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metrics.py
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metrics.py
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import numpy as np
from keras import backend as K
from sklearn.metrics import jaccard_similarity_score
smooth_default = 1.
def dice_coef(y_true, y_pred, smooth = smooth_default, per_batch = True):
if not per_batch:
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
else:
y_true_f = K.batch_flatten(y_true)
y_pred_f = K.batch_flatten(y_pred)
intersec = 2. * K.sum(y_true_f * y_pred_f, axis=1, keepdims=True) + smooth
union = K.sum(y_true_f, axis=1, keepdims=True) + K.sum(y_pred_f, axis=1, keepdims=True) + smooth
return K.mean(intersec / union)
def jacc_coef(y_true, y_pred, smooth = smooth_default):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + smooth)
def jacc_loss(y_true, y_pred):
return -jacc_coef(y_true, y_pred)
def dice_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def dice_jacc_single(mask_true, mask_pred, smooth = smooth_default):
bool_true = mask_true.reshape(-1).astype(np.bool)
bool_pred = mask_pred.reshape(-1).astype(np.bool)
if bool_true.shape != bool_pred.shape:
raise ValueError("Masks of different sizes.")
bool_sum = bool_true.sum() + bool_pred.sum()
if bool_sum == 0:
print "Empty mask"
return 0,0
intersec = np.logical_and(bool_true, bool_pred).sum()
dice = 2. * intersec / bool_sum
jacc = jaccard_similarity_score(bool_true.reshape((1, -1)), bool_pred.reshape((1, -1)), normalize=True, sample_weight=None)
return dice, jacc
def dice_jacc_mean(mask_true, mask_pred, smooth = smooth_default):
dice = 0
jacc = 0
for i in range(mask_true.shape[0]):
current_dice, current_jacc = dice_jacc_single(mask_true=mask_true[i],mask_pred=mask_pred[i], smooth= smooth)
dice = dice + current_dice
jacc = jacc + current_jacc
return dice/mask_true.shape[0], jacc/mask_true.shape[0]