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Labeled comprehension channel axis #381

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13 changes: 8 additions & 5 deletions dask_image/ndmeasure/__init__.py
Original file line number Diff line number Diff line change
@@ -4,12 +4,12 @@
import functools
import operator
import warnings
from dask import compute, delayed

import dask.array as da
import dask.bag as db
import dask.dataframe as dd
import numpy as np
from dask import compute, delayed

from . import _utils
from ._utils import _label
@@ -378,7 +378,6 @@ def label(image, structure=None, wrap_axes=None):
relabeled = _label.relabel_blocks(block_labeled, new_labeling)
n = da.max(relabeled)


return (relabeled, n)


@@ -402,7 +401,9 @@ def labeled_comprehension(image,
Parameters
----------
image : ndarray
N-D image data
Intensity image with same size as ``label_image``, plus optionally
an extra dimension for multichannel data. The extra channel dimension,
if present, must be the last axis.
label_image : ndarray, optional
Image features noted by integers. If None (default), all values.
index : int or sequence of ints, optional
@@ -441,14 +442,16 @@ def labeled_comprehension(image,
args = (image,)
if pass_positions:
positions = _utils._ravel_shape_indices(
image.shape, chunks=image.chunks
image.shape, chunks=image.chunks, skip_trailing_dim=image.ndim != label_image.ndim
)
args = (image, positions)

result = np.empty(index.shape, dtype=object)
for i in np.ndindex(index.shape):
lbl_mtch_i = (label_image == index[i])
args_lbl_mtch_i = tuple(e[lbl_mtch_i] for e in args)
args_lbl_mtch_i = tuple(
e[lbl_mtch_i] if e.ndim == lbl_mtch_i.ndim else e.reshape(-1, e.shape[-1])[lbl_mtch_i.reshape(-1)] for e in
args)
result[i] = _utils._labeled_comprehension_func(
func, out_dtype, default_1d, *args_lbl_mtch_i
)
9 changes: 5 additions & 4 deletions dask_image/ndmeasure/_utils/__init__.py
Original file line number Diff line number Diff line change
@@ -31,9 +31,10 @@ def _norm_input_labels_index(image, label_image=None, index=None):
FutureWarning
)

if image.shape != label_image.shape:
image_shape = image.shape if image.ndim == label_image.ndim else image.shape[:-1]
if image_shape != label_image.shape: # allow trailing channel
raise ValueError(
"The image and label_image arrays must be the same shape."
f"The image and label_image arrays must be the same shape. {image_shape} != {label_image.shape}"
)

return (image, label_image, index)
@@ -47,7 +48,7 @@ def _ravel_shape_indices_kernel(*args):
return sum(args2)


def _ravel_shape_indices(dimensions, dtype=int, chunks=None):
def _ravel_shape_indices(dimensions, dtype=int, chunks=None, skip_trailing_dim:bool=False):
"""
Gets the raveled indices shaped like input.
"""
@@ -60,7 +61,7 @@ def _ravel_shape_indices(dimensions, dtype=int, chunks=None):
dtype=dtype,
chunks=c
)
for i, c in enumerate(chunks)
for i, c in enumerate(chunks[:-1] if skip_trailing_dim else chunks)
]

indices = da.blockwise(