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同步操作将从 SwagyChill/pytorch-captcha-recognition 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
# -*- 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)
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