avg_dict["classes"][label_name]["ap25%"] = np.average(aps[ d_inf,li,o25])
return avg_dict
def assign_instances_for_scan(pred_file, gt_file, pred_path):
try:
pred_info = util_3d.read_instance_prediction_file(pred_file, pred_path)
except Exception, e:
util.print_error('unable to load ' + pred_file + ': ' + str(e))
try:
gt_ids = util_3d.load_ids(gt_file)
except Exception, e:
util.print_error('unable to load ' + gt_file + ': ' + str(e))
# get gt instances
gt_instances = util_3d.get_instances(gt_ids, VALID_CLASS_IDS, CLASS_LABELS, ID_TO_LABEL)
# associate
gt2pred = deepcopy(gt_instances)
for label in gt2pred:
for gt in gt2pred[label]:
gt['matched_pred'] = []
pred2gt = {}
for label in CLASS_LABELS:
pred2gt[label] = []
num_pred_instances = 0
# mask of void labels in the groundtruth
bool_void = np.logical_not(np.in1d(gt_ids//1000, VALID_CLASS_IDS))
# go thru all prediction masks
for pred_mask_file in pred_info:
label_id = int(pred_info[pred_mask_file]['label_id'])
conf = pred_info[pred_mask_file]['conf']
def assign_instances_for_scan(pred_file, gt_file, pred_path):
pred_info = util_3d.read_instance_prediction_file(pred_file, pred_path)
try:
pred_info = util_3d.read_instance_prediction_file(pred_file, pred_path)
except:
util.print_error('unable to load ' + pred_file)
try:
gt_ids = util_3d.load_ids(gt_file)
except:
util.print_error('unable to load ' + gt_file)
# get gt instances
gt_instances = util_3d.get_instances(gt_ids, VALID_CLASS_IDS, CLASS_LABELS,
ID_TO_LABEL)
# associate
gt2pred = deepcopy(gt_instances)
for label in gt2pred:
for gt in gt2pred[label]:
gt['matched_pred'] = []
pred2gt = {}
for label in CLASS_LABELS:
pred2gt[label] = []
num_pred_instances = 0
# mask of void labels in the groundtruth
bool_void = np.logical_not(np.in1d(gt_ids // 1000, VALID_CLASS_IDS))
# go thru all prediction masks
for pred_index, pred_mask_file in enumerate(pred_info):
label_id = int(pred_info[pred_mask_file]['label_id'])
conf = pred_info[pred_mask_file]['conf']
if not label_id in ID_TO_LABEL:
continue
label_name = ID_TO_LABEL[label_id]
# read the mask
pred_mask = util_3d.load_ids(pred_mask_file)
if len(pred_mask) != len(gt_ids):
util.print_error(
'wrong number of lines in ' + pred_mask_file +
'(%d) vs #mesh vertices (%d), please double check and/or re-download the mesh'
% (len(pred_mask), len(gt_ids)))
# convert to binary
pred_mask = np.not_equal(pred_mask, 0)
num = np.count_nonzero(pred_mask)
if num < opt.min_region_sizes[0]:
continue # skip if empty
pred_instance = {}
pred_instance['filename'] = pred_mask_file
pred_instance['pred_id'] = num_pred_instances
pred_instance['label_id'] = label_id
pred_instance['vert_count'] = num
pred_instance['confidence'] = conf
pred_instance['void_intersection'] = np.count_nonzero(
np.logical_and(bool_void, pred_mask))
# matched gt instances
matched_gt = []
# go thru all gt instances with matching label
#print(gt2pred[label_name])
for (gt_num, gt_inst) in enumerate(gt2pred[label_name]):
intersection = np.count_nonzero(
np.logical_and(gt_ids == gt_inst['instance_id'], pred_mask))
#print((gt_ids == gt_inst['instance_id']).sum(), pred_mask.sum(), intersection)
if intersection > 0:
gt_copy = gt_inst.copy()
pred_copy = pred_instance.copy()
gt_copy['intersection'] = intersection
pred_copy['intersection'] = intersection
matched_gt.append(gt_copy)
gt2pred[label_name][gt_num]['matched_pred'].append(pred_copy)
#print(label_name, pred_index, num_pred_instances, num, conf, pred_instance['void_intersection'], len(matched_gt))
pred_instance['matched_gt'] = matched_gt
num_pred_instances += 1
pred2gt[label_name].append(pred_instance)
return gt2pred, pred2gt