CamVid数据集介绍及读取，附代码（Pytorch版本）

#CamVid数据集介绍及读取，附代码（Pytorch版本）

最近想在Bisenet网络上测试一下CamVid数据集，CamVid数据集也是第一次接触，然后想借阅一些别人的博客，抄近道，一查之后才发现。。。。。。。。
所以我想把自己理解的跟大家分享一下。
我也会把自己跑通的Bisenet代码也会分享到该博客上。

##数据集简介
CamVid数据集的文件布局如下所示：

其中class_dict是将数据集中每个物体使用RGB三通道的颜色值进行分类的。

这里面的class_11说的是数据集中存在的，且进行分割的物体。（这个是我个人理解，如果有误，欢迎各位在评论区留言。）
这里说了只是简介，想了解更多，只需要自己下载，查看一下便知。

读取代码解析

上代码！！！！！
下面的是保存为CamVid.py

import os
import torch
import glob
import os
from torchvision import transforms
#import cv2
from PIL import Image
import pandas as pd
import numpy as np
#from imgaug import augmenters as iaa
#import imgaug as ia
from utils import get_label_info, one_hot_it, RandomCrop, reverse_one_hot, one_hot_it_v11, one_hot_it_v11_dice
import random

def augmentation():
    # augment images with spatial transformation: Flip, Affine, Rotation, etc...
    # see https://github.com/aleju/imgaug for more details
    pass


def augmentation_pixel():
    # augment images with pixel intensity transformation: GaussianBlur, Multiply, etc...
    pass



class CamVid(torch.utils.data.Dataset):
    def __init__(self, image_path, label_path, csv_path, scale, loss='dice', mode='train'):
        super().__init__()
        self.mode = mode
        self.image_list = []
        if not isinstance(image_path, list):
            image_path = [image_path]
        for image_path_ in image_path:
            self.image_list.extend(glob.glob(os.path.join(image_path_, '*.png')))
        self.image_list.sort()
        self.label_list = []
        if not isinstance(label_path, list):
            label_path = [label_path]
        for label_path_ in label_path:
            self.label_list.extend(glob.glob(os.path.join(label_path_, '*.png')))
        self.label_list.sort()
        # self.image_name = [x.split('/')[-1].split('.')[0] for x in self.image_list]
        # self.label_list = [os.path.join(label_path, x + '_L.png') for x in self.image_list]
#        self.fliplr = iaa.Fliplr(0.5)
        self.label_info = get_label_info(csv_path)
        # resize
        # self.resize_label = transforms.Resize(scale, Image.NEAREST)
        # self.resize_img = transforms.Resize(scale, Image.BILINEAR)
        # normalization
        self.to_tensor = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
            ])
        # self.crop = transforms.RandomCrop(scale, pad_if_needed=True)
        self.image_size = scale
        self.scale = [0.5, 1, 1.25, 1.5, 1.75, 2]
        self.loss = loss

    def __getitem__(self, index):
        # load image and crop
        seed = random.random()
        img = Image.open(self.image_list[index])
        img.show()
        # random crop image
        # =====================================
        # w,h = img.size
        # th, tw = self.scale
        # i = random.randint(0, h - th)
        # j = random.randint(0, w - tw)
        # img = F.crop(img, i, j, th, tw)
        # =====================================
#        print(self.scale)
        scale = random.choice(self.scale)
#        print(scale)
        scale = (int(self.image_size[0] * scale), int(self.image_size[1] * scale))
#        print(scale)
#        print(self.image_size)
#        print(scale)
        # randomly resize image and random crop
        # =====================================
        if self.mode == 'train':
            img = transforms.Resize(scale, Image.BILINEAR)(img)
            img = RandomCrop(self.image_size, seed, pad_if_needed=True)(img)
        # =====================================
        img = np.array(img)
#        # load label
        label = Image.open(self.label_list[index])
        label.show()
#        # crop the corresponding label
#        # =====================================
#        # label = F.crop(label, i, j, th, tw)
#        # =====================================
#
#        # randomly resize label and random crop
#        # =====================================
        if self.mode == 'train':
            label =