This answer for this Question in StackOverflow.
Theory: Thresholding in HSV Space. Reference Link
Python Code Reference Link
import numpy as np
import matplotlib.pyplot as plt
import cv2
# Read image
img = cv2.imread('roofs.jpg')
converted = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Reference: https://www.pyimagesearch.com/2014/08/18/skin-detection-step-step-example-using-python-opencv/
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
lower = np.array([0, 48, 80], dtype = "uint8")
upper = np.array([12, 255, 255], dtype = "uint8")
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
skinMask = cv2.morphologyEx(skinMask, cv2.MORPH_CLOSE, kernel, iterations = 1)
# blur the mask to help remove noise, then apply the
# mask to the img
skinMask = cv2.GaussianBlur(skinMask, (5, 5), 0)
skin = cv2.bitwise_and(img, img, mask = skinMask)
# show the skin in the image along with the mask
cv2.imshow("images", np.hstack([img, skin]))
# waits for user to press any key
# (this is necessary to avoid Python kernel form crashing)
cv2.waitKey(0)
# closing all open windows
cv2.destroyAllWindows() 
