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fix trying to make this implementation more like slic

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Andreas Mueller
2012-06-23 01:07:32 +02:00
parent a779952619
commit 7b646ad7ea
1 changed files with 9 additions and 2 deletions
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skimage/segmentation/km_segmentation.py
+9 -2
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@@ -1,7 +1,10 @@
import numpy as np
from scipy import ndimage
from ..util import img_as_float
def km_segmentation(image, n_segments=100, ratio=50, max_iter=100):
def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
image = ndimage.gaussian_filter(img_as_float(image), sigma)
# initialize on grid:
height, width = image.shape[:2]
# approximate grid size for desired n_segments
@@ -12,7 +15,11 @@ def km_segmentation(image, n_segments=100, ratio=50, max_iter=100):
means_color = image[means_y, means_x, :]
means = np.dstack([means_y, means_x, means_color]).reshape(-1, 5)
image = np.dstack([grid_y, grid_x, image * ratio])
# we do the scaling of ratio in the same way as in the SLIC paper
# so the values have the same meaning
ratio = (ratio / float(step)) ** 2
print(ratio)
image = np.dstack([grid_y, grid_x, image / ratio])
nearest_mean = np.zeros((height, width), dtype=np.int)
distance = np.ones((height, width), dtype=np.float) * np.inf