Add cucim.skimage.morphology.medial_axis (#342)

closes #336

This PR adds a function for skeletonization of 2D images via the medial axis transform. 

It should be reviewed after #318 is merged. The new commits only start from 19a6fed.

There is one sequential component to this algorithm that still must be run on the CPU, but the majority of the computations are on the GPU and acceleration is good. I will add benchmark results here soon.

Authors:
  - Gregory Lee (https://github.com/grlee77)
  - https://github.com/jakirkham

Approvers:
  - https://github.com/jakirkham
  - Gigon Bae (https://github.com/gigony)

URL: #342

python/cucim/src/cucim/skimage/morphology/__init__.py

@@ -1,4 +1,4 @@
-from ._skeletonize import thin
+from ._skeletonize import medial_axis, thin
 from .binary import (binary_closing, binary_dilation, binary_erosion,
  binary_opening)
 from .footprints import (ball, cube, diamond, disk, octagon, octahedron,
@@ -32,4 +32,5 @@
  "remove_small_objects",
  "remove_small_holes",
  "thin",
+ "medial_axis",
 ]

python/cucim/src/cucim/skimage/morphology/_medial_axis_lookup.py

@@ -0,0 +1,67 @@
+import numpy as np
+
+# medial axis lookup tables (independent of image content)
+#
+# Note: lookup table generated using scikit-image code from
+# https://github.com/scikit-image/scikit-image/blob/38b595d60befe3a0b4c0742995b9737200a079c6/skimage/morphology/_skeletonize.py#L449-L458 # noqa
+
+lookup_table = np.array(
+ [
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1,
+ 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
+ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
+ 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
+ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
+ 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0
+ ],
+ dtype=bool,
+)
+
+
+cornerness_table = np.array(
+ [
+ 9, 8, 8, 7, 8, 7, 7, 6, 8, 7, 7, 6, 7, 6, 6, 5, 8, 7, 7, 6, 7, 6,
+ 6, 5, 7, 6, 6, 5, 6, 5, 5, 4, 8, 7, 7, 6, 7, 6, 6, 5, 7, 6, 6, 5,
+ 6, 5, 5, 4, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3, 8, 7,
+ 7, 6, 7, 6, 6, 5, 7, 6, 6, 5, 6, 5, 5, 4, 7, 6, 6, 5, 6, 5, 5, 4,
+ 6, 5, 5, 4, 5, 4, 4, 3, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4,
+ 4, 3, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 8, 7, 7, 6,
+ 7, 6, 6, 5, 7, 6, 6, 5, 6, 5, 5, 4, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5,
+ 5, 4, 5, 4, 4, 3, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3,
+ 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 7, 6, 6, 5, 6, 5,
+ 5, 4, 6, 5, 5, 4, 5, 4, 4, 3, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3,
+ 4, 3, 3, 2, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 5, 4,
+ 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, 8, 7, 7, 6, 7, 6, 6, 5,
+ 7, 6, 6, 5, 6, 5, 5, 4, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4,
+ 4, 3, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3, 6, 5, 5, 4,
+ 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5,
+ 5, 4, 5, 4, 4, 3, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2,
+ 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 5, 4, 4, 3, 4, 3,
+ 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, 7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4,
+ 5, 4, 4, 3, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 6, 5,
+ 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, 5, 4, 4, 3, 4, 3, 3, 2,
+ 4, 3, 3, 2, 3, 2, 2, 1, 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3,
+ 3, 2, 5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, 5, 4, 4, 3,
+ 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, 4, 3, 3, 2, 3, 2, 2, 1, 3, 2,
+ 2, 1, 2, 1, 1, 0
+ ],
+ dtype=np.uint8,
+)

python/cucim/src/cucim/skimage/morphology/_skeletonize.py

@@ -1,8 +1,15 @@
+import warnings
+
 import cupy as cp
 import cucim.skimage._vendored.ndimage as ndi
 import numpy as np
 
+from cucim.core.operations.morphology import distance_transform_edt
+
 from .._shared.utils import check_nD, deprecate_kwarg
+from ._medial_axis_lookup import \
+ cornerness_table as _medial_axis_cornerness_table
+from ._medial_axis_lookup import lookup_table as _medial_axis_lookup_table
 
