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import numpy as np
from unittest import TestCase
from datumaro.components.project import Dataset
from datumaro.components.extractor import (Extractor, DatasetItem,
Mask, Polygon, PolyLine, Points, Bbox, Label,
LabelCategories, MaskCategories, AnnotationType
)
import datumaro.util.mask_tools as mask_tools
import datumaro.plugins.transforms as transforms
from datumaro.util.test_utils import compare_datasets
class TransformsTest(TestCase):
def test_reindex(self):
source = Dataset.from_iterable([
DatasetItem(id=10),
DatasetItem(id=10, subset='train'),
DatasetItem(id='a', subset='val'),
])
expected = Dataset.from_iterable([
DatasetItem(id=5),
DatasetItem(id=6, subset='train'),
DatasetItem(id=7, subset='val'),
])
actual = transforms.Reindex(source, start=5)
compare_datasets(self, expected, actual)
def test_mask_to_polygons(self):
source = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[
Mask(np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]),
),
]),
])
expected = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[
Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5]),
Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5]),
]),
])
actual = transforms.MasksToPolygons(source)
compare_datasets(self, expected, actual)
def test_mask_to_polygons_small_polygons_message(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[
Mask(np.array([
[0, 0, 0],
[0, 1, 0],
[0, 0, 0],
]),
),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3))), ])
with self.assertLogs(level=log.DEBUG) as logs:
actual = transforms.MasksToPolygons(source_dataset)
compare_datasets(self, target_dataset, actual)
self.assertRegex('\n'.join(logs.output), 'too small polygons')
def test_polygons_to_masks(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[
Polygon([0, 0, 4, 0, 4, 4]),
Polygon([5, 0, 9, 0, 5, 5]),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[
Mask(np.array([
[0, 0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]),
),
Mask(np.array([
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]),
),
]),
])
actual = transforms.PolygonsToMasks(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_crop_covered_segments(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[
# The mask is partially covered by the polygon
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0]],
), z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]],
), z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1),
]),
])
actual = transforms.CropCoveredSegments(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_merge_instance_segments(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]],
),
z_order=0, group=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
z_order=1, group=1),
Polygon([0, 0, 0, 2, 2, 2, 2, 0],
z_order=1),
]
),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 0],
[1, 1, 1, 0, 0]],
),
z_order=0, group=1),
Mask(np.array([
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
),
z_order=1),
]
),
])
actual = transforms.MergeInstanceSegments(source_dataset,
include_polygons=True)
compare_datasets(self, target_dataset, actual)
def test_map_subsets(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='a'),
DatasetItem(id=2, subset='b'),
DatasetItem(id=3, subset='c'),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset=''),
DatasetItem(id=2, subset='a'),
DatasetItem(id=3, subset='c'),
])
actual = transforms.MapSubsets(source_dataset,
{ 'a': '', 'b': 'a' })
compare_datasets(self, target_dataset, actual)
def test_shapes_to_boxes(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]],
), id=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4], id=2),
PolyLine([1, 1, 2, 1, 2, 2, 1, 2], id=3),
Points([2, 2, 4, 2, 4, 4, 2, 4], id=4),
]
),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 4, 4, id=1),
Bbox(1, 1, 3, 3, id=2),
Bbox(1, 1, 1, 1, id=3),
Bbox(2, 2, 2, 2, id=4),
]
),
])
actual = transforms.ShapesToBoxes(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_id_from_image(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image='path.jpg'),
DatasetItem(id=2),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id='path', image='path.jpg'),
DatasetItem(id=2),
])
actual = transforms.IdFromImageName(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_boxes_to_masks(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 3, 3, z_order=1),
Bbox(0, 0, 3, 1, z_order=2),
Bbox(0, 2, 3, 1, z_order=3),
]
),
])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1, image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
),
z_order=1),
Mask(np.array([
[1, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
),
z_order=2),
Mask(np.array([
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
),
z_order=3),
]
),
])
actual = transforms.BoxesToMasks(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_random_split(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset="a"),
DatasetItem(id=2, subset="a"),
DatasetItem(id=3, subset="b"),
DatasetItem(id=4, subset="b"),
DatasetItem(id=5, subset="b"),
DatasetItem(id=6, subset=""),
DatasetItem(id=7, subset=""),
])
actual = transforms.RandomSplit(source_dataset, splits=[
('train', 4.0 / 7.0),
('test', 3.0 / 7.0),
])
self.assertEqual(4, len(actual.get_subset('train')))
self.assertEqual(3, len(actual.get_subset('test')))
def test_random_split_gives_error_on_wrong_ratios(self):
source_dataset = Dataset.from_iterable([DatasetItem(id=1)])
with self.assertRaises(Exception):
transforms.RandomSplit(source_dataset, splits=[
('train', 0.5),
('test', 0.7),
])
with self.assertRaises(Exception):
transforms.RandomSplit(source_dataset, splits=[])
with self.assertRaises(Exception):
transforms.RandomSplit(source_dataset, splits=[
('train', -0.5),
('test', 1.5),
])
def test_remap_labels(self):
src_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
# Should be remapped
Label(1),
Bbox(1, 2, 3, 4, label=2),
Mask(image=np.array([1]), label=3),
# Should be kept
Polygon([1, 1, 2, 2, 3, 4], label=4),
PolyLine([1, 3, 4, 2, 5, 6])
])
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label%s' % i for i in range(5)),
AnnotationType.mask: MaskCategories(
colormap=mask_tools.generate_colormap(5)),
})
dst_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(1),
Bbox(1, 2, 3, 4, label=0),
Mask(image=np.array([1]), label=1),
Polygon([1, 1, 2, 2, 3, 4], label=2),
PolyLine([1, 3, 4, 2, 5, 6], label=None)
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
['label0', 'label9', 'label4']),
AnnotationType.mask: MaskCategories(colormap={
k: v for k, v in mask_tools.generate_colormap(5).items()
if k in { 0, 1, 3, 4 }
})
})
actual = transforms.RemapLabels(src_dataset, mapping={
'label1': 'label9',
'label2': 'label0',
'label3': 'label9',
}, default='keep')
compare_datasets(self, dst_dataset, actual)
def test_remap_labels_delete_unspecified(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[ Label(0) ])
], categories=['label0'])
target_dataset = Dataset.from_iterable([
DatasetItem(id=1),
], categories=[])
actual = transforms.RemapLabels(source_dataset,
mapping={}, default='delete')
compare_datasets(self, target_dataset, actual)
def test_transform_labels(self):
src_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(1),
Bbox(1, 2, 3, 4, label=2),
Bbox(1, 3, 3, 3),
Mask(image=np.array([1]), label=3),
Polygon([1, 1, 2, 2, 3, 4], label=4),
PolyLine([1, 3, 4, 2, 5, 6], label=5)
])
], categories=['label%s' % i for i in range(6)])
dst_dataset = Dataset.from_iterable([
DatasetItem(id=1, annotations=[
Label(1),
Label(2),
Label(3),
Label(4),
Label(5)
]),
], categories=['label%s' % i for i in range(6)])
actual = transforms.AnnsToLabels(src_dataset)
compare_datasets(self, dst_dataset, actual)
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