用PaddlePaddle（飞浆）实现车牌识别
【摘要】
项目描述：本次实践是一个多分类任务，需要将照片中的每个字符分别进行识别，完成车牌的识别
实践平台：百度AI实训平台-AI Studio、PaddlePaddle1.8.0 动态图
数据集介绍（自己去网上下载车牌识别数据集）
数据集文件名为characterData.zip，其中有65个文件夹 包含0-9，A-Z，以及各省简...
项目描述：本次实践是一个多分类任务，需要将照片中的每个字符分别进行识别，完成车牌的识别
实践平台：百度AI实训平台-AI Studio、PaddlePaddle1.8.0 动态图
数据集介绍（自己去网上下载车牌识别数据集）
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数据集文件名为characterData.zip，其中有65个文件夹
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包含0-9，A-Z，以及各省简称
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图片为12020的灰度图像
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本次实验中，取其中的10%作为测试集，90%作为训练集
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#导入需要的包
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import os
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import zipfile
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import random
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import json
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import cv2
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import numpy as np
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from PIL import Image
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.dygraph import Linear
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import matplotlib.pyplot as plt
1、数据准备
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'''
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参数配置
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'''
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train_parameters = {
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"input_size": [1, 20, 20], #输入图片的shape
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"class_dim": -1, #分类数
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"src_path":"data/data23617/characterData.zip", #原始数据集路径
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"target_path":"/home/aistudio/data/dataset", #要解压的路径
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"train_list_path": "./train_data.txt", #train_data.txt路径
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"eval_list_path": "./val_data.txt", #eval_data.txt路径
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"label_dict":{}, #标签字典
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"readme_path": "/home/aistudio/data/readme.json", #readme.json路径
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"num_epochs": 100, #训练轮数
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"train_batch_size": 32, #批次的大小
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"learning_strategy": { #优化函数相关的配置
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"lr": 0.0005 #超参数学习率
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}
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}
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def unzip_data(src_path,target_path):
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'''
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解压原始数据集，将src_path路径下的zip包解压至data/dataset目录下
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'''
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if(not os.path.isdir(target_path)):
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z = zipfile.ZipFile(src_path, 'r')
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z.extractall(path=target_path)
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z.close()
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else:
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print("文件已解压")
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def get_data_list(target_path,train_list_path,eval_list_path):
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'''
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生成数据列表
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'''
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#存放所有类别的信息
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class_detail = []
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#获取所有类别保存的文件夹名称
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data_list_path=target_path
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class_dirs = os.listdir(data_list_path)
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if '__MACOSX' in class_dirs:
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class_dirs.remove('__MACOSX')
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# #总的图像数量
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all_class_images = 0
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# #存放类别标签
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class_label=0
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# #存放类别数目
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class_dim = 0
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# #存储要写进eval.txt和train.txt中的内容
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trainer_list=[]
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eval_list=[]
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#读取每个类别
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for class_dir in class_dirs:
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if class_dir != ".DS_Store":
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class_dim += 1
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#每个类别的信息
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class_detail_list = {}
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eval_sum = 0
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trainer_sum = 0
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#统计每个类别有多少张图片
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class_sum = 0
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#获取类别路径
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path = os.path.join(data_list_path,class_dir)
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# print(path)
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# 获取所有图片
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img_paths = os.listdir(path)
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for img_path in img_paths: # 遍历文件夹下的每个图片
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if img_path =='.DS_Store':
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continue
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name_path = os.path.join(path,img_path) # 每张图片的路径
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if class_sum % 10 == 0: # 每10张图片取一个做验证数据
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eval_sum += 1 # eval_sum为测试数据的数目
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eval_list.append(name_path + "\t%d" % class_label + "\n")
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else:
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trainer_sum += 1
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trainer_list.append(name_path + "\t%d" % class_label + "\n")#trainer_sum测试数据的数目
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class_sum += 1 #每类图片的数目
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all_class_images += 1 #所有类图片的数目
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# 说明的json文件的class_detail数据
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class_detail_list['class_name'] = class_dir #类别名称
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class_detail_list['class_label'] = class_label #类别标签
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class_detail_list['class_eval_images'] = eval_sum #该类数据的测试集数目
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class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目
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class_detail.append(class_detail_list)
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#初始化标签列表
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train_parameters['label_dict'][str(class_label)] = class_dir
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class_label += 1
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#初始化分类数
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train_parameters['class_dim'] = class_dim
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print(train_parameters)
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#乱序
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random.shuffle(eval_list)
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with open(eval_list_path, 'a') as f:
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for eval_image in eval_list:
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f.write(eval_image)
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#乱序
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random.shuffle(trainer_list)
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with open(train_list_path, 'a') as f2:
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for train_image in trainer_list:
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f2.write(train_image)
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# 说明的json文件信息
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readjson = {}
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readjson['all_class_name'] = data_list_path #文件父目录
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readjson['all_class_images'] = all_class_images
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readjson['class_detail'] = class_detail
