Cut objects out

import numpy as np

# The true outline of an L-shaped object in a 6 x 8 picture (True = object).
truth = np.zeros((6, 8), dtype=bool)
truth[1:5, 2] = True       # the upright
truth[4, 2:7] = True       # the foot

def iou(a, b):
    """Intersection over union: overlap divided by the area either one covers."""
    return (a & b).sum() / (a | b).sum()

# Two guesses: just the upright, and the whole L plus one stray pixel.
upright = np.zeros_like(truth)
upright[1:5, 2] = True
whole = truth.copy()
whole[0, 7] = True
print(f"upright: IoU {iou(upright, truth):.2f}")
print(f"whole:   IoU {iou(whole, truth):.2f}")  # SAM predicts this number for each guess

# How much of its own bounding box the object fills.
rows, cols = np.nonzero(truth)
box = (rows.max() - rows.min() + 1) * (cols.max() - cols.min() + 1)
print(f"the L fills {truth.sum() / box:.0%} of its box")

# Cutting out with a soft matte: each pixel mixes the photo and a new backdrop.
alpha = np.array([0.0, 0.3, 1.0])   # background, a wisp of hair, subject
photo, backdrop = 200.0, 50.0       # brightness of one photo pixel and the backdrop
print("composite:", alpha * photo + (1 - alpha) * backdrop)

upright: IoU 0.50
whole:   IoU 0.89
the L fills 40% of its box
composite: [ 50.  95. 200.]