How to correctly recognize white (and its shades) in an image?

Question

Python

Hello.
It is required to recognize "blots" of white color (and its shades) on the image: highlight the areas of "blots" on the image and calculate the area of ​​"blots" as a percentage of the total size of the image.

import cv2 as cv
import numpy as np
h_min = np.array((50, 50, 50), np.uint8)
h_max = np.array((255, 255, 255), np.uint8)
img = cv.imread(r'C:\\1.jpg')
hsv = cv.cvtColor( img, cv.COLOR_BGR2HSV )
thresh = cv.inRange( hsv, h_min, h_max )
contours, hierarchy = cv.findContours( thresh.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
cv.drawContours( img, contours, -1, (255,0,0), 3, cv.LINE_AA, hierarchy, 1 )
cv.imshow('result', img)
cv.waitKey(234234)

The blob is always surrounded by a black border.
In this case, what is the best way to proceed, to carry out morphological processing of the image in order to smooth out the noise for a better search for blobs? Although the "blot" is in shades of white, it is not monophonic. How to take into account only the blobs that are surrounded by a black border, look for black objects, and then only look inside them for my "blobs"?
Thank you.