import numpy as np
from .helper import _validate_array_and_make_bool
[docs]
def total_error(mask_gt, mask_pred, weight_fn=1, weight_fp=1):
"""
Computes the percent of misclassified pixels on an image.
In the case where one is more important than the
other, a weighted average may be used: c0FP + c1FN
'https://stats.stackexchange.com/questions/273537/f1-dice-score-vs-iou <https://stats.stackexchange.com/questions/273537/f1-dice-score-vs-iou>'_
Parameters
----------
mask_gt : bool ndarray
groudn truth mask.
mask_predicted : bool ndarray
DESCRIPTION.
weight_fn : int, optional
scalar weight applied to false negatives. The default is 1.
weight_fp : int, optional
scalar weight applied to false positives. The default is 1.
Returns
-------
percent: float
total error of the predicted mask given a ground truth mask.
"""
# convert to bool
mask_gt = _validate_array_and_make_bool(mask_gt)
mask_pred = _validate_array_and_make_bool(mask_pred)
# subtraction operation requires arrays to be int
# > 0 then convers then back to bool
fp = (mask_pred.astype(int) - mask_gt.astype(int)) > 0 # bool
fn = (mask_gt.astype(int) - mask_pred.astype(int)) > 0 # bool
n_rows, n_cols = mask_gt.shape
# return (np.sum(fn)*weight_fn + np.sum(fp)*weight_fp) / (n_rows * n_cols )
return (np.sum(fn) * weight_fn + np.sum(fp) * weight_fp) / (
(n_rows * n_cols) * ((weight_fp + weight_fn) / 2)
)