AC: handle undefined values for detection report (#2698)

* AC: handl undefined values for detection report

* Update tools/accuracy_checker/accuracy_checker/metrics/detection.py

* Update tools/accuracy_checker/accuracy_checker/metrics/detection.py

Author: eaidova

tools/accuracy_checker/accuracy_checker/metrics/coco_metrics.py

@@ -196,10 +196,10 @@ def prepare_summary(label_stats):
             lamrs.append(lamr)
             total_objects_cnt += len(recall[-1])
             per_class_summary[label_idx] = {
-                'precision': precision_v_,
-                'recall': recall_v_,
-                'result': precision_v_,
-                'ap': ap_v,
+                'precision': precision_v_ if not np.isnan(precision_v_) else -1,
+                'result': precision_v_ if not np.isnan(precision_v_) else -1,
+                'recall': recall_v_ if not np.isnan(recall_v_) else -1,
+                'ap': ap_v if not np.isnan(ap_v) else -1,
                 "scale": 100,
                 "result_postfix": "%",
                 'objects_count': len(recall[-1]),
@@ -214,10 +214,10 @@ def prepare_summary(label_stats):
         precision_res = np.nanmean(precision_v)
         return {
             'summary_result': {
-                'result': precision_res,
-                'precision': precision_res,
-                'recall': recall_res,
-                'ap': ap_res,
+                'result': precision_res if not np.isnan(precision_res) else -1,
+                'precision': precision_res if not np.isnan(precision_res) else -1,
+                'recall': recall_res if not np.isnan(recall_res) else -1,
+                'ap': ap_res if not np.isnan(ap_res) else -1,
                 'result_scale': 100,
                 'result_postfix': '%',
                 'charts': {

tools/accuracy_checker/accuracy_checker/metrics/detection.py

@@ -232,9 +232,9 @@ def prepare_summary(label_stats):
                 continue
             ap.append(stat['result'])
             recall.append(stat['recall'])
-            recall_v.append(recall[-1][-1])
+            recall_v.append(recall[-1][-1] if np.size(recall[-1]) else np.NAN)
             precision.append(stat['precision'])
-            precision_v.append(precision[-1][-1])
+            precision_v.append(precision[-1][-1] if np.size(precision[-1]) else np.NAN)
             fppi = 1 - precision[-1]
             mr = stat['miss_rate']
             if np.size(recall[-1]) and np.size(precision[-1]):
@@ -260,9 +260,9 @@ def prepare_summary(label_stats):
             lamrs.append(lamr)
             total_objects_cnt += len(recall[-1])
             per_class_summary[label_idx] = {
-                'precision': precision[-1][-1],
-                'recall': recall[-1][-1],
-                'result': ap[-1],
+                'precision': precision[-1][-1] if np.size(precision[-1]) else -1,
+                'recall': recall[-1][-1] if np.size(recall[-1]) else -1,
+                'result': ap[-1] if not np.isnan(ap[-1]) else -1,
                 "scale": 100,
                 "result_postfix": "%",
                 'objects_count': len(recall[-1]),