Check config template is working

config/config_det.template.yaml

@@ -7,23 +7,19 @@
 prepare_data.py: 
   srs: "EPSG:2056"  # Projection of the input file
   datasets:
-    shapefile: ./input/input_det/AoI/[AoI_YEAR_Shapefile]
-  output_folder: ./output/output_det/[YEAR]
+    shapefile: ./input/input_det/AoI/swissimage_footprint_SWISSIMAGE_YEAR.shp
+  output_folder: ./output/output_det/SWISSIMAGE_YEAR
   zoom_level: 16  # z, keep between 15 and 18  
 
 # 2-Request tiles according to the provided AOI and tiles parameters
 generate_tilesets.py:
   debug_mode: False
   datasets:
-    aoi_tiles_geojson: ./output/output_det/[YEAR]/tiles.geojson
+    aoi_tiles_geojson: ./output/output_det/SWISSIMAGE_YEAR/tiles.geojson
     orthophotos_web_service:
-      # type: WMS # supported values: 1. MIL = Map Image Layer 2. WMS
-      # url: https://wms.geo.admin.ch/service
-      # layers: ch.swisstopo.swissimage
-      # srs: "EPSG:2056"
       type: XYZ # supported values: 1. MIL = Map Image Layer 2. WMS 3. XYZ
-      url: https://wmts.geo.admin.ch/1.0.0/ch.swisstopo.swissimage-product/default/[YEAR]/3857/{z}/{x}/{y}.jpeg
-  output_folder: ./output/output_det/[YEAR]
+      url: https://wmts.geo.admin.ch/1.0.0/ch.swisstopo.swissimage-product/default/SWISSIMAGE_YEAR/3857/{z}/{x}/{y}.jpeg
+  output_folder: ./output/output_det/SWISSIMAGE_YEAR
   tile_size: 256 # per side, in pixels
   overwrite: True
   n_jobs: 10
@@ -42,7 +38,7 @@ generate_tilesets.py:
 
 # 3-Perform the object detection based on the optimized trained model
 make_detections.py:
-  working_folder: ./output/output_det/[YEAR]
+  working_folder: ./output/output_det/SWISSIMAGE_YEAR
   log_subfolder: logs
   sample_tagged_img_subfolder: sample_tagged_images
   COCO_files: # relative paths, w/ respect to the working_folder
@@ -52,20 +48,20 @@ make_detections.py:
     pth_file: '../../../input/input_det/logs/model_0002999.pth' # trained model minimizing the validation loss curve, 
                                                                 # monitoring of the training process via tensorboard (tensorboard --logdir </logs>)
                                                                 # for the provided parameters model_0002999.pth is chosen 
-  image_metadata_json: './output/output_det/[YEAR]/img_metadata.json'
+  image_metadata_json: './output/output_det/SWISSIMAGE_YEAR/img_metadata.json'
   rdp_simplification: # rdp = Ramer-Douglas-Peucker
     enabled: True
     epsilon: 2.0 # cf. https://rdp.readthedocs.io/en/latest/
   score_lower_threshold: 0.3
 
 # 4-Filtering and merging detection polygons to improve results
 filter_detections.py:
-  year: [YEAR]
-  input: ./output/output_det/[YEAR]/oth_detections_at_0dot3_threshold.gpkg
-  shapefile: ./input/input_det/AoI/swissimage_footprint_[YEAR].shp
+  year: SWISSIMAGE_YEAR
+  detections: ./output/output_det/SWISSIMAGE_YEAR/oth_detections_at_0dot3_threshold.gpkg
+  shapefile: ./input/input_det/AoI/swissimage_footprint_SWISSIMAGE_YEAR.shp
   dem: ./input/input_det/DEM/switzerland_dem_EPSG2056.tif
   elevation: 1200.0 # m, altitude threshold
   score: 0.95 # detection score (from 0 to 1) provided by detectron2
   distance: 10 # m, distance use as a buffer to merge close polygons (likely to belong to the same object) together
   area: 5000.0 # m2, area threshold under which polygons are discarded
-  output: ./output/output_det/[YEAR]/oth_detections_at_0dot3_threshold_year-{year}_score-{score}_area-{area}_elevation-{elevation}_distance-{distance}.geojson
+  output: ./output/output_det/SWISSIMAGE_YEAR/oth_detections_at_0dot3_threshold_year-{year}_score-{score}_area-{area}_elevation-{elevation}_distance-{distance}.geojson

config/config_det.yaml

@@ -1,5 +1,5 @@
 ###################################
-####### Inference detection ####### 
+##### Detection by inference #####
 # Automatic detection of Quarries and Mineral Extraction Sites (MES) in images
 
 # 1-Produce tiles geometry according to the AoI extent and zoom level