FishServer/FishMeasure/run_predict_from_svo2_fish9.sh

#!/usr/bin/env bash
# Single case: fish9 only (same flags as run_predict_from_svo2.sh).
# Layout: output_weight_estimator/fish9/<svo_stem>/{cloud,images,weight_prediction.json,...}
#
if [ -z "${BASH_VERSION:-}" ]; then
  exec bash "$0" "$@"
fi

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "$SCRIPT_DIR"

SESSION_ROOT="/home/ubuntu/data/fish/2016-1-22-last"
FISH_NAME="fish1"
fish_dir="${SESSION_ROOT}/${FISH_NAME}/"
OUT_PARENT="output_weight_estimator"
save_out="${OUT_PARENT}/${FISH_NAME}"

if [[ ! -d "$fish_dir" ]]; then
  echo "ERROR: not a directory: $fish_dir" >&2
  exit 1
fi

shopt -s nullglob
svos=("${fish_dir}"*.svo2)
shopt -u nullglob
if [[ ${#svos[@]} -eq 0 ]]; then
  echo "ERROR: no .svo2 in $fish_dir" >&2
  exit 1
fi

echo "============================================================"
echo " ${FISH_NAME}  (${#svos[@]} .svo2)  →  ${save_out}/<svo_stem>/"
echo "============================================================"

python3 predict_weigth_from_svo2.py \
  --batch-svo-folder "$fish_dir" \
  --weight-checkpoint weight_estimator/runs/dgcnn_20260312_171043/best.pt \
  --save-output "$save_out" \
  --yolo-model "/home/ubuntu/projects/FishMeasure/runs/train/fish_detection_20251127_104658/weights/best.pt" \
  --conf 0.8 \
  --imgsz 640 \
  --sam-device cuda \
  --max-frames 0 \
  --filter-pointcloud \
  --use-density-filter \
  --pointcloud-classifier "/home/ubuntu/projects/FishMeasure/pointcloud_classifier/Pointnet_Pointnet2_pytorch/log/classification/fish_pointnet2_finetune/checkpoints/best_model.pth" \
  --use-pointcloud-classifier \
  --pointcloud-classifier-threshold 0.7 \
  --use-flatness-filter \
  --flatness-threshold 55.0 \
  --frame-stride 1 \
  --weight-top-k 5 \
  --weight-top-by-length 
# Optional: append --no-weight-top-by-length if you want top-K by predicted weight only.