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jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': '))
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with open(train_parameters['readme_path'],'w') as f:
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f.write(jsons)
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print ('生成数据列表完成！')
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def data_reader(file_list):
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'''
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自定义data_reader
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'''
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def reader():
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with open(file_list, 'r') as f:
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lines = [line.strip() for line in f]
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for line in lines:
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img_path, lab = line.strip().split('\t')
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img = cv2.imread(img_path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = np.array(img).astype('float32')
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img = img/255.0
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yield img, int(lab)
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return reader
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'''
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参数初始化
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'''
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src_path=train_parameters['src_path']
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target_path=train_parameters['target_path']
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train_list_path=train_parameters['train_list_path']
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eval_list_path=train_parameters['eval_list_path']
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batch_size=train_parameters['train_batch_size']
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'''
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解压原始数据到指定路径
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'''
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unzip_data(src_path,target_path)
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#每次生成数据列表前，首先清空train.txt和eval.txt
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with open(train_list_path, 'w') as f:
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f.seek(0)
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f.truncate()
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with open(eval_list_path, 'w') as f:
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f.seek(0)
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f.truncate()
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#生成数据列表
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get_data_list(target_path,train_list_path,eval_list_path)
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'''
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构造数据提供器
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'''
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train_reader = paddle.batch(data_reader(train_list_path),
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batch_size=batch_size,
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drop_last=True)
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eval_reader = paddle.batch(data_reader(eval_list_path),
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batch_size=batch_size,
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drop_last=True)
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Batch=0
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Batchs=[]
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all_train_accs=[]
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def draw_train_acc(Batchs, train_accs):
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title="training accs"
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plt.title(title, fontsize=24)
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plt.xlabel("batch", fontsize=14)
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plt.ylabel("acc", fontsize=14)
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plt.plot(Batchs, train_accs, color='green', label='training accs')
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plt.legend()
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plt.grid()
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plt.show()
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all_train_loss=[]
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def draw_train_loss(Batchs, train_loss):
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title="training loss"
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plt.title(title, fontsize=24)
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plt.xlabel("batch", fontsize=14)
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plt.ylabel("loss", fontsize=14)
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plt.plot(Batchs, train_loss, color='red', label='training loss')
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plt.legend()
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plt.grid()
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plt.show()
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Batch=0
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Batchs=[]
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all_train_accs=[]
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def draw_train_acc(Batchs, train_accs):
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title="training accs"
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plt.title(title, fontsize=24)
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plt.xlabel("batch", fontsize=14)
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plt.ylabel("acc", fontsize=14)
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plt.plot(Batchs, train_accs, color='green', label='training accs')
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plt.legend()
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plt.grid()
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plt.show()
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all_train_loss=[]
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def draw_train_loss(Batchs, train_loss):
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title="training loss"
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plt.title(title, fontsize=24)
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plt.xlabel("batch", fontsize=14)
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plt.ylabel("loss", fontsize=14)
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plt.plot(Batchs, train_loss, color='red', label='training loss')
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plt.legend()
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plt.grid()
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plt.show()
2、定义模型
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定义DNN网络
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class MyDNN(fluid.dygraph.Layer):
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'''
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DNN网络
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'''
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def __init__(self):
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super(MyDNN,self).__init__()
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self.hidden1= Linear(20*20,400,act='relu')
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self.hidden2 = Linear(400,200,act='relu')
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self.hidden3 = Linear(200,100,act='relu')
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self.out = Linear(100,65,act='softmax')
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def forward(self,input): # forward 定义执行实际运行时网络的执行逻辑
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'''前向计算'''
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x = fluid.layers.reshape(input, shape=[-1,20*20]) #-1 表示这个维度的值是从x的元素总数和剩余维度推断出来的，有且只能有一个维度设置为-1
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x = self.hidden1(x)
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x = self.hidden2(x)
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# print('2',x.shape)
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x = self.hidden3(x)
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# print('3',x.shape)
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y = self.out(x)
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# print('4',y.shape)
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return y
3、训练模型
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with fluid.dygraph.guard():
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model=MyDNN() #模型实例化
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model.train() #训练模式
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opt=fluid.optimizer.SGDOptimizer(learning_rate=train_parameters['learning_strategy']['lr'], parameter_list=model.parameters())#优化器选用SGD随机梯度下降，学习率为0.001.
