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Modifed code to use seam map

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This commit is contained in:
Vighnesh Birodkar
2015-06-08 23:11:57 +05:30
parent 66caaa5b9f
commit 37d8a8447b
3 changed files with 94 additions and 31 deletions
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doc/examples/plot_seam_carving.py
+8 -6
View File
@@ -18,21 +18,23 @@ from skimage import transform
from skimage import filters
from matplotlib import pyplot as plt
def nothing(img):
return img
img = data.coins()
img = data.coins()#io.imread('/home/vighnesh/images/seam_bw.png')
out = transform.seam_carve(img, 'vertical', 80, energy_func=filters.sobel)
out = transform.seam_carve(out, 'horizontal', 70, 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(img, plugin='matplotlib')
plt.figure()
plt.title('Resized Image')
io.imshow(resized, plugin='matplotlib')
#io.imshow(resized, plugin='matplotlib')
plt.figure()
plt.title('Resized using Seam-Carving')
io.imshow(out, plugin='matplotlib')
#io.imshow(out, plugin='matplotlib')
io.show()
#io.show()
skimage/transform/_seam_carving.pyx
+84 -23
View File
@@ -1,7 +1,3 @@
# cython: cdivision=True
# cython: boundscheck=False
# cython: nonecheck=False
# cython: wraparound=False
import numpy as np
cimport numpy as cnp
@@ -112,6 +108,65 @@ cdef remove_seam_v(cnp.double_t[:, :, ::1] img, Py_ssize_t[::1] seam,
img[row, idx, :] = img[row, idx + 1, :]
cdef _preprocess_image(cnp.double_t[:, ::1] energy_img,
cnp.double_t[:, ::1] cumulative_img,
cnp.int8_t[:, ::1] track_img,
Py_ssize_t cols):
cdef Py_ssize_t r, c, offset, c_idx
cdef Py_ssize_t rows = energy_img.shape[0]
cdef cnp.double_t min_cost = DBL_MAX
for c in range(cols):
cumulative_img[0, c] = energy_img[0, c]
for r in range(1, rows):
for c in range(cols):
min_cost = DBL_MAX
for offset in range(-1, 2):
c_idx = c + offset
if (c_idx > cols - 1) or (c_idx < 0) :
continue
if cumulative_img[r-1, c_idx] < min_cost:
min_cost = cumulative_img[r-1, c_idx]
track_img[r, c] = offset
#print "min_cost = ", min_cost
cumulative_img[r,c] = min_cost + energy_img[r, c]
#print "-------Cumulative Image --------"
#print np.array(cumulative_img)
#print "-------Energy Image --------"
#print np.array(energy_img)
cdef cnp.uint8_t mark_seam(cnp.int8_t[:, ::1] track_img, Py_ssize_t start_index,
cnp.uint8_t[:, ::1] seam_map):
cdef Py_ssize_t rows = track_img.shape[0]
cdef Py_ssize_t[::1] current_seam_indices = np.zeros(rows, dtype=np.int)
cdef Py_ssize_t row, col
cdef cnp.int8_t offset
cdef Py_ssize_t seams
current_seam_indices[rows - 1] = start_index
for row in range(rows - 2, -1, -1):
col = current_seam_indices[row+1]
offset = track_img[row, col]
col = col + offset
current_seam_indices[row] = col
if seam_map[row, col]:
return 0
for row in range(rows):
col = current_seam_indices[row]
seam_map[row, col] = 1
return 1
def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
""" Carve vertical seams off an image.
@@ -120,7 +175,7 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
Parameters
----------
image : (M, N) or (M, N, 3) ndarray
img : (M, N) or (M, N, 3) ndarray
Input image whose vertical seams are to be removed.
iters : int
Number of vertical seams are to be removed.
@@ -151,29 +206,35 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
"Seam Carving for Content-Aware Image Resizing"
http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf
"""
cdef Py_ssize_t[::1] seam
cdef Py_ssize_t ndim = img.ndim
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 Py_ssize_t cols = img.shape[1]
track_img = np.zeros(img.shape[0:2], dtype=np.int8)
cdef cnp.double_t[:, :, ::1] image = img
cdef cnp.int8_t[:, ::1] track_img = np.zeros(img.shape[0:2], dtype=np.int8)
cdef cnp.double_t[:, ::1] cumulative_img = np.zeros(img.shape[0:2], dtype=np.float)
cdef cnp.double_t[:, ::1] energy_img
current_cost = np.zeros_like(track_img[0], dtype = img.dtype)
prev_cost = np.zeros_like(track_img[0], dtype = img.dtype)
energy_img_obj = energy_func(np.squeeze(img))
energy_img = energy_img_obj
for i in range(iters):
energy_img_obj[:, 0:border] = DBL_MAX
energy_img_obj[:, cols-border:cols] = DBL_MAX
sliced_img = np.squeeze(img[:, 0:cols])
energy_img = energy_func(sliced_img, *extra_args, **extra_kwargs)
_preprocess_image(energy_img, cumulative_img, track_img, cols)
last_row[...] = cumulative_img[-1, :]
sorted_indices = np.argsort(last_row_obj)
#print "Sorted Indices = ", np.array(sorted_indices)
#print "First sorted Index = ", sorted_indices[0]
#print "Last Row = ", np.array(energy_img[-1, :])
#print np.array()
# So that borders are ignored.
energy_img[:, 0:border] = DBL_MAX
energy_img[:, cols-border:cols] = DBL_MAX
seam = _find_seam_v(energy_img, track_img, current_cost, prev_cost,
cols)
remove_seam_v(img, seam, cols)
cols -= 1
from skimage import io
io.imshow(seam_map_obj*255)
io.show()
return img[:, 0:cols]
skimage/transform/seam_carving.py
+2 -2
View File
@@ -60,11 +60,11 @@ def seam_carve(img, mode, num, energy_func, extra_args=[],
if image.ndim == 2:
image = image[..., np.newaxis]
if mode == 'vertical':
if mode == 'horizontal':
image = np.transpose(image, (1, 0, 2))
image = np.ascontiguousarray(image)
out = _seam_carve_v(image, num, energy_func, extra_args, extra_kwargs, border)
if mode == 'vertical':
if mode == 'horizontal':
out = np.transpose(out, (1, 0, 2))
return np.squeeze(out)