Added example and test

doc/examples/plot_seam_carving.py

@@ -1,19 +1,38 @@
+"""
+============
+Seam Carving
+============
+
+This example demonstrates how images can be resized using seam carving [1]_.
+Resizing often distorts contents in the image. Seam carving tries to resize
+images while trying to keep important content intact. 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
+
+"""
 from skimage import io, data
 from skimage import transform
 from skimage import color, filters
 from matplotlib import pyplot as plt
 
-def custom_sobel(img):
-    if img.ndim == 3:
-        img = color.rgb2gray(img)
-
-    return filters.sobel(img)
 
 img = data.coins()
-out  = transform.seam_carve(img, 'vertical', 80, energy_func = custom_sobel)
-out  = transform.seam_carve(out, 'horizontal', 70, energy_func = custom_sobel)
+out  = transform.seam_carve(img, 'vertical', 80, energy_func = filters.sobel)
+out  = transform.seam_carve(out, 'horizontal', 70, energy_func = filters.sobel)
+resized = transform.resize(img, out.shape)
+
+plt.title('Original Image')
+io.imshow(img, plugin='matplotlib')
 
-io.imshow(out)
 plt.figure()
-io.imshow(img)
+plt.title('Resized Image Image')
+io.imshow(resized, plugin='matplotlib')
+
+plt.figure()
+plt.title('Resized Image Image')
+io.imshow(out, plugin='matplotlib')
+
 io.show()

skimage/transform/_seam_carving.pyx

@@ -84,7 +84,7 @@ cdef find_seam_v(cnp.double_t[:, ::1] energy_img, cnp.int8_t[:, ::1] track_img,
     return seam
 
 
-cdef remove_seam_h_2d(cnp.double_t[:, ::1] img, Py_ssize_t[::1] seam,
+cdef remove_seam_v_2d(cnp.double_t[:, ::1] img, Py_ssize_t[::1] seam,
                       Py_ssize_t cols):
     cdef Py_ssize_t rows, row, col, idx
     rows = img.shape[0]
@@ -105,14 +105,14 @@ cdef remove_seam_h_2d(cnp.double_t[:, ::1] img, Py_ssize_t[::1] seam,
     -----
     `seam` is passed as an argument so that we don't have to reallocate it for
     each iteration in `_seam_carve_v`.
-    """"
+    """
     
     for row in range(rows):
         for idx in range(seam[row], cols - 1):
             img[row, idx] = img[row, idx + 1]
 
 
-cdef remove_seam_h_3d(cnp.double_t[:, :, ::1] img, Py_ssize_t[::1] seam,
+cdef remove_seam_v_3d(cnp.double_t[:, :, ::1] img, Py_ssize_t[::1] seam,
                       Py_ssize_t cols):
     """ Removes one horizontal seam from the image.
 
@@ -131,7 +131,7 @@ cdef remove_seam_h_3d(cnp.double_t[:, :, ::1] img, Py_ssize_t[::1] seam,
     -----
     `seam` is passed as an argument so that we don't have to reallocate it for
     each iteration in `_seam_carve_v`.
-    """"
+    """
     cdef Py_ssize_t rows, row, col, idx
     rows = img.shape[0]
 
@@ -201,9 +201,9 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
                            cols)
 
         if ndim == 2:
-            remove_seam_h_2d(img, seam, cols)
+            remove_seam_v_2d(img, seam, cols)
         elif ndim == 3:
-            remove_seam_h_3d(img, seam, cols)
+            remove_seam_v_3d(img, seam, cols)
 
         cols -= 1
 

skimage/transform/seam_carving.py

@@ -1,4 +1,4 @@
-from _seam_carving import _seam_carve_h
+from _seam_carving import _seam_carve_v
 from ..import filters
 from .. import util
 from .._shared import utils
@@ -47,6 +47,12 @@ def seam_carve(img, mode, num, energy_func, extra_args = [],
     -------
     out : ndarray
         The cropped image with the seams removed.
+
+    References
+    ----------
+    .. [1] Shai Avidan and Ariel Shamir
+           "Seam Carving for Content-Aware Image Resizing"
+           http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf
     """
 
     utils.assert_nD(img, (2,3))
@@ -55,7 +61,7 @@ def seam_carve(img, mode, num, energy_func, extra_args = [],
 
     if mode == 'horizontal':
         img = np.ascontiguousarray(img)
-        return _seam_carve_h(img, num, energy_func, extra_args ,extra_kwargs,
+        return _seam_carve_v(img, num, energy_func, extra_args ,extra_kwargs,
                              border)
     elif mode == 'vertical' :
         if img.ndim == 3:
@@ -64,7 +70,7 @@ def seam_carve(img, mode, num, energy_func, extra_args = [],
             img = img.T
 
         img = np.ascontiguousarray(img)
-        out = _seam_carve_h(img, num, energy_func, extra_args , extra_kwargs,
+        out = _seam_carve_v(img, num, energy_func, extra_args , extra_kwargs,
                             border)
 
         if img.ndim == 3:

skimage/transform/tests/test_seam_carving.py

@@ -0,0 +1,33 @@
+from skimage import transform
+import numpy as np
+from numpy import testing
+
+def energy(img):
+    if(img.ndim == 3):
+        return np.ascontiguousarray(img[:, :, 0])
+    return (1 - img)
+
+def test_seam_carving():
+    img = np.array([[0, 0, 1, 0, 0],
+                    [0, 0, 1, 0, 0],
+                    [0, 0, 1, 0, 0],
+                    [0, 1, 0, 0, 0],
+                    [1, 0 , 0, 0, 0]], dtype = np.float )
+
+    out = transform.seam_carve(img, 'horizontal', 1, energy, border=0)
+    testing.assert_allclose(out, 0)
+
+    img = img.T
+    out = transform.seam_carve(img, 'vertical', 1, energy, border=0)
+    testing.assert_allclose(out, 0)
+
+    img = img.T
+
+    img3 = np.dstack([img, img, img])
+
+    out = transform.seam_carve(img3, 'horizontal', 1, energy, border=0)
+    testing.assert_allclose(out, 0)
+
+
+    out = transform.seam_carve(img3, 'vertical', 1, energy, border=0)
+    testing.assert_allclose(out, 0)