Replace pixel color by size as a condition in an image

Curious G.

I have this image:

It's a meat peace, and I would like to measure the marble (white part). To do this I used this:

import numpy as np
import cv2

cv2.namedWindow('Image2', cv2.WINDOW_NORMAL)        # Create window with freedom of dimensions
cv2.resizeWindow('Image2', 600, 600)

def increase_brightness(img, value=30):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)

    lim = 255 - value
    v[v > lim] = 0
    v[v <= lim] += value

    final_hsv = cv2.merge((h, s, v))
    return final_hsv

image = cv2.imread('image.jpeg')

image2 = increase_brightness(image)

lower_range=np.array([0, 155, 0])
upper_range=np.array([255,255,255])
mask=cv2.inRange(mask, lower_range, upper_range)

and I obtained this image:

but there is a noise in the final image. Is there a way to replace the black smallest pixels by white pixels, using, for example, the pixel size as a condition?

OR Is there a better approach to do this?

fmw42

Here is one way using Python/OpenCV by using cv2.inRange() to threshold on white.

Input:

import cv2
import numpy as np

# load image
img = cv2.imread("meat.jpg")

# threshold on white
lower = (120,140,190)
upper = (255,255,255)

# create the mask and use it to change the colors
thresh = cv2.inRange(img, lower, upper)

# apply morphology to clean up small spots
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# count number of non-zero pixels
count = np.count_nonzero(thresh)

print("total white pixels =", count)

# write thresh to disk
cv2.imwrite("meat_thresh.png", thresh)

# display it
cv2.imshow("thresh", thresh)
cv2.waitKey(0)