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ENH reasonable speed for felzenszwalbs's segmentation

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This commit is contained in:
Andreas Mueller
2012-08-03 11:37:10 +01:00
parent 0c19899825
commit 7a5e7e49ea
1 changed files with 16 additions and 7 deletions
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skimage/segmentation/felzenszwalb.pyx
+16 -7
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@@ -3,6 +3,7 @@ cimport numpy as np
from collections import defaultdict
import scipy
#from ..util import img_as_float
#from ..color import rgb2grey
#from skimage.morphology.ccomp cimport find_root, join_trees
@@ -58,8 +59,6 @@ def felzenszwalb_segmentation(image, k, sigma=0.8):
image = scipy.ndimage.gaussian_filter(image, sigma=sigma)
# compute edge weights in 8 connectivity:
#right_cost = np.sum((image[1:, :, :] - image[:-1, :, :]) ** 2, axis=2)
#down_cost = np.sum((image[:, 1:, :] - image[:, :-1, :]) ** 2, axis=2)
right_cost = np.abs((image[1:, :] - image[:-1, :]))
down_cost = np.abs((image[:, 1:] - image[:, :-1]))
dright_cost = np.abs((image[1:, 1:] - image[:-1, :-1]))
@@ -77,25 +76,35 @@ def felzenszwalb_segmentation(image, k, sigma=0.8):
# initialize data structures for segment size
# and inner cost, then start greedy iteration over edges.
edge_queue = np.argsort(costs)
edges = np.ascontiguousarray(edges[edge_queue])
costs = np.ascontiguousarray(costs[edge_queue])
cdef np.int_t *segments_p = <np.int_t*>segments.data
cdef np.int_t *edges_p = <np.int_t*>edges.data
cdef np.float_t *costs_p = <np.float_t*>costs.data
cdef np.ndarray[np.int_t, ndim=1] segment_size = np.ones(width * height, dtype=np.int)
# inner cost of segments
cdef np.ndarray[np.float_t, ndim=1] cint = np.zeros(width * height)
cdef int seg0, seg1, seg_new
cdef float cost, inner_cost0, inner_cost1
for edge, cost in zip(edges[edge_queue], costs[edge_queue]):
seg0 = find_root(segments_p, edge[0])
seg1 = find_root(segments_p, edge[1])
# set costs_p back one. we increase it before we use it
# since we might continue before that.
costs_p -= 1
for e in xrange(costs.size):
seg0 = find_root(segments_p, edges_p[0])
seg1 = find_root(segments_p, edges_p[1])
edges_p += 2
costs_p += 1
if seg0 == seg1:
continue
inner_cost0 = cint[seg0] + k / segment_size[seg0]
inner_cost1 = cint[seg1] + k / segment_size[seg1]
if cost < min(inner_cost0, inner_cost1):
if costs_p[0] < min(inner_cost0, inner_cost1):
# update size and cost
join_trees(segments_p, seg0, seg1)
seg_new = find_root(segments_p, seg0)
segment_size[seg_new] = segment_size[seg0] + segment_size[seg1]
cint[seg_new] = cost
cint[seg_new] = costs_p[0]
# unravel the union find tree
flat = segments.ravel()
old = np.zeros_like(flat)