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Added proper handling of nifti RGB formats #46

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786dfc2
2 format of NIfTI are properly supported
Feb 18, 2026
1c389a7
added discussion about nibabel limitation about rgb structured format
Feb 18, 2026
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11 changes: 11 additions & 0 deletions src/neuroglancer_scripts/data_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,12 +80,23 @@ def chunk_transformer(chunk, preserve_input=True):

def get_dtype_from_vol(volume):
zero_index = tuple(0 for _ in volume.shape)

# RGB format test case 1: datatype == 2 (unsigned char, 4 signed short, 8 signed int, 16 float, 64 double
# 256 signed char, 512 unsigned short, 768 unsigned int, 1024 long long, 1280 unsigned long long
# 1536 long double
# rgb values are stored as 3 uint array on the 4th axis
if len(volume.shape) == 5 and volume.shape[4] == 3:
logger.info('RGB uint8 * 3 format detected')
return volume[zero_index].dtype, True

return get_dtype(volume[zero_index].dtype)


def get_dtype(input_dtype):
if input_dtype.names is None:
return input_dtype, False
# RGB format case 2: when nifti datatype == 128, the values are stored in structured format
# np.dtype([('R', 'u1'), ('G', 'u1'), ('B', 'u1')])
if input_dtype.names not in NG_MULTICHANNEL_DATATYPES:
err = f'tuple datatype {input_dtype.names} not yet supported'
raise NotImplementedError(err)
Expand Down
81 changes: 64 additions & 17 deletions src/neuroglancer_scripts/volume_reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,8 +94,11 @@ def nibabel_image_to_info(img,
zero_index = tuple(0 for _ in shape)
input_dtype = proxy[zero_index].dtype

input_dtype, is_rgb = neuroglancer_scripts.data_types.get_dtype(
input_dtype)
if len(shape) == 5 and shape[4] == 3:
is_rgb = True
else:
input_dtype, is_rgb = neuroglancer_scripts.data_types.get_dtype(
input_dtype)
if is_rgb:
shape = shape + (3,)

Expand All @@ -111,10 +114,23 @@ def nibabel_image_to_info(img,
guessed_dtype = input_dtype.name
else:
guessed_dtype = "float32"


# RGB nifti file has two arrangements, number 1 datatype as integer values with axis 4 to be the components
# the other type is datatype == 128, then the data is stored structurally
if is_rgb:
if len(shape) == 4:
num_channels = 3
elif len(shape) == 5:
num_channels = shape[4]
else:
num_channels = 3
else:
num_channels = 1
formatted_info = f"""\
{{
"type": "image",
"num_channels": {shape[3] if len(shape) >= 4 else 1},
"num_channels": {num_channels},
"data_type": "{guessed_dtype}",
"scales": [
{{
Expand Down Expand Up @@ -180,7 +196,7 @@ def volume_to_precomputed(pyramid_writer, volume, chunk_transformer=None):
# Volumes given by nibabel are using Fortran indexing (X, Y, Z, T)
assert volume.shape[:3] == tuple(size)
if len(volume.shape) > 3:
assert volume.shape[3] == num_channels
assert volume.shape[4] == num_channels

progress_bar = tqdm(
total=(((size[0] - 1) // chunk_size[0] + 1)
Expand All @@ -207,6 +223,8 @@ def volume_to_precomputed(pyramid_writer, volume, chunk_transformer=None):
elif len(volume.shape) == 3:
chunk = volume[x_slicing, y_slicing, z_slicing]
chunk = chunk[..., np.newaxis]
elif len(volume.shape) == 5:
chunk = volume[x_slicing, y_slicing, z_slicing, 0, :]

if chunk_transformer is not None:
chunk = chunk_transformer(chunk, preserve_input=False)
Expand Down Expand Up @@ -250,24 +268,30 @@ def nibabel_image_to_precomputed(img,
# read a value from the file to see the result of the scaling
zero_index = tuple(0 for _ in shape)
input_dtype = proxy[zero_index].dtype

affine = img.affine
voxel_sizes = nibabel.affines.voxel_sizes(affine)

