基于CNN训练的一套 端到端 的验证码识别模型，使用深度学习+训练数据+大量计算力.zip

# -*- coding: UTF-8 -*- import os from torch.utils.data import DataLoader,Dataset import torchvision.transforms as transforms from PIL import Image import one_hot_encoding as ohe import captcha_setting class mydataset(Dataset): def __init__(self, folder, transform=None): self.train_image_file_paths = [os.path.join(folder, image_file) for image_file in os.listdir(folder)] self.transform = transform def __len__(self): return len(self.train_image_file_paths) def __getitem__(self, idx): image_root = self.train_image_file_paths[idx] image_name = image_root.split(os.path.sep)[-1] image = Image.open(image_root) if self.transform is not None: image = self.transform(image) label = ohe.encode(image_name.split('_')[0]) # 为了方便，在生成图片的时候，图片文件的命名格式 "4个数字或者数字_时间戳.PNG", 4个字母或者即是图片的验证码的值，字母大写,同时对该值做 one-hot 处理 return image, label transform = transforms.Compose([ # transforms.ColorJitter(), transforms.Grayscale(), transforms.ToTensor(), # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def get_train_data_loader(): dataset = mydataset(captcha_setting.TRAIN_DATASET_PATH, transform=transform) return DataLoader(dataset, batch_size=64, shuffle=True) def get_test_data_loader(): dataset = mydataset(captcha_setting.TEST_DATASET_PATH, transform=transform) return DataLoader(dataset, batch_size=1, shuffle=True) def get_predict_data_loader(): dataset = mydataset(captcha_setting.PREDICT_DATASET_PATH, transform=transform) return DataLoader(dataset, batch_size=1, shuffle=True)