File size: 9,785 Bytes
d21cb06 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | import os.path as osp
from collections import OrderedDict
from functools import partial
from unittest import TestCase
import datumaro.plugins.camvid_format as Camvid
import numpy as np
from datumaro.components.extractor import (AnnotationType, DatasetItem,
Extractor, LabelCategories, Mask)
from datumaro.components.project import Dataset, Project
from datumaro.plugins.camvid_format import CamvidConverter, CamvidImporter
from datumaro.util.test_utils import (TestDir, compare_datasets,
test_save_and_load)
class CamvidFormatTest(TestCase):
def test_can_write_and_parse_labelmap(self):
src_label_map = Camvid.CamvidLabelMap
with TestDir() as test_dir:
file_path = osp.join(test_dir, 'label_colors.txt')
Camvid.write_label_map(file_path, src_label_map)
dst_label_map = Camvid.parse_label_map(file_path)
self.assertEqual(src_label_map, dst_label_map)
DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'camvid_dataset')
class TestExtractorBase(Extractor):
def _label(self, camvid_label):
return self.categories()[AnnotationType.label].find(camvid_label)[0]
def categories(self):
return Camvid.make_camvid_categories()
class CamvidImportTest(TestCase):
def test_can_import(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='0001TP_008550', subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=1),
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=18),
Mask(image=np.array([[0, 0, 0, 1, 1]]), label=22),
]
),
DatasetItem(id='0001TP_008580', subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=2),
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4),
Mask(image=np.array([[0, 0, 0, 1, 1]]), label=27),
]
),
DatasetItem(id='0001TP_006690', subset='train',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 1, 0, 1, 1]]), label=3),
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=18),
]
),
DatasetItem(id='0016E5_07959', subset = 'val',
image=np.ones((1, 5, 3)),
annotations=[
Mask(image=np.array([[1, 1, 1, 0, 0]]), label=1),
Mask(image=np.array([[0, 0, 0, 1, 1]]), label=8),
]
),
], categories=Camvid.make_camvid_categories())
parsed_dataset = Project.import_from(DUMMY_DATASET_DIR, 'camvid').make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_detect_camvid(self):
self.assertTrue(CamvidImporter.detect(DUMMY_DATASET_DIR))
class CamvidConverterTest(TestCase):
def _test_save_and_load(self, source_dataset, converter, test_dir,
target_dataset=None, importer_args=None):
return test_save_and_load(self, source_dataset, converter, test_dir,
importer='camvid',
target_dataset=target_dataset, importer_args=importer_args)
def test_can_save_camvid_segm(self):
class TestExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id='a/b/1', subset='test',
image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0),
Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3),
Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4),
]),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
partial(CamvidConverter.convert, label_map='camvid'),
test_dir)
def test_can_save_camvid_segm_unpainted(self):
class TestExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0),
Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3),
Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4),
]),
])
class DstExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=0),
Mask(image=np.array([[0, 1, 1, 0, 0]]), label=3),
Mask(image=np.array([[1, 0, 0, 0, 1]]), label=4),
]),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
partial(CamvidConverter.convert,
label_map='camvid', apply_colormap=False),
test_dir, target_dataset=DstExtractor())
def test_can_save_dataset_with_no_subsets(self):
class TestExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 0, 0, 1, 0]]), label=0),
Mask(image=np.array([[0, 1, 1, 0, 1]]), label=3),
]),
DatasetItem(id=2, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]), label=1),
Mask(image=np.array([[0, 0, 1, 0, 1]]), label=2),
]),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
partial(CamvidConverter.convert, label_map='camvid'), test_dir)
def test_can_save_with_no_masks(self):
class TestExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id='a/b/1', subset='test',
image=np.ones((2, 5, 3)),
),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
partial(CamvidConverter.convert, label_map='camvid'),
test_dir)
def test_dataset_with_source_labelmap_undefined(self):
class SrcExtractor(TestExtractorBase):
def __iter__(self):
yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]), label=0),
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=1),
])
def categories(self):
label_cat = LabelCategories()
label_cat.add('Label_1')
label_cat.add('label_2')
return {
AnnotationType.label: label_cat,
}
class DstExtractor(TestExtractorBase):
def __iter__(self):
yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]), label=self._label('Label_1')),
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=self._label('label_2')),
])
def categories(self):
label_map = OrderedDict()
label_map['background'] = None
label_map['Label_1'] = None
label_map['label_2'] = None
return Camvid.make_camvid_categories(label_map)
with TestDir() as test_dir:
self._test_save_and_load(SrcExtractor(),
partial(CamvidConverter.convert, label_map='source'),
test_dir, target_dataset=DstExtractor())
def test_dataset_with_source_labelmap_defined(self):
class SrcExtractor(TestExtractorBase):
def __iter__(self):
yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]), label=1),
Mask(image=np.array([[0, 0, 1, 0, 1]]), label=2),
])
def categories(self):
label_map = OrderedDict()
label_map['background'] = (0, 0, 0)
label_map['label_1'] = (1, 2, 3)
label_map['label_2'] = (3, 2, 1)
return Camvid.make_camvid_categories(label_map)
class DstExtractor(TestExtractorBase):
def __iter__(self):
yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
Mask(image=np.array([[1, 1, 0, 1, 0]]), label=self._label('label_1')),
Mask(image=np.array([[0, 0, 1, 0, 1]]), label=self._label('label_2')),
])
def categories(self):
label_map = OrderedDict()
label_map['background'] = (0, 0, 0)
label_map['label_1'] = (1, 2, 3)
label_map['label_2'] = (3, 2, 1)
return Camvid.make_camvid_categories(label_map)
with TestDir() as test_dir:
self._test_save_and_load(SrcExtractor(),
partial(CamvidConverter.convert, label_map='source'),
test_dir, target_dataset=DstExtractor())
|