namemap

user-friendly-metrics.py

@@ -108,7 +108,12 @@ def num_gt_ids(df):
 def calculate(predictions, 
               references, 
               max_iou: float = 0.5,
-              recognition_thresholds: list = [0.3, 0.5, 0.8],):
+              recognition_thresholds: list = [0.3, 0.5, 0.8],
+              namemap = {"num_misses": "fn",
+                         "num_false_positives": "fp",
+                         "num_detections": "tp"}
+            ):
+    
     """Returns the scores"""
     
     try: 
@@ -142,14 +147,14 @@ def calculate(predictions,
         acc.update(refs[:,0].astype('int').tolist(), preds[:,0].astype('int').tolist(), C)
 
     mh = mm.metrics.create()
-    summary = mh.compute(acc, metrics=['recall', 'precision', "num_objects", "num_false_positives", "num_matches", "num_misses", "num_detections", "num_predictions"]).to_dict()
+    summary = mh.compute(acc, metrics=['recall', 'precision', 'num_misses', 'num_false_positives', 'num_detections']).to_dict()
 
     df = events_to_df_map(acc.events)
     tr_ratios = track_ratios(df, obj_frequencies(df))
     unique_gt_ids = num_gt_ids(df)
 
     for key in summary:
-        summary[key] = float(summary[key][0])
+        summary[namemap[key]] = float(summary[key][0])
 
     summary["num_gt_ids"] = unique_gt_ids
 
@@ -188,7 +193,6 @@ def calculate_from_payload(payload: dict,
         frames = payload['sequences'][sequence][gt_field_name]
         
         all_formated_references = {"all": []}
-        print("filters: ", filters)
         for filter, filter_ranges in filters.items():
             all_formated_references[filter] = {}
             for filter_range in filter_ranges: