# 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()