dnn: expose only float variant of NMSBoxes for bindings

the float variant was always shadowed by the int version as
Rect2d is implicitly convertible to Rect.
This swaps things which is fine, as the vector of boxes was always
copied and the computation was done in double.

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):