一、数据标注

（1）使用labelme来进行分割标注，标注完之后会得到一个json，数据格式如下：

二、获取数据信息

        读取json文件来得到标签信息，读取jpg文件获取图像。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。

    def get_label(self, img_path, labelme_json_path, img_size):
        # h, w 是图片的宽高
        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"]
            # 获取类别的索引
            category_idx = self.category_types.index(category)
            points = shape["points"]
            points_array = np.array(points, dtype=np.int32)
            temp = mask_array[category_idx, ...]   # 获取出mask_array对应的类别层
            # 将标注的坐标点连接成一个区域，并将区域内的值填为255
            mask_array[category_idx, ...] = cv2.fillPoly(temp, [points_array], 255)
            # 可以将每一层的输出来看mask图
        # 交换维度
        mask_array = np.transpose(mask_array, (1, 2, 0, 3)).squeeze(axis=-1)
        # 将mask转为tensor
        mask_tensor = ut.i2t(mask_array, False)
        return mask_tensor

 如果一张图片只有一个类别，那其他类的mask图就是黑的，效果展示如下：

完整的读取数据代码如下:dataloader_labelme.py

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 osp

device = 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_folder
        self.img_size = img_size
        self.category_types = category_types
        self.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_tensor

    def 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_tensor

    def 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_lists

    def 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	est\%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_tensor



if __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)