def stat_of_all_models(self, img_true, n_out):
'''
return two lists of dice coefficient and error of all models in the ensemble
'''
dices = []
errs = []
count = 1
for pred_raw in self.pred_raws:
# Compute img_pred
pred_all = np.argmax(pred_raw, axis=1)
img_pred = create_img_from_pred(self.vx_all, pred_all,
img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
dices.append(dice_regions.mean())
err_global = error_rate(pred, true)
errs.append(err_global)
count = count + 1
return dices, errs
for atlas_id in xrange(n_files):
print "Atlas: {}".format(atlas_id)
ls_vx = []
ls_pred = []
brain_batches = data_gen.generate_single_atlas(atlas_id, None,
region_centroids,
batch_size, True)
vx_all, pred_all = net.predict_from_generator(brain_batches, scaler,
pred_functions)
# Construct the predicted image
img_true = data_gen.atlases[atlas_id][1]
img_pred = create_img_from_pred(vx_all, pred_all, img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
end_time = time.clock()
print "It took {} seconds to evaluate the whole brain.".format(
end_time - start_time)
print "The mean dice is {}".format(dice_regions.mean())
print "The error rateis {}".format(err_global)
# Save the results
errors = np.zeros((n_files,))
pred_functions = {}
for atlas_id in xrange(n_files):
print "Atlas: {}".format(atlas_id)
ls_vx = []
ls_pred = []
brain_batches = data_gen.generate_single_atlas(atlas_id, None, region_centroids, batch_size, True)
vx_all, pred_all = net.predict_from_generator(brain_batches, scaler, pred_functions)
# Construct the predicted image
img_true = data_gen.atlases[atlas_id][1]
img_pred = create_img_from_pred(vx_all, pred_all, img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
end_time = time.clock()
print "It took {} seconds to evaluate the whole brain.".format(end_time - start_time)
print "The mean dice is {}".format(dice_regions.mean())
print "The error rateis {}".format(err_global)
# Save the results
errors[atlas_id] = err_global