recognition_th

mot-metrics.py → user-friendly-metrics.py

@@ -15,6 +15,10 @@
 import evaluate
 import datasets
 import motmetrics as mm
+from motmetrics.metrics import (events_to_df_map, 
+                                obj_frequencies,
+                                track_ratios,
+                                num_gt_ids)
 import numpy as np
 
 _CITATION = """\
@@ -53,7 +57,7 @@ Args:
 
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class MotMetrics(evaluate.Metric):
+class UserFriendlyMetrics(evaluate.Metric):
     """TODO: Short description of my evaluation module."""
 
     def _info(self):
@@ -89,7 +93,14 @@ class MotMetrics(evaluate.Metric):
         return calculate_from_payload(payload, max_iou, debug)
         #return calculate(predictions, references, max_iou)
 
-def calculate(predictions, references, max_iou: float = 0.5):
+def recognition(track_ratios, th = 0.5):
+    """Number of objects tracked for at least 20 percent of lifespan."""
+    return track_ratios[track_ratios >= th].count()
+
+def calculate(predictions, 
+              references, 
+              max_iou: float = 0.5,
+              recognition_thresholds: list = [0.3, 0.5, 0.8],):
     """Returns the scores"""
     
     try: 
@@ -124,8 +135,16 @@ def calculate(predictions, references, max_iou: float = 0.5):
 
     mh = mm.metrics.create()
     summary = mh.compute(acc).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] = summary[key][0]
+
+    for th in recognition_thresholds:
+        summary[f'recognition_{th}'] = recognition(tr_ratios, th)
     
     return summary