Add rasterize-lidar tool

idefix/helpers.py

@@ -208,7 +208,7 @@ def rasterize(pc_file, feature='elevation', resolution=1.,
 
     vxl = bin(grid, pc.spatial, fval, bin_method)
 
-    squash_method = squash_method if isinstance(squash_method, tuple) else (squash_method,)
+    squash_method = squash_method if not isinstance(squash_method, str) else (squash_method,)
 
     rasters = []
     for s in squash_method:

idefix/tools/config.json

@@ -0,0 +1,30 @@
+{
+ "global": {
+  "resolution": 1,
+  "interpolation": "idw",
+  "out_dir": "./results"
+ },
+ "rasters": [
+  {
+   "feature": "elevation",
+   "bin_structure": "voxel",
+   "bin_method": "mean",
+   "squash_method": ["top", "bottom", "std"]
+  },
+  {
+   "feature": "elevation",
+   "bin_structure": "pixel",
+   "bin_method": "mean"
+  },
+  {
+   "bin_structure": "pixel",
+   "bin_method": "density"
+  },
+  {
+   "feature": "num_returns",
+   "bin_structure": "pixel",
+   "bin_method": "mode"
+  }
+ ]
+}
+

idefix/tools/rasterize-lidar.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# file rasterize-lidar.py
+# author Florent Guiotte <florent.guiotte@irisa.fr>
+# version 0.0
+# date 18 févr. 2021
+"""Create raster of LiDAR features.
+
+Use npz point clouds from Idefix.
+"""
+
+import json
+import argparse
+from multiprocessing import Pool
+from tqdm.auto import tqdm
+from pathlib import Path
+
+import idefix as ix
+
+parser = argparse.ArgumentParser(description='Compute features rasters from LiDAR point cloud.',
+                                 formatter_class=argparse.RawDescriptionHelpFormatter,
+                                 epilog="""
+The config file can contain any parameters of the
+idefix.helpers.rasterize function in a json file.
+
+You can define 'global' parameters (for all the rasters) and raster
+specific parameters in a list 'rasters'.
+
+See the following `config.json` example file:
+
+{
+ "global": {
+  "resolution": 5,
+  "interpolation": "idw",
+  "out_dir": "./results"
+ },
+ "rasters": [
+  {
+   "feature": "elevation",
+   "bin_structure": "voxel",
+   "bin_method": "mean",
+   "squash_method": ["top", "bottom", "std"]
+  },
+  {
+   "feature": "elevation",
+   "bin_structure": "pixel",
+   "bin_method": "mean"
+  },
+  {
+   "bin_structure": "pixel",
+   "bin_method": "density"
+  },
+  {
+   "feature": "num_returns",
+   "bin_structure": "pixel",
+   "bin_method": "mode"
+  }
+ ]
+}
+""")
+
+parser.add_argument('-c', '--config', type=str, help='json file to setup the rasterization processes', dest='c')
+parser.add_argument('-n', '--nprocess', type=int, help='number of child processes to use', default=1, dest='n')
+parser.add_argument('in_dir', type=str, help='the path to the point cloud directory')
+parser.add_argument('out_dir', type=str, help='path to output raster results')
+
+args = parser.parse_args()
+
+def _map_rasterize(kwargs):
+    return ix.helpers.rasterize(**kwargs)
+
+def main():
+    in_dir = Path(args.in_dir)
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(exist_ok=True)
+
+
+    pc_files = list(in_dir.glob('*.npz'))
+
+
+    config = {'global': {'out_dir': out_dir}}
+    config |= json.load(open(args.c)) if args.c else {}
+
+    globalc = config['global']
+    rasters = config['rasters'] if 'rasters' in config else [{}]
+    
+    queue = []
+    for pc_file in pc_files:
+        for raster in rasters:
+            job = globalc.copy()
+            job |= raster
+            job |= {'pc_file': pc_file}
+            queue += [job]
+
+    pool = Pool(processes=args.n)
+    for _ in tqdm(pool.imap_unordered(_map_rasterize, queue), total=len(queue)):
+        pass
+
+if __name__ == '__main__':
+    main()