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import torch
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import cv2
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import os
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import glob
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from torch.utils.data import Dataset
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import random
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import matplotlib.pyplot as plt
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class ISBI_Loader(Dataset):
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def __init__(self, data_path):
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# 初始化函数，读取所有data_path下的图片
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self.data_path = data_path
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self.imgs_path = glob.glob(os.path.join(data_path, '*.png'))
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def augment(self, image, flipCode):
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# 使用cv2.flip进行数据增强，filpCode为1水平翻转，0垂直翻转，-1水平+垂直翻转
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flip = cv2.flip(image, flipCode)
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return flip
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def __getitem__(self, index):
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# 根据index读取图片
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image_path = self.imgs_path[index]
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# 根据image_path生成label_path
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label_path = image_path.replace('normal_jpg', 'jpg')
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# 读取训练图片和标签图片
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image = cv2.imread(image_path)
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label = cv2.imread(label_path)
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# 将数据转为单通道的图片
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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label = cv2.cvtColor(label, cv2.COLOR_BGR2GRAY)
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image = image.reshape(1, image.shape[0], image.shape[1])
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label = label.reshape(1, label.shape[0], label.shape[1])
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# 处理标签，将像素值为255的改为1
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if label.max() > 1:
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label = label / 255
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# 随机进行数据增强，为2时不做处理
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flipCode = random.choice([-1, 0, 1, 2])
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if flipCode != 2:
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image = self.augment(image, flipCode)
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label = self.augment(label, flipCode)
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return image, label
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def __len__(self):
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# 返回训练集大小
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return len(self.imgs_path)
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if __name__ == "__main__":
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isbi_dataset = ISBI_Loader(r'F:/unet_program/H/normal_jpg/')
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print("数据个数：", len(isbi_dataset))
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train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,
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batch_size=2,
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shuffle=True)
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for image, label in train_loader:
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print(image.shape)
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