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Remove PMF, fix NaN in gradient visualizations, fix pos_open/neg_open shared param

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- Remove PMF from ground classification options (PDAL recommends SMRF over PMF)
- Auto-detection now uses CSF for urban/complex terrain instead of PMF
- Add z_std > 30m heuristic to auto-select CSF for complex terrain
- Fix pos_open/neg_open lambda missing 'shared' parameter (NameError in workers)
- Fix NaN mask not restored in hillshade, slope, aspect, curvature
  (gradient-based products computed on filled DEM lost NaN transparency)
- Add nan_mask parameter to _save_tif for centralized NaN restoration
- DTM TIF kept by default (no longer deleted after WebP conversion)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
2026-05-14 00:50:45 +02:00
parent eac482874d
commit 1cf8e1752f
6 changed files with 66 additions and 63 deletions
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13
CLAUDE.md
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@ -16,9 +16,8 @@ All commands run inside Docker. Use `./run.sh` as the primary interface.
./run.sh -g -r 0.2 # High resolution (0.2m/px)
./run.sh --test # Run unit tests
./run.sh -g --file LHD_FXX_1000_6882_PTS_LAMB93_IGN69.copc # Single file
./run.sh --ground-classification pmf # Force PMF ground classification
./run.sh -g --keep-tif # Keep intermediate TIFF files (default: kept)
./run.sh -g --no-keep-tif # Delete intermediate TIFF files
./run.sh --ground-classification csf # Force CSF ground classification (complex terrain)
./run.sh -g --keep-tif # Keep TIFF files (allows WebP regeneration without recalculating DTM)
./run.sh # Print help (no args)
```
@ -34,7 +33,7 @@ docker run --rm --gpus all -v $(pwd)/input:/data/input:ro -v $(pwd)/output:/data
- **`cli.py`** — argparse + logging setup. Entry point via `python -m lidar_pipeline`.
- **`pipeline.py`** — `LidarArchaeoPipeline` orchestrator. `VIZ_STEPS` registry maps names to generate functions. `FilePrefixFilter` for parallel logging. Creates `SharedDEM` once per file and passes it to all visualizations.
- **`dtm.py`** — PDAL ground classification (SMRF/PMF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
- **`dtm.py`** — PDAL ground classification (SMRF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
- **`visualizations.py`** — 15 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution, shared=None)` and return a TIF path or None. `SharedDEM` class pre-computes gradient, NaN mask, LRM to avoid redundant I/O and computation.
- **`gpu.py`** — CuPy/numpy abstraction: `HAS_GPU`, `to_gpu()`, `to_cpu()`, `xp_gaussian_filter()`, `xp_uniform_filter()`, `xp_minimum_filter()`, `gpu_cleanup()`. Falls back to CPU gracefully.
- **`ign.py`** — IGN WMTS tile download + overlay generation for orthophoto and topographic maps.
@ -60,11 +59,11 @@ Three places must be updated:
### Ground classification
Auto-detection in `dtm.py` `detect_ground_method()`:
- Single-return ratio > 0.6 → PMF (urban terrain)
- Single-return ratio > 0.6 → CSF (urban terrain, cloth simulation)
- Height std > 30m → CSF (complex/mountainous terrain)
- Default → SMRF (natural terrain)
Override with `--ground-classification {auto,smrf,pmf,csf}`.
Override with `--ground-classification {auto,smrf,csf}`.
### NaN handling
@ -83,6 +82,6 @@ Uses `ProcessPoolExecutor` with `'spawn'` start method (required for CUDA). Each
- **Language**: UI messages and comments in French. Code identifiers in English.
- **Logging**: Use `logger = logging.getLogger("lidar")`. Prefix per-file logs via `_file_filter.basename`.
- **GPU pattern**: `arr_gpu = to_gpu(arr)` → compute → `result = to_cpu(arr_gpu)` → `gpu_cleanup()` between visualizations.
- **Output format**: Visualizations saved as WebP. DTM TIFF kept by default for reuse (use `--no-keep-tif` to delete). `--force` regenerates WebPs without re-classifying if DTM exists. No COGs or viewer — only WebP + PDF report remain.
- **Output format**: Visualizations saved as WebP. TIFF intermediates deleted by default. Use `--keep-tif` to keep DTM+TIF for WebP regeneration with `--force`. No COGs or viewer.
- **Compression**: TIF intermediates use `deflate` compression (faster than LZW for float32 data).
- **Tests**: Run only inside Docker via `./run.sh --test`. Synthetic DEM fixture in `tests/conftest.py`.