Fix initialization in km_segmentation, prettier examples

doc/examples/plot_km_segmentation.py

@@ -6,31 +6,16 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 from skimage.data import lena
-from skimage.segmentation import km_segmentation
+from skimage.segmentation import km_segmentation, visualize_boundaries
 from skimage.util import img_as_float
+from skimage.color import rgb2lab
 
 img = img_as_float(lena()).copy("C")
-segments = km_segmentation(img, ratio=2.0, n_segments=200)
+segments = km_segmentation(rgb2lab(img), ratio=10.0, n_segments=1000)
 
 print("number of segments: %d" % len(np.unique(segments)))
 
-plt.subplot(131, title="original")
-plt.imshow(img, interpolation='nearest')
+boundaries_mine = visualize_boundaries(img, segments)
+plt.imshow(boundaries_mine)
 plt.axis("off")
-
-plt.subplot(132, title="superpixels")
-# shuffle the labels for better visualization
-plt.imshow(segments, interpolation='nearest', cmap=plt.cm.prism)
-plt.axis("off")
-
-plt.subplot(133, title="mean color")
-colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
-        xrange(img.shape[2])]
-counts = np.bincount(segments.ravel())
-colors = np.vstack(colors) / counts
-plt.imshow(colors.T[segments], interpolation='nearest')
-plt.axis("off")
-
-plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
-                    bottom=0.02, left=0.02, right=0.98)
 plt.show()

doc/examples/plot_quickshift.py

@@ -25,30 +25,19 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 from skimage.data import lena
-from skimage.segmentation import quickshift
+from skimage.segmentation import quickshift, visualize_boundaries
 from skimage.util import img_as_float
+from skimage.color import rgb2lab
 
 img = img_as_float(lena())[::2, ::2, :].copy("C")
-segments = quickshift(img, kernel_size=5, max_dist=20)
+segments = quickshift(rgb2lab(img), kernel_size=5, max_dist=20)
+segments_rgb = quickshift(img, kernel_size=5, max_dist=20)
 
 print("number of segments: %d" % len(np.unique(segments)))
-
-fig, (ax_org, ax_sp, ax_mean) = plt.subplots(1, 3)
-ax_org.set_title("original")
-ax_org.imshow(img, interpolation='nearest')
-ax_org.axis("off")
-
-ax_sp.set_title("superpixels")
-ax_sp.imshow(segments, interpolation='nearest', cmap=plt.cm.prism)
-ax_sp.axis("off")
-
-colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
-        xrange(img.shape[2])]
-counts = np.bincount(segments.ravel())
-colors = np.vstack(colors) / counts
-ax_mean.set_title("mean color")
-ax_mean.imshow(colors.T[segments], interpolation='nearest')
-ax_mean.axis("off")
-fig.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
-                    bottom=0.02, left=0.02, right=0.98)
+boundaries = visualize_boundaries(img, segments)
+boundaries_rgb = visualize_boundaries(img, segments_rgb)
+plt.imshow(boundaries)
+plt.figure()
+plt.imshow(boundaries_rgb)
+plt.axis("off")
 plt.show()

skimage/__init__.py

@@ -88,6 +88,7 @@ test = _setup_test()
 test_verbose = _setup_test(verbose=True)
 
 
+
 def get_log(name=None):
     """Return a console logger.
 

skimage/filter/tests/test_thresholding.py

@@ -77,11 +77,13 @@ def test_otsu_camera_image():
     assert 86 < threshold_otsu(camera) < 88
 
 
+
 def test_otsu_coins_image():
     coins = skimage.img_as_ubyte(data.coins())
     assert 106 < threshold_otsu(coins) < 108
 
 
+
 def test_otsu_coins_image_as_float():
     coins = skimage.img_as_float(data.coins())
     assert 0.41 < threshold_otsu(coins) < 0.42

skimage/segmentation/__init__.py

@@ -2,6 +2,7 @@ from .random_walker_segmentation import random_walker
 from .felzenszwalb import felzenszwalb_segmentation
 from .km_segmentation import km_segmentation
 from .quickshift import quickshift
+from .boundaries import find_boundaries, visualize_boundaries
 
 __all__ = [random_walker, quickshift, felzenszwalb_segmentation,
-    km_segmentation]
+    km_segmentation, find_boundaries, visualize_boundaries]

skimage/segmentation/boundaries.py

@@ -0,0 +1,19 @@
+import numpy as np
+from ..morphology import dilation, square
+from ..util import img_as_float
+
+
+def find_boundaries(label_img):
+    boundaries = np.zeros(label_img.shape, dtype=np.bool)
+    boundaries[1:, :] += label_img[1:, :] != label_img[:-1, :]
+    boundaries[:, 1:] += label_img[:, 1:] != label_img[:, :-1]
+    return boundaries
+
+
+def visualize_boundaries(img, label_img):
+    img = img_as_float(img, force_copy=True)
+    boundaries = find_boundaries(label_img)
+    outer_boundaries = dilation(boundaries.astype(np.uint8), square(2))
+    img[outer_boundaries != 0, :] = np.array([0, 0, 0])  # black
+    img[boundaries, :] = np.array([1, 1, 0])  # yellow
+    return img

skimage/segmentation/km_segmentation.pyx

@@ -5,12 +5,12 @@ from scipy import ndimage
 from ..util import img_as_float
 
 
-def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
+def km_segmentation(image, n_segments=100, ratio=10., max_iter=10, sigma=1):
     """Segments image using k-means clustering in Color-(x,y) space.
 
     Parameters
     ----------
-    image: (width, height, 3) ndarray 
+    image: (width, height, 3) ndarray
         Input image
     ratio: float
         Balances color-space proximity and image-space proximity.
@@ -50,7 +50,8 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
     means_y = grid_y[::step, ::step]
     means_x = grid_x[::step, ::step]
 
-    means_color = image[means_y, means_x, :]
+    n_seeds = len(means_y)
+    means_color = np.zeros((n_seeds, n_seeds, 3))
     cdef np.ndarray[dtype=np.float_t, ndim=2] means = np.dstack([means_y, means_x, means_color]).reshape(-1, 5)
     cdef np.float_t* current_mean
     cdef np.float_t* mean_entry
@@ -63,13 +64,14 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
     cdef double dist_mean
 
     cdef np.ndarray[dtype=np.int_t, ndim=2] nearest_mean = np.zeros((height, width), dtype=np.int)
-    cdef np.ndarray[dtype=np.float_t, ndim=2] distance = np.ones((height, width), dtype=np.float) * np.inf
+    cdef np.ndarray[dtype=np.float_t, ndim=2] distance = np.empty((height, width))
     cdef np.float_t* image_p = <np.float_t*> image_yx.data
     cdef np.float_t* distance_p = <np.float_t*> distance.data
     cdef np.float_t* current_distance
     cdef np.float_t* current_pixel
     cdef double tmp
     for i in xrange(max_iter):
+        distance.fill(np.inf)
         changes = 0
         current_mean = <np.float_t*> means.data
         # assign pixels to means
@@ -105,5 +107,6 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
         means_list = [np.bincount(nearest_mean.ravel(), image_yx[:, :, j].ravel())
                 for j in xrange(5)]
         in_mean = np.bincount(nearest_mean.ravel())
+        in_mean[in_mean == 0] = 1
         means = (np.vstack(means_list) / in_mean).T.copy("C")
     return nearest_mean