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# Copyright 2023 The TensorFlow 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.
"""TFDS Semantic Segmentation decoders."""
import tensorflow as tf, tf_keras
from official.vision.dataloaders import decoder
class CityScapesDecorder(decoder.Decoder):
"""A tf.Example decoder for tfds cityscapes datasets."""
def __init__(self):
# Original labels to trainable labels map, 255 is the ignore class.
self._label_map = {
-1: 255,
0: 255,
1: 255,
2: 255,
3: 255,
4: 255,
5: 255,
6: 255,
7: 0,
8: 1,
9: 255,
10: 255,
11: 2,
12: 3,
13: 4,
14: 255,
15: 255,
16: 255,
17: 5,
18: 255,
19: 6,
20: 7,
21: 8,
22: 9,
23: 10,
24: 11,
25: 12,
26: 13,
27: 14,
28: 15,
29: 255,
30: 255,
31: 16,
32: 17,
33: 18,
}
def decode(self, serialized_example):
# Convert labels according to the self._label_map
label = serialized_example['segmentation_label']
for original_label in self._label_map:
label = tf.where(label == original_label,
self._label_map[original_label] * tf.ones_like(label),
label)
sample_dict = {
'image/encoded':
tf.io.encode_jpeg(serialized_example['image_left'], quality=100),
'image/height': serialized_example['image_left'].shape[0],
'image/width': serialized_example['image_left'].shape[1],
'image/segmentation/class/encoded':
tf.io.encode_png(label),
}
return sample_dict
TFDS_ID_TO_DECODER_MAP = {
'cityscapes': CityScapesDecorder,
'cityscapes/semantic_segmentation': CityScapesDecorder,
'cityscapes/semantic_segmentation_extra': CityScapesDecorder,
}
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