第P10周：Pytorch实现车牌识别

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊
  一、导入数据

#一、导入数据
'''
1.1设置GPU
'''
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings
import torch.nn.functional as F
import matplotlib.pyplot as plt
import pandas as pd
from torchvision.io import read_image
from torch.utils.data import Dataset
import torch.utils.data as data
from PIL import Image
import copy
 
warnings.filterwarnings("ignore")        #忽略警告信息--？？？怎么突然增加一个这个
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

'''
1.2 获取类别名
'''
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
 
data_dir = '/home/aiusers/space_yjl/深度学习训练营/pytorch入门实战/第P10周：Pytorch实现车牌识别/015_licence_plate'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("/")[8].split("_")[1].split(".")[0] for path in data_paths]
print(classeNames)
#这里一个车牌就是一类，因为我们的这里的数据是没有分类的，一个车牌就是一类
 
data_paths = list(data_dir.glob('*'))
data_paths_str = [str(path) for path in data_paths]
print(data_paths_str)

'''
1.3 数据可视化
'''
plt.figure(figsize=(14, 5))
plt.suptitle("数据示例", fontsize=15)
for i in range(18):
    plt.subplot(3, 6, i+1)
    images = plt.imread(data_paths_str[i])
    plt.imshow(images)
plt.show()

'''
1.4 标签数字化
'''
import numpy as np
char_enum = ["京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏",