Update csv_eval.py

retinanet/csv_eval.py

@@ -177,6 +177,8 @@ def evaluate(
     all_annotations    = _get_annotations(generator)
 
     average_precisions = {}
+    recalls = {}
+    precisions = {}
 
     for label in range(generator.num_classes()):
         false_positives = np.zeros((0,))
@@ -231,17 +233,19 @@ def evaluate(
         # compute average precision
         average_precision  = _compute_ap(recall, precision)
         average_precisions[label] = average_precision, num_annotations
+        recalls[label] = recall
+        precisions[label] = precision
 
 
     print('\nmAP:')
     for label in range(generator.num_classes()):
         label_name = generator.label_to_name(label)
         print('{}: {}'.format(label_name, average_precisions[label][0]))
-        print("Precision: ",precision[-1])
-        print("Recall: ",recall[-1])
+        print("Precision: ",precisions[label][-1])
+        print("Recall: ",recalls[label][-1])
 
         if save_path!=None:
-            plt.plot(recall,precision)
+            plt.plot(recalls[label],precisions[label])
             # naming the x axis 
             plt.xlabel('Recall') 
             # naming the y axis