def reduceWidth (im, energyImage):
cum_energy = cumulative_minimum_energy_map(energyImage, "VERTICAL")
vSeam = find_optimal_vertical_seam(cum_energy)
#new_im = np.zeros(im.shape[0], im.shape[1]-1)
#new_energy_map = np.empty_like(energyImage)
new_im = np.empty_like(im)
crop_im = new_im[:, :(im.shape[1]-1)]
# print "crop_im size is"
# print crop_im.shape
new_energy_map = np.empty_like(energyImage)
# print "new_energy_map size is"
# print new_energy_map.shape
crop_energy_map = new_energy_map[:, :(new_energy_map.shape[1]-1)]
# print "crop_energy_map size is"
# print crop_energy_map.shape
for row in range(im.shape[0]):
for col in range(im.shape[1]):
if( col < vSeam[row]):
#print "Col is %d and vSeam[row] is %d" % (col, vSeam[row])
crop_im[row][col] = im[row][col]
crop_energy_map[row][col] = energyImage[row][col]
elif(col == vSeam[row]):
pass
else:
crop_im[row][col-1] = im[row][col]
crop_energy_map[row][col-1] = energyImage[row][col]
return crop_im, crop_energy_map
def reduceWidth(im, energyImage):
mapV = cumulative_minimum_energy_map(energyImage, "VERTICAL")
seam = find_optimal_vertical_seam(mapV)
newIm = np.zeros((im.shape[0], im.shape[1] - 1, im.shape[2])).astype(int)
for i in xrange(len(seam)):
newIm[i] = np.delete(im[i], seam[i], 0)
return [newIm, energy_image(newIm)]
def reduceWidth(im, energyImage):
m, n = im.shape[:2]
cumulativeMinimumEnergyMap = cumulative_minimum_energy_map(
energyImage, 'VERTICAL')
verticalSeam = find_optimal_vertical_seam(cumulativeMinimumEnergyMap)
reducedColorImage = np.zeros((m, n - 1, 3), dtype=np.uint8)
reducedEnergyImage = np.zeros((m, n - 1), dtype=np.float64)
for rows in range(m):
imageCopy = im[rows, :, :]
mask = np.ones(n, dtype=bool)
mask[verticalSeam[rows]] = False
reducedColorImage[rows, :, :] = imageCopy[mask]
reducedEnergyImage = energy_image(reducedColorImage)
return [reducedColorImage, reducedEnergyImage]
def reduceWidth(im, energyImage):
map = cumulative_minimum_energy_map(energyImage, 'VERTICAL')
x = find_optimal_vertical_seam(map)
y = np.arange(im.shape[0])
height, width, channels = im.shape
e_height, e_width = energyImage.shape
final_im = np.zeros((height, width - 1, channels), dtype=np.uint8)
energy_im = np.zeros((e_height, e_width - 1), dtype=float)
for y_index in range(0, height):
left = im[y_index][:x[y_index]][:]
right = im[y_index][x[y_index] + 1:][:]
total = np.concatenate((left, right), axis=0)
final_im[y_index] = total
left_e = energyImage[y_index][:x[y_index]]
right_e = energyImage[y_index][x[y_index] + 1:]
total_e = np.concatenate((left_e, right_e), axis=0)
energy_im[y_index] = total_e
return (final_im, energy_im)
def reduceWidth(im, energyImage):
"""
Args:
energyImage: 2D matrix of datatype double
im: MxNx3 matrix of datatype uint8
Returns:
reducedColorImage: 3D matrix same as the input image but with its width reduced by one pixel
reducedEnergyImage: 2D matrix same as the inputenergyImage, but with its width reduced by one pixel
"""
row=np.size(energyImage,0)
col=np.size(energyImage,1)
cumap=cumulative_minimum_energy_map(energyImage, 'VERTICAL')
seam=find_optimal_vertical_seam(cumap)
reducedColorImage=np.zeros((row,col-1,3),dtype=np.uint8)
reducedEnergyImage=np.zeros((row,col-1),dtype=np.double)
for i in range(row):
reducedColorImage[i,0:seam[i],:]=im[i,0:seam[i],:]
reducedEnergyImage[i,0:seam[i]]=energyImage[i,0:seam[i]]
reducedColorImage[i,seam[i]:col-1,:]=im[i,seam[i]+1:col,:]
reducedEnergyImage[i,seam[i]:col-1]=energyImage[i,seam[i]+1:col]
return reducedColorImage, reducedEnergyImage
plt.figure()
plt.title('Cumulative Vertical Intensity')
plt.imshow(v_cum_min_energy)
plt.show()
h_cum_min_energy = cumulative_minimum_energy_map(en_image, 'HORIZONTAL')
plt.figure()
plt.title('Cumulative Horizontal Intensity')
plt.imshow(h_cum_min_energy)
plt.show()
plt.figure()
plt.title('Original Image')
plt.imshow(img)
plt.show()
displaySeam(img, find_optimal_vertical_seam(v_cum_min_energy), 'VERTICAL')
displaySeam(img, find_optimal_horizontal_seam(h_cum_min_energy), 'HORIZONTAL')
"""
#vSeam = find_optimal_horizontal_seam(cum_min_energy)
vSeam = find_optimal_vertical_seam(cum_min_energy)
#print find_optimal_horizontal_seam(w)
displaySeam(img, vSeam, 'VERTICAL')
plt.figure(2)
plt.hold(False)
plt.subplot(121)
plt.title('Display after seam')
plt.imshow(img)
plt.imshow(energyImage, cmap=plt.get_cmap('gray'))
# plt.savefig('./energyImagePrague.png')
plt.show()
### cumulative energy map
mapV = cumulative_minimum_energy_map(energyImage, "VERTICAL")
mapH = cumulative_minimum_energy_map(energyImage, "HORIZONTAL")
plt.imshow(mapV, cmap=plt.get_cmap('gray'))
# plt.savefig('./verticalCumulativeEnergyMapPrague.png')
plt.show()
plt.imshow(mapH, cmap=plt.get_cmap('gray'))
# plt.savefig('./horizontalCumulativeEnergyMapPrague.png')
plt.show()
### vertical seam
vSeam = find_optimal_vertical_seam(mapV)
### horizontal seam
hSeam = find_optimal_horizontal_seam(mapH)
### display seam
displaySeam(img, vSeam, "VERTICAL")
displaySeam(img, hSeam, "HORIZONTAL")
### mall
img = mpimg.imread('./inputSeamCarvingMall.jpg')
plt.imshow(img)
# plt.savefig('./energyImagePrague.png')
plt.show()
### energyImage
