calcuate average precision for each category

tools/cocoeval.py

@@ -453,11 +453,16 @@ def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
                 mean_s = -1
             else:
                 mean_s = np.mean(s[s>-1])
+
+                #cacluate AP(average precision) for each category
+                num_classes = 80
                 avg_ap = 0.0
-                for i in range(0,80):
-                    print('category : {0} : {1}'.format(i,np.mean(s[:,:,i,:])))
-                    avg_ap +=np.mean(s[:,:,i,:])
-                print('mAP : {}'.format(avg_ap / 80))
+                if ap == 1:
+                    for i in range(0, num_classes):
+                        print('category : {0} : {1}'.format(i,np.mean(s[:,:,i,:])))
+                        avg_ap +=np.mean(s[:,:,i,:])
+                    print('(all categories) mAP : {}'.format(avg_ap / num_classes))
+
             print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
             return mean_s
         def _summarizeDets():