ade20k.py

###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################

import os
import sys
import numpy as np
import random
import math
from PIL import Image, ImageOps, ImageFilter

import torch
import torch.utils.data as data
import torchvision.transforms as transform

from .base import BaseDataset

class ADE20KSegmentation(BaseDataset):
    BASE_DIR = 'ADEChallengeData2016'
    NUM_CLASS = 150
    def __init__(self, root=os.path.expanduser('~/.encoding/data'), split='train',
                 mode=None, transform=None, target_transform=None, **kwargs):
        super(ADE20KSegmentation, self).__init__(
            root, split, mode, transform, target_transform, **kwargs)
        # assert exists and prepare dataset automatically
        root = os.path.join(root, self.BASE_DIR)
        assert os.path.exists(root), "Please setup the dataset using" + \
            "encoding/scripts/prepare_ade20k.py"
        self.images, self.masks = _get_ade20k_pairs(root, split)
        if split != 'test':
            assert (len(self.images) == len(self.masks))
        if len(self.images) == 0:
            raise(RuntimeError("Found 0 images in subfolders of: \
                " + root + "\n"))

    def __getitem__(self, index):
        img = Image.open(self.images[index]).convert('RGB')
        if self.mode == 'test':
            if self.transform is not None:
                img = self.transform(img)
            return img, os.path.basename(self.images[index])
        mask = Image.open(self.masks[index])
        # synchrosized transform
        if self.mode == 'train':
            img, mask = self._sync_transform(img, mask)
        elif self.mode == 'val':
            img, mask = self._val_sync_transform(img, mask)
        else:
            assert self.mode == 'testval'
            mask = self._mask_transform(mask)
        # general resize, normalize and toTensor
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            mask = self.target_transform(mask)
        return img, mask

    def _sync_transform(self, img, mask):
        # random mirror
        if random.random() < 0.5:
            img = img.transpose(Image.FLIP_LEFT_RIGHT)
            mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
        crop_size = self.crop_size
        w, h = img.size
        long_size = random.randint(int(self.base_size*0.5), int(self.base_size*2.5))
        if h > w:
            oh = long_size
            ow = int(1.0 * w * long_size / h + 0.5)
            short_size = ow
        else:
            ow = long_size
            oh = int(1.0 * h * long_size / w + 0.5)
            short_size = oh
        img = img.resize((ow, oh), Image.BILINEAR)
        mask = mask.resize((ow, oh), Image.NEAREST)
        # pad crop
        if short_size < crop_size:
            padh = crop_size - oh if oh < crop_size else 0
            padw = crop_size - ow if ow < crop_size else 0
            img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
            mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=0)
        # random crop crop_size
        w, h = img.size
        x1 = random.randint(0, w - crop_size)
        y1 = random.randint(0, h - crop_size)
        img = img.crop((x1, y1, x1+crop_size, y1+crop_size))
        mask = mask.crop((x1, y1, x1+crop_size, y1+crop_size))
        # final transform
        return img, self._mask_transform(mask)

    def _mask_transform(self, mask):
        target = np.array(mask).astype('int64') - 1
        return torch.from_numpy(target)

    def __len__(self):
        return len(self.images)

    @property
    def pred_offset(self):
        return 1


def _get_ade20k_pairs(folder, split='train'):
    def get_path_pairs(img_folder, mask_folder):
        img_paths = []
        mask_paths = []
        for filename in os.listdir(img_folder):
            basename, _ = os.path.splitext(filename)
            if filename.endswith(".jpg"):
                imgpath = os.path.join(img_folder, filename)
                maskname = basename + '.png'
                maskpath = os.path.join(mask_folder, maskname)
                if os.path.isfile(maskpath):
                    img_paths.append(imgpath)
                    mask_paths.append(maskpath)
                else:
                    print('cannot find the mask:', maskpath)
        return img_paths, mask_paths

    if split == 'train':
        img_folder = os.path.join(folder, 'images/training')
        mask_folder = os.path.join(folder, 'annotations/training')
        img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
        print('len(img_paths):', len(img_paths))
        assert len(img_paths) == 20210
    elif split == 'val':
        img_folder = os.path.join(folder, 'images/validation')
        mask_folder = os.path.join(folder, 'annotations/validation')
        img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
        assert len(img_paths) == 2000
    else:
        assert split == 'trainval'
        train_img_folder = os.path.join(folder, 'images/training')
        train_mask_folder = os.path.join(folder, 'annotations/training')
        val_img_folder = os.path.join(folder, 'images/validation')
        val_mask_folder = os.path.join(folder, 'annotations/validation')
        train_img_paths, train_mask_paths = get_path_pairs(train_img_folder, train_mask_folder)
        val_img_paths, val_mask_paths = get_path_pairs(val_img_folder, val_mask_folder)
        img_paths = train_img_paths + val_img_paths
        mask_paths = train_mask_paths + val_mask_paths
        assert len(img_paths) == 22210
    return img_paths, mask_paths