.. _example_transform_plot_seam_carving.py:


============
Seam Carving
============

This example demonstrates how images can be resized using seam carving [1]_.
Resizing to a new aspect ratio distorts image contents. Seam carving attempts
to resize *without* distortion, by removing regions of an image which are less
important. In this example we are using the Sobel filter to signify the
importance of each pixel.

.. [1] Shai Avidan and Ariel Shamir
       "Seam Carving for Content-Aware Image Resizing"
       http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf



.. code-block:: python

	from skimage import data, draw
	from skimage import transform, util
	import numpy as np
	from skimage import filters, color
	from matplotlib import pyplot as plt
	
	
	hl_color = np.array([0, 1, 0])
	
	img = data.rocket()
	img = util.img_as_float(img)
	eimg = filters.sobel(color.rgb2gray(img))
	
	plt.title('Original Image')
	plt.imshow(img)
	
	

.. image:: images/plot_seam_carving_1.png


.. code-block:: python

	
	resized = transform.resize(img, (img.shape[0], img.shape[1] - 200))
	plt.figure()
	plt.title('Resized Image')
	plt.imshow(resized)
	
	

.. image:: images/plot_seam_carving_2.png


.. code-block:: python

	
	out = transform.seam_carve(img, eimg, 'vertical', 200)
	plt.figure()
	plt.title('Resized using Seam Carving')
	plt.imshow(out)
	
	

.. image:: images/plot_seam_carving_3.png

Resizing distorts the rocket and surrounding objects, whereas seam carving
removes empty spaces and preserves object proportions.

Object Removal
--------------

Seam carving can also be used to remove artifacts from images.
This requires weighting the artifact with low values. Recall lower weights are
preferentially removed in seam carving. The following code masks the rocket's
region with low weights, indicating it should be removed.



.. code-block:: python

	
	masked_img = img.copy()
	
	poly = [(404, 281), (404, 360), (359, 364), (338, 337), (145, 337), (120, 322),
	        (145, 304), (340, 306), (362, 284)]
	pr = np.array([p[0] for p in poly])
	pc = np.array([p[1] for p in poly])
	rr, cc = draw.polygon(pr, pc)
	
	masked_img[rr, cc, :] = masked_img[rr, cc, :]*0.5 + hl_color*.5
	plt.figure()
	plt.title('Object Marked')
	
	plt.imshow(masked_img)
	
	

.. image:: images/plot_seam_carving_4.png


.. code-block:: python

	
	eimg[rr, cc] -= 1000
	
	plt.figure()
	plt.title('Object Removed')
	out = transform.seam_carve(img, eimg, 'vertical', 90)
	resized = transform.resize(img, out.shape)
	plt.imshow(out)
	plt.show()
	
	

.. image:: images/plot_seam_carving_5.png



**Python source code:** :download:`download <plot_seam_carving.py>`
(generated using ``skimage`` |version|)



**IPython Notebook:** :download:`download <./notebook/plot_seam_carving.ipynb>`
(generated using ``skimage`` |version|)

