image_segmentation.py

from __future__ import print_function
import argparse

from skimage.segmentation import felzenszwalb, slic, quickshift
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
from skimage.data import imread

import matplotlib.pyplot as plt
import numpy as np

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help = "Path to the image")
args = vars(ap.parse_args())
# load the image and convert it to a floating point data type
img = img_as_float(imread(args["image"]))

segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)

print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print("Slic number of segments: %d" % len(np.unique(segments_slic)))
print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))

fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)

ax[0].imshow(mark_boundaries(img, segments_fz))
ax[0].set_title("Felzenszwalbs's method")
ax[1].imshow(mark_boundaries(img, segments_slic))
ax[1].set_title("SLIC")
ax[2].imshow(mark_boundaries(img, segments_quick))
ax[2].set_title("Quickshift")
for a in ax:
    a.set_xticks(())
    a.set_yticks(())

plt.show()