import numpy as np
np.random.seed(0)
true = np.random.rand(10, 5, 5, 4)>0.5
pred = np.random.rand(10, 5, 5, 4)>0.5
def single_dice_coef(y_true, y_pred_bin):
# shape of y_true and y_pred_bin: (height, width)
intersection = np.sum(y_true * y_pred_bin)
if (np.sum(y_true)==0) and (np.sum(y_pred_bin)==0):
return 1
return (2*intersection) / (np.sum(y_true) + np.sum(y_pred_bin))
def mean_dice_coef(y_true, y_pred_bin):
# shape of y_true and y_pred_bin: (n_samples, height, width, n_channels)
batch_size = y_true.shape[0]
channel_num = y_true.shape[-1]
mean_dice_channel = 0.
for i in range(batch_size):
for j in range(channel_num):
channel_dice = single_dice_coef(y_true[i, :, :, j], y_pred_bin[i, :, :, j])
mean_dice_channel += channel_dice/(channel_num*batch_size)
return mean_dice_channel
def dice_coef2(y_true, y_pred):
y_true_f = y_true.flatten()
y_pred_f = y_pred.flatten()
union = np.sum(y_true_f) + np.sum(y_pred_f)
if union==0: return 1
intersection = np.sum(y_true_f * y_pred_f)
return 2. * intersection / union
print(mean_dice_coef(true, pred))
print(dice_coef2(true, pred))
# 0.4884357140842496
# 0.499001996007984
Here is what the above code is Doing:
1. flatten the true and predicted arrays
2. calculate the intersection of the two arrays
3. calculate the union of the two arrays
4. calculate the dice coefficient