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# coding=utf-8
# Copyright 2021 The Deeplab2 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.
"""Tests for build_coco_data."""
import json
import os
from absl import flags
import numpy as np
from PIL import Image
import tensorflow as tf
from deeplab2.data import build_coco_data
from deeplab2.data import coco_constants
FLAGS = flags.FLAGS
_TEST_FILE_NAME = '000000123456.png'
class BuildCOCODataTest(tf.test.TestCase):
def setUp(self):
super().setUp()
self.data_dir = FLAGS.test_tmpdir
self.height = 100
self.width = 100
self.split = 'train'
image_path = os.path.join(self.data_dir,
build_coco_data._FOLDERS_MAP[self.split]['image'])
panoptic_map_path = os.path.join(self.data_dir,
build_coco_data._FOLDERS_MAP
[self.split]['label'])
tf.io.gfile.makedirs(panoptic_map_path)
panoptic_map_path = os.path.join(panoptic_map_path,
'panoptic_%s2017' % self.split)
tf.io.gfile.makedirs(image_path)
tf.io.gfile.makedirs(panoptic_map_path)
self.panoptic_maps = {}
image_id = int(_TEST_FILE_NAME[:-4])
self.panoptic_maps[image_id] = self._create_image_and_panoptic_map(
image_path, panoptic_map_path, image_id)
def _create_image_and_panoptic_map(self, image_path, panoptic_path, image_id):
def id2rgb(id_map):
id_map_copy = id_map.copy()
rgb_shape = tuple(list(id_map.shape) + [3])
rgb_map = np.zeros(rgb_shape, dtype=np.uint8)
for i in range(3):
rgb_map[..., i] = id_map_copy % 256
id_map_copy //= 256
return rgb_map
# Creates dummy images and panoptic maps.
# Dummy image.
image = np.random.randint(
0, 255, (self.height, self.width, 3), dtype=np.uint8)
with tf.io.gfile.GFile(
os.path.join(image_path, '%012d.jpg' % image_id), 'wb') as f:
Image.fromarray(image).save(f, format='JPEG')
# Dummy panoptic map.
semantic = np.random.randint(
0, 201, (self.height, self.width), dtype=np.int32)
instance_ = np.random.randint(
0, 100, (self.height, self.width), dtype=np.int32)
id_mapping = coco_constants.get_id_mapping()
valid_semantic = id_mapping.keys()
for i in range(201):
if i not in valid_semantic:
mask = (semantic == i)
semantic[mask] = 0
instance_[mask] = 0
instance = instance_.copy()
segments_info = []
for sem in np.unique(semantic):
ins_id = 1
if sem == 0:
continue
if id_mapping[sem] in build_coco_data._CLASS_HAS_INSTANCE_LIST:
for ins in np.unique(instance_[semantic == sem]):
instance[np.logical_and(semantic == sem, instance_ == ins)] = ins_id
area = np.logical_and(semantic == sem, instance_ == ins).sum()
idx = sem * 256 + ins_id
iscrowd = 0
segments_info.append({
'id': idx.tolist(),
'category_id': sem.tolist(),
'area': area.tolist(),
'iscrowd': iscrowd,
})
ins_id += 1
else:
instance[semantic == sem] = 0
area = (semantic == sem).sum()
idx = sem * 256
iscrowd = 0
segments_info.append({
'id': idx.tolist(),
'category_id': sem.tolist(),
'area': area.tolist(),
'iscrowd': iscrowd,
})
encoded_panoptic_map = semantic * 256 + instance
encoded_panoptic_map = id2rgb(encoded_panoptic_map)
with tf.io.gfile.GFile(
os.path.join(panoptic_path, '%012d.png' % image_id), 'wb') as f:
Image.fromarray(encoded_panoptic_map).save(f, format='PNG')
for i in range(201):
if i in valid_semantic:
mask = (semantic == i)
semantic[mask] = id_mapping[i]
decoded_panoptic_map = semantic * 256 + instance
# Write json file
json_annotation = {
'annotations': [
{
'file_name': _TEST_FILE_NAME,
'image_id': int(_TEST_FILE_NAME[:-4]),
'segments_info': segments_info
}
]
}
json_annotation_path = os.path.join(self.data_dir,
build_coco_data._FOLDERS_MAP
[self.split]['label'],
'panoptic_%s2017.json' % self.split)
with tf.io.gfile.GFile(json_annotation_path, 'w') as f:
json.dump(json_annotation, f, indent=2)
return decoded_panoptic_map
def test_build_coco_dataset_correct(self):
build_coco_data._convert_dataset(
coco_root=self.data_dir,
dataset_split=self.split,
output_dir=FLAGS.test_tmpdir)
output_record = os.path.join(
FLAGS.test_tmpdir, '%s-%05d-of-%05d.tfrecord' %
(self.split, 0, build_coco_data._NUM_SHARDS))
self.assertTrue(tf.io.gfile.exists(output_record))
# Parses tf record.
image_ids = sorted(self.panoptic_maps)
for i, raw_record in enumerate(
tf.data.TFRecordDataset([output_record]).take(5)):
image_id = image_ids[i]
example = tf.train.Example.FromString(raw_record.numpy())
panoptic_map = np.fromstring(
example.features.feature['image/segmentation/class/encoded']
.bytes_list.value[0],
dtype=np.int32).reshape((self.height, self.width))
np.testing.assert_array_equal(panoptic_map, self.panoptic_maps[image_id])
if __name__ == '__main__':
tf.test.main()
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