hole-filling.py

"""
Read in the image.
Threshold the input image to obtain a binary image.
Flood fill from pixel (0, 0). Notice the difference between the outputs of step 2 and step 3 is that the background in step 3 is now white.
Invert the flood filled image ( i.e. black becomes white and white becomes black ).
Combine the thresholded image with the inverted flood filled image using bitwise OR operation to obtain the final foreground mask with holes filled in. The image in Step 4 has some black areas inside the boundary. By design the image in Step 2 has those holes filled in. So we combine the two to get the mask.
"""

import cv2;
import numpy as np;

# Read image
im_in = cv2.imread("../nickel.jpg", cv2.IMREAD_GRAYSCALE);

# Threshold.
# Set values equal to or above 220 to 0.
# Set values below 220 to 255.

th, im_th = cv2.threshold(im_in, 220, 255, cv2.THRESH_BINARY_INV);

# Copy the thresholded image.
im_floodfill = im_th.copy()

# Mask used to flood filling.
# Notice the size needs to be 2 pixels than the image.
h, w = im_th.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)

# Floodfill from point (0, 0)
cv2.floodFill(im_floodfill, mask, (0,0), 255);

# Invert floodfilled image
im_floodfill_inv = cv2.bitwise_not(im_floodfill)

# Combine the two images to get the foreground.
im_out = im_th | im_floodfill_inv

# Display images.
cv2.imshow("Original Image", im_in)
cv2.imshow("Thresholded Image", im_th)
cv2.imshow("Floodfilled Image", im_floodfill)
cv2.imshow("Inverted Floodfilled Image", im_floodfill_inv)
cv2.imshow("Foreground", im_out)
cv2.waitKey(0)
cv2.destroyAllWindows()