VIEW_FREQ=5    # sample frequency of the frames
THRES_CONNECT="0.9,0.3,5"   # dynamic threshold for region growing
# THRES_CONNECT="0.9,0.5,5"   # dynamic threshold for region growing
MAX_NEIGHBOR_DISTANCE=2    # farthest distance to take neighbors into account
THRES_MERGE=200 #400           # merge small groups with less than THRES_MERGE points during post-processing
DIS_DECAY=0.5              # decay rate of the distance weight
SIMILAR_METRIC="2-norm"    # metric for similarity measurement
MASK_NAME="semantic-sam"   # mask name for loading mask
ALIAS_MASK_NAME="semantic-sam"   # mask name for saving results


TEXT_HEAD="scannet"
TEXT="${TEXT_HEAD}_${VIEW_FREQ}view"
HEAD="${TEXT_HEAD}_${VIEW_FREQ}view_merge${THRES_MERGE}_${SIMILAR_METRIC}_${ALIAS_MASK_NAME}_connect${THRES_CONNECT}_depth${MAX_NEIGHBOR_DISTANCE}"
# EVAL_DIR="data/ScanNet/results/${HEAD}"   # directory to export results
EVAL_DIR="/YourPath/results/${HEAD}"   # directory to export results

python sam2object.py \
 --thres_merge=$THRES_MERGE \
 --similar_metric=$SIMILAR_METRIC \
 --thres_connect=$THRES_CONNECT \
 --mask_name=$MASK_NAME \
 --text=$TEXT \
 --max_neighbor_distance=$MAX_NEIGHBOR_DISTANCE \
 --view_freq=$VIEW_FREQ \
 --use_torch \
 --dis_decay=$DIS_DECAY \
 --eval_dir=$EVAL_DIR 