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dnn: add a documentation for NMS, fix missing experimantal namespace
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@ -734,18 +734,20 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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*/
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CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst);
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/** @brief
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* @param bboxes
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* @param scores
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* @param score_threshold
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* @param nms_threshold
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* @param eta
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* @param top_k
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* @param indices
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/** @brief Performs non maximum suppression given boxes and corresponding scores.
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* @param bboxes a set of bounding boxes to apply NMS.
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* @param scores a set of corresponding confidences.
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* @param score_threshold a threshold used to filter boxes by score.
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* @param nms_threshold a threshold used in non maximum suppression.
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* @param indices the kept indices of bboxes after NMS.
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* @param eta a coefficient in adaptive threshold formula: \f$nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\f$.
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* @param top_k if `>0`, keep at most @p top_k picked indices.
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*/
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CV_EXPORTS_W void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
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const float score_threshold, const float nms_threshold,
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const float eta, const int top_k, CV_OUT std::vector<int>& indices);
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CV_OUT std::vector<int>& indices,
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const float eta = 1.f, const int top_k = 0);
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//! @}
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@ -48,25 +48,12 @@ inline void GetMaxScoreIndex(const std::vector<float>& scores, const float thres
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SortScorePairDescend<int>);
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// Keep top_k scores if needed.
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if (top_k > -1 && top_k < (int)score_index_vec.size())
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if (top_k > 0 && top_k < (int)score_index_vec.size())
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{
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score_index_vec.resize(top_k);
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}
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}
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template <typename BoxType>
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struct NMSOverlap
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{
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float operator() (const BoxType& a, const BoxType& b);
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};
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template <>
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inline float NMSOverlap<Rect>::operator() (const Rect& a, const Rect& b)
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{
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float rectIntersectionArea = (float)(a & b).area();
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return rectIntersectionArea / (a.area() + b.area() - rectIntersectionArea);
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}
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// Do non maximum suppression given bboxes and scores.
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// Inspired by Piotr Dollar's NMS implementation in EdgeBox.
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// https://goo.gl/jV3JYS
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@ -74,13 +61,13 @@ inline float NMSOverlap<Rect>::operator() (const Rect& a, const Rect& b)
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// scores: a set of corresponding confidences.
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// score_threshold: a threshold used to filter detection results.
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// nms_threshold: a threshold used in non maximum suppression.
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// top_k: if not -1, keep at most top_k picked indices.
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// top_k: if not > 0, keep at most top_k picked indices.
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// indices: the kept indices of bboxes after nms.
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template <typename BoxType>
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inline void NMSFast_(const std::vector<BoxType>& bboxes,
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const std::vector<float>& scores, const float score_threshold,
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const float nms_threshold, const float eta, const int top_k,
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std::vector<int>& indices, NMSOverlap<BoxType> computeOverlap)
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std::vector<int>& indices, float (*computeOverlap)(const BoxType&, const BoxType&))
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{
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CV_Assert(bboxes.size() == scores.size());
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@ -91,8 +78,8 @@ inline void NMSFast_(const std::vector<BoxType>& bboxes