 # --------- Skeletonization and thinning based on Guo and Hall 1989 ---------
 
@@ -39,7 +46,7 @@
  0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)
 
 
-@deprecate_kwarg({'max_iter': 'max_num_iter'}, removed_version="23.02.00",
+@deprecate_kwarg({"max_iter": "max_num_iter"}, removed_version="23.02.00",
  deprecated_version="22.02.00")
 def thin(image, max_num_iter=None):
  """
@@ -131,7 +138,7 @@ def thin(image, max_num_iter=None):
  # perform the two "subiterations" described in the paper
  for lut in [G123_LUT, G123P_LUT]:
  # correlate image with neighborhood mask
- N = ndi.correlate(skel, mask, mode='constant')
+ N = ndi.correlate(skel, mask, mode="constant")
  # take deletion decision from this subiteration's LUT
  D = cp.take(lut, N)
  # perform deletion
@@ -141,3 +148,218 @@ def thin(image, max_num_iter=None):
  num_iter += 1
 
  return skel.astype(bool)
+
+
+# --------- Skeletonization by medial axis transform --------
+
+
+def _get_tiebreaker(n, random_seed):
+ # CuPy generator doesn't currently have the permutation method, so
+ # fall back to cp.random.permutation instead.
+ cp.random.seed(random_seed)
+ if n < 2 << 31:
+ dtype = np.int32
+ else:
+ dtype = np.intp
+ tiebreaker = cp.random.permutation(cp.arange(n, dtype=dtype))
+ return tiebreaker
+
+
+def medial_axis(image, mask=None, return_distance=False, *, random_state=None):
+ """Compute the medial axis transform of a binary image.
+
+ Parameters
+ ----------
+ image : binary ndarray, shape (M, N)
+ The image of the shape to be skeletonized.
+ mask : binary ndarray, shape (M, N), optional
+ If a mask is given, only those elements in `image` with a true
+ value in `mask` are used for computing the medial axis.
+ return_distance : bool, optional
+ If true, the distance transform is returned as well as the skeleton.
+ random_state : {None, int, `numpy.random.Generator`}, optional
+ If `random_state` is None the `numpy.random.Generator` singleton is
+ used.
+ If `random_state` is an int, a new ``Generator`` instance is used,
+ seeded with `random_state`.
+ If `random_state` is already a ``Generator`` instance then that
+ instance is used.
+
+ .. versionadded:: 0.19
+
+ Returns
+ -------
+ out : ndarray of bools
+ Medial axis transform of the image
+ dist : ndarray of ints, optional
+ Distance transform of the image (only returned if `return_distance`
+ is True)
+
+ See Also
+ --------
+ skeletonize
+
+ Notes
+ -----
+ This algorithm computes the medial axis transform of an image
+ as the ridges of its distance transform.
+
+ The different steps of the algorithm are as follows
+ * A lookup table is used, that assigns 0 or 1 to each configuration of
+ the 3x3 binary square, whether the central pixel should be removed
+ or kept. We want a point to be removed if it has more than one neighbor
+ and if removing it does not change the number of connected components.
+
+ * The distance transform to the background is computed, as well as
+ the cornerness of the pixel.
+
+ * The foreground (value of 1) points are ordered by
+ the distance transform, then the cornerness.
+
+ * A cython function is called to reduce the image to its skeleton. It
+ processes pixels in the order determined at the previous step, and
+ removes or maintains a pixel according to the lookup table. Because
+ of the ordering, it is possible to process all pixels in only one
+ pass.
+
+ Examples
+ --------
+ >>> square = np.zeros((7, 7), dtype=np.uint8)
+ >>> square[1:-1, 2:-2] = 1
+ >>> square
+ array([[0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+ >>> medial_axis(square).astype(np.uint8)
+ array([[0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 1, 0, 1, 0, 0],
+ [0, 0, 0, 1, 0, 0, 0],
+ [0, 0, 0, 1, 0, 0, 0],
+ [0, 0, 0, 1, 0, 0, 0],
+ [0, 0, 1, 0, 1, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+
