def test_map_semantic_img_fast_uint16_stress_test():
"""
Test fast method on large example. Map 60,000 classes to a
different 60,000 classes with a fast array conversion.
Should take less than 1 millisecond.
"""
semantic_img = np.array(range(60000)).reshape(3000, 20).astype(np.uint16)
# will expect uint16 back since 60000 < 65535, which is uint16 max
gt_mapped_img1 = np.array(range(60000)).reshape(3000, 20).astype(
np.uint16) + 1
label_mapping = {i: i + 1 for i in range(60000)}
label_mapping_copy = copy.deepcopy(label_mapping)
label_mapping_arr = form_label_mapping_array(label_mapping)
dict_is_equal(label_mapping, label_mapping_copy)
assert label_mapping == label_mapping_copy
start = time.time()
mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr)
end = time.time()
print(f'Took {end-start} sec.')
gt_mapped_img2 = map_semantic_img_slow(semantic_img, label_mapping)
assert np.allclose(gt_mapped_img1, mapped_img)
assert np.allclose(gt_mapped_img2, mapped_img)
assert gt_mapped_img1.dtype == mapped_img.dtype
assert gt_mapped_img2.dtype == mapped_img.dtype
def test_map_semantic_img_fast_dontwidenvalue():
"""
Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale.
"""
semantic_img = np.array([[300, 301, 302], [300, 301, 302]],
dtype=np.uint16)
label_mapping = {300: 0, 301: 1, 302: 2}
label_mapping_arr = form_label_mapping_array(label_mapping)
mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr)
# Expect uint8 since max class index in values <= 255, so uint16 unnecessary
gt_mapped_img1 = np.array([[0, 1, 2], [0, 1, 2]], dtype=np.uint8)
assert np.allclose(gt_mapped_img1, mapped_img)
assert mapped_img.dtype == np.uint8
def test_map_semantic_img_fast_widenvalue():
"""
Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale.
"""
semantic_img = np.array([[255, 255, 255], [255, 255, 255]], dtype=np.uint8)
label_mapping = {255: 256}
label_mapping_arr = form_label_mapping_array(label_mapping)
mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr)
# Expect uint8 since max class index <= 255, so uint16 unnecessary
gt_mapped_img1 = np.array([[256, 256, 256], [256, 256, 256]],
dtype=np.uint16)
assert np.allclose(gt_mapped_img1, mapped_img)
assert mapped_img.dtype == np.uint16
def test_map_semantic_img_fast():
"""
Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale.
"""
semantic_img = np.array([[254, 0, 1], [7, 8, 9]], dtype=np.uint8)
label_mapping = {254: 253, 0: 255, 1: 0, 7: 6, 8: 7, 9: 8}
label_mapping_arr = form_label_mapping_array(label_mapping)
mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr)
# Expect uint8 since max class index <= 255, so uint16 unnecessary
gt_mapped_img1 = np.array([[253, 255, 0], [6, 7, 8]], dtype=np.uint8)
gt_mapped_img2 = map_semantic_img_slow(semantic_img, label_mapping)
assert np.allclose(gt_mapped_img1, mapped_img)
assert np.allclose(gt_mapped_img2, mapped_img)
assert gt_mapped_img1.dtype == mapped_img.dtype
assert gt_mapped_img2.dtype == mapped_img.dtype
def relabel_pair(old_dataroot: str,
new_dataroot: str,
orig_pair: Tuple[str, str],
remapped_pair: Tuple[str, str],
label_mapping_arr: np.ndarray,
dataset_colors: Optional[np.ndarray] = None):
"""
No need to copy the RGB files again. We just update the label file paths.
Args:
- old_dataroot:
- new_dataroot:
- orig_pair: Tuple containing relative path to RGB image and label image
- remapped_pair: Tuple containing relative path to RGB image and label image
- label_mapping_arr:
- dataset_colors:
Returns:
- None
"""
_, orig_rel_label_fpath = orig_pair
_, remapped_rel_label_fpath = remapped_pair
old_label_fpath = f'{old_dataroot}/{orig_rel_label_fpath}'
if dataset_colors is None:
label_img = imageio.imread(old_label_fpath)
else:
# remap from RGB encoded labels to 1-channel class indices
label_img_rgb = cv2_imread_rgb(old_label_fpath)
label_img = rgb_img_to_obj_cls_img(label_img_rgb, dataset_colors)
remapped_img = map_semantic_img_fast(label_img, label_mapping_arr)
new_label_fpath = f'{new_dataroot}/{remapped_rel_label_fpath}'
create_leading_fpath_dirs(new_label_fpath)
imageio.imwrite(new_label_fpath, remapped_img)
