In [4]:
%matplotlib inline
from matplotlib import pyplot as plt, cm
from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
from skimage import io
from imutils.convenience import resize

In [6]:
# load the image and convert it to a floating point data type
image = img_as_float(io.imread("./data/IMG_3835_1024.jpg"))
image = resize(image, height = 500)

# loop over the number of segments
for numSegments in (100, 200, 300):
	# apply SLIC and extract (approximately) the supplied number
	# of segments
	segments = slic(image, n_segments = numSegments, sigma = 5)
	# show the output of SLIC
	fig = plt.figure("Superpixels -- %d segments" % (numSegments))
	ax = fig.add_subplot(1, 1, 1)
	ax.imshow(mark_boundaries(image, segments))

In [ ]:
# The first is the is the n_segments  argument which defines how many superpixel 
# segments we want to generate. This value defaults to 100 segments.
# We then supply sigma , which is the smoothing Gaussian kernel applied prior to segmentation.
# Other optional parameters can be utilized in the function, such as max_iter , 
# which the maximum number of iterations for k-means, compactness , 
# which balances the color-space proximity with image space-proximity, and 
# convert2lab  which determines whether the input image should be converted 
# to the L*a*b* color space prior to forming superpixels (in nearly all cases, 
# having convert2lab  set to True  is a good idea).