Changed signatures to use energy map

doc/examples/plot_seam_carving.py

@@ -21,10 +21,11 @@ from matplotlib import pyplot as plt
 def nothing(img):
     return img
 
-#img = io.imread('/home/vighnesh/images/castle.jpg')
+img = io.imread('/home/vighnesh/images/rocket.jpg')
 #img = color.rgb2gray(img)
-img = data.camera()
-out = transform.seam_carve(img, 'vertical', 50, energy_func=filters.sobel)
+eimg = filters.sobel(color.rgb2gray(img))
+#img = data.camera()
+out = transform.seam_carve(img, eimg, 'vertical', 200)
 #out = transform.seam_carve(out, 'horizontal', 70, energy_func=filters.sobel)
 resized = transform.resize(img, out.shape)
 

skimage/transform/_seam_carving.pyx

@@ -78,11 +78,11 @@ cdef remove_seam(cnp.double_t[:, :, ::1] img,
             for ch in range(channels):
                 img[r, c, ch] = img[r, c + shift, ch]
 
-def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
+def _seam_carve_v(img, energy_map, iters, border):
     """ Carve vertical seams off an image.
 
     Carves out vertical seams off an image while using the given energy
-    function to decide the importance of each pixel.[1]
+    map to decide the importance of each pixel.[1]
 
     Parameters
     ----------
@@ -90,25 +90,21 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
         Input image whose vertical seams are to be removed.
     iters : int
         Number of vertical seams are to be removed.
-    energy_func : callable
-        The function used to decide the importance of each pixel. The higher
+    energy_map : (M, N) ndarray
+        The array to decide the importance of each pixel. The higher
         the value corresponding to a pixel, the more the algorithm will try
-        to keep it in the image. For every iteration `energy_func` is called
-        as `energy_func(image, *extra_args, **extra_kwargs)`, where `image`
-        is the cropped image during each iteration and is expected to return a
-        (M, N) ndarray depicting each pixel's importance.
-    extra_args : iterable
-        The extra arguments supplied to `energy_func`.
-    extra_kwargs : dict
-        The extra keyword arguments supplied to `energy_func`.
-    border : int
-        The number of pixels in the right and left end of the image to be
-        excluded from being considered for a seam. This is important as certain
-        filters just ignore image boundaries and set them to `0`.
+        to keep it in the image.
+    num : int
+        Number of seams are to be removed.
+    border : int, optional
+        The number of pixels in the right, left and bottom end of the image
+        to be excluded from being considered for a seam. This is important as
+        certain filters just ignore image boundaries and set them to `0`.
+        By default border is set to `1`.
 
     Returns
     -------
-    image : (M, N - iters) or (M, N - iters, 3) ndarray
+    image : (M, N - iters, 3) ndarray of float
         The cropped image with the vertical seams removed.
 
     References
@@ -118,11 +114,10 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
            http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf
     """
     last_row_obj = np.zeros(img.shape[1], dtype=np.float)
-    seam_map_obj = np.zeros(img.shape[0:2], dtype=np.uint8)
 
     cdef cnp.double_t[::1] last_row = last_row_obj
     cdef Py_ssize_t[::1] sorted_indices
-    cdef cnp.uint8_t[:, ::1] seam_map = seam_map_obj
+    cdef cnp.uint8_t[:, ::1] seam_map = np.zeros(img.shape[0:2], dtype=np.uint8)
     cdef Py_ssize_t cols = img.shape[1]
     cdef Py_ssize_t rows = img.shape[0]
     cdef Py_ssize_t seams_left = iters
@@ -134,33 +129,25 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
     cdef cnp.double_t[:, ::1] cumulative_img = np.zeros(img.shape[0:2], dtype=np.float)
     cdef cnp.double_t[:, :, ::1] energy_img
 
-    energy_img_obj = energy_func(np.squeeze(img))[:, :, np.newaxis]**2
-    energy_img_obj = np.ascontiguousarray(energy_img_obj)
-    energy_img = energy_img_obj
-
-    energy_img_obj[:, 0:border, 0] = DBL_MAX
-    energy_img_obj[:, cols-border:cols, 0] = DBL_MAX
-    energy_img_obj[rows-border:rows,:,0] = energy_img_obj[rows-2*border:rows-border,:,0]
+    energy_map[:, 0:border] = DBL_MAX
+    energy_map[:, cols-border:cols] = DBL_MAX
+    energy_map[rows-border:rows, :] = energy_map[rows-2*border:rows-border, :]
 
+    energy_map = np.ascontiguousarray(energy_map[:, :, np.newaxis])
+    energy_img = energy_map
 
