在语义分割中用到的数据无非就是原始图片（image）和标注后得到的mask图片，所以在读取数据的时候只要返回图片和标签信息就OK 了。

import torch
import os
import numpy as np
from torch.utils.data import Dataset
from utils_func import seg_utils as ut
import cv2
from torchvision.transforms.functional import rotate as tensor_rotate
from torchvision.transforms.functional import vflip, hflip
from torchvision.transforms.functional import adjust_brightness
import random
import base64
import json
import os
import os.path as ospdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")class train_data(Dataset):def __init__(self, image_folder, img_size, category_types):self.image_folder = image_folderself.img_size = img_sizeself.category_types = category_typesself.num_classes = len(category_types)self.data_list = self.generate_mask()# print(self.data_list)self.img_and_mask = []for idx, data in enumerate(self.data_list):img_tensor, mask_tensor = self.get_image_and_label(data[0], data[1], self.img_size)self.img_and_mask.append([img_tensor, mask_tensor])def __len__(self):return len(self.img_and_mask)def __getitem__(self, index):img_tensor = self.img_and_mask[index][0].to(device)mask_tensor = self.img_and_mask[index][1].to(device)# 如果有数据增强就在这里处理return img_tensor, mask_tensordef data_augment(self, img_tensor, mask_tensor, aug_flag):if aug_flag[0] == 0:angel = random.choice(aug_flag[1])img_tensor = tensor_rotate(img_tensor, int(angel))mask_tensor = tensor_rotate(mask_tensor, int(angel))elif aug_flag[0] == 1:factor = aug_flag[1]img_tensor = adjust_brightness(img_tensor, factor)elif aug_flag[0] == 2:flip_type = random.choice(aug_flag[1])if flip_type == 1:img_tensor = vflip(img_tensor)mask_tensor = vflip(mask_tensor)else:img_tensor = hflip(img_tensor)mask_tensor = hflip(mask_tensor)return img_tensor, mask_tensordef generate_mask(self):data_lists = []for file_name in os.listdir(self.image_folder):if file_name.endswith("json"):json_path = os.path.join(self.image_folder, file_name)img_path = osp.join(self.image_folder, "%s.jpg" % file_name.split(".")[0])data_lists.append([img_path, json_path])return data_listsdef get_image_and_label(self, img_path, labelme_json_path, img_size):# print("==================================================")# print(img_path)# print("==================================================")img = ut.p2i(img_path)h, w = img.shape[:2]img = cv2.resize(img, (img_size[0], img_size[1]))# cv2.imwrite(r"C:\Users\HJ\Desktop\test\%s.jpg"%img_path.split(".")[0][-7:], img)img_tensor = ut.i2t(img)mask_array = np.zeros([self.num_classes, h, w, 1], dtype=np.uint8)with open(labelme_json_path, "r") as f:json_data = json.load(f)shapes = json_data["shapes"]for shape in shapes:category = shape["label"]category_idx = self.category_types.index(category)points = shape["points"]points_array = np.array(points, dtype=np.int32)temp = mask_array[category_idx, ...]mask_array[category_idx, ...] = cv2.fillPoly(temp, [points_array], 255)mask_array = np.transpose(mask_array, (1, 2, 0, 3)).squeeze(axis=-1)mask_array = cv2.resize(mask_array, (self.img_size[0], self.img_size[1])).astype(np.uint8)mask_tensor = ut.i2t(mask_array, False)return img_tensor, mask_tensorif __name__ == '__main__':img_folder = r"D:\finish_code\SegmentationProject\datasets\data2"img_size1 = [256, 512]category_types = ["background", "person", "car", "road"]t = train_data(img_folder, img_size1, category_types)t.__getitem__(1)