print('Calculating error: {}, {}, {}, {}, {}, {}'.format(
error_type, method, dataset_str, scene_id, im_id, obj_id))
# Load depth image if VSD is selected
if error_type == 'vsd' and im_id != im_id_prev:
depth_path = dp['test_depth_mpath'].format(scene_id, im_id)
# depth_im = inout.load_depth(depth_path)
depth_im = inout.load_depth2(depth_path) # Faster
depth_im *= dp['cam']['depth_scale'] # to [mm]
# Load camera matrix
if error_type in ['vsd', 'cou']:
K = scene_info[im_id]['cam_K']
# Load pose estimates
res = inout.load_results_sixd17(res_path)
ests = res['ests']
# Sort the estimates by score (in descending order)
ests_sorted = sorted(enumerate(ests),
key=lambda x: x[1]['score'],
reverse=True)
# Select the required number of top estimated poses
if n_top == 0: # All estimates are considered
n_top_curr = None
elif n_top == -1: # Given by the number of GT poses
n_gt = sum([gt['obj_id'] == obj_id for gt in scene_gt[im_id]])
n_top_curr = n_gt
else:
n_top_curr = n_top
print('Calculating error: {}, {}, {}, {}, {}, {}'.format(
error_type, method, dataset_str, scene_id, im_id, obj_id))
# Load depth image if VSD is selected
if error_type == 'vsd' and im_id != im_id_prev:
depth_path = dp['test_depth_mpath'].format(scene_id, im_id)
# depth_im = inout.load_depth(depth_path)
depth_im = inout.load_depth2(depth_path) # Faster
depth_im *= dp['cam']['depth_scale'] # to [mm]
# Load camera matrix
if error_type in ['vsd', 'cou']:
K = scene_info[im_id]['cam_K']
# Load pose estimates
res = inout.load_results_sixd17(res_path)
ests = res['ests']
# Sort the estimates by score (in descending order)
ests_sorted = sorted(enumerate(ests), key=lambda x: x[1]['score'],
reverse=True)
# Select the required number of top estimated poses
if n_top == 0: # All estimates are considered
n_top_curr = None
elif n_top == -1: # Given by the number of GT poses
n_gt = sum([gt['obj_id'] == obj_id for gt in scene_gt[im_id]])
n_top_curr = n_gt
else:
n_top_curr = n_top
ests_sorted = ests_sorted[slice(0, n_top_curr)]