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epochs_num=train_parameters['num_epochs'] #迭代次数
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for pass_num in range(epochs_num):
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for batch_id,data in enumerate(train_reader()):
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images=np.array([x[0].reshape(1,20,20) for x in data],np.float32)
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labels = np.array([x[1] for x in data]).astype('int64')
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labels = labels[:, np.newaxis]
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image=fluid.dygraph.to_variable(images)
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label=fluid.dygraph.to_variable(labels)
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predict=model(image) #数据传入model
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loss=fluid.layers.cross_entropy(predict,label)
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avg_loss=fluid.layers.mean(loss)#获取loss值
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acc=fluid.layers.accuracy(predict,label)#计算精度
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if batch_id!=0 and batch_id%50==0:
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Batch = Batch+50
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Batchs.append(Batch)
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all_train_loss.append(avg_loss.numpy()[0])
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all_train_accs.append(acc.numpy()[0])
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print("train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(pass_num,batch_id,avg_loss.numpy(),acc.numpy()))
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avg_loss.backward()
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opt.minimize(avg_loss) #优化器对象的minimize方法对参数进行更新
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model.clear_gradients() #model.clear_gradients()来重置梯度
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fluid.save_dygraph(model.state_dict(),'MyDNN')#保存模型
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draw_train_acc(Batchs,all_train_accs)
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draw_train_loss(Batchs,all_train_loss)
训练结果：
4、模型评估
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#模型评估
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with fluid.dygraph.guard():
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accs = []
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model_dict, _ = fluid.load_dygraph('MyDNN')
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model = MyDNN()
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model.load_dict(model_dict) #加载模型参数
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model.eval() #训练模式
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for batch_id,data in enumerate(eval_reader()):#测试集
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images=np.array([x[0].reshape(1,20,20) for x in data],np.float32)
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labels = np.array([x[1] for x in data]).astype('int64')
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labels = labels[:, np.newaxis]
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image=fluid.dygraph.to_variable(images)
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label=fluid.dygraph.to_variable(labels)
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predict=model(image)
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acc=fluid.layers.accuracy(predict,label)
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accs.append(acc.numpy()[0])
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avg_acc = np.mean(accs)
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print(avg_acc)
输出精确率为：
0.9289216
5、使用模型
5.1对车牌图像进行预处理
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# 对车牌图片进行处理，分割出车牌中的每一个字符并保存
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license_plate = cv2.imread('work/车牌.png')
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gray_plate = cv2.cvtColor(license_plate, cv2.COLOR_RGB2GRAY)
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ret, binary_plate = cv2.threshold(gray_plate, 175, 255, cv2.THRESH_BINARY) #ret：阈值，binary_plate：根据阈值处理后的图像数据
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# 按列统计像素分布
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result = []
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for col in range(binary_plate.shape[1]):
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result.append(0)
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for row in range(binary_plate.shape[0]):
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result[col] = result[col] + binary_plate[row][col]/255
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# print(result)
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#记录车牌中字符的位置
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character_dict = {}
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num = 0
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i = 0
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while i < len(result):
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if result[i] == 0:
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i += 1
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else:
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index = i + 1
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while result[index] != 0:
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index += 1
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character_dict[num] = [i, index-1]
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num += 1
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i = index
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# print(character_dict)
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#将每个字符填充，并存储
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characters = []
