Spaces:
Runtime error
Runtime error
File size: 6,047 Bytes
0924f30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# 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()
|