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
#导入需要的包
import os
import zipfile
import random
import json
import cv2
import numpy as np
from PIL import Image
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph import Linear
import matplotlib.pyplot as plt
1、数据准备
'''
参数配置
'''
train_parameters = {"input_size": [1, 20, 20], #输入图片的shape"class_dim": -1, #分类数"src_path":"data/data23617/characterData.zip", #原始数据集路径"target_path":"/home/aistudio/data/dataset", #要解压的路径 "train_list_path": "./train_data.txt", #train_data.txt路径"eval_list_path": "./val_data.txt", #eval_data.txt路径"label_dict":{}, #标签字典"readme_path": "/home/aistudio/data/readme.json", #readme.json路径"num_epochs": 100, #训练轮数"train_batch_size": 32, #批次的大小"learning_strategy": { #优化函数相关的配置"lr": 0.0005 #超参数学习率}
}def unzip_data(src_path,target_path):'''解压原始数据集，将src_path路径下的zip包解压至data/dataset目录下'''if(not os.path.isdir(target_path)): z = zipfile.ZipFile(src_path, 'r')z.extractall(path=target_path)z.close()else:print("文件已解压")def get_data_list(target_path,train_list_path,eval_list_path):'''生成数据列表'''#存放所有类别的信息class_detail = []#获取所有类别保存的文件夹名称data_list_path=target_pathclass_dirs = os.listdir(data_list_path)if '__MACOSX' in class_dirs:class_dirs.remove('__MACOSX')# #总的图像数量all_class_images = 0# #存放类别标签class_label=0# #存放类别数目class_dim = 0# #存储要写进eval.txt和train.txt中的内容trainer_list=[]eval_list=[]#读取每个类别for class_dir in class_dirs:if class_dir != ".DS_Store":class_dim += 1#每个类别的信息class_detail_list = {}eval_sum = 0trainer_sum = 0#统计每个类别有多少张图片class_sum = 0#获取类别路径 path = os.path.join(data_list_path,class_dir)# print(path)# 获取所有图片img_paths = os.listdir(path)for img_path in img_paths: # 遍历文件夹下的每个图片if img_path =='.DS_Store':continuename_path = os.path.join(path,img_path) # 每张图片的路径if class_sum % 10 == 0: # 每10张图片取一个做验证数据eval_sum += 1 # eval_sum为测试数据的数目eval_list.append(name_path + "\t%d" % class_label + "\n")else:trainer_sum += 1 trainer_list.append(name_path + "\t%d" % class_label + "\n")#trainer_sum测试数据的数目class_sum += 1 #每类图片的数目all_class_images += 1 #所有类图片的数目# 说明的json文件的class_detail数据class_detail_list['class_name'] = class_dir #类别名称class_detail_list['class_label'] = class_label #类别标签class_detail_list['class_eval_images'] = eval_sum #该类数据的测试集数目class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目class_detail.append(class_detail_list) #初始化标签列表train_parameters['label_dict'][str(class_label)] = class_dirclass_label += 1#初始化分类数train_parameters['class_dim'] = class_dimprint(train_parameters)#乱序 random.shuffle(eval_list)with open(eval_list_path, 'a') as f:for eval_image in eval_list:f.write(eval_image) #乱序 random.shuffle(trainer_list) with open(train_list_path, 'a') as f2:for train_image in trainer_list:f2.write(train_image) # 说明的json文件信息readjson = {}readjson['all_class_name'] = data_list_path #文件父目录readjson['all_class_images'] = all_class_imagesreadjson['class_detail'] = class_detailjsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': '))with open(train_parameters['readme_path'],'w') as f:f.write(jsons)print ('生成数据列表完成！')
def data_reader(file_list):'''自定义data_reader'''def reader():with open(file_list, 'r') as f:lines = [line.strip() for line in f]for line in lines:img_path, lab = line.strip().split('\t')img = cv2.imread(img_path)img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)img = np.array(img).astype('float32')img = img/255.0yield img, int(lab) return reader'''
参数初始化
'''
src_path=train_parameters['src_path']
target_path=train_parameters['target_path']
train_list_path=train_parameters['train_list_path']
eval_list_path=train_parameters['eval_list_path']
batch_size=train_parameters['train_batch_size']
'''
解压原始数据到指定路径
'''
unzip_data(src_path,target_path)#每次生成数据列表前，首先清空train.txt和eval.txt
with open(train_list_path, 'w') as f: f.seek(0)f.truncate()
with open(eval_list_path, 'w') as f: f.seek(0)f.truncate() #生成数据列表
get_data_list(target_path,train_list_path,eval_list_path)'''
构造数据提供器
'''
train_reader = paddle.batch(data_reader(train_list_path),batch_size=batch_size,drop_last=True)
