def _evaluate_semisupervised(all_gt_masks, all_res_masks, all_void_masks,
metric):
if all_res_masks.shape[0] > all_gt_masks.shape[0]:
sys.stdout.write(
"\nIn your PNG files there is an index higher than the number of objects in the sequence!"
)
sys.exit()
elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
zero_padding = np.zeros(
(all_gt_masks.shape[0] - all_res_masks.shape[0],
*all_res_masks.shape[1:]))
all_res_masks = np.concatenate([all_res_masks, zero_padding],
axis=0)
j_metrics_res, f_metrics_res = np.zeros(
all_gt_masks.shape[:2]), np.zeros(all_gt_masks.shape[:2])
for ii in range(all_gt_masks.shape[0]):
if 'J' in metric:
j_metrics_res[ii, :] = db_eval_iou(all_gt_masks[ii, ...],
all_res_masks[ii, ...],
all_void_masks)
if 'F' in metric:
f_metrics_res[ii, :] = db_eval_boundary(
all_gt_masks[ii, ...], all_res_masks[ii, ...],
all_void_masks)
return j_metrics_res, f_metrics_res
def test_void_masks():
gt = np.zeros((2, 200, 200))
mask = np.zeros((2, 200, 200))
void = np.zeros((2, 200, 200))
gt[:, 100:150, 100:150] = 1
void[:, 50:100, 100:150] = 1
mask[:, 50:150, 100:150] = 1
assert np.mean(db_eval_iou(gt, mask, void)) == 1
assert np.mean(db_eval_boundary(gt, mask, void)) == 1
def _evaluate_unsupervised(all_gt_masks,
all_res_masks,
all_void_masks,
metric,
max_n_proposals=20):
if all_res_masks.shape[0] > max_n_proposals:
sys.stdout.write(
f"\nIn your PNG files there is an index higher than the maximum number ({max_n_proposals}) of proposals allowed!"
)
sys.exit()
elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
zero_padding = np.zeros(
(all_gt_masks.shape[0] - all_res_masks.shape[0],
*all_res_masks.shape[1:]))
all_res_masks = np.concatenate([all_res_masks, zero_padding],
axis=0)
j_metrics_res = np.zeros(
(all_res_masks.shape[0], all_gt_masks.shape[0],
all_gt_masks.shape[1]))
f_metrics_res = np.zeros(
(all_res_masks.shape[0], all_gt_masks.shape[0],
all_gt_masks.shape[1]))
for ii in range(all_gt_masks.shape[0]):
for jj in range(all_res_masks.shape[0]):
if 'J' in metric:
j_metrics_res[jj,
ii, :] = db_eval_iou(all_gt_masks[ii, ...],
all_res_masks[jj, ...],
all_void_masks)
if 'F' in metric:
f_metrics_res[jj, ii, :] = db_eval_boundary(
all_gt_masks[ii, ...], all_res_masks[jj, ...],
all_void_masks)
if 'J' in metric and 'F' in metric:
all_metrics = (np.mean(j_metrics_res, axis=2) +
np.mean(f_metrics_res, axis=2)) / 2
else:
all_metrics = np.mean(j_metrics_res,
axis=2) if 'J' in metric else np.mean(
f_metrics_res, axis=2)
row_ind, col_ind = linear_sum_assignment(-all_metrics)
return j_metrics_res[row_ind, col_ind, :], f_metrics_res[row_ind,
col_ind, :]
