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prepare: add index module, update pipeline, tests, Dockerfile, and run.sh

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This commit is contained in:
Antoine Jacquin
2026-07-28 23:20:20 +02:00
parent c58ca3f477
commit 54dbec145e
9 changed files with 924 additions and 35 deletions
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21
lidar_pipeline/visualizations.py
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@ -16,7 +16,7 @@ from pathlib import Path
import numpy as np
import rasterio
from .gpu import HAS_GPU, to_gpu, to_cpu, xp_gaussian_filter, xp_uniform_filter, xp_minimum_filter, xp_maximum_filter, gpu_cleanup
from .gpu import to_gpu, to_cpu, xp_gaussian_filter, xp_uniform_filter, gpu_cleanup
from . import gpu as _gpu_mod
logger = logging.getLogger("lidar")
@ -37,7 +37,6 @@ class _XPProxy:
def __getattr__(self, name):
global _cp
from . import gpu as _gpu_mod
if _gpu_mod.HAS_GPU:
if _cp is None:
try:
@ -272,20 +271,21 @@ def _filter_nanaware(arr, filter_func, *args, use_gpu=True, **kwargs):
def _prepare_dem_for_raycast(dem_file, shared, resolution):
"""Load DEM and prepare padded array for ray-tracing.
Returns (dem_gpu_or_cpu, dem_np, rows, cols, res, nan_mask,
transform, crs, padded) ready for ray-tracing.
Returns (dem_filled, dem_np, rows, cols, res, nan_mask,
transform, crs) ready for ray-tracing.
dem_filled is a CPU numpy array (filled, no NaN).
"""
if shared:
dem_np = shared.dem_np
nan_mask = shared.nan_mask
transform = shared.transform
crs = shared.crs
dem = to_gpu(shared.filled) if _gpu_mod.HAS_GPU else shared.filled
dem = shared.filled
else:
dem_np, transform, crs = _read_dem(dem_file)
nan_mask = np.isnan(dem_np)
filled, _ = _fill_nans(dem_np)
dem = to_gpu(filled) if _gpu_mod.HAS_GPU else filled
dem = filled
res = resolution
rows, cols = dem_np.shape
return dem, dem_np, rows, cols, res, nan_mask, transform, crs
@ -304,7 +304,7 @@ def _ray_trace_horizons(dem, rows, cols, res, n_dirs, max_dist, radii_m=None):
processed and results are streamed back to CPU.
Args:
dem: GPU or CPU filled DEM array (rows, cols).
dem: CPU numpy array — filled DEM (no NaN), shape (rows, cols).
rows, cols: dimensions.
res: resolution in m/px.
n_dirs: number of directions.
@ -329,13 +329,14 @@ def _ray_trace_horizons(dem, rows, cols, res, n_dirs, max_dist, radii_m=None):
# Pad on CPU (numpy) — avoids GPU memory pressure and
# pre-compiled kernel issues (NO_BINARY_FOR_GPU on sm_89).
dem_np = to_cpu(dem)
padded_np = np.pad(dem_np, max_dist, mode='constant', constant_values=np.nan)
padded_np = np.pad(dem, max_dist, mode='constant', constant_values=np.nan)
# Transfer padded DEM to GPU for computation
padded = to_gpu(padded_np)
# GPU view of central region — reference elevation for ray-tracing
dem = padded[max_dist:max_dist+rows, max_dist:max_dist+cols]
# Free the CPU copy — we don't need it anymore
del dem_np, padded_np
del padded_np
# Process one direction at a time to limit GPU memory.
# Store results as flat CPU arrays — transfer back to GPU at the end.