| import logging as log |
| 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=[ |
| |
| 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=[ |
| |
| Label(1), |
| Bbox(1, 2, 3, 4, label=2), |
| Mask(image=np.array([1]), label=3), |
|
|
| |
| 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) |
|
|
