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# 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
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