Pytorch入门实战：10-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

在之前的案例中，我们多是使用`datasets.ImageFolder`函数直接导入已经分类好的数据集形成`Dataset`，然后使用`DataLoader`加载`Dataset`，但是如果对无法分类的数据集，我们如何导入，并进行识别呢？

本周我将自定义一个`MyDataset`加载车牌数据集并完成车牌识别

一、导入数据

from torchvision.transforms import transforms
from torch.utils.data       import DataLoader
from torchvision            import datasets
import torchvision.models   as models
import torch.nn.functional  as F
import torch.nn             as nn
import torch,torchvision

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

👉代码输出:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

1. 获取类别名

import os,PIL,random,pathlib
import matplotlib.pyplot as plt
# 支持中文
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 = './015_licence_plate/'
data_dir = pathlib.Path(data_dir)

data_paths  = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1].split("_")[1].split(".")[0] for path in data_paths]
print(classeNames)

👉代码输出

['川W9BR26', '藏WP66B0', '沪E264UD', '津D8Z15T'...]

data_paths     = list(data_dir.glob('*'))
data_paths_str = [str(path) for path in data_paths]
data_paths_str

👉代码输出

['015_licence_plate\\000000000_川W9BR26.jpg',
 '015_licence_plate\\000000000_藏WP66B0.jpg',
 '015_licence_plate\\000000001_沪E264UD.jpg',
 
 ......
 
 '015_licence_plate\\000000003_甘G24298.jpg',
 '015_licence_plate\\000000003_青SN18Q3.jpg',
 '015_licence_plate\\000000004_云HZR899.jpg',
 ...]

2. 数据可视化

plt.figure(figsize=(14,5))
plt.suptitle("数据示例（K同学啊）",fontsize=15)

for i in range(18):
    plt.subplot(3,6,i+1)
    # plt.xticks([])
    # plt.yticks([])
    # plt.grid(False)
    
    # 显示图片
    images = plt.imread(data_paths_str[i])
    plt.imshow(images)

plt.show()

3. 标签数字化

import numpy as np

char_enum = ["京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏",&#