Merge branch 'wms' of https://github.com/swiss-territorial-data-lab/s…

…wimming-pool-detector into wms

config_NE.yaml

@@ -36,7 +36,7 @@ assess_predictions_NE.py:
       trn: output-NE/trn_predictions_at_0dot05_threshold.pkl
       val: output-NE/val_predictions_at_0dot05_threshold.pkl
       tst: output-NE/tst_predictions_at_0dot05_threshold.pkl
-      #all: output-NE/all_predictions_at_0dot05_threshold.pkl
+      all: output-NE/all_predictions_at_0dot05_threshold.pkl
   folders:
     output: output-NE
 

config_NEuGE.yaml

@@ -0,0 +1,42 @@
+generate_training_sets_NE.py:
+  datasets:
+    ground_truth_sectors_shapefile: data-update/Sectors_groundtruth.shp
+    other_sectors_shapefile: data-update/Sectors_other.shp
+    ground_truth_swimming_pools_shapefile: data-update/sp_gt_missing_manuell.shp
+    other_swimming_pools_shapefile: data-update/Swimmingpools_other.shp
+    orthophotos_web_map_service:
+      url: https://sitn.ne.ch/mapproxy95/service
+      layers: ortho2019
+      srs: "EPSG:900913"
+  folders:
+    output: output-comb
+  tile_size: 256 # per side, in pixels
+  save_image_metadata: True
+  overwrite: False
+  n_jobs: 100
+  COCO_metadata:
+    year: 2020
+    version: 1.0
+    description: 2019 orthophotos w/ Swimming Pool segmentations
+    contributor: Système d'information du territoire Neuchâtelois (SITN)
+    url: https://sitn.ne.ch
+    license:
+      name: Unknown
+      url: 
+
+
+assess_predictions_NE.py:
+  n_jobs: 100
+  datasets:
+    labels_geojson: output-comb/labels-NE-manuell-fuse.geojson
+    image_metadata_json: output-comb/img_metadata.json
+    OK_tiles_geojson: output-comb/OK_tiles.geojson # tiles including at least one swimming pool, split in trn/val/tst subsets
+    aoi_tiles_geojson: output-comb/aoi_z18_tiles.geojson # aoi = Area of Interest
+    predictions:
+      trn: output-comb/trn_predictions_at_0dot05_threshold.pkl
+      val: output-comb/val_predictions_at_0dot05_threshold.pkl
+      tst: output-comb/tst_predictions_at_0dot05_threshold.pkl
+      all: output-comb/all_predictions_at_0dot05_threshold.pkl
+  folders:
+    output: output-comb/assessment-GEuNE
+

data-update/Swimmimgpools_groundtruth_missing.cpg

@@ -0,0 +1 @@
+UTF-8

data-update/Swimmimgpools_groundtruth_missing.prj

@@ -0,0 +1 @@
+PROJCS["CH1903+_LV95",GEOGCS["GCS_CH1903+",DATUM["D_CH1903+",SPHEROID["Bessel_1841",6377397.155,299.1528128]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Hotine_Oblique_Mercator_Azimuth_Center"],PARAMETER["latitude_of_center",46.95240555555556],PARAMETER["longitude_of_center",7.439583333333333],PARAMETER["azimuth",90],PARAMETER["scale_factor",1],PARAMETER["false_easting",2600000],PARAMETER["false_northing",1200000],UNIT["Meter",1]]

data-update/Swimmimgpools_groundtruth_missing.qpj

@@ -0,0 +1 @@
+PROJCS["CH1903+ / LV95",GEOGCS["CH1903+",DATUM["CH1903+",SPHEROID["Bessel 1841",6377397.155,299.1528128,AUTHORITY["EPSG","7004"]],TOWGS84[674.374,15.056,405.346,0,0,0,0],AUTHORITY["EPSG","6150"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4150"]],PROJECTION["Hotine_Oblique_Mercator_Azimuth_Center"],PARAMETER["latitude_of_center",46.95240555555556],PARAMETER["longitude_of_center",7.439583333333333],PARAMETER["azimuth",90],PARAMETER["rectified_grid_angle",90],PARAMETER["scale_factor",1],PARAMETER["false_easting",2600000],PARAMETER["false_northing",1200000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","2056"]]

data-update/Swimmingpools_groundtruth_modifie.prj

@@ -0,0 +1 @@
+PROJCS["CH1903+_LV95",GEOGCS["GCS_CH1903+",DATUM["D_CH1903+",SPHEROID["Bessel_1841",6377397.155,299.1528128]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Hotine_Oblique_Mercator_Azimuth_Center"],PARAMETER["False_Easting",2600000.0],PARAMETER["False_Northing",1200000.0],PARAMETER["Scale_Factor",1.0],PARAMETER["Azimuth",90.0],PARAMETER["Longitude_Of_Center",7.439583333333333],PARAMETER["Latitude_Of_Center",46.95240555555556],UNIT["Meter",1.0],AUTHORITY["EPSG",2056]]

data-update/sp_gt_missing_manuell.cpg

@@ -0,0 +1 @@
+UTF-8

helpers/MIL.py

@@ -110,12 +110,12 @@ def get_geotiff(mil_url, bbox, width, height, filename, imageSR=2056, bboxSR=205
 
     #print('Fetching image...')
     url = image_metadata['href']
-    r = requests.get(url, allow_redirects=True, verify=False)
-    #print(f'Writing image file: {tiff_filename}')
+    r = requests.get(url, allow_redirects=True, verify=False, timeout=10)
+    print(f'Writing image file: {tiff_filename}')
     with open(tiff_filename, 'wb') as fp:
         fp.write(r.content)
 
-    #print(f'TIFF -> GeoTIFF: {geotiff_filename}')
+    print(f'TIFF -> GeoTIFF: {geotiff_filename}')
     src_ds = gdal.Open(tiff_filename)
     #dst_ds = gdal.Translate(geotiff_filename, src_ds, options='-of GTiff -a_srs EPSG:2056 -a_nodata 253')
     gdal.Translate(geotiff_filename, src_ds, options=f'-of GTiff -a_srs EPSG:{imageSR}')

helpers/misc.py

@@ -3,6 +3,7 @@
 import sys
 import pandas as pd
 import geopandas as gpd
+import numpy as np
 
 from shapely.affinity import affine_transform, scale
 from shapely.geometry import box
@@ -75,16 +76,17 @@ def fast_predictions_to_features(predictions_dict, img_metadata_dict):
 
         kk = k.split('/')[-1]
         this_img_metadata = img_metadata_dict[kk]
-
+        #print(this_img_metadata)
+
         crs = f"EPSG:{this_img_metadata['extent']['spatialReference']['latestWkid']}"
         transform = image_metadata_to_affine_transform(this_img_metadata)
-
+        #print(transform)
         for pred in v:
-
+            #print(pred)
             if 'pred_mask' in pred.keys():
 
-                pred_mask_int = pred['pred_mask'].astype(int)
-
+                pred_mask_int = pred['pred_mask'].astype(np.uint8)
+                #print(pred_mask_int)
                 feats += [{'type': 'Feature', 
                             'properties': {'raster_val': v, 'score': pred['score'], 'crs': crs}, 
                             'geometry': s