CLAUDE: Add v88_to_colmap_dense.sh — full pipeline from v88 to fused.ply

scripts/v88_to_colmap_dense.sh

+#!/usr/bin/env bash
+# =============================================================================
+# v88_to_colmap_dense.sh — Elphel LWIR-16 v88 folder → COLMAP dense 3D model
+#
+# Copyright (C) 2026 Elphel, Inc.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# =============================================================================
+#
+# Full pipeline from a v88 processing folder to a dense point cloud:
+#
+#   Step 1  Python: inverse-shift TERRAIN-DISP-MERGED frames using ELEV_GND
+#           → COLMAP text sparse model (cameras.txt / images.txt / PNGs)
+#
+#   Step 2  colmap model_converter: text → binary sparse model
+#
+#   Step 3  colmap image_undistorter: prepare dense workspace
+#
+#   Step 4  Python: write sequential patch-match.cfg (±N temporal neighbours)
+#           (required because we skip COLMAP feature matching)
+#
+#   Step 5  colmap patch_match_stereo: GPU PatchMatch depth maps
+#           (with filter pass so stereo_fusion accepts the result)
+#
+#   Step 6  colmap stereo_fusion: merge depth maps → fused.ply point cloud
+#
+# Usage:
+#   bash scripts/v88_to_colmap_dense.sh \
+#       --v88  /path/to/.../1763232146_700470/v88 \
+#       --out  /tmp/colmap_run
+#
+# Optional:
+#   --neighbours N     source frames each side for PatchMatch  (default: 10)
+#   --depth_min  M     minimum depth in metres                 (default: auto)
+#   --depth_max  M     maximum depth in metres                 (default: auto)
+#   --hfov       deg   camera horizontal FoV in degrees        (default: 32)
+#
+# Prerequisites:
+#   • conda-forge COLMAP with CUDA in PATH:
+#       export PATH="/home/elphel/miniforge3/bin:$PATH"
+#   • Python 3 with numpy, scipy, Pillow (system or conda env)
+#   • foliage repo checked out; script must be run from its root:
+#       bash scripts/v88_to_colmap_dense.sh ...
+# =============================================================================
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+PYTHON="${PYTHON:-/usr/bin/python3}"
+
+# ── Defaults ─────────────────────────────────────────────────────────────────
+V88=""
+OUT=""
+NEIGHBOURS=10
+DEPTH_MIN=""
+DEPTH_MAX=""
+HFOV=32.0
+
+# ── Argument parsing ──────────────────────────────────────────────────────────
+usage() {
+    echo "Usage: $0 --v88 <path> --out <dir> [--neighbours N] [--depth_min M] [--depth_max M] [--hfov deg]" >&2
+    exit 1
+}
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --v88)         V88="$2";         shift 2 ;;
+        --out)         OUT="$2";         shift 2 ;;
+        --neighbours)  NEIGHBOURS="$2";  shift 2 ;;
+        --depth_min)   DEPTH_MIN="$2";   shift 2 ;;
+        --depth_max)   DEPTH_MAX="$2";   shift 2 ;;
+        --hfov)        HFOV="$2";        shift 2 ;;
+        *)             usage ;;
+    esac
+done
+[[ -n "$V88" && -n "$OUT" ]] || usage
+
+# ── Locate input files ────────────────────────────────────────────────────────
+# Timestamp = name of the parent directory of v88
+TS="$(basename "$(dirname "$V88")")"
+TIFF="$V88/${TS}-TERRAIN-DISP-MERGED.tiff"
+XML="$V88/${TS}-INTERFRAME.corr-xml"
+ELEV="$V88/${TS}-ELEV_GND.tiff"
+
+for f in "$TIFF" "$XML" "$ELEV"; do
+    [[ -f "$f" ]] || { echo "ERROR: missing $f" >&2; exit 1; }
+done
+echo "=== Input files verified ==="
+echo "  TIFF:     $TIFF"
+echo "  XML:      $XML"
+echo "  ELEV_GND: $ELEV"
+
+# ── Auto-detect depth range from ELEV_GND if not specified ───────────────────
