使用PyTorch实现验证码识别

本文通过PyTorch从零实现一个验证码图像识别系统，包括数据生成、模型搭建、训练和测试。

1. 安装必要库
   首先安装所需Python库：
   pip install torch torchvision pillow captcha numpy2. 生成验证码数据集
   使用​​captcha​​库生成包含数字和大写字母的验证码图像。
   from captcha.image import ImageCaptcha
   import random
   import string
   import os
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   characters = string.digits + string.ascii_uppercase
   captcha_length = 4
   image_width, image_height = 160, 60

def generate_dataset(num_images=10000, save_path="dataset"):
os.makedirs(save_path, exist_ok=True)
generator = ImageCaptcha(width=image_width, height=image_height)
for i in range(num_images):
label = ''.join(random.choices(characters, k=captcha_length))
image = generator.generate_image(label)
image.save(os.path.join(save_path, f"{label}_{i}.png"))

generate_dataset()3. 定义数据集类
构建自定义数据集用于读取图像和对应标签。
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import torch

class CaptchaDataset(Dataset):
def init(self, root_dir):
self.root_dir = root_dir
self.image_list = os.listdir(root_dir)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
self.char_to_idx = {char: idx for idx, char in enumerate(characters)}

def __len__(self):
    return len(self.image_list)

def __getitem__(self, idx):
    file_name = self.image_list[idx]
    label_text = file_name.split('_')[0]
    image = Image.open(os.path.join(self.root_dir, file_name)).convert('RGB')
    image = self.transform(image)
    label = torch.tensor([self.char_to_idx[c] for c in label_text], dtype=torch.long)
    return image, label

dataset = CaptchaDataset("dataset")
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)4. 构建模型
模型由卷积层提取特征，LSTM建模序列关系。
import torch.nn as nn

class CaptchaModel(nn.Module):
def init(self):
super(CaptchaModel, self).init()
self.cnn = nn.Sequential(
nn.Conv2d(3, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(),
nn.MaxPool2d((2,1))
)
self.rnn = nn.LSTM(128 * 7, 128, num_layers=2, bidirectional=True, batch_first=True)
self.fc = nn.Linear(256, len(characters))

def forward(self, x):
    x = self.cnn(x)
    x = x.permute(0, 3, 1, 2)  # [batch, width, channels, height]
    B, W, C, H = x.shape
    x = x.view(B, W, C*H)
    x, _ = self.rnn(x)
    x = self.fc(x)
    return x5. 训练模型

设置损失函数、优化器并开始训练。
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = CaptchaModel().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(20):
model.train()
total_loss = 0
for images, labels in dataloader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = sum(criterion(outputs[:, i, :], labels[:, i]) for i in range(captcha_length))
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}, Loss: {total_loss:.4f}")6. 测试模型
加载一张图片并进行预测。
def predict(model, img_path):
model.eval()
image = Image.open(img_path).convert('RGB')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
image = transform(image).unsqueeze(0).to(device)
with torch.no_grad():
output = model(image)
pred = output.argmax(dim=2)
pred_text = ''.join([characters[i] for i in pred[0]])
return pred_text

test_img = "dataset/7H2K_0.png"
print("Predicted:", predict(model, test_img))