Update ref-metrics.py

ref-metrics.py

@@ -90,62 +90,60 @@ class UserFriendlyMetrics(evaluate.Metric):
         """Optional: download external resources useful to compute the scores"""
         # TODO: Download external resources if needed
         pass  
-
     
     def compute_from_payload(
-            self,
-            payload,
-            max_iou: float = 0.5,
-            filters={},
-            recognition_thresholds=[0.3, 0.5, 0.8],
-            area_ranges_tuples=None,  # Optional parameter
-            debug: bool = False,
-        ):
-            """
-            Call the required functions to compute the metrics and return it.
-        
-            Returns:
-                dict: A dictionary containing the computed metrics based on the provided area in the area_ranges_tuples.
-            """            
-            return self.dummy_values(area_ranges_tuples)
-    
+        self,
+        payload,
+        max_iou: float = 0.5,
+        filters={},
+        recognition_thresholds=[0.3, 0.5, 0.8],
+        area_ranges_tuples=None,  # Optional parameter
+        debug: bool = False,
+    ):
+        """
+        Call the required functions to compute the metrics and return it.
+
+        Returns:
+            dict: A dictionary containing the computed metrics based on the provided area in the area_ranges_tuples,
+            if a range area is provided it will be displayed in the output.
+        """
+        return self.dummy_values(area_ranges_tuples)
+
     def dummy_values(self, area_ranges_tuples=None):
         """Dummy randome values in the expected format that all new metrics need to return"""
 
         # Use default ranges if none are provided
         if area_ranges_tuples is None:
-            area_ranges_tuples = [
-            ("all", [0, 1e5**2]),
-            ("small", [0**2, 6**2]),
-            ("medium", [6**2, 12**2]),
-            ("large", [12**2, 1e5**2]),
-        ]
-        
+            area_names = ["all", "small", "medium", "large"]
+        else:
+            area_names = list(area_ranges_tuples.keys())
+
         # Generate random dummy values
         def generate_random_values():
             return {
                 "tp": random.randint(0, 100),  # Random integer between 0 and 100
-                "fp": random.randint(0, 50),   # Random integer between 0 and 50
-                "fn": random.randint(0, 50),   # Random integer between 0 and 50
-                "precision": round(random.uniform(0.5, 1.0), 2),  # Random float between 0.5 and 1.0
-                "recall": round(random.uniform(0.5, 1.0), 2),     # Random float between 0.5 and 1.0
-                "f1": round(random.uniform(0.5, 1.0), 2)          # Random float between 0.5 and 1.0
+                "fp": random.randint(0, 50),  # Random integer between 0 and 50
+                "fn": random.randint(0, 50),  # Random integer between 0 and 50
+                "precision": round(
+                    random.uniform(0.5, 1.0), 2
+                ),  # Random float between 0.5 and 1.0
+                "recall": round(
+                    random.uniform(0.5, 1.0), 2
+                ),  # Random float between 0.5 and 1.0
+                "f1": round(
+                    random.uniform(0.5, 1.0), 2
+                ),  # Random float between 0.5 and 1.0
             }
-        
+
         # Initialize output structure
-        dummy_output = {
-            "model_1": {
-                "overall": {},
-                "per_sequence": {
-                    "sequence_1": {}
-                }
-            }
-        }
-        
+        dummy_output = {"model_1": {"overall": {}, "per_sequence": {"sequence_1": {}}}}
+
         # Populate only the ranges specified in area_ranges_tuples with random values
-        for range_name, _ in area_ranges_tuples:
-            dummy_output["model_1"]["overall"][range_name] = generate_random_values()
-            dummy_output["model_1"]["per_sequence"]["sequence_1"][range_name] = generate_random_values()
-        
+        for area_name in area_names:
+            dummy_output["model_1"]["overall"][area_name] = generate_random_values()
+            dummy_output["model_1"]["per_sequence"]["sequence_1"][
+                area_name
+            ] = generate_random_values()
+
         return dummy_output