     _preprocess_image(energy_img, cumulative_img, track_img, cols)
     last_row[...] = cumulative_img[-1, :]
     sorted_indices = np.argsort(last_row_obj)
     seam_idx = 0
 
-
     while seams_left > 0:
-        #print "sorted indices", np.array(sorted_indices)[:10]
-        #print "sorted array ", np.sort(last_row_obj)[:10]
-        #print "Seam starting at : ", sorted_indices[seam_idx]
         if mark_seam(track_img, sorted_indices[seam_idx], seam_map):
             seams_left -= 1
             cols -= 1
-            #print "Seam marked ", seam_idx
             seam_idx += 1
             continue
         else:
-            print "Seams removed = ", seam_idx
             seam_idx = 0
             remove_seam(image, seam_map, cols)
             remove_seam(energy_img, seam_map, cols)
@@ -169,7 +156,4 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
             last_row[:cols] = cumulative_img[-1, :cols]
             sorted_indices = np.argsort(last_row_obj)
 
-    #from skimage import io
-    #io.imshow(seam_map_obj*255)
-    #io.show()
-    return img#[:, 0:cols]
+    return img[:, 0:cols]

skimage/transform/seam_carving.py

@@ -4,43 +4,36 @@ from .._shared import utils
 import numpy as np
 
 
-def seam_carve(img, mode, num, energy_func, extra_args=[],
-               extra_kwargs={}, border=1, force_copy=True):
+def seam_carve(img, energy_map, mode, num, border=1, force_copy=True):
     """ Carve vertical or horizontal seams off an image.
 
     Carves out vertical/horizontal seams off an image while using the given
-    energy function to decide the importance of each pixel.
+    energy map to decide the importance of each pixel.
 
     Parameters
     ----------
     image : (M, N) or (M, N, 3) ndarray
-        Input image whose vertical seams are to be removed.
+        Input image whose seams are to be removed.
+    energy_map : (M, N) ndarray
+        The array to decide the importance of each pixel. The higher
+        the value corresponding to a pixel, the more the algorithm will try
+        to keep it in the image.
     mode : str {'horizontal', 'vertical'}
         Indicates whether seams are to be removed vertically or horizontally.
         Removing seams horizontally will decrease the height whereas removing
         vertically will decrease the width.
     num : int
         Number of seams are to be removed.
-    energy_func : callable
-        The function used to decide the importance of each pixel. The higher
-        the value corresponding to a pixel, the more the algorithm will try
-        to keep it in the image. For every iteration `energy_func` is called
-        as `energy_func(image, *extra_args, **extra_kwargs)`, where `image`
-        is the cropped image during each iteration and is expected to return a
-        (M, N) ndarray depicting each pixel's importance.
-    extra_args : iterable, optional
-        The extra arguments supplied to `energy_func`.
-    extra_kwargs : dict, optional
-        The extra keyword arguments supplied to `energy_func`.
     border : int, optional
-        The number of pixels in the right and left end of the image to be
-        excluded from being considered for a seam. This is important as certain
-        filters just ignore image boundaries and set them to `0`. By default
-        border is set to `1`.
+        The number of pixels in the right, left and bottom end of the image
+        to be excluded from being considered for a seam. This is important as
+        certain filters just ignore image boundaries and set them to `0`.
+        By default border is set to `1`.
     force_copy : bool, optional
-        If set, the image is copied before being used by the method which
-        modifies it in place. Set this to `False` if the original image is no
-        loner needed after this opetration.
+        If set, the `image` and `energy_map` are copied before being used by
+        the method which modifies it in place. Set this to `False` if the
+        original image and the energy map are no longer needed after
+        this opetration.
 
     Returns
     -------
@@ -55,7 +48,8 @@ def seam_carve(img, mode, num, energy_func, extra_args=[],
     """
 
     utils.assert_nD(img, (2, 3))
-    image = util.img_as_float(img)
+    image = util.img_as_float(img, force_copy)
+    energy_map = util.img_as_float(energy_map, force_copy)
 
     if image.ndim == 2:
         image = image[..., np.newaxis]
@@ -64,7 +58,8 @@ def seam_carve(img, mode, num, energy_func, extra_args=[],
         image = np.transpose(image, (1, 0, 2))
 
     image = np.ascontiguousarray(image)
-    out = _seam_carve_v(image, num, energy_func, extra_args, extra_kwargs, border)
+    out = _seam_carve_v(image, energy_map, num, border)
+
     if mode == 'horizontal':
         out = np.transpose(out, (1, 0, 2))
     return np.squeeze(out)