info = precomputed_writer.info

output_dtype = np.dtype(info["data_type"])
info_voxel_sizes = 1e-6 * np.asarray(info["scales"][0]["resolution"])
if not np.allclose(voxel_sizes, info_voxel_sizes):
logger.warning("voxel size is inconsistent with resolution in the "
"info file (%s mm)",
" × ".join(str(sz) for sz in info_voxel_sizes))

if not np.can_cast(input_dtype, output_dtype, casting="safe"):
logger.warning("The volume has data type %s, but chunks will be "
"saved with %s. You should make sure that the cast "
"does not lose range/accuracy.",
input_dtype.name, output_dtype.name)
if input_dtype == [('R', 'u1'), ('G', 'u1'), ('B', 'u1')]:
# numpy cannot case RGB*u1 to uint8, the rgb structure need to be handled separately
# a structured RGB*uint16 might need additional tweak but the format is extremely rare
# assume all structured RGB images are 8-bits
structured_rgb = True
logger.warning('Structured RGB data detected. Converting to uint8 view to feed the pipeline')
else:
structured_rgb = False
if not np.can_cast(input_dtype, output_dtype, casting="safe"):
logger.warning("The volume has data type %s, but chunks will be "
"saved with %s. You should make sure that the cast "
"does not lose range/accuracy.",
input_dtype.name, output_dtype.name)

# Scaling according to --input-min and --input-max. We modify the
# slope/inter values used by Nibabel rather than re-implementing
Expand All @@ -290,16 +314,36 @@ def nibabel_image_to_precomputed(img,
proxy._inter = prescaling_inter * postscaling_slope + postscaling_inter

# Transformations applied to the voxel values
chunk_transformer = (
neuroglancer_scripts.data_types.get_chunk_dtype_transformer(
input_dtype, output_dtype
# data_types.get_chunk_dtype_transformer does not support transform from structured RGB to uint8
# force the conversion to be from a uint8 * 3 view for structured RGB format
if structured_rgb:
chunk_transformer = (
neuroglancer_scripts.data_types.get_chunk_dtype_transformer(
np.uint8, output_dtype
)
)
else:
chunk_transformer = (
neuroglancer_scripts.data_types.get_chunk_dtype_transformer(
input_dtype, output_dtype
)
)
)
if load_full_volume:
logger.info("Loading full volume to memory... ")
volume = np.asanyarray(img.dataobj)
# the structured rgb format handling is explained in this stackoverflow question
# https://stackoverflow.com/questions/40534333/how-to-write-a-color-3d-nifti-with-nibabel
if structured_rgb:
volume = np.asanyarray(img.dataobj)
volume = volume.copy().view(dtype=np.uint8).reshape(volume.shape[0:4] + (3,))
else:
volume = np.asanyarray(img.dataobj)
else:
volume = proxy
if structured_rgb:
logger.warning('The mmap access for structured RGB nifti format is not supported, reading all data into memory')
logger.warning('This process may use a lot of memory, be warned')
volume = proxy[:].view(dtype=np.uint8, type=np.ndarray).reshape(proxy.shape[0:4] + (3,))
else:
volume = proxy
logger.info("Writing chunks... ")
volume_to_precomputed(precomputed_writer, volume,
chunk_transformer=chunk_transformer)
Expand All @@ -316,6 +360,8 @@ def volume_file_to_precomputed(volume_filename,
dtype, is_rgb = neuroglancer_scripts.data_types.get_dtype_from_vol(
img.dataobj)
if is_rgb:
pass
"""
proxy = np.asarray(img.dataobj)
new_proxy = proxy.view(dtype=np.uint8, type=np.ndarray)
third = int(new_proxy.shape[0] / 3)
Expand All @@ -325,6 +371,7 @@ def volume_file_to_precomputed(volume_filename,
new_proxy[2*third:]
], axis=-1)
img = nibabel.Nifti1Image(new_dataobj, img.affine)
"""

accessor = neuroglancer_scripts.accessor.get_accessor_for_url(
dest_url, options
Expand Down