update the dummy example

ref-metrics.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
+import random
 import datetime
 import os
 
@@ -89,91 +91,59 @@ class UserFriendlyMetrics(evaluate.Metric):
         # 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],
-        debug: bool = False,
-    ):
-        """Returns the scores"""
-        # this practically call _compute
-        return self.dummy_values()
-        # return calculate(predictions, references, max_iou)
-
-    def dummy_values(self):
-        return {
+            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,
+        ):
+            
+            """Returns the metrics"""
+            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
+        # Default area ranges if not provided
+        default_area_ranges = [
+            ("all", [0, 1e5**2]),
+            ("small", [0**2, 6**2]),
+            ("medium", [6**2, 12**2]),
+            ("large", [12**2, 1e5**2]),
+        ]
+        
+        # Use default ranges if none are provided
+        if area_ranges_tuples is None:
+            area_ranges_tuples = default_area_ranges
+        
+        # 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
+            }
+        
+        # Initialize output structure
+        dummy_output = {
             "model_1": {
-                "overall": {
-                    "all": {
-                        "tp": 50,
-                        "fp": 20,
-                        "fn": 10,
-                        "precision": 0.71,
-                        "recall": 0.83,
-                        "f1": 0.76
-                    },
-                    "small": {
-                        "tp": 15,
-                        "fp": 5,
-                        "fn": 2,
-                        "precision": 0.75,
-                        "recall": 0.88,
-                        "f1": 0.81
-                    },
-                    "medium": {
-                        "tp": 25,
-                        "fp": 10,
-                        "fn": 5,
-                        "precision": 0.71,
-                        "recall": 0.83,
-                        "f1": 0.76
-                    },
-                    "large": {
-                        "tp": 10,
-                        "fp": 5,
-                        "fn": 3,
-                        "precision": 0.67,
-                        "recall": 0.77,
-                        "f1": 0.71
-                    }
-                },
+                "overall": {},
                 "per_sequence": {
-                    "sequence_1": {
-                        "all": {
-                            "tp": 30,
-                            "fp": 15,
-                            "fn": 7,
-                            "precision": 0.67,
-                            "recall": 0.81,
-                            "f1": 0.73
-                        },
-                        "small": {
-                            "tp": 10,
-                            "fp": 3,
-                            "fn": 1,
-                            "precision": 0.77,
-                            "recall": 0.91,
-                            "f1": 0.83
-                        },
-                        "medium": {
-                            "tp": 15,
-                            "fp": 7,
-                            "fn": 2,
-                            "precision": 0.68,
-                            "recall": 0.88,
-                            "f1": 0.77
-                        },
-                        "large": {
-                            "tp": 5,
-                            "fp": 2,
-                            "fn": 1,
-                            "precision": 0.71,
-                            "recall": 0.83,
-                            "f1": 0.76
-                        }
-                    }
+                    "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()
+        
+        return dummy_output
+