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_boxes_to_masks(self): class SrcExtractor(Extractor): def __iter__(self): return iter([ 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), ]), ]) class DstExtractor(Extractor): def __iter__(self): return iter([ 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(SrcExtractor()) compare_datasets(self, DstExtractor(), actual)