Foreground Extrac is any method that allows the foreground of an image to be extracted for further processing like object recognition, tracking, etc. The algorithm used to extract the foreground is here GrabCut algorithm. In this algorithm, an area is drawn according to the foreground, a rectangle is drawn above it. This is the rectangle that surrounds our main object. The coordinates of the region are determined by the foreground mask. But this segmentation is not ideal as it can mark some foreground as background and vice versa. This problem can be avoided manually. This foreground extraction technique functions like a green screen in cinematography.
# Python program for illustration
# fetch foreground using
# GrabCut algorithm
# arrange imports
import numpy as np
import cv2
from matplotlib import pyplot as plt
# the path to the specified input image and
# the image is loaded using the imread command
image = cv2.imread ( `image.jpg` )
# create a simple mask similar to the image
# to the uploaded image, with
# form and type of return
mask = np.zeros (image.shape [: 2 ], np.uint8)
# specify foreground and background model
# using numpy, the array consists of 1 line
# and 65 columns and all elements of the array are 0
# Data type for the array np.float64 ( default)
backgroundModel = np. zeros (( 1 , 65 ), np.float64)
foregroundModel = np.zeros (( 1 , 65 ), np.float64)
# define the ROI
# as rectangle coordinates
# where values are entered as
# (start_x, start_y, width, heights a)
# these coordinates correspond to the input image
# may differ for different images
rectangle = ( 20 , 100 , 150 , 150 )
# apply the capture algorithm with matching
# values as parameters, number of iterations = 3
# cv2.GC_INIT_WITH_RECT is used because
# rectangle mode is used
cv2.grabCut (image, mask, rectangle,
backgroundModel, foregroundModel,
3 , cv2.GC_INIT_WITH_RECT)
# In the new mask image, pixels will be
# marked with four flags
# four flags indicate background / foreground
# mask changed, all 0 and 2 pixels
# converted to background
# mask changed, all 1 and 3 pixels
# now part of the foreground
# the return type is also mentioned,
# this gives us the final mask
mask2 = np.where ((mask = 2 ) | (mask = = 0 ), 0 , 1 ). astype ( ` uint8` )
# The final mask is multiplied by
# input image to give a segmented image .
image = image * mask2 [:,:, np.newaxis]
# output a segmented image with a color scale
plt.imsh ow (image)
plt.colorbar ()
plt.show ()
Input image:
Output:
Here we took the input image is 500X281 and coordinates for the rectangle have been determined accordingly. The output image shows how the object to the left of the image becomes part of the foreground and the background is subtracted.
