This is perhaps the most interesting application of seam carving. We can make an object completely disappear from an image. Let's consider the following image:
Let's select the region of interest:
After you remove the chair on the right, it will look something like this:
It's as if the chair never existed! Before we look at the code, it's important to know that this takes a while to run. So, just wait for a couple of minutes to get an idea of the processing time. You can adjust the input image size accordingly! Let's take a look at the code:
import sys
import cv2
import numpy as np
# Draw rectangle on top of the input image
def draw_rectangle(event, x, y, flags, params):
global x_init, y_init, drawing, top_left_pt, bottom_right_pt, img_orig
# Detecting a mouse click
if event == cv2.EVENT_LBUTTONDOWN:
drawing = True
x_init, y_init = x, y
# Detecting mouse movement
elif event == cv2.EVENT_MOUSEMOVE:
if drawing:
top_left_pt, bottom_right_pt = (x_init,y_init), (x,y)
img[y_init:y, x_init:x] = 255 - img_orig[y_init:y, x_init:x]
cv2.rectangle(img, top_left_pt, bottom_right_pt, (0,255,0), 2)
# Detecting the mouse button up event
elif event == cv2.EVENT_LBUTTONUP:
drawing = False
top_left_pt, bottom_right_pt = (x_init,y_init), (x,y)
# Create the "negative" film effect for the selected # region
img[y_init:y, x_init:x] = 255 - img[y_init:y, x_init:x]
# Draw rectangle around the selected region
cv2.rectangle(img, top_left_pt, bottom_right_pt, (0,255,0), 2)
rect_final = (x_init, y_init, x-x_init, y-y_init)
# Remove the object in the selected region
remove_object(img_orig, rect_final)
# Computing the energy matrix using modified algorithm
def compute_energy_matrix_modified(img, rect_roi):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Compute the X derivative
sobel_x = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=3)
# Compute the Y derivative
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)
# Compute weighted summation i.e. 0.5*X + 0.5*Y
energy_matrix = cv2.addWeighted(abs_sobel_x, 0.5, abs_sobel_y, 0.5, 0)
x,y,w,h = rect_roi
# We want the seams to pass through this region, so make sure the energy values in this region are set to 0
energy_matrix[y:y+h, x:x+w] = 0
return energy_matrix
# Compute energy matrix
def compute_energy_matrix(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Compute X derivative
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
# Compute Y derivative
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 weighted summation i.e. 0.5*X + 0.5*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
seam = np.zeros(img.shape[0])
# Initialize the 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; using double loop to compute the paths
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 vertical seam to the input 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
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
# Remove the object from the input region of interest
def remove_object(img, rect_roi):
num_seams = rect_roi[2] + 10
energy = compute_energy_matrix_modified(img, rect_roi)
# Start a loop and remove one seam at a time
for i in xrange(num_seams):
# Find the vertical seam that can be removed
seam = find_vertical_seam(img, energy)
# Remove that vertical seam
img = remove_vertical_seam(img, seam)
x,y,w,h = rect_roi
# Compute energy matrix after removing the seam
energy = compute_energy_matrix_modified(img, (x,y,w-i,h))
print 'Number of seams removed =', i+1
img_output = np.copy(img)
# Fill up the region with surrounding values so that the size # of the image remains unchanged
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()
if __name__=='__main__':
img_input = cv2.imread(sys.argv[1])
drawing = False
img = np.copy(img_input)
img_orig = np.copy(img_input)
cv2.namedWindow('Input')
cv2.setMouseCallback('Input', draw_rectangle)
while True:
cv2.imshow('Input', img)
c = cv2.waitKey(10)
if c == 27:
break
cv2.destroyAllWindows()The basic logic remains the same here. We are using seam carving to remove an object. Once we select the region of interest, we make all the seams pass through this region. We do this by manipulating the energy matrix after every iteration. We have added a new function called compute_energy_matrix_modified to achieve this. Once we compute the energy matrix, we assign a value of 0 to this region of interest. This way, we force all the seams to pass through this area. After we remove all the seams related to this region, we keep adding the seams until we expand the image to its original width.
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