How can I remove background from images using OpenCV python ?

import cv2

import numpy as np

#== Parameters =======================================================================

BLUR = 21

CANNY_THRESH_1 = 10

CANNY_THRESH_2 = 200

MASK_DILATE_ITER = 10

MASK_ERODE_ITER = 10

MASK_COLOR = (0.0,0.0,1.0) # In BGR format

#== Processing =======================================================================

#-- Read image -----------------------------------------------------------------------

img = cv2.imread('C:/Temp/person.jpg')

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#-- Edge detection -------------------------------------------------------------------

edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)

edges = cv2.dilate(edges, None)

edges = cv2.erode(edges, None)

#-- Find contours in edges, sort by area ---------------------------------------------

contour_info = []

_, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

# Previously, for a previous version of cv2, this line was: 

#  contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

# Thanks to notes from commenters, I've updated the code but left this note

for c in contours:

    contour_info.append((

        c,

        cv2.isContourConvex(c),

        cv2.contourArea(c),

    ))

contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)

max_contour = contour_info[0]

#-- Create empty mask, draw filled polygon on it corresponding to largest contour ----

# Mask is black, polygon is white

mask = np.zeros(edges.shape)

cv2.fillConvexPoly(mask, max_contour[0], (255))

#-- Smooth mask, then blur it --------------------------------------------------------

mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)

mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)

mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)

mask_stack = np.dstack([mask]*3)    # Create 3-channel alpha mask

#-- Blend masked img into MASK_COLOR background --------------------------------------

mask_stack  = mask_stack.astype('float32') / 255.0          # Use float matrices, 

img         = img.astype('float32') / 255.0                 #  for easy blending

masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR) # Blend

masked = (masked * 255).astype('uint8')                     # Convert back to 8-bit 

cv2.imshow('img', masked)                                   # Display

cv2.waitKey()

#cv2.imwrite('C:/Temp/person-masked.jpg', masked)           # Save