+if [[ -z "$DEPTH_MIN" || -z "$DEPTH_MAX" ]]; then
+    read DEPTH_MIN DEPTH_MAX < <($PYTHON - <<PYEOF
+import numpy as np
+from PIL import Image
+elev = np.array(Image.open("$ELEV"), dtype=np.float32)
+z = -elev   # positive AGL depth
+# Add 40 % margin around the observed range
+lo = z.min() * 0.6
+hi = z.max() * 1.4
+print(f"{lo:.0f} {hi:.0f}")
+PYEOF
+    )
+    echo "  Auto depth range: ${DEPTH_MIN} – ${DEPTH_MAX} m AGL"
+fi
+
+mkdir -p "$OUT"
+
+# ── Step 1: Perspective remap → COLMAP text sparse model ─────────────────────
+COLMAP_TEXT="$OUT/colmap_text"
+echo ""
+echo "=== Step 1: Perspective remap ==="
+$PYTHON "$REPO_ROOT/scripts/elphel_to_colmap_perspective.py" \
+    --tiff     "$TIFF" \
+    --xml      "$XML"  \
+    --elev_gnd "$ELEV" \
+    --out      "$COLMAP_TEXT" \
+    --hfov     "$HFOV"
+
+# ── Step 2: Convert text sparse model → binary ───────────────────────────────
+DENSE_ROOT="$OUT/dense_workspace"
+echo ""
+echo "=== Step 2: Convert sparse model text → binary ==="
+mkdir -p "$DENSE_ROOT/sparse"
+colmap model_converter \
+    --input_path  "$COLMAP_TEXT/sparse/0" \
+    --output_path "$DENSE_ROOT/sparse" \
+    --output_type BIN
+
+# ── Step 3: Undistort images (prepares dense/ workspace structure) ────────────
+echo ""
+echo "=== Step 3: Undistort images ==="
+colmap image_undistorter \
+    --image_path   "$COLMAP_TEXT/images" \
+    --input_path   "$DENSE_ROOT/sparse" \
+    --output_path  "$DENSE_ROOT/dense" \
+    --output_type  COLMAP
+
+# ── Step 4: Write sequential patch-match.cfg ─────────────────────────────────
+echo ""
+echo "=== Step 4: Write patch-match.cfg (neighbours = ±${NEIGHBOURS}) ==="
+$PYTHON - <<PYEOF
+import os
+from pathlib import Path
+
+images_dir = Path("$DENSE_ROOT/dense/images")
+images = sorted(os.listdir(images_dir))
+N = $NEIGHBOURS
+cfg_lines = []
+for i, img in enumerate(images):
+    sources = [images[j] for j in range(max(0, i-N), min(len(images), i+N+1)) if j != i]
+    cfg_lines.append(img)
+    cfg_lines.append(', '.join(sources))
+
+cfg_path = Path("$DENSE_ROOT/dense/stereo/patch-match.cfg")
+cfg_path.write_text('\n'.join(cfg_lines) + '\n')
+print(f"  Wrote {len(images)} entries to {cfg_path}")
+PYEOF
+
+# ── Step 5: PatchMatch stereo (GPU) ──────────────────────────────────────────
+echo ""
+echo "=== Step 5: PatchMatch stereo  depth=[${DEPTH_MIN}, ${DEPTH_MAX}] m ==="
+echo "    (this takes ~20-30 min on RTX 5060 Ti for 185 frames)"
+colmap patch_match_stereo \
+    --workspace_path              "$DENSE_ROOT/dense" \
+    --workspace_format            COLMAP \
+    --PatchMatchStereo.depth_min  "$DEPTH_MIN" \
+    --PatchMatchStereo.depth_max  "$DEPTH_MAX" \
+    --PatchMatchStereo.geom_consistency true \
+    --PatchMatchStereo.filter     true
+
+# ── Step 6: Stereo fusion → dense point cloud ─────────────────────────────────
+echo ""
+echo "=== Step 6: Stereo fusion ==="
+colmap stereo_fusion \
+    --workspace_path                    "$DENSE_ROOT/dense" \
+    --workspace_format                  COLMAP \
+    --input_type                        geometric \
+    --output_path                       "$OUT/fused.ply" \
+    --StereoFusion.min_num_pixels       2 \
+    --StereoFusion.max_depth_error      0.05 \
+    --StereoFusion.max_reproj_error     4
+
+echo ""
+echo "=== Done ==="
+echo "Dense point cloud: $OUT/fused.ply"
+echo "Size: $(du -sh "$OUT/fused.ply" 2>/dev/null | cut -f1)"
+echo ""
+echo "Open in Blender:"
+echo "  DISPLAY=:0 blender --python-expr \"import bpy; bpy.ops.wm.ply_import(filepath='$OUT/fused.ply')\" &"