eval_reader = paddle.batch(data_reader(eval_list_path),batch_size=batch_size,drop_last=True)Batch=0
Batchs=[]
all_train_accs=[]
def draw_train_acc(Batchs, train_accs):title="training accs"plt.title(title, fontsize=24)plt.xlabel("batch", fontsize=14)plt.ylabel("acc", fontsize=14)plt.plot(Batchs, train_accs, color='green', label='training accs')plt.legend()plt.grid()plt.show()all_train_loss=[]
def draw_train_loss(Batchs, train_loss):title="training loss"plt.title(title, fontsize=24)plt.xlabel("batch", fontsize=14)plt.ylabel("loss", fontsize=14)plt.plot(Batchs, train_loss, color='red', label='training loss')plt.legend()plt.grid()plt.show()Batch=0
Batchs=[]
all_train_accs=[]
def draw_train_acc(Batchs, train_accs):title="training accs"plt.title(title, fontsize=24)plt.xlabel("batch", fontsize=14)plt.ylabel("acc", fontsize=14)plt.plot(Batchs, train_accs, color='green', label='training accs')plt.legend()plt.grid()plt.show()all_train_loss=[]
def draw_train_loss(Batchs, train_loss):title="training loss"plt.title(title, fontsize=24)plt.xlabel("batch", fontsize=14)plt.ylabel("loss", fontsize=14)plt.plot(Batchs, train_loss, color='red', label='training loss')plt.legend()plt.grid()plt.show()2、定义模型
定义DNN网络
class MyDNN(fluid.dygraph.Layer):'''DNN网络'''def __init__(self):super(MyDNN,self).__init__()self.hidden1= Linear(20*20,400,act='relu')self.hidden2 = Linear(400,200,act='relu')self.hidden3 = Linear(200,100,act='relu')self.out = Linear(100,65,act='softmax')def forward(self,input): # forward 定义执行实际运行时网络的执行逻辑'''前向计算'''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-1x = self.hidden1(x)x = self.hidden2(x)# print('2',x.shape)x = self.hidden3(x)# print('3',x.shape)y = self.out(x)# print('4',y.shape)return y3、训练模型
with fluid.dygraph.guard():model=MyDNN() #模型实例化model.train() #训练模式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.epochs_num=train_parameters['num_epochs'] #迭代次数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) #数据传入modelloss=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:Batch = Batch+50 Batchs.append(Batch)all_train_loss.append(avg_loss.numpy()[0])all_train_accs.append(acc.numpy()[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) #优化器对象的minimize方法对参数进行更新 model.clear_gradients() #model.clear_gradients()来重置梯度fluid.save_dygraph(model.state_dict(),'MyDNN')#保存模型draw_train_acc(Batchs,all_train_accs)
draw_train_loss(Batchs,all_train_loss)训练结果：
4、模型评估
#模型评估
with fluid.dygraph.guard():accs = []model_dict, _ = fluid.load_dygraph('MyDNN')model = MyDNN()model.load_dict(model_dict) #加载模型参数model.eval() #训练模式for batch_id,data in enumerate(eval_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) acc=fluid.layers.accuracy(predict,label)accs.append(acc.numpy()[0])avg_acc = np.mean(accs)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
# 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('work/车牌.png')
gray_plate = cv2.cvtColor(license_plate, cv2.COLOR_RGB2GRAY)
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
# 按列统计像素分布
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
# print(result)
#记录车牌中字符的位置
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 = index
# print(character_dict)
#将每个字符填充，并存储
characters = []
for i in range(8):if i==2:continuepadding = (170 - (character_dict[i][1] - character_dict[i][0])) / 2#将单个字符图像填充为170*170ndarray = 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('work/' + str(i) + '.png', ndarray)characters.append(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 img5.2 对标签进行转换
#将标签进行转换
print('Label:',train_parameters['label_dict'])
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 train_parameters['label_dict'].values():LABEL[str(L)] = match[V]L += 1
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
#构建预测动态图过程
with fluid.dygraph.guard():model=MyDNN()#模型实例化model_dict,_=fluid.load_dygraph('MyDNN')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('work/' + 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('work/车牌.png'))
for i in range(len(lab)):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
