import numpy as np
from .helper import _validate_array_and_make_bool
# https://gist.github.com/brunodoamaral/e130b4e97aa4ebc468225b7ce39b3137
[docs]
def dice(im1, im2, empty_score=1.0):
"""
Computes the dice coefficient/F1 score, a measure of average similarity.
Parameters
----------
im1 : array-like, bool
Any array of arbitrary size. If not boolean, will be converted.
im2 : array-like, bool
Any other array of identical size. If not boolean, will be converted.
empty-score : int
Any other array of identical size. If not boolean, will be converted.
Returns
-------
float
Dice coefficient as a float on range [0,1]. \n
Maximum similarity = 1 \n
No similarity = 0 \n
Both are empty (sum eq to zero) = empty_score
Note
-----
The order of inputs for `dice` is irrelevant. The result will be
identical if `im1` and `im2` are switched.
"""
im1 = _validate_array_and_make_bool(im1)
im2 = _validate_array_and_make_bool(im2)
if im1.shape != im2.shape:
raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")
im_sum = im1.sum() + im2.sum()
if im_sum == 0: # no true values in either image
print("warning: no true values in either array, returning empty_score=1")
return empty_score
# Compute Dice coefficient
intersection = np.logical_and(im1, im2)
return 2.0 * intersection.sum() / im_sum