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for i in range(8):
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if i==2:
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continue
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padding = (170 - (character_dict[i][1] - character_dict[i][0])) / 2
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#将单个字符图像填充为170*170
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ndarray = np.pad(binary_plate[:,character_dict[i][0]:character_dict[i][1]], ((0,0), (int(padding), int(padding))), 'constant', constant_values=(0,0))
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ndarray = cv2.resize(ndarray, (20,20))
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cv2.imwrite('work/' + str(i) + '.png', ndarray)
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characters.append(ndarray)
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def load_image(path):
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img = paddle.dataset.image.load_image(file=path, is_color=False)
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img = img.astype('float32')
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img = img[np.newaxis, ] / 255.0
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return img
5.2 对标签进行转换
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#将标签进行转换
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print('Label:',train_parameters['label_dict'])
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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',
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'O':'O','P':'P','Q':'Q','R':'R','S':'S','T':'T','U':'U','V':'V','W':'W','X':'X','Y':'Y','Z':'Z',
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'yun':'云','cuan':'川','hei':'黑','zhe':'浙','ning':'宁','jin':'津','gan':'赣','hu':'沪','liao':'辽','jl':'吉','qing':'青','zang':'藏',
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'e1':'鄂','meng':'蒙','gan1':'甘','qiong':'琼','shan':'陕','min':'闽','su':'苏','xin':'新','wan':'皖','jing':'京','xiang':'湘','gui':'贵',
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'yu1':'渝','yu':'豫','ji':'冀','yue':'粤','gui1':'桂','sx':'晋','lu':'鲁',
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'0':'0','1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9'}
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L = 0
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LABEL ={}
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for V in train_parameters['label_dict'].values():
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LABEL[str(L)] = match[V]
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L += 1
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print(LABEL)
输出label为：
Label: {'0': 'sx', '1': 'e1', '2': 'yu1', '3': 'yue', '4': 'K', '5': 'P', '6': '3', '7': 'qing', '8': 'yu', '9': 'E', '10': 'zang', '11': 'xin', '12': 'J', '13': 'hei', '14': 'M', '15': 'lu', '16': 'S', '17': '6', '18': '0', '19': 'hu', '20': 'U', '21': 'A', '22': 'D', '23': 'shan', '24': 'zhe', '25': 'liao', '26': 'H', '27': 'Z', '28': 'wan', '29': 'N', '30': 'W', '31': 'C', '32': 'meng', '33': 'X', '34': '8', '35': 'F', '36': 'jl', '37': 'R', '38': 'ji', '39': 'Q', '40': 'Y', '41': 'yun', '42': 'gan1', '43': 'L', '44': 'cuan', '45': '9', '46': 'su', '47': 'jin', '48': 'min', '49': 'V', '50': '1', '51': 'gui1', '52': 'B', '53': '7', '54': 'xiang', '55': 'qiong', '56': 'G', '57': 'jing', '58': 'ning', '59': 'T', '60': '5', '61': 'gui', '62': '2', '63': '4', '64': 'gan'}
{'0': '晋', '1': '鄂', '2': '渝', '3': '粤', '4': 'K', '5': 'P', '6': '3', '7': '青', '8': '豫', '9': 'E', '10': '藏', '11': '新', '12': 'J', '13': '黑', '14': 'M', '15': '鲁', '16': 'S', '17': '6', '18': '0', '19': '沪', '20': 'U', '21': 'A', '22': 'D', '23': '陕', '24': '浙', '25': '辽', '26': 'H', '27': 'Z', '28': '皖', '29': 'N', '30': 'W', '31': 'C', '32': '蒙', '33': 'X', '34': '8', '35': 'F', '36': '吉', '37': 'R', '38': '冀', '39': 'Q', '40': 'Y', '41': '云', '42': '甘', '43': 'L', '44': '川', '45': '9', '46': '苏', '47': '津', '48': '闽', '49': 'V', '50': '1', '51': '桂', '52': 'B', '53': '7', '54': '湘', '55': '琼', '56': 'G', '57': '京', '58': '宁', '59': 'T', '60': '5', '61': '贵', '62': '2', '63': '4', '64': '赣'}
5.3 使用模型进行预测
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#构建预测动态图过程
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with fluid.dygraph.guard():
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model=MyDNN()#模型实例化
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model_dict,_=fluid.load_dygraph('MyDNN')
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model.load_dict(model_dict)#加载模型参数
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model.eval()#评估模式
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lab=[]
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for i in range(8):
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if i==2:
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continue
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infer_imgs = []
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infer_imgs.append(load_image('work/' + str(i) + '.png'))
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infer_imgs = np.array(infer_imgs)
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infer_imgs = fluid.dygraph.to_variable(infer_imgs)
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result=model(infer_imgs)
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lab.append(np.argmax(result.numpy()))
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print(lab)
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display(Image.open('work/车牌.png'))
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for i in range(len(lab)):
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print(LABEL[str(lab[i])],end='')
输出：[15, 21, 17, 34, 17, 9, 12]
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=722x170 at 0x7F8E98B3C410>
预测：鲁A686EJ。。表示预测正确
文章来源: blog.csdn.net，作者：小小谢先生，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/xiewenrui1996/article/details/107714711
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