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FEAT - combined API from is_local_maximum() into peak_local_max()

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is_local_maximum() is a wrapper function for peak_local_max()
is_local_maximum() runs much faster (~20% of prior runtime, nearly = to peak_local_max())
All tests in .feature and .morphology subpackages pass as written with these changes.

Todo:
  * Fully document API
  * remove commented-out old algorithm in is_local_maximum()
  * add new tests for full coverage of new, more complex peak_local_max()
This commit is contained in:
Josh Warner (Mac)
2012-11-19 23:38:58 -06:00
parent 13c61b9694
commit 63b5c5a4a0
2 changed files with 120 additions and 77 deletions
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skimage/morphology/watershed.py
+58 -54
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@@ -28,6 +28,7 @@ from _heapq import heappush, heappop
import numpy as np
import scipy.ndimage
from ..filter import rank_order
from ..feature import peak_local_max
from . import _watershed
@@ -281,62 +282,65 @@ def is_local_maximum(image, labels=None, footprint=None):
[False, False, False, False],
[False, True, False, True]], dtype=bool)
"""
if labels is None:
labels = np.ones(image.shape, dtype=np.uint8)
if footprint is None:
footprint = np.ones([3] * image.ndim, dtype=np.uint8)
assert((np.all(footprint.shape) & 1) == 1)
footprint = (footprint != 0)
footprint_extent = (np.array(footprint.shape) - 1) // 2
if np.all(footprint_extent == 0):
return labels > 0
result = (labels > 0).copy()
#
# Create a labels matrix with zeros at the borders that might be
# hit by the footprint.
#
big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2,
labels.dtype)
big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels
#
# Find the relative indexes of each footprint element
#
image_strides = np.array(image.strides) // image.dtype.itemsize
big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize
result_strides = np.array(result.strides) // result.dtype.itemsize
footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]]
# if labels is None:
# labels = np.ones(image.shape, dtype=np.uint8)
# if footprint is None:
# footprint = np.ones([3] * image.ndim, dtype=np.uint8)
# assert((np.all(footprint.shape) & 1) == 1)
# footprint = (footprint != 0)
# footprint_extent = (np.array(footprint.shape) - 1) // 2
# if np.all(footprint_extent == 0):
# return labels > 0
# result = (labels > 0).copy()
# #
# # Create a labels matrix with zeros at the borders that might be
# # hit by the footprint.
# #
# big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2,
# labels.dtype)
# big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels
# #
# # Find the relative indexes of each footprint element
# #
# image_strides = np.array(image.strides) // image.dtype.itemsize
# big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize
# result_strides = np.array(result.strides) // result.dtype.itemsize
# footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]]
fp_image_offsets = np.sum(image_strides[:, np.newaxis] *
footprint_offsets[:, footprint], 0)
fp_big_offsets = np.sum(big_strides[:, np.newaxis] *
footprint_offsets[:, footprint], 0)
#
# Get the index of each labeled pixel in the image and big_labels arrays
#
indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0]
image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0)
big_indexes = np.sum(big_strides[:, np.newaxis] *
(indexes + footprint_extent[:, np.newaxis]), 0)
result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0)
#
# Now operate on the raveled images
#
big_labels_raveled = big_labels.ravel()
image_raveled = image.ravel()
result_raveled = result.ravel()
#
# A hit is a hit if the label at the offset matches the label at the pixel
# and if the intensity at the pixel is greater or equal to the intensity
# at the offset.
#
for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets):
same_label = (big_labels_raveled[big_indexes + fp_big_offset] ==
big_labels_raveled[big_indexes])
less_than = (image_raveled[image_indexes[same_label]] <
image_raveled[image_indexes[same_label] + fp_image_offset])
result_raveled[result_indexes[same_label][less_than]] = False
# fp_image_offsets = np.sum(image_strides[:, np.newaxis] *
# footprint_offsets[:, footprint], 0)
# fp_big_offsets = np.sum(big_strides[:, np.newaxis] *
# footprint_offsets[:, footprint], 0)
# #
# # Get the index of each labeled pixel in the image and big_labels arrays
# #
# indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0]
# image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0)
# big_indexes = np.sum(big_strides[:, np.newaxis] *
# (indexes + footprint_extent[:, np.newaxis]), 0)
# result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0)
# #
# # Now operate on the raveled images
# #
# big_labels_raveled = big_labels.ravel()
# image_raveled = image.ravel()
# result_raveled = result.ravel()
# #
# # A hit is a hit if the label at the offset matches the label at the pixel
# # and if the intensity at the pixel is greater or equal to the intensity
# # at the offset.
# #
# for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets):
# same_label = (big_labels_raveled[big_indexes + fp_big_offset] ==
# big_labels_raveled[big_indexes])
# less_than = (image_raveled[image_indexes[same_label]] <
# image_raveled[image_indexes[same_label] + fp_image_offset])
# result_raveled[result_indexes[same_label][less_than]] = False
return result
# return result
return peak_local_max(image, labels=labels, min_distance=1,
footprint=footprint, indices=False,
exclude_border=False)
# ---------------------- deprecated ------------------------------