+ """
+ try:
+ from skimage.morphology._skeletonize_cy import _skeletonize_loop
+ except ImportError as e:
+ warnings.warn(
+ "Could not find required private skimage Cython function:\n"
+ "\tskimage.morphology._skeletonize_cy._skeletonize_loop\n"
+ )
+ raise e
+
+ if mask is None:
+ # masked_image is modified in-place later so make a copy of the input
+ masked_image = image.astype(bool, copy=True)
+ else:
+ masked_image = image.astype(bool, copy=True)
+ masked_image[~mask] = False
+
+ # Load precomputed lookup table based on three conditions:
+ # 1. Keep only positive pixels
+ # AND
+ # 2. Keep if removing the pixel results in a different connectivity
+ # (if the number of connected components is different with and
+ # without the central pixel)
+ # OR
+ # 3. Keep if # pixels in neighborhood is 2 or less
+ # Note that this table is independent of the image
+ table = _medial_axis_lookup_table
+
+ # Build distance transform
+ distance = distance_transform_edt(masked_image)
+ if return_distance:
+ store_distance = distance.copy()
+
+ # Corners
+ # The processing order along the edge is critical to the shape of the
+ # resulting skeleton: if you process a corner first, that corner will
+ # be eroded and the skeleton will miss the arm from that corner. Pixels
+ # with fewer neighbors are more "cornery" and should be processed last.
+ # We use a cornerness_table lookup table where the score of a
+ # configuration is the number of background (0-value) pixels in the
+ # 3x3 neighborhood
+ cornerness_table = cp.asarray(_medial_axis_cornerness_table)
+ corner_score = _table_lookup(masked_image, cornerness_table)
+
+ # Define arrays for inner loop
+ distance = distance[masked_image]
+ i, j = cp.where(masked_image)
+
+ # Determine the order in which pixels are processed.
+ # We use a random # for tiebreaking. Assign each pixel in the image a
+ # predictable, random # so that masking doesn't affect arbitrary choices
+ # of skeletons
+ tiebreaker = _get_tiebreaker(n=distance.size, random_seed=random_state)
+ order = cp.lexsort(
+ cp.stack(
+ (tiebreaker, corner_score[masked_image], distance),
+ axis=0
+ )
+ )
+
+ # Call _skeletonize_loop on the CPU. It requies a single pass over the
+ # full array using a specific pixel order, so cannot be run multithreaded!
+ order = cp.asnumpy(order.astype(cp.int32, copy=False))
+ table = cp.asnumpy(table.astype(cp.uint8, copy=False))
+ i = cp.asnumpy(i).astype(dtype=np.intp, copy=False)
+ j = cp.asnumpy(j).astype(dtype=np.intp, copy=False)
+ result = cp.asnumpy(masked_image)
+ # Remove pixels not belonging to the medial axis
+ _skeletonize_loop(result.view(np.uint8), i, j, order, table)
+ result = cp.asarray(result.view(bool), dtype=bool)
+
+ if mask is not None:
+ result[~mask] = image[~mask]
+ if return_distance:
+ return result, store_distance
+ else:
+ return result
+
+
+def _table_lookup(image, table):
+ """
+ Perform a morphological transform on an image, directed by its
+ neighbors
+
+ Parameters
+ ----------
+ image : ndarray
+ A binary image
+ table : ndarray
+ A 512-element table giving the transform of each pixel given
+ the values of that pixel and its 8-connected neighbors.
+
+ Returns
+ -------
+ result : ndarray of same shape as `image`
+ Transformed image
+
+ Notes
+ -----
+ The pixels are numbered like this::
+
+ 0 1 2
+ 3 4 5
+ 6 7 8
+
+ The index at a pixel is the sum of 2**<pixel-number> for pixels
+ that evaluate to true.
+ """
+ #
+ # We accumulate into the indexer to get the index into the table
+ # at each point in the image
+ #
+ # max possible value of indexer is 512, so just use int16 dtype
+ kernel = cp.array(
+ [[256, 128, 64], [32, 16, 8], [4, 2, 1]],
+ dtype=cp.int16
+ )
+ indexer = ndi.convolve(image, kernel, output=np.int16, mode="constant")
+ image = table[indexer]
+ return image