File size: 4,480 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 | from functools import partial
import numpy as np
import os.path as osp
from unittest import TestCase
from datumaro.components.project import Dataset
from datumaro.components.extractor import (DatasetItem,
AnnotationType, Bbox, LabelCategories
)
from datumaro.components.project import Project
from datumaro.plugins.mot_format import MotSeqGtConverter, MotSeqImporter
from datumaro.util.test_utils import (TestDir, compare_datasets,
test_save_and_load)
class MotConverterTest(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='mot_seq',
target_dataset=target_dataset, importer_args=importer_args)
def test_can_save_bboxes(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2, attributes={
'occluded': True,
}),
Bbox(0, 4, 4, 4, label=3, attributes={
'visibility': 0.4,
}),
Bbox(2, 4, 4, 4, attributes={
'ignored': True
}),
]
),
DatasetItem(id=2, subset='val',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(1, 2, 4, 2, label=3),
]
),
DatasetItem(id=3, subset='test',
image=np.ones((5, 4, 3)) * 3,
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
target_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2, attributes={
'occluded': True,
'visibility': 0.0,
'ignored': False,
}),
Bbox(0, 4, 4, 4, label=3, attributes={
'occluded': False,
'visibility': 0.4,
'ignored': False,
}),
Bbox(2, 4, 4, 4, attributes={
'occluded': False,
'visibility': 1.0,
'ignored': True,
}),
]
),
DatasetItem(id=2,
image=np.ones((8, 8, 3)),
annotations=[
Bbox(1, 2, 4, 2, label=3, attributes={
'occluded': False,
'visibility': 1.0,
'ignored': False,
}),
]
),
DatasetItem(id=3,
image=np.ones((5, 4, 3)) * 3,
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(
source_dataset,
partial(MotSeqGtConverter.convert, save_images=True),
test_dir, target_dataset=target_dataset)
DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mot_dataset')
class MotImporterTest(TestCase):
def test_can_detect(self):
self.assertTrue(MotSeqImporter.detect(DUMMY_DATASET_DIR))
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2, attributes={
'occluded': False,
'visibility': 1.0,
'ignored': False,
}),
]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
dataset = Project.import_from(DUMMY_DATASET_DIR, 'mot_seq') \
.make_dataset()
compare_datasets(self, expected_dataset, dataset) |