data/voc.py

# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# 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.

"""Pascal VOC dataset."""

import numpy as np
from PIL import Image
# pylint: disable=g-importing-member
from torchvision.datasets import VOCSegmentation

CLASS2ID = {
    'Background': 0,
    'Aero plane': 1,
    'Bicycle': 2,
    'Bird': 3,
    'Boat': 4,
    'Bottle': 5,
    'Bus': 6,
    'Car': 7,
    'Cat': 8,
    'Chair': 9,
    'Cow': 10,
    'Dining table': 11,
    'Dog': 12,
    'Horse': 13,
    'Motorbike': 14,
    'Person': 15,
    'Potted plant': 16,
    'Sheep': 17,
    'Sofa': 18,
    'Train': 19,
    'Tv/Monitor': 20,
    # ... add more entries as needed
    'Border': 255,
}


VOC_CLASSES = [
    'aeroplane',
    'bicycle',
    'bird avian',
    'boat',
    'bottle',
    'bus',
    'car',
    'cat',
    'chair seat',
    'cow',
    'diningtable',
    'dog',
    'horse',
    'motorbike',
    'person with clothes,people,human',
    'pottedplant',
    'sheep',
    'sofa',
    'train',
    'tvmonitor screen',
]


BACKGROUND_CATEGORY = [
    'ground',
    'land',
    'grass',
    'tree',
    'building',
    'wall',
    'sky',
    'lake',
    'water',
    'river',
    'sea',
    'keyboard',
    'helmet',
    'cloud',
    'house',
    'mountain',
    'ocean',
    'road',
    'rock',
    'street',
    'valley',
    'bridge',
    'sign',
]


class VOCDataset(VOCSegmentation):
  """Pascal VOC dataset."""

  def __init__(
      self,
      root='/datasets/jianhaoy/PASCAL/',
      year='2012',
      split='val',
      target_transform=None,
      download=False,
      transform=None,
  ):
    super(VOCDataset, self).__init__(
        root=root,
        image_set=split,
        year=year,
        target_transform=transform,
        download=download,
        transform=transform,
    )
    self.idx_to_class = {val: key for (key, val) in CLASS2ID.items()}

  def __getitem__(self, index):
    image_path = self.images[index]
    image = Image.open(image_path).convert('RGB')
    target = np.asarray(Image.open(self.masks[index]), dtype=np.int32)

    _, unique_values = self.process_target(np.array(target))
    classnames = [self.idx_to_class[idx] for idx in unique_values]

    if self.transforms:
      image = self.transform(image)

    return image, str(image_path), target, classnames

  def process_target(self, arr):
    # Set values 0 and 255 to 1
    arr[(arr == 0) | (arr == 255)] = 0

    # Find unique values (excluding 0 and 255)
    unique_values = np.unique(arr[(arr != 0) & (arr != 255)])

    # Create separate masks for each unique value
    masks = [arr == value for value in unique_values]
    masks = [Image.fromarray(arr) for arr in masks]
    masks = [self.target_transform(arr) for arr in masks]

    return masks, unique_values