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# Metrics between masks in numpy
import numpy as np
def dice(img1: np.ndarray, img2: np.ndarray, epsilon=0.00001):
assert np.min(img1) >= 0.
assert np.max(img1) <= 1.
assert np.min(img2) >= 0.
assert np.max(img2) <= 1.
return (np.sum(np.multiply(img1, img2)) * 2 + epsilon) / (np.sum(img1) + np.sum(img2) + epsilon)
def harden(img: np.ndarray):
h_img = np.zeros_like(img, dtype=np.uint8)
h_img[img > 0.5] = 1
return h_img
def restrict(m: np.ndarray):
rm = np.copy(m)
rm[rm<0]=0.
rm[rm>1]=1.
return rm
def check_mask(m):
if np.min(m) < 0.:
print("WARNING: Mask contains pixels below 0, constraining it to be over 0")
if np.max(m) > 1.:
print("WARNING: Mask contains pixels over 1, constraining them to be below 1")
assert np.min(m) >= 0.
assert np.max(m) <= 1.
def hard_dice(img1: np.ndarray, img2: np.ndarray, epsilon=0.00001):
check_mask(img1)
check_mask(img2)
return dice(harden(img1), harden(img2), epsilon)
def rel_size_diff(img_r: np.ndarray, img: np.ndarray, epsilon=0.00001):
assert np.min(img) >= 0.
assert np.max(img) <= 1.
s_r = np.sum(img_r)
s = np.sum(img)
return np.abs(s_r-s) / s_r
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