fix

README.md

@@ -22,15 +22,30 @@ This metric can be used to calculate object detection metrics. It has an option
 ```
 >>> import evaluate
 >>> from seametrics.fo_to_payload.utils import fo_to_payload
->>> payload = fo_to_payload(
-        dataset=dataset, 
-        gt_field=gt_field, 
-        models=model_list
-    )
->>> for model in payload["models"]:
->>>     module = evaluate.load("./detection_metric.py", iou_thresholds=0.9)
->>>     module.add_batch(payload, model=model)
->>>     result = module.compute()
+>>> b = fo_to_payload(
+>>>         dataset="SAILING_DATASET_QA",
+>>>         gt_field="ground_truth_det",
+>>>         models=["yolov5n6_RGB_D2304-v1_9C"],
+>>>         sequence_list=["Trip_14_Seq_1"],
+>>>         data_type="rgb"
+>>>    )
+>>> module = evaluate.load("SEA-AI/det-metrics.py")
+>>> module.add_batch(b)
+>>> res = module.compute()
+>>> print(res)
+{'all': {'range': [0, 10000000000.0],
+  'iouThr': '0.00',
+  'maxDets': 100,
+  'tp': 89,
+  'fp': 13,
+  'fn': 15,
+  'duplicates': 1,
+  'precision': 0.8725490196078431,
+  'recall': 0.8557692307692307,
+  'f1': 0.8640776699029126,
+  'support': 104,
+  'fpi': 0,
+  'nImgs': 22}}
 ```
 
 ### Metric Settings

det-metrics.py

@@ -177,9 +177,19 @@ class DetectionMetric(evaluate.Metric):
             gt_normalized = seq_data[data["gt_field_name"]] # shape: (n_frames, m_gts)
             pred_normalized = seq_data[model] # shape: (n_frames, l_preds)
             img_res = seq_data["resolution"] # (h, w)
-            for gt_frame, pred_frame in zip(gt_normalized, pred_normalized): # iterate over all frames
-                processed_pred = self._fo_dets_to_metrics_dict(pred_frame, w=img_res[1], h=img_res[0])
-                processed_gt = self._fo_dets_to_metrics_dict(gt_frame, w=img_res[1], h=img_res[0])
+            for gt_frame, pred_frame in zip(gt_normalized, pred_normalized): # iterate over all frame
+                processed_pred = self._fo_dets_to_metrics_dict(
+                    fo_dets=pred_frame, 
+                    w=img_res[1], 
+                    h=img_res[0], 
+                    include_scores=True
+                )
+                processed_gt = self._fo_dets_to_metrics_dict(
+                    fo_dets=gt_frame, 
+                    w=img_res[1],
+                    h=img_res[0], 
+                    include_scores=False
+                )
                 predictions.append(processed_pred[0]["boxes"].tolist())
                 references.append(processed_gt[0]["boxes"].tolist())
 
@@ -204,7 +214,8 @@ class DetectionMetric(evaluate.Metric):
     @staticmethod
     def _fo_dets_to_metrics_dict(fo_dets: list,
                                  w: int,
-                                 h: int) -> List[Dict[str, np.ndarray]]:
+                                 h: int,
+                                 include_scores: bool = False) -> List[Dict[str, np.ndarray]]:
         """Convert list of fiftyone detections to format that is
             required by PrecisionRecallF1Support() function of seametrics library
 
@@ -236,14 +247,21 @@ class DetectionMetric(evaluate.Metric):
             detections.append(
                 [bbox[0]*w, bbox[1]*h, bbox[2]*w, bbox[3]*h]
             )
-            scores.append(det["confidence"]) # None for gt
-            #labels.append(bbox["label"])
+            scores.append(det["confidence"] if det["confidence"] is not None else 1.0) # None for gt
             labels.append(1)
-            
-        return [
-            dict(
-                boxes=np.array(detections),
-                scores=np.array(scores),
-                labels=np.array(labels)
-            )
-        ]
+
+        if include_scores: 
+            return [
+                dict(
+                    boxes=np.array(detections),
+                    scores=np.array(scores),
+                    labels=np.array(labels)
+                )
+            ]
+        else:
+            return [
+                dict(
+                    boxes=np.array(detections),
+                    labels=np.array(labels)
+                )
+            ]