pytorch图片验证码识别问题代码

以下是一个基于PyTorch的图片验证码识别问题代码的示例：

import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
import os

# 定义模型结构
class CaptchaModel(torch.nn.Module):
    def __init__(self):
        super(CaptchaModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.pool1 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool2 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool3 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.fc1 = torch.nn.Linear(128*4*13, 1024)
        self.fc2 = torch.nn.Linear(1024, 4*10)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.nn.functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = torch.nn.functional.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = torch.nn.functional.relu(x)
        x = self.pool3(x)
        x = x.view(-1, 128*4*13)
        x = self.fc1(x)
        x = torch.nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 加载模型
model = CaptchaModel()
model.load_state_dict(torch.load('captcha_model.pth'))

# 定义验证码字符集
charset = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'

# 定义预处理函数
preprocess = transforms.Compose([
    transforms.Resize((50, 200)),
    transforms.ToTensor(),
])

# 定义测试函数
def test_captcha(image_path):
    # 加载图像
    image = Image.open(image_path).convert('RGB')
    # 预处理图像
    image = preprocess(image)
    # 扩展维度
    image = image.unsqueeze(0)
    # 预测结果
    output = model(image)
    # 获取预测结果的索引
    output = torch.argmax(output, dim=1)
    # 将索引转换成字符
    captcha = ''.join([charset[i] for i in output])
    return captcha

# 定义测试函数
def test(dataset_path):
    # 加载数据集
    dataset = ImageFolder(dataset_path, transform=preprocess)
    # 创建数据加载器
    dataloader = DataLoader(dataset, batch_size=1)
    # 初始化正确率
    accuracy = 0
    # 遍历数据集
    for images, labels in dataloader:
        # 获取图像路径
        image_path = dataset.samples[labels[0]][0]
        # 获取真实验证码
        captcha = os.path.splitext(os.path.basename(image_path))[0]
        # 预测验证码
        predict_captcha = test_captcha(image_path)
        # 判断是否正确
        if captcha == predict_captcha:
            accuracy += 1
        else:
            print(f'Error: {captcha} -> {predict_captcha}')
    # 输出正确率
    print(f'Accuracy: {accuracy}/{len(dataset)} = {accuracy/len(dataset)*100:.2f}%')

# 测试
test('captcha_dataset')

需要注意的是，在运行代码之前，需要准备好验证码数据集，其中每个子文件夹包含了相同字符数量的验证码图像。例如，如果字符集中包含数字和大小写字母，那么数据集的目录结构可以如下所示：

captcha_dataset
├── 0
│   ├── 0001.png
│   ├── 0002.png
│   ├── ...
├── 1
│   ├── 0001.png
│   ├── 0002.png
│   ├── ...
├── ...
├── z
│   ├── 0001.png
│   ├── 0002.png
│   ├── ...
└── z
    ├── 0001.png
    ├── 0002.png
    ├── ...

另外，这只是一个简单的验证码识别问题代码，针对不同的验证码类型，可能需要进行一些调整，比如修改模型结构、调整字符集等。