cleanups, fix in feedback

cleanups, fix in feedback

brdr/aligner.py

@@ -618,7 +618,7 @@ def get_formula(self, geometry, with_geom=False):
                 "geometry": geom,
             }
 
-        self.feedback_info(dict_formula)
+        self.feedback_debug(str(dict_formula))
         return dict_formula
 
     def get_last_version_date(self, geometry, grb_type=GRBType.ADP):

brdr/utils.py

@@ -168,7 +168,7 @@ def get_oe_dict_by_ids(objectids, oetype='aanduidingsobjecten'):
             geom = shape(response["locatie"]["contour"])
             dict_thematic[key] = geom
         else:
-            logging.warning('object met id ' + str(a) +' werd niet gevonden in ' + oetype)
+            logging.warning('object id ' + str(a) +' not available in ' + oetype)
     return dict_thematic
 
 
@@ -398,7 +398,8 @@ def diffs_from_dict_series(dict_series, dict_thematic):
 
             #calculate the diffs you want to have
             #diff = results_diff[key].area * 100 / results[key].area #percentage of change
-            diff = results[key].area - dict_thematic[key].area #difference (m²) between area of resulting geometry and original geometry
+            diff = results[key].area - dict_thematic[key].area#difference (m²) between area of resulting geometry and original geometry
+            diff = round(diff, 1)#round, so the detected changes are within 10cm²
             #diff = abs(results[key].area - dict_thematic[key].area) #absolute difference (m²) between area of resulting geometry and original geometry
             #diff = abs(results[key].area - dict_thematic[key].area)*100/dict_thematic[key].area #absolute difference (%) between area of resulting geometry and original geometry
             #TODO: determine a good diff-value for determination

examples/example_ao.py

@@ -0,0 +1,34 @@
+import numpy as np
+
+from brdr.aligner import Aligner
+from brdr.utils import get_oe_dict_by_ids
+from examples import show_map, plot_series
+
+if __name__ == "__main__":
+    # EXAMPLE to test the algorithm for erfgoedobject with relevant distance 0.2m and od_strategy SNAP_ALL_SIDE
+
+    # Initiate brdr
+    aligner = Aligner()
+    # Load thematic data & reference data
+    #dict_theme = get_oe_dict_by_ids([206363], oetype='erfgoedobjecten')
+    aanduidingsobjecten = range(1,10)
+    dict_theme = get_oe_dict_by_ids(aanduidingsobjecten, oetype='aanduidingsobjecten')
+    aligner.load_thematic_data_dict(dict_theme)
+    aligner.load_reference_data_grb_actual(grb_type="adp", partition=1000)
+
+    #RESULTS
+    # rel_dist = 0.2
+    # dict_results_by_distance = {}
+    # #put resulting tuple in a dictionary
+    # dict_results_by_distance[rel_dist] = aligner.process_dict_thematic(rel_dist,2)
+    # aligner.export_results("output/")
+    # show_map(dict_results_by_distance, aligner.dict_thematic, aligner.dict_reference)
+
+    series = np.arange(0, 500, 20, dtype=int)/100
+    #predict which relevant distances are interesting to propose as resulting geometry
+    dict_predicted, diffs = aligner.predictor(relevant_distances=series, od_strategy=2,treshold_overlap_percentage=50)
+    for key in dict_predicted.keys():
+        diff ={}
+        diff[key]= diffs[key]
+        plot_series(series,diff)
+        show_map(dict_predicted[key], {key:aligner.dict_thematic[key]}, aligner.dict_reference)

examples/example_eo.py

@@ -13,13 +13,13 @@
     #dict_theme = get_oe_dict_by_ids([206363], oetype='erfgoedobjecten')
 
     erfgoedobjecten =[206407,
-# 206403,
-# 206372,
-# 206369,
-# 206377,
-# 206371,
-# 206370,
-# 206368,
+206403,
+206372,
+206369,
+206377,
+206371,
+206370,
+206368,
 206786
 ]
     dict_theme = get_oe_dict_by_ids(erfgoedobjecten, oetype='erfgoedobjecten')

tests/test_examples.py

@@ -132,6 +132,6 @@ def test_example_predictor(self):
 
         series = np.arange(0, 300, 10, dtype=int)/100
         # predict which relevant distances are interesting to propose as resulting geometry
-        dict_predicted,diffs = aligner.predictor(relevant_distances=series, od_strategy=4, treshold_overlap_percentage=50)
+        dict_predicted, diffs = aligner.predictor(relevant_distances=series, od_strategy=4, treshold_overlap_percentage=50)
         for key in dict_predicted.keys():
             continue