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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, Points, Polygon, PolyLine, Bbox, Label,
LabelCategories,
)
from datumaro.plugins.cvat_format.extractor import CvatImporter
from datumaro.plugins.cvat_format.converter import CvatConverter
from datumaro.util.image import Image
from datumaro.util.test_utils import (TestDir, compare_datasets,
test_save_and_load)
DUMMY_IMAGE_DATASET_DIR = osp.join(osp.dirname(__file__),
'assets', 'cvat_dataset', 'for_images')
DUMMY_VIDEO_DATASET_DIR = osp.join(osp.dirname(__file__),
'assets', 'cvat_dataset', 'for_video')
class CvatImporterTest(TestCase):
def test_can_detect_image(self):
self.assertTrue(CvatImporter.detect(DUMMY_IMAGE_DATASET_DIR))
def test_can_detect_video(self):
self.assertTrue(CvatImporter.detect(DUMMY_VIDEO_DATASET_DIR))
def test_can_load_image(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='img0', subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0, z_order=1,
attributes={
'occluded': True,
'a1': True, 'a2': 'v3'
}),
PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
attributes={'occluded': False}),
], attributes={'frame': 0}),
DatasetItem(id='img1', subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Polygon([1, 2, 3, 4, 6, 5], z_order=1,
attributes={'occluded': False}),
Points([1, 2, 3, 4, 5, 6], label=1, z_order=2,
attributes={'occluded': False}),
], attributes={'frame': 1}),
], categories={
AnnotationType.label: LabelCategories.from_iterable([
['label1', '', {'a1', 'a2'}],
['label2'],
])
})
parsed_dataset = CvatImporter()(DUMMY_IMAGE_DATASET_DIR).make_dataset()
compare_datasets(self, expected_dataset, parsed_dataset)
def test_can_load_video(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='frame_000010', subset='annotations',
image=255 * np.ones((20, 25, 3)),
annotations=[
Bbox(3, 4, 7, 1, label=2,
id=0,
attributes={
'occluded': True,
'outside': False, 'keyframe': True,
'track_id': 0
}),
Points([21.95, 8.00, 2.55, 15.09, 2.23, 3.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': False, 'keyframe': True,
'track_id': 1, 'hgl': 'hgkf',
}),
], attributes={'frame': 10}),
DatasetItem(id='frame_000013', subset='annotations',
image=255 * np.ones((20, 25, 3)),
annotations=[
Bbox(7, 6, 7, 2, label=2,
id=0,
attributes={
'occluded': False,
'outside': True, 'keyframe': True,
'track_id': 0
}),
Points([21.95, 8.00, 9.55, 15.09, 5.23, 1.16],
label=0,
id=1,
attributes={
'occluded': False,
'outside': True, 'keyframe': True,
'track_id': 1, 'hgl': 'jk',
}),
PolyLine([7.85, 13.88, 3.50, 6.67, 15.90, 2.00, 13.31, 7.21],
label=2,
id=2,
attributes={
'occluded': False,
'outside': False, 'keyframe': True,
'track_id': 2,
}),
], attributes={'frame': 13}),
DatasetItem(id='frame_000016', subset='annotations',
image=Image(path='frame_0000016.png', size=(20, 25)),
annotations=[
Bbox(8, 7, 6, 10, label=2,
id=0,
attributes={
'occluded': False,
'outside': True, 'keyframe': True,
'track_id': 0
}),
PolyLine([7.85, 13.88, 3.50, 6.67, 15.90, 2.00, 13.31, 7.21],
label=2,
id=2,
attributes={
'occluded': False,
'outside': True, 'keyframe': True,
'track_id': 2,
}),
], attributes={'frame': 16}),
], categories={
AnnotationType.label: LabelCategories.from_iterable([
['klhg', '', {'hgl'}],
['z U k'],
['II']
]),
})
parsed_dataset = CvatImporter()(DUMMY_VIDEO_DATASET_DIR).make_dataset()
compare_datasets(self, expected_dataset, parsed_dataset)
class CvatConverterTest(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='cvat',
target_dataset=target_dataset, importer_args=importer_args)
def test_can_save_and_load(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
label_categories.items[2].attributes.update(['a1', 'a2', 'empty'])
label_categories.attributes.update(['occluded'])
source_dataset = Dataset.from_iterable([
DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=1, group=4,
attributes={ 'occluded': True}),
Points([1, 1, 3, 2, 2, 3],
label=2,
attributes={ 'a1': 'x', 'a2': 42, 'empty': '',
'unknown': 'bar' }),
Label(1),
Label(2, attributes={ 'a1': 'y', 'a2': 44 }),
]
),
DatasetItem(id=1, subset='s1',
annotations=[
PolyLine([0, 0, 4, 0, 4, 4],
label=3, id=4, group=4),
Bbox(5, 0, 1, 9,
label=3, id=4, group=4),
]
),
DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4], z_order=1,
label=3, group=4,
attributes={ 'occluded': False }),
PolyLine([5, 0, 9, 0, 5, 5]), # will be skipped as no label
]
),
DatasetItem(id=3, subset='s3', image=Image(
path='3.jpg', size=(2, 4))),
], categories={
AnnotationType.label: label_categories,
})
target_dataset = Dataset.from_iterable([
DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=1, group=4,
attributes={ 'occluded': True }),
Points([1, 1, 3, 2, 2, 3],
label=2,
attributes={ 'occluded': False, 'empty': '',
'a1': 'x', 'a2': 42 }),
Label(1),
Label(2, attributes={ 'a1': 'y', 'a2': 44 }),
], attributes={'frame': 0}
),
DatasetItem(id=1, subset='s1',
annotations=[
PolyLine([0, 0, 4, 0, 4, 4],
label=3, group=4,
attributes={ 'occluded': False }),
Bbox(5, 0, 1, 9,
label=3, group=4,
attributes={ 'occluded': False }),
], attributes={'frame': 1}
),
DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4], z_order=1,
label=3, group=4,
attributes={ 'occluded': False }),
], attributes={'frame': 0}
),
DatasetItem(id=3, subset='s3', image=Image(
path='3.jpg', size=(2, 4)),
attributes={'frame': 0}),
], categories={
AnnotationType.label: label_categories,
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CvatConverter.convert, save_images=True), test_dir,
target_dataset=target_dataset)
def test_relative_paths(self):
source_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))),
], categories={ AnnotationType.label: LabelCategories() })
target_dataset = Dataset.from_iterable([
DatasetItem(id='1', image=np.ones((4, 2, 3)),
attributes={'frame': 0}),
DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3)),
attributes={'frame': 1}),
DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3)),
attributes={'frame': 2}),
], categories={
AnnotationType.label: LabelCategories()
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CvatConverter.convert, save_images=True), test_dir,
target_dataset=target_dataset)
def test_preserve_frame_ids(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='some/name1', image=np.ones((4, 2, 3)),
attributes={'frame': 40}),
], categories={
AnnotationType.label: LabelCategories()
})
with TestDir() as test_dir:
self._test_save_and_load(expected_dataset,
CvatConverter.convert, test_dir)
def test_reindex(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='some/name1', image=np.ones((4, 2, 3)),
attributes={'frame': 40}),
], categories={ AnnotationType.label: LabelCategories() })
expected_dataset = Dataset.from_iterable([
DatasetItem(id='some/name1', image=np.ones((4, 2, 3)),
attributes={'frame': 0}),
], categories={ AnnotationType.label: LabelCategories() })
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(CvatConverter.convert, reindex=True), test_dir,
target_dataset=expected_dataset)
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