Computer Vision & Image Processing using Python and OpenCV

Computer Vision for Beginners: How to do a simple image segmentation with superpixels using Python and OpenCV

# import the necessary packages
import warnings
warnings.filterwarnings("ignore")

from sklearn.cluster import KMeans
from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
from skimage import io
import numpy as np
import matplotlib.pyplot as plt
import cv2

# load the image and grab its width and height
image = cv2.imread("python_one.jpg")
(h, w) = image.shape[:2]

plt.imshow(image)
plt.show()

print((h, w))

(900, 1200)

# load the image and convert it to a floating point data type
image = img_as_float(image)

# loop over the number of segments
for numSegments in (200, 300, 500, 800):
	# 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))
	plt.axis("off")
    
# show the plots
plt.show()

# load the image and grab its width and height
image = cv2.imread("python.jpg")
(h, w) = image.shape[:2]

plt.imshow(image)
plt.show()

print((h, w))

# load the image and convert it to a floating point data type
image = img_as_float(image)

# loop over the number of segments
for numSegments in (200, 300, 500, 800):
	# 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))
	plt.axis("off")
    
# show the plots
plt.show()

(410, 612)

# load the image and grab its width and height
image = cv2.imread("datascience.png")
(h, w) = image.shape[:2]

plt.imshow(image)
plt.show()

print((h, w))

# load the image and convert it to a floating point data type
image = img_as_float(image)

# loop over the number of segments
for numSegments in (200, 300, 500, 800):
	# 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))
	plt.axis("off")
    
# show the plots
plt.show()

(840, 1600)

Summary

In this coding recipe, we discussed how to do a simple image segmentation with superpixels using Python and OpenCV.

Specifically, we have learned the followings:

• How to do a simple image segmentation with superpixels using Python and OpenCV.