7f4cdd9459
- main.py: extract frames from two videos, run COLMAP feature extraction - match_features.py: Python-based within-video SIFT matching via OpenCV (replaces colmap exhaustive_matcher which segfaults on ARM64 in COLMAP 4.x) - match_crossvideo.py: exhaustive cross-video matching (v1×v2) to stitch two flights into a single COLMAP model - run.sh: entry point for frame extraction + feature extraction - train_splat.sh: ns-process-data → splatfacto → .ply export, with correct PATH for Homebrew ffmpeg and MPS device flags for Apple Silicon - .gitignore: exclude videos, generated scene data, venv, logs - README.md: full pipeline walkthrough, all known issues and fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
121 lines
5.0 KiB
Bash
Executable File
121 lines
5.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Re-runnable pipeline: COLMAP output → Nerfstudio → splatfacto → .ply
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# Skips COLMAP (assumes my_scene/sparse/0/ already exists).
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set -euo pipefail
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cd "$(dirname "$0")"
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source venv/bin/activate
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# Use the full Homebrew ffmpeg (nerfstudio's bundled one lacks split/fps filters)
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export PATH="/opt/homebrew/opt/ffmpeg/bin:$PATH"
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SCENE=my_scene
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NS_DATA=$SCENE/ns_data
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EXPORT_DIR=$SCENE/exports
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PLY=$EXPORT_DIR/splat.ply
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# ── 1. Verify COLMAP output ────────────────────────────────────────────────
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echo ""
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echo "=== Step 1: Verifying COLMAP output ==="
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if [ ! -d "$SCENE/sparse" ] || [ -z "$(ls -A $SCENE/sparse 2>/dev/null)" ]; then
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echo "ERROR: $SCENE/sparse/ not found or empty. Run main.py + match_crossvideo.py first."
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exit 1
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fi
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# Pick the model with the most registered images
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BEST_MODEL=$(python3 -c "
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import struct, os, sys
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best_dir, best_imgs = '', 0
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for m in sorted(os.listdir('$SCENE/sparse')):
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d = '$SCENE/sparse/' + m
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f = d + '/images.bin'
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if not os.path.isfile(f): continue
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with open(f,'rb') as fh: n = struct.unpack('<Q', fh.read(8))[0]
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if n > best_imgs: best_imgs, best_dir = n, d
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print(best_dir)
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")
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if [ -z "$BEST_MODEL" ]; then
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echo "ERROR: no valid COLMAP model found in $SCENE/sparse/"
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exit 1
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fi
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for f in cameras.bin images.bin points3D.bin; do
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if [ ! -f "$BEST_MODEL/$f" ]; then
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echo "ERROR: missing $BEST_MODEL/$f"
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exit 1
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fi
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done
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NUM_IMGS=$(python3 -c "import struct; f=open('$BEST_MODEL/images.bin','rb'); print(struct.unpack('<Q',f.read(8))[0])")
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NUM_PTS=$(python3 -c "import struct; f=open('$BEST_MODEL/points3D.bin','rb'); print(struct.unpack('<Q',f.read(8))[0])")
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echo " Best model: $BEST_MODEL (images=$NUM_IMGS points3D=$NUM_PTS)"
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num_models=$(find "$SCENE/sparse" -mindepth 1 -maxdepth 1 -type d | wc -l | tr -d ' ')
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if [ "$num_models" -gt 1 ]; then
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echo " WARNING: $num_models disconnected models — using largest ($BEST_MODEL)."
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echo " Run match_crossvideo.py and re-map to attempt a full stitch."
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fi
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# ── 2. Convert COLMAP → Nerfstudio format ─────────────────────────────────
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echo ""
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echo "=== Step 2: ns-process-data (COLMAP → Nerfstudio) ==="
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ns-process-data images \
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--data "$(pwd)/$SCENE/images" \
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--output-dir "$(pwd)/$NS_DATA" \
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--skip-colmap \
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--colmap-model-path "$(pwd)/$BEST_MODEL"
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# ── 3. Train splatfacto with browser viewer ────────────────────────────────
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echo ""
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echo "=== Step 3: Training splatfacto ==="
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echo ""
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echo " ┌──────────────────────────────────────────────────────┐"
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echo " │ Live viewer (fly around during training): │"
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echo " │ http://localhost:7007 │"
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echo " └──────────────────────────────────────────────────────┘"
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echo ""
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ns-train splatfacto \
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--data "$NS_DATA" \
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--vis viewer \
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--viewer.quit-on-train-completion True
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# ── 4. Find the latest training output ────────────────────────────────────
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TRAIN_OUT=$(ls -td outputs/*/splatfacto/*/ 2>/dev/null | head -1)
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if [ -z "$TRAIN_OUT" ]; then
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echo "ERROR: could not find training output folder under outputs/"
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exit 1
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fi
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CONFIG_PATH="$TRAIN_OUT/config.yml"
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echo " Training output: $TRAIN_OUT"
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# ── 5. Export .ply ────────────────────────────────────────────────────────
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echo ""
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echo "=== Step 4: Exporting Gaussian splat to .ply ==="
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mkdir -p "$EXPORT_DIR"
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ns-export gaussian-splat \
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--load-config "$CONFIG_PATH" \
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--output-dir "$EXPORT_DIR"
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# ── 6. Final summary ──────────────────────────────────────────────────────
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echo ""
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echo "======================================================================"
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if [ -f "$PLY" ]; then
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echo " .ply exported: $(pwd)/$PLY"
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else
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echo " WARNING: splat.ply not found at $PLY — check $EXPORT_DIR/"
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fi
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echo ""
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echo " View during training : http://localhost:7007"
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echo ""
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echo " View final .ply (Option A — recommended):"
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echo " Drag $(pwd)/$PLY into:"
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echo " https://playcanvas.com/supersplat/editor"
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echo " Runs 100% in-browser; the file stays on your machine."
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echo ""
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echo " View final .ply (Option B — fully offline):"
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echo " python3 -m http.server 8080 --directory \$(dirname $PLY)"
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echo " Then open http://localhost:8080/splat.ply in gsplat viewer"
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echo " (requires a separate gsplat.js page — Option A is simpler)"
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echo "======================================================================"
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