auto_nue.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import glob
import random
import cv2
import paddle
import numpy as np
from PIL import Image

from paddleseg.cvlibs import manager
from paddleseg.transforms import Compose

# Random seed is set to ensure that after shuffling dataset per epoch during multi-gpu training, the data sequences of all gpus are consistent.
random.seed(100)


@manager.DATASETS.add_component
class AutoNue(paddle.io.Dataset):
    """
    You need to to download and convert the [India Driving Dataset](https://idd.insaan.iiit.ac.in/evaluation/autonue21/#bm5)
    following the instructions of Segmentation Track. IDD_Dectection dataset also need for pseudo-labeling.

    And then, you need to organize data following the below structure.

    IDD_Segmentation
    |
    |--leftImg8bit
    |  |--train
    |  |--val
    |  |--test
    |
    |--gtFine
    |  |--train
    |  |--val
    |  |--test

    Args:
        transforms (list): Transforms for image.
        dataset_root (str): Cityscapes dataset directory.
        mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
        coarse_multiple (float|int, optional): Multiple of the amount of coarse data relative to fine data. Default: 1
        add_val (bool, optional): Whether to add val set in training. Default: False
    """

    def __init__(self,
                 transforms,
                 dataset_root,
                 mode='train',
                 coarse_multiple=1,
                 add_val=False):
        self.dataset_root = dataset_root
        self.transforms = Compose(transforms)
        self.file_list = list()
        mode = mode.lower()
        self.mode = mode
        self.num_classes = 26
        self.ignore_index = 255
        self.coarse_multiple = coarse_multiple

        if mode not in ['train', 'val', 'test']:
            raise ValueError(
                "mode should be 'train', 'val' or 'test', but got {}.".format(
                    mode))

        if self.transforms is None:
            raise ValueError("`transforms` is necessary, but it is None.")

        img_dir = os.path.join(self.dataset_root, 'leftImg8bit')
        label_dir = os.path.join(self.dataset_root, 'gtFine')
        if self.dataset_root is None or not os.path.isdir(
                self.dataset_root) or not os.path.isdir(
                    img_dir) or not os.path.isdir(label_dir):
            raise ValueError(
                "The dataset is not Found or the folder structure is nonconfoumance."
            )

        label_files = sorted(
            glob.glob(
                os.path.join(label_dir, mode, '*',
                             '*_gtFine_labellevel3Ids.png')))
        img_files = sorted(
            glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.*')))

        self.file_list = [
            [img_path, label_path]
            for img_path, label_path in zip(img_files, label_files)
        ]
        # for ii in range(len(self.file_list)):
        #     print(self.file_list[ii])
        # print(len(self.file_list))
        self.num_files = len(self.file_list)
        self.total_num_files = self.num_files

        if mode == 'train':
            # whether to add val set in training
            if add_val:
                label_files = sorted(
                    glob.glob(
                        os.path.join(label_dir, 'val', '*',
                                     '*_gtFine_labellevel3Ids.png')))
                img_files = sorted(
                    glob.glob(
                        os.path.join(img_dir, 'val', '*', '*_leftImg8bit.*')))
                val_file_list = [
                    [img_path, label_path]
                    for img_path, label_path in zip(img_files, label_files)
                ]
                self.file_list.extend(val_file_list)
                for ii in range(len(self.file_list)):
                    print(self.file_list[ii])
                print(len(self.file_list))
                self.num_files = len(self.file_list)
                self.total_num_files = self.num_files

            # use coarse dataset only in training
            # img_dir = os.path.join('data/IDD_Detection/JPEGImages/all')
            # label_dir = os.path.join('data/IDD_Detection/AutoLabel/pred_refine')

            # if self.dataset_root is None or not os.path.isdir(
            #         self.dataset_root) or not os.path.isdir(
            #             img_dir) or not os.path.isdir(label_dir):
            #     raise ValueError(
            #         "The coarse dataset is not Found or the folder structure is nonconfoumance."
            #     )

            # coarse_label_files = sorted(
            #     glob.glob(os.path.join(label_dir, '*', '*')))
            # coarse_img_files = sorted(
            #     glob.glob(os.path.join(img_dir, '*', '*')))
            # if len(coarse_img_files) != len(coarse_label_files):
            #     raise ValueError(
            #         "The number of images = {} is not equal to the number of labels = {} in Cityscapes Autolabeling dataset."
            #         .format(len(coarse_img_files), len(coarse_label_files)))

            # self.coarse_file_list = [[img_path, label_path]
            #                          for img_path, label_path in zip(
            #                              coarse_img_files, coarse_label_files)]
            # random.shuffle(self.coarse_file_list)

            # self.total_num_files = int(self.num_files * (1 + coarse_multiple))

    def __getitem__(self, idx):
        if self.mode == 'test':
            image_path, label_path = self.file_list[idx]
            im, _ = self.transforms(im=image_path)
            im = im[np.newaxis, ...]
            return im, image_path
        elif self.mode == 'val':
            image_path, label_path = self.file_list[idx]
            im, _ = self.transforms(im=image_path)
            label = np.asarray(Image.open(label_path))
            #             label = cv2.resize(label, (1280, 720), interpolation=cv2.INTER_NEAREST)
            label = label[np.newaxis, :, :]
            return im, label
        else:
            if idx >= self.num_files:
                image_path, label_path = self.coarse_file_list[idx -
                                                               self.num_files]
            else:
                image_path, label_path = self.file_list[idx]

            im, label = self.transforms(im=image_path, label=label_path)
            return im, label

    def __len__(self):
        return self.total_num_files