WIP:: update script

.github/workflows/cicd_docker.yml

@@ -10,8 +10,8 @@ env:
   DOCKER_IMAGE_NAME: pdal_ign_plugin
 
 jobs:
-
   build_docker_image_and_run_tests:
+
     runs-on: ubuntu-latest
 
     steps:
@@ -24,3 +24,4 @@ jobs:
     - name: Run tests in docker image
       run: docker run ${{ env.DOCKER_IMAGE_NAME }}:test python -m pytest
 
+

Dockerfile

@@ -9,7 +9,6 @@ RUN apt-get update && apt-get install --no-install-recommends -y cmake make buil
 
 COPY src src
 COPY CMakeLists.txt CMakeLists.txt
-
 RUN cmake -G"Unix Makefiles" -DCONDA_PREFIX=$CONDA_PREFIX -DCMAKE_BUILD_TYPE=Release
 RUN make -j4 install
 
@@ -31,4 +30,4 @@ RUN pip install .
 
 # Add example scripts + test data (to be able to test inside the docker image)
 COPY scripts /pdal_ign_plugin/scripts
-COPY test /pdal_ign_plugin/test
+COPY test /pdal_ign_plugin/test

README.md

@@ -74,7 +74,7 @@ The name of the folder informs of the plugIN nature (reader, writer, filter).
 The code should respect the documentation proposed by pdal: [build a pdal plugin](https://pdal.io/en/2.6.0/development/plugins.html).
 Be careful to change if the plugIn is a reader, a writer or a filter.
 
-The CMakeList should contains:
+The CMakeList should contain:
 
 ```
 file( GLOB_RECURSE GD_SRCS ${CMAKE_SOURCE_DIR} *)
@@ -90,14 +90,14 @@ install(TARGETS pdal_plugin_filter_my_new_PI)
 ```
 
 You should complete the main CMakeList by adding the new plugIN:
-
 ```
 add_subdirectory(src/filter_my_new_PI)
 ```
 
 Each plugIN has his own md file in the doc directory, structured as the [model](./doc/_doc_model_plugIN.md).
 
 Don't forget to update [the list](#list-of-filters) with a link to the documentation.
+<<<<<<< Updated upstream
 
 ## `macro` python module usage
 

environment_docker.yml

@@ -7,7 +7,9 @@ dependencies:
   - python-pdal
   - gdal
   - pytest
+
   # --------- pip & pip libraries --------- #
   - pip
   - pip:
     - ign-pdal-tools
+

scripts/ex_filtering_points_with_add_dimensions.py → scripts/mark_points_to_use_for_digital_models_with_new_dimension.py

@@ -1,5 +1,7 @@
 import argparse
+
 import pdal
+
 from macro import macro
 
 """
@@ -15,24 +17,48 @@ def parse_args():
     parser.add_argument(
         "--output_las", "-o", type=str, required=True, help="Output cloud las file"
     )
-    parser.add_argument("--output_dsm", "-s", type=str, required=True, help="Output dsm tiff file")
-    parser.add_argument("--output_dtm", "-t", type=str, required=True, help="Output dtm tiff file")
+    parser.add_argument(
+        "--dsm_dimension",
+        type=str,
+        required=False,
+        default="dsm_marker",
+        help="Dimension name for the output DSM marker",
+    )
+    parser.add_argument(
+        "--dtm_dimension",
+        type=str,
+        required=False,
+        default="dtm_marker",
+        help="Dimension name for the output DTM marker",
+    )
+    parser.add_argument(
+        "--output_dsm", "-s", type=str, required=False, default="", help="Output dsm tiff file"
+    )
+    parser.add_argument(
+        "--output_dtm", "-t", type=str, required=False, default="", help="Output dtm tiff file"
+    )
     return parser.parse_args()
 
 
 if __name__ == "__main__":
     args = parse_args()
 
-    pipeline = pdal.Reader.las(args.input)
+    pipeline = pdal.Pipeline() | pdal.Reader.las(args.input)
+    dsm_dim = args.dsm_dimension
+    dtm_dim = args.dtm_dimension
+
+    # Récupération des dimensions du fichier en entrée
+    input_dimensions = pipeline.quickinfo["readers.las"]["dimensions"].split(", ")
 
     # 0 - ajout de dimensions temporaires
     pipeline |= pdal.Filter.ferry(
-        dimensions=f"=>PT_GRID_DSM, =>PT_VEG_DSM, =>PT_GRID_DTM, =>PT_ON_BRIDGE"
+        dimensions="=>PT_GRID_DSM, =>PT_VEG_DSM, =>PT_GRID_DTM, =>PT_ON_BRIDGE"
     )
 
-    ## 1 - recherche des points max de végétation (4,5) sur une grille régulière, avec prise en compte des points sol (2) et basse
-    ##     vegetation (3) proche de la végétation
-    ##     pour le calcul du DSM
+    # 1 - recherche des points max de végétation (4,5) sur une grille régulière, avec prise en
+    # compte des points sol (2) et basse
+    #     vegetation (3) proche de la végétation
+    #     pour le calcul du DSM
 
     pipeline |= pdal.Filter.assign(
         value=["PT_VEG_DSM = 1 WHERE " + macro.build_condition("Classification", [4, 5])]
@@ -71,7 +97,7 @@ def parse_args():
         resolution=0.75, value="PT_GRID_DSM=1", output_type="max", where="PT_VEG_DSM==1"
     )
 
-    ## 2 - sélection des points pour DTM et DSM
+    # 2 - sélection des points pour DTM et DSM
 
     # selection de points DTM (max) sur une grille régulière
     pipeline |= pdal.Filter.gridDecimation(
@@ -98,7 +124,7 @@ def parse_args():
         condition_out="PT_GRID_DSM=1",
     )
 
-    ## 3 - gestion des ponts
+    # 3 - gestion des ponts
     #      bouche trou : on filtre les points (2,3,4,5,9) au milieu du pont en les mettant à PT_ON_BRIDGE=1
 
     pipeline = macro.add_radius_assign(
@@ -119,25 +145,29 @@ def parse_args():
     )
     pipeline |= pdal.Filter.assign(value=["PT_GRID_DSM=0 WHERE PT_ON_BRIDGE==1"])
 
-    ## 4 - point pour DTM servent au DSM également
+    # 4 - point pour DTM servent au DSM également
     pipeline |= pdal.Filter.assign(value=["PT_GRID_DSM=1 WHERE PT_GRID_DTM==1"])
 
-    ## 5 - export du nuage et des DSM
+    # 5 - export du nuage et des DSM
+    # Ajout des dimensions de sortie
+    pipeline |= pdal.Filter.ferry(dimensions=f"PT_GRID_DSM=>{dsm_dim}, PT_GRID_DTM=>{dtm_dim}")
 
-    pipeline |= pdal.Writer.las(extra_dims="all", forward="all", filename=args.output_las)
+    pipeline |= pdal.Writer.las(
+        extra_dims=input_dimensions + [dtm_dim, dsm_dim], forward="all", filename=args.output_las
+    )
     pipeline |= pdal.Writer.gdal(
         gdaldriver="GTiff",
         output_type="max",
         resolution=2.0,
         filename=args.output_dtm,
-        where="PT_GRID_DTM==1",
+        where=f"{dtm_dim}==1",
     )
     pipeline |= pdal.Writer.gdal(
         gdaldriver="GTiff",
         output_type="max",
         resolution=2.0,
         filename=args.output_dsm,
-        where="PT_GRID_DSM==1",
+        where=f"{dsm_dim}==1",
     )
 
     pipeline.execute()