File size: 3,976 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 | from functools import partial
import numpy as np
from unittest import TestCase
from datumaro.components.project import Dataset
from datumaro.components.extractor import (DatasetItem,
AnnotationType, Label, Mask, Points, Polygon,
PolyLine, Bbox, Caption,
LabelCategories, MaskCategories, PointsCategories
)
from datumaro.plugins.datumaro_format.extractor import DatumaroImporter
from datumaro.plugins.datumaro_format.converter import DatumaroConverter
from datumaro.util.mask_tools import generate_colormap
from datumaro.util.image import Image
from datumaro.util.test_utils import (TestDir, compare_datasets_strict,
test_save_and_load)
class DatumaroConverterTest(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='datumaro',
target_dataset=target_dataset, importer_args=importer_args,
compare=compare_datasets_strict)
@property
def test_dataset(self):
label_categories = LabelCategories()
for i in range(5):
label_categories.add('cat' + str(i))
mask_categories = MaskCategories(
generate_colormap(len(label_categories.items)))
points_categories = PointsCategories()
for index, _ in enumerate(label_categories.items):
points_categories.add(index, ['cat1', 'cat2'], joints=[[0, 1]])
return Dataset.from_iterable([
DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)),
annotations=[
Caption('hello', id=1),
Caption('world', id=2, group=5),
Label(2, id=3, attributes={
'x': 1,
'y': '2',
}),
Bbox(1, 2, 3, 4, label=4, id=4, z_order=1, attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, id=5, group=5),
Points([1, 2, 2, 0, 1, 1], label=0, id=5, z_order=4),
Mask(label=3, id=5, z_order=2, image=np.ones((2, 3))),
]),
DatasetItem(id=21, subset='train',
annotations=[
Caption('test'),
Label(2),
Bbox(1, 2, 3, 4, label=5, id=42, group=42)
]),
DatasetItem(id=2, subset='val',
annotations=[
PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11, z_order=1),
Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12, z_order=4),
]),
DatasetItem(id=42, subset='test',
attributes={'a1': 5, 'a2': '42'}),
DatasetItem(id=42),
DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))),
], categories={
AnnotationType.label: label_categories,
AnnotationType.mask: mask_categories,
AnnotationType.points: points_categories,
})
def test_can_save_and_load(self):
with TestDir() as test_dir:
self._test_save_and_load(self.test_dataset,
partial(DatumaroConverter.convert, save_images=True), test_dir)
def test_can_detect(self):
with TestDir() as test_dir:
DatumaroConverter.convert(self.test_dataset, save_dir=test_dir)
self.assertTrue(DatumaroImporter.detect(test_dir))
def test_relative_paths(self):
test_dataset = Dataset.from_iterable([
DatasetItem(id='1', image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3))),
])
with TestDir() as test_dir:
self._test_save_and_load(test_dataset,
partial(DatumaroConverter.convert, save_images=True), test_dir)
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