region_centroids = RegionCentroids(134)
region_centroids.update_barycentres(ds_testing.vx, pred_wo_centroids)
# Generate and evaluate the dataset
start_time = time.clock()
dices = np.zeros((n_files, 134))
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()
region_centroids.update_barycentres(ds_testing.vx, pred_wo_centroids)
# Generate and evaluate the dataset
start_time = time.clock()
dices = np.zeros((n_files, 134))
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)
# region_centroids = RegionCentroids(134)
# region_centroids.update_barycentres(ds_testing.vx, pred_wo_centroids)
# Generate and evaluate the dataset
dices = np.zeros((n_files, 134))
errors = np.zeros((n_files, ))
pred_functions = {}
# for atlas_id in xrange(n_files):
for atlas_id in xrange(1):
start_time = time.clock()
print "Atlas: {}".format(atlas_id)
ls_vx = []
ls_pred = []
brain_batches = data_gen.generate_single_atlas(atlas_id, None, None,
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 = net.compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
