quality_control: correct the per-class accuracy formula

The current formula used to calculate `ConfusionMatrix.accuracy` is, in fact,
not accuracy, but the Jaccard index. Replace it with the correct formula.

Since the Jaccard index is a useful metric in its own right, calculate it too,
but save it in another attribute of `ConfusionMatrix`.

cvat/apps/quality_control/quality_reports.py

@@ -218,6 +218,7 @@ class ConfusionMatrix(_Serializable):
     precision: np.array
     recall: np.array
     accuracy: np.array
+    jaccard_index: np.array
 
     @property
     def axes(self):
@@ -240,6 +241,7 @@ def from_dict(cls, d: dict):
             precision=np.asarray(d["precision"]),
             recall=np.asarray(d["recall"]),
             accuracy=np.asarray(d["accuracy"]),
+            jaccard_index=np.asarray(d["jaccard_index"]),
         )
 
 
@@ -1934,17 +1936,23 @@ def _generate_annotations_summary(
         matched_ann_counts = np.diag(confusion_matrix)
         ds_ann_counts = np.sum(confusion_matrix, axis=1)
         gt_ann_counts = np.sum(confusion_matrix, axis=0)
+        total_annotations_count = np.sum(confusion_matrix)
 
-        label_accuracies = _arr_div(
+        label_jaccard_indices = _arr_div(
             matched_ann_counts, ds_ann_counts + gt_ann_counts - matched_ann_counts
         )
         label_precisions = _arr_div(matched_ann_counts, ds_ann_counts)
         label_recalls = _arr_div(matched_ann_counts, gt_ann_counts)
+        label_accuracies = (
+            total_annotations_count # TP + TN + FP + FN
+            - (ds_ann_counts - matched_ann_counts) # - FP
+            - (gt_ann_counts - matched_ann_counts) # - FN
+            # ... = TP + TN
+        ) / (total_annotations_count or 1)
 
         valid_annotations_count = np.sum(matched_ann_counts)
         missing_annotations_count = np.sum(confusion_matrix[cls._UNMATCHED_IDX, :])
         extra_annotations_count = np.sum(confusion_matrix[:, cls._UNMATCHED_IDX])
-        total_annotations_count = np.sum(confusion_matrix)
         ds_annotations_count = np.sum(ds_ann_counts[: cls._UNMATCHED_IDX])
         gt_annotations_count = np.sum(gt_ann_counts[: cls._UNMATCHED_IDX])
 
@@ -1961,6 +1969,7 @@ def _generate_annotations_summary(
                 precision=label_precisions,
                 recall=label_recalls,
                 accuracy=label_accuracies,
+                jaccard_index=label_jaccard_indices,
             ),
         )