# Copyright (c) Facebook, Inc. and its affiliates. import copy import numpy as np import os import unittest import pycocotools.mask as mask_util from detectron2.data import MetadataCatalog, detection_utils from detectron2.data import transforms as T from detectron2.structures import BitMasks, BoxMode from detectron2.utils.file_io import PathManager class TestTransformAnnotations(unittest.TestCase): def test_transform_simple_annotation(self): transforms = T.TransformList([T.HFlipTransform(400)]) anno = { "bbox": np.asarray([10, 10, 200, 300]), "bbox_mode": BoxMode.XYXY_ABS, "category_id": 3, "segmentation": [[10, 10, 100, 100, 100, 10], [150, 150, 200, 150, 200, 200]], } output = detection_utils.transform_instance_annotations(anno, transforms, (400, 400)) self.assertTrue(np.allclose(output["bbox"], [200, 10, 390, 300])) self.assertEqual(len(output["segmentation"]), len(anno["segmentation"])) self.assertTrue(np.allclose(output["segmentation"][0], [390, 10, 300, 100, 300, 10])) detection_utils.annotations_to_instances([output, output], (400, 400)) def test_transform_empty_annotation(self): detection_utils.annotations_to_instances([], (400, 400)) def test_flip_keypoints(self): transforms = T.TransformList([T.HFlipTransform(400)]) anno = { "bbox": np.asarray([10, 10, 200, 300]), "bbox_mode": BoxMode.XYXY_ABS, "keypoints": np.random.rand(17, 3) * 50 + 15, } output = detection_utils.transform_instance_annotations( copy.deepcopy(anno), transforms, (400, 400), keypoint_hflip_indices=detection_utils.create_keypoint_hflip_indices( ["keypoints_coco_2017_train"] ), ) # The first keypoint is nose self.assertTrue(np.allclose(output["keypoints"][0, 0], 400 - anno["keypoints"][0, 0])) # The last 16 keypoints are 8 left-right pairs self.assertTrue( np.allclose( output["keypoints"][1:, 0].reshape(-1, 2)[:, ::-1], 400 - anno["keypoints"][1:, 0].reshape(-1, 2), ) ) self.assertTrue( np.allclose( output["keypoints"][1:, 1:].reshape(-1, 2, 2)[:, ::-1, :], anno["keypoints"][1:, 1:].reshape(-1, 2, 2), ) ) def test_crop(self): transforms = T.TransformList([T.CropTransform(300, 300, 10, 10)]) keypoints = np.random.rand(17, 3) * 50 + 15 keypoints[:, 2] = 2 anno = { "bbox": np.asarray([10, 10, 200, 400]), "bbox_mode": BoxMode.XYXY_ABS, "keypoints": keypoints, } output = detection_utils.transform_instance_annotations( copy.deepcopy(anno), transforms, (10, 10) ) # box is shifted and cropped self.assertTrue((output["bbox"] == np.asarray([0, 0, 0, 10])).all()) # keypoints are no longer visible self.assertTrue((output["keypoints"][:, 2] == 0).all()) def test_transform_RLE(self): transforms = T.TransformList([T.HFlipTransform(400)]) mask = np.zeros((300, 400), order="F").astype("uint8") mask[:, :200] = 1 anno = { "bbox": np.asarray([10, 10, 200, 300]), "bbox_mode": BoxMode.XYXY_ABS, "segmentation": mask_util.encode(mask[:, :, None])[0], "category_id": 3, } output = detection_utils.transform_instance_annotations( copy.deepcopy(anno), transforms, (300, 400) ) mask = output["segmentation"] self.assertTrue((mask[:, 200:] == 1).all()) self.assertTrue((mask[:, :200] == 0).all()) inst = detection_utils.annotations_to_instances( [output, output], (400, 400), mask_format="bitmask" ) self.assertTrue(isinstance(inst.gt_masks, BitMasks)) def test_transform_RLE_resize(self): transforms = T.TransformList( [T.HFlipTransform(400), T.ScaleTransform(300, 400, 400, 400, "bilinear")] ) mask = np.zeros((300, 400), order="F").astype("uint8") mask[:, :200] = 1 anno = { "bbox": np.asarray([10, 10, 200, 300]), "bbox_mode": BoxMode.XYXY_ABS, "segmentation": mask_util.encode(mask[:, :, None])[0], "category_id": 3, } output = detection_utils.transform_instance_annotations( copy.deepcopy(anno), transforms, (400, 400) ) inst = detection_utils.annotations_to_instances( [output, output], (400, 400), mask_format="bitmask" ) self.assertTrue(isinstance(inst.gt_masks, BitMasks)) def test_gen_crop(self): instance = {"bbox": [10, 10, 100, 100], "bbox_mode": BoxMode.XYXY_ABS} t = detection_utils.gen_crop_transform_with_instance((10, 10), (150, 150), instance) # the box center must fall into the cropped region self.assertTrue(t.x0 <= 55 <= t.x0 + t.w) def test_gen_crop_outside_boxes(self): instance = {"bbox": [10, 10, 100, 100], "bbox_mode": BoxMode.XYXY_ABS} with self.assertRaises(AssertionError): detection_utils.gen_crop_transform_with_instance((10, 10), (15, 15), instance) def test_read_sem_seg(self): cityscapes_dir = MetadataCatalog.get("cityscapes_fine_sem_seg_val").gt_dir sem_seg_gt_path = os.path.join( cityscapes_dir, "frankfurt", "frankfurt_000001_083852_gtFine_labelIds.png" ) if not PathManager.exists(sem_seg_gt_path): raise unittest.SkipTest( "Semantic segmentation ground truth {} not found.".format(sem_seg_gt_path) ) sem_seg = detection_utils.read_image(sem_seg_gt_path, "L") self.assertEqual(sem_seg.ndim, 3) self.assertEqual(sem_seg.shape[2], 1) self.assertEqual(sem_seg.dtype, np.uint8) self.assertEqual(sem_seg.max(), 32) self.assertEqual(sem_seg.min(), 1) def test_read_exif_orientation(self): # https://github.com/recurser/exif-orientation-examples/raw/master/Landscape_5.jpg URL = "detectron2://assets/Landscape_5.jpg" img = detection_utils.read_image(URL, "RGB") self.assertEqual(img.ndim, 3) self.assertEqual(img.dtype, np.uint8) self.assertEqual(img.shape, (1200, 1800, 3)) # check that shape is not transposed def test_opencv_exif_orientation(self): import cv2 URL = "detectron2://assets/Landscape_5.jpg" with PathManager.open(URL, "rb") as f: img = cv2.imdecode(np.frombuffer(f.read(), dtype="uint8"), cv2.IMREAD_COLOR) self.assertEqual(img.dtype, np.uint8) self.assertEqual(img.shape, (1200, 1800, 3)) if __name__ == "__main__": unittest.main()