batched nms impl

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

@@ -1194,6 +1194,27 @@ CV__DNN_INLINE_NS_BEGIN
                              CV_OUT std::vector<int>& indices,
                              const float eta = 1.f, const int top_k = 0);
 
+    /** @brief Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
+
+     * @param bboxes a set of bounding boxes to apply NMS.
+     * @param scores a set of corresponding confidences.
+     * @param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
+     * @param score_threshold a threshold used to filter boxes by score.
+     * @param nms_threshold a threshold used in non maximum suppression.
+     * @param indices the kept indices of bboxes after NMS.
+     * @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 void NMSBoxesBatched(const std::vector<Rect>& bboxes, const std::vector<float>& scores, const std::vector<int>& class_ids,
+                                    const float score_threshold, const float nms_threshold,
+                                    CV_OUT std::vector<int>& indices,
+                                    const float eta = 1.f, const int top_k = 0);
+
+    CV_EXPORTS_W void NMSBoxesBatched(const std::vector<Rect2d>& bboxes, const std::vector<float>& scores, const std::vector<int>& class_ids,
+                                      const float score_threshold, const float nms_threshold,
+                                      CV_OUT std::vector<int>& indices,
+                                      const float eta = 1.f, const int top_k = 0);
+
     /**
      * @brief Enum of Soft NMS methods.
      * @see softNMSBoxes