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ENH: make quickshift more tolerant to input type, just convert to float. Also keep track of random seed for reproducable tests.

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Finally, do a unique on the output and add testing.
This commit is contained in:
Andreas Mueller
2012-08-03 11:37:11 +01:00
parent 4d10749a0e
commit ce26467ad4
3 changed files with 70 additions and 9 deletions
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skimage/segmentation/quickshift.pyx
+20 -8
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@@ -3,12 +3,14 @@ cimport numpy as np
from itertools import product
from ..util import img_as_float
cdef extern from "math.h":
double exp(double)
def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, tau=10, return_tree=False):
def quickshift(image, sigma=5, tau=10, return_tree=False, random_seed=None):
"""Segments image using quickshift clustering in Color-(x,y) space.
Produces an oversegmentation of the image using the quickshift mode-seeking algorithm.
@@ -25,6 +27,8 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
Higher means less clusters.
return_tree: bool
Whether to return the full segmentation hierarchy tree
random_seed: None or int
Random seed used for breaking ties
Returns
-------
@@ -42,6 +46,13 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
"""
image = np.atleast_3d(image)
cdef np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image_c = img_as_float(np.ascontiguousarray(image))
if random_seed is None:
random_state = np.random.RandomState()
else:
random_state = np.random.RandomState(random_seed)
# We compute the distances twice since otherwise
# we get crazy memory overhead (width * height * windowsize**2)
@@ -55,13 +66,13 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
raise ValueError("Sigma should be >= 1")
cdef int w = int(2 * sigma)
cdef int width = image.shape[0]
cdef int height = image.shape[1]
cdef int channels = image.shape[2]
cdef int width = image_c.shape[0]
cdef int height = image_c.shape[1]
cdef int channels = image_c.shape[2]
cdef float closest, dist
cdef int x, y, xx, yy, x_, y_
cdef np.float_t* image_p = <np.float_t*> image.data
cdef np.float_t* image_p = <np.float_t*> image_c.data
cdef np.float_t* current_pixel_p = image_p
cdef np.float_t* current_entry_p
@@ -74,14 +85,14 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
dist = 0
current_entry_p = current_pixel_p
for c in xrange(channels):
dist += (current_pixel_p[c] - image[x_, y_, c])**2
dist += (current_pixel_p[c] - image_c[x_, y_, c])**2
dist += (x - x_)**2 + (y - y_)**2
densities[x, y] += exp(-dist / sigma)
current_pixel_p += channels
# this will break ties that otherwise would give us headache
densities += np.random.normal(scale=0.00001, size=(width, height))
densities += random_state.normal(scale=0.00001, size=(width, height))
# default parent to self:
cdef np.ndarray[dtype=np.int_t, ndim=2] parent = np.arange(width * height).reshape(width, height)
cdef np.ndarray[dtype=np.float_t, ndim=2] dist_parent = np.zeros((width, height))
@@ -96,7 +107,7 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
if densities[x_, y_] > current_density:
dist = 0
for c in xrange(channels):
dist += (current_pixel_p[c] - image[x_, y_, c])**2
dist += (current_pixel_p[c] - image_c[x_, y_, c])**2
dist += (x - x_)**2 + (y - y_)**2
if dist < closest:
closest = dist
@@ -111,6 +122,7 @@ def quickshift(np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image, sigma=5, ta
while (old != flat).any():
old = flat
flat = flat[flat]
flat = np.unique(flat, return_inverse=True)[1]
flat = flat.reshape(width, height)
if return_tree:
return flat, parent