1])
fc1 = self.fc1(rs_1)
drop1 = fluid.layers.dropout(fc1,self.drop_ratiol)
y = self.fc2(drop1)
return y
上面这里就设计了一个卷积神经网络 其中 W2=(W1-F+2P)/S+1
H2=(H2-F+2P)/S+1
最后的input_dim=num_filters最后输出的大小(如22 3*3）
with fluid.dygraph.guard():
model = DensenNet() # 模型实例化
model.train() # 训练模式
opt = fluid.optimizer.SGDOptimizer(learning_rate=0.003,
parameter_list=model.parameters()) # 优化器选用SGD随机梯度下降，学习率为0.003.
epochs_num = 125# 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
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
规范化操作 ，可以调节学习率和训练次数来提高模型的准确率
#模型校验
with fluid.dygraph.guard():
accs = []
model=DensenNet()#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=DensenNet()#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(LABEL[str(lab[i])],end='')
成功运行 AE86
