We know that we can use seam carving to reduce the width of an image without deteriorating the interesting regions. So naturally, we need to ask ourselves if we can expand an image without deteriorating the interesting regions? As it turns out, we can do it using the same logic. When we compute the seams, we just need to add an extra column instead of deleting it.
If you expand the ducks image naively, it will look something like this:
If you do it in a smarter way, that is, by using seam carving, it will look something like this:
As you can see here, the width of the image has increased and the ducks don't look stretched. Following is the code to do it:
import sys
import cv2
import numpy as np
# Compute the energy matrix from the input image
def compute_energy_matrix(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
abs_sobel_x = cv2.convertScaleAbs(sobel_x)
abs_sobel_y = cv2.convertScaleAbs(sobel_y)
return cv2.addWeighted(abs_sobel_x, 0.5, abs_sobel_y, 0.5, 0)
# Find the vertical seam
def find_vertical_seam(img, energy):
rows, cols = img.shape[:2]
# Initialize the seam vector with 0 for each element
seam = np.zeros(img.shape[0])
# Initialize distance and edge matrices
dist_to = np.zeros(img.shape[:2]) + sys.maxint
dist_to[0,:] = np.zeros(img.shape[1])
edge_to = np.zeros(img.shape[:2])
# Dynamic programming; iterate using double loop and compute
#the paths efficiently
for row in xrange(rows-1):
for col in xrange(cols):
if col != 0:
if dist_to[row+1, col-1] > dist_to[row, col] + energy[row+1, col-1]:
dist_to[row+1, col-1] = dist_to[row, col] + energy[row+1, col-1]
edge_to[row+1, col-1] = 1
if dist_to[row+1, col] > dist_to[row, col] + energy[row+1, col]:
dist_to[row+1, col] = dist_to[row, col] + energy[row+1, col]
edge_to[row+1, col] = 0
if col != cols-1:
if dist_to[row+1, col+1] > dist_to[row, col] + energy[row+1, col+1]:
dist_to[row+1, col+1] = dist_to[row, col] + energy[row+1, col+1]
edge_to[row+1, col+1] = -1
# Retracing the path
seam[rows-1] = np.argmin(dist_to[rows-1, :])
for i in (x for x in reversed(xrange(rows)) if x > 0):
seam[i-1] = seam[i] + edge_to[i, int(seam[i])]
return seam
# Add a vertical seam to the image
def add_vertical_seam(img, seam, num_iter):
seam = seam + num_iter
rows, cols = img.shape[:2]
zero_col_mat = np.zeros((rows,1,3), dtype=np.uint8)
img_extended = np.hstack((img, zero_col_mat))
for row in xrange(rows):
for col in xrange(cols, int(seam[row]), -1):
img_extended[row, col] = img[row, col-1]
# To insert a value between two columns, take the average # value of the neighbors. It looks smooth this way and we # can avoid unwanted artifacts.
for i in range(3):
v1 = img_extended[row, int(seam[row])-1, i]
v2 = img_extended[row, int(seam[row])+1, i]
img_extended[row, int(seam[row]), i] = (int(v1)+int(v2))/2
return img_extended
# Remove vertical seam from the image
def remove_vertical_seam(img, seam):
rows, cols = img.shape[:2]
for row in xrange(rows):
for col in xrange(int(seam[row]), cols-1):
img[row, col] = img[row, col+1]
img = img[:, 0:cols-1]
return img
if __name__=='__main__':
img_input = cv2.imread(sys.argv[1])
num_seams = int(sys.argv[2])
img = np.copy(img_input)
img_output = np.copy(img_input)
energy = compute_energy_matrix(img)
for i in xrange(num_seams):
seam = find_vertical_seam(img, energy)
img = remove_vertical_seam(img, seam)
img_output = add_vertical_seam(img_output, seam, i)
energy = compute_energy_matrix(img)
print 'Number of seams added =', i+1
cv2.imshow('Input', img_input)
cv2.imshow('Output', img_output)
cv2.waitKey()We added an extra function, add_vertical_seam, in this code. We use it to add vertical seams so that the image looks natural.