百度飞桨python训练营结营创意赛项目——车牌检测

纸船

Paddlehub简介

PaddleHub是飞桨预训练模型管理工具和迁移学习工具，可以便捷地获取PaddlePaddle生态下的预训练模型，涵盖了图像分类、目标检测、词法分析、语义模型、情感分析、语言模型、视频分类、图像生成、图像分割九类主流模型。本课程将持续更新关于PaddleHub产品使用的案例及教程。

官网：https://paddlepaddle.org.cn/hub
GitHub：https://github.com/PaddlePaddle/PaddleHub

设计理念：模型即软件

• 模型一键下载、管理、预测
• 一键自动超参优化
• 一键私有化部署

预训练模型库，资源强大快速验证

• Master模式，百度AI生态不断输送自有高性能模型
• 支持命令行运行，快速高效
• 迁移学习工具，满足实际业务应用需求
• 丰富的fine-tune API，十行代码完成迁移学习

【适合对象】

• 广泛，初学者友好（基本Python编程能力）
• 私有化友好（基于PaddlePaddle开源框架，局域网、私有数据都OK）

项目说明

随着科技的进步，时代的发展，智能网联技术已愈发成熟，在此背景下，传统汽车也向着智能化、网联化的方向发展，例如，现在火热的无人驾驶汽车，下图是百度的无人驾驶汽车系列之一——Apollo

无人驾驶汽车系统可以分为三个部分：感知信息、路径规划和决策控制。其中感知层作为最重要的部分之一，包括信号灯检测、行人检测、车道线检测等内容，其中车牌检测也是部分之一，而车牌信息检测也广泛的应用在其它场景当中，例如：停车场、小区或学校的关卡等，都设有车牌信息识别系统。
本项目以此背景为依托，以paddlehub作为深度学习的框架，来进行车牌检测的建设。

代码展示

#解压车牌信息数据集文件
!unzip -q /home/aistudio/data/data23617/characterData.zip

#导入需要的包
import os
import cv2
import numpy as np
import paddle as paddle
import paddle.fluid as fluid
from PIL import Image
import matplotlib.pyplot as plt
from multiprocessing import cpu_count
from paddle.fluid.dygraph import Pool2D,Conv2D
from paddle.fluid.dygraph import Linear

# 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':continueprint(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 = sampleimg = paddle.dataset.image.load_image(file=img, is_color=False)img = img.flatten().astype('float32') / 255.0return 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)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

import os
import paddle.fluid as fluid
place = fluid.CUDAPlace(0)
with fluid.dygraph.guard(place):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= 300 #迭代次数为2for 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

#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 += 1else:index = i + 1while result[index] != 0:index += 1character_dict[num] = [i, index - 1]num += 1i = indexfor i in range(8):if i == 2:continuepadding = (170 - (character_dict[i][1] - character_dict[i][0])) / 2ndarray = 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,] / 255.0return 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:continueinfer_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()))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='')

总结心得

本项目平台依托百度paddlehub和AI Studio以及python来实现车牌信息的检测，方便我们可以进行下一步的操作。当然，对于此项目的算法。我们最终的准确率是0.97104911，网络类型采用的是Lenet,所以为了提高识别速率和准确率也还可以换用其他类型的网络，或者对数据集的图片进行归一化、灰度化处理等操作来增强图像的特征，感兴趣的同学可以自行尝试一下。