ENH renamed "felzenszwalb_segmentation" to "felzenszwalb", remove debug output from slic

doc/examples/plot_segmentations.py

@@ -63,12 +63,12 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 from skimage.data import lena
-from skimage.segmentation import felzenszwalb_segmentation, \
+from skimage.segmentation import felzenszwalb, \
     visualize_boundaries, slic, quickshift
 from skimage.util import img_as_float
 
 img = img_as_float(lena()[::2, ::2])
-segments_fz = felzenszwalb_segmentation(img, scale=100, sigma=0.5, min_size=50)
+segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
 segments_slic = slic(img, ratio=10, n_segments=250, sigma=1)
 segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
 

skimage/segmentation/__init__.py

@@ -1,5 +1,5 @@
 from .random_walker_segmentation import random_walker
-from .felzenszwalb import felzenszwalb_segmentation
+from .felzenszwalb import felzenszwalb
 from .slic import slic
 from .quickshift import quickshift
 from .boundaries import find_boundaries, visualize_boundaries

skimage/segmentation/_felzenszwalb.pyx

@@ -7,7 +7,7 @@ from skimage.morphology.ccomp cimport find_root, join_trees
 from ..util import img_as_float
 
 
-def _felzenszwalb_segmentation_grey(image, scale=1, sigma=0.8, min_size=20):
+def _felzenszwalb_grey(image, scale=1, sigma=0.8, min_size=20):
     """Felzenszwalb's efficient graph based segmentation for a single channel.
 
     Produces an oversegmentation of a 2d image using a fast, minimum spanning

skimage/segmentation/felzenszwalb.py

@@ -1,10 +1,10 @@
 import warnings
 import numpy as np
 
-from ._felzenszwalb import _felzenszwalb_segmentation_grey
+from ._felzenszwalb import _felzenszwalb_grey
 
 
-def felzenszwalb_segmentation(image, scale=1, sigma=0.8, min_size=20):
+def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
     """Computes Felsenszwalb's efficient graph based image segmentation.
 
     Produces an oversegmentation of a multichannel (i.e. RGB) image
@@ -46,7 +46,7 @@ def felzenszwalb_segmentation(image, scale=1, sigma=0.8, min_size=20):
     #image = img_as_float(image)
     if image.ndim == 2:
         # assume single channel image
-        return _felzenszwalb_segmentation_grey(image, scale=scale, sigma=sigma)
+        return _felzenszwalb_grey(image, scale=scale, sigma=sigma)
 
     elif image.ndim != 3:
         raise ValueError("Got image with ndim=%d, don't know"
@@ -61,7 +61,7 @@ def felzenszwalb_segmentation(image, scale=1, sigma=0.8, min_size=20):
     # compute quickshift for each channel
     for c in xrange(n_channels):
         channel = np.ascontiguousarray(image[:, :, c])
-        s = _felzenszwalb_segmentation_grey(channel, scale=scale, sigma=sigma,
+        s = _felzenszwalb_grey(channel, scale=scale, sigma=sigma,
                 min_size=min_size)
         segmentations.append(s)
 

skimage/segmentation/slic.pyx

@@ -57,7 +57,6 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
     grid_y, grid_x = np.mgrid[:height, :width]
     means_y = grid_y[::step, ::step]
     means_x = grid_x[::step, ::step]
-    print(means_y, means_x)
 
     means_color = np.zeros((means_y.shape[0], means_y.shape[1], 3))
     cdef np.ndarray[dtype=np.float_t, ndim=2] means \