def SNIC(filename,compactness, numSegments):
# load image
color_image = np.array(Image.open(resource_stream(__name__, filename)))
color_image = cv2.imread(filename)
number_of_pixels = color_image.shape[0] * color_image.shape[1]
# compute grid
grid = compute_grid(color_image.shape, numSegments)
seeds = list(chain.from_iterable(grid))
seed_len = len(seeds)
# choose a distance metric
distance_metric = create_augmented_snic_distance(color_image.shape, seed_len, compactness)
segmentation, distances, numSegments = snic(
skimage.color.rgb2lab(color_image).tolist(),
seeds,
compactness, nd_computations["3"], distance_metric)
return np.array(segmentation)
from pkg_resources import resource_stream
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import skimage.color
from skimage.segmentation import mark_boundaries
from pysnic.algorithms.snic import snic
# load image
color_image = np.array(
Image.open(resource_stream(__name__, "../data/20x.png")).convert("RGB"))
lab_image = skimage.color.rgb2lab(color_image).tolist()
number_of_pixels = color_image.shape[0] * color_image.shape[1]
# SNIC parameters
number_of_segments = 100
compactness = 10.00
segmentation, _, number_of_segments, centroids = snic(
lab_image,
number_of_segments,
compactness,
update_func=lambda num_pixels: print("processed %05.2f%%" % (
num_pixels * 100 / number_of_pixels)))
# show the output of SNIC
fig = plt.figure("SNIC with %d segments" % number_of_segments)
plt.imshow(mark_boundaries(color_image, np.array(segmentation)))
plt.show()
# compute grid
grid = compute_grid(color_image.shape, numSegments)
seeds = list(chain.from_iterable(grid))
seed_len = len(seeds)
# choose a distance metric
distance_metric = create_augmented_snic_distance(color_image.shape, seed_len,
compactness)
start = timer()
segmentation, distances, centroids = snic(
skimage.color.rgb2lab(color_image).tolist(),
seeds,
compactness,
nd_computations["3"],
distance_metric,
update_func=lambda num_pixels: print("processed %05.2f%%" % (
num_pixels * 100 / number_of_pixels)))
end = timer()
print("superpixelation took: %fs" % (end - start))
# show the output of SNIC
plt.figure("SNIC with %d segments" % len(centroids))
plt.imshow(mark_boundaries(color_image, np.array(segmentation)))
plt.show()
plt.figure("segmentations")
plt.imshow(segmentation, cmap="prism")
plt.show()