Merge pull request #16828 from paroj:nmspy

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -1038,7 +1038,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      * @param eta a coefficient in adaptive threshold formula: \f$nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\f$.
      * @param top_k if `>0`, keep at most @p top_k picked indices.
      */
-    CV_EXPORTS_W void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
+    CV_EXPORTS void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
                                const float score_threshold, const float nms_threshold,
                                CV_OUT std::vector<int>& indices,
                                const float eta = 1.f, const int top_k = 0);

modules/dnn/misc/python/test/test_dnn.py

@@ -230,6 +230,12 @@ class dnn_test(NewOpenCVTests):
                 self.assertTrue(ret)
                 normAssert(self, refs[i], result, 'Index: %d' % i, 1e-10)
 
+    def test_nms(self):
+        confs = (1, 1)
+        rects = ((0, 0, 0.4, 0.4), (0, 0, 0.2, 0.4)) # 0.5 overlap
+
+        self.assertTrue(all(cv.dnn.NMSBoxes(rects, confs, 0, 0.6).ravel() == (0, 1)))
+
     def test_custom_layer(self):
         class CropLayer(object):
             def __init__(self, params, blobs):