v

my_metricv2.py

@@ -109,11 +109,9 @@ class MyMetricv2(evaluate.Metric):
         """Returns the scores"""
         # TODO: Compute the different scores of the module
 
-        # <frame number>, <object id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <confidence>, <x>, <y>, <z>
-
         return calculate(predictions, references)
 
-def calculate(predictions, references):
+def calculate(predictions, references, max_iou: float = 0.5):
     """Returns the scores"""
     try: 
         np_predictions = np.array(predictions)
@@ -123,14 +121,29 @@ def calculate(predictions, references):
     try:
         np_references = np.array(references)
     except:
-        raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]")
+        raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]")
     
-    num_frames = max(np_references[:, 0].max(), np_predictions[:, 0].max())+1
-        # acc = mm.MOTAccumulator(auto_id=True)
-    print(num_frames)
+    if np_predictions.shape[1] != 7:
+        raise ValueError("The predictions should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]")
+    if np_references.shape[1] != 6:
+        raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]")
+
+    if np_predictions[:, 0].min() <= 0:
+        raise ValueError("The frame number in the predictions should be a positive integer")
+    if np_references[:, 0].min() <= 0:
+        raise ValueError("The frame number in the references should be a positive integer")
+
+
+    num_frames = max(np_references[:, 0].max(), np_predictions[:, 0].max())
+
+    acc = mm.MOTAccumulator(auto_id=True)
+    for i in range(1, num_frames+1):
+        preds = np_predictions[np_predictions[:, 0] == i, 1:6]
+        refs = np_references[np_references[:, 0] == i, 1:6]
+        print(preds)
+        print(refs)
+
     return {
-        
             "predictions": predictions,
             "references": references
-    
         }