#!/usr/bin/env bash
# SUM Parts - put the Seosan prediction somewhere it can be opened and looked at
#
# Two files, because they answer different questions:
#   *_pred_viewer.ply   class colours   -> where did each class land
#   *_rgb_viewer.ply    photo texture   -> what is actually there
#
# Open both, flip between them. That is how you find out whether the 21% the
# model calls water is asphalt, shadow, or something else entirely.
set -uo pipefail

SCRIPTS="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
LOGROOT="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle/examples/segmentation/log/sumv2_triangle"
DEST="/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/output"

source "$HOME/miniconda3/etc/profile.d/conda.sh"
conda activate sumparts

mkdir -p "$DEST"

PRED=$(find "$LOGROOT" -name 'seosan*_pred.ply' -printf '%T@ %p\n' 2>/dev/null \
       | sort -rn | head -1 | cut -d' ' -f2-)
[ -n "$PRED" ] || { echo "no seosan prediction found"; exit 1; }

echo "prediction : $PRED"
cp "$PRED" "$DEST/seosan_pred_viewer.ply"
echo "  -> $DEST/seosan_pred_viewer.ply"

SRC="$HOME/sum-parts/data/korea_poc/seosan_BlockYBA_tile0.ply"
if [ -f "$SRC" ]; then
    python "$SCRIPTS/ply_for_viewer.py" "$SRC" "$DEST/seosan_rgb_viewer.ply"
fi

echo
echo "=== class colour legend ==="
python - <<'PY'
CLASSES = ['unclassified', 'terrain', 'high_vegetation', 'facade_surface',
           'water', 'car', 'boat', 'roof_surface', 'chimney', 'dormer',
           'balcony', 'roof_installation', 'wall']
COLORS = [(0,0,0), (170,85,0), (0,255,0), (255,255,0), (0,255,255),
          (255,0,255), (0,0,153), (85,85,127), (255,50,50), (85,0,127),
          (50,125,150), (50,0,50), (215,160,140)]
for i, (n, c) in enumerate(zip(CLASSES, COLORS)):
    print(f"  {i:>2}  {n:<20} RGB {c}")
PY

echo
ls -lh "$DEST"/seosan_*viewer.ply