Update det-metrics.py

moved the _add_batch and _fo_dets_to_metrics_dict to the seametrics utils

det-metrics.py

@@ -20,6 +20,8 @@ import datasets
 import numpy as np
 
 from seametrics.detection import PrecisionRecallF1Support
+from seametrics.fo_utils.utils import _fo_dets_to_metrics_dict
+from seametrics.fo_utils.utils import _add_batch
 
 _CITATION = """\
 @InProceedings{coco:2020,
@@ -167,34 +169,9 @@ class DetectionMetric(evaluate.Metric):
         """
         # populate two empty lists in format suitable for hugging face metric
         # nothing is computed based on them but prevents huggingface error
-        predictions, references = [], []
 
-        if model is None:
-            model = data["models"][0]
 
-        for sequence in data["sequence_list"]:
-            seq_data = data["sequences"][sequence]
-            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 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())
-
-                 # where the magic happens: update metric with data from current frame
-                self.coco_metric.update(processed_pred, processed_gt)
+        self, predictions,references = _add_batch(self, data, model)    
 
         # prevents hugging face error, doesn't do a lot       
         super(evaluate.Metric, self).add_batch(
@@ -202,6 +179,7 @@ class DetectionMetric(evaluate.Metric):
             references=references
         )
 
+
     def _compute(
             self, 
             predictions, 
@@ -210,58 +188,3 @@ class DetectionMetric(evaluate.Metric):
         """Returns the scores"""
         result = self.coco_metric.compute()["metrics"]
         return result
-
-    @staticmethod
-    def _fo_dets_to_metrics_dict(fo_dets: list,
-                                 w: int,
-                                 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
-
-        Args:
-            fo_dets (list): list containing fiftyone detections (or empty if frame without any detections)
-                note: bounding boxes in fo-detections are in format xywhn
-            w (int): width in pixel of image
-            h (int): height in pixel of image
-
-        Returns:
-            List[Dict[str, np.ndarray]]: list holding single dict with items:
-                "boxes": denormalized bounding boxes of whole frame in numpy array (shape: n_bboxes, 4)
-                "scores": confidence scores in numpy array (shape: n_bboxes)
-                "labels": labels in numpy array (shape: n_bboxes)
-        """
-        detections = []
-        scores = []
-        labels = [] #TODO: map to numbers
-        if len(fo_dets) == 0:
-            return [
-                dict(
-                    boxes=np.array([]),
-                    scores=np.array([]),
-                    labels=np.array([])
-                )
-            ]
-        for det in fo_dets:
-            bbox = det["bounding_box"]
-            detections.append(
-                [bbox[0]*w, bbox[1]*h, bbox[2]*w, bbox[3]*h]
-            )
-            scores.append(det["confidence"] if det["confidence"] is not None else 1.0) # None for gt
-            labels.append(1)
-
-        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)
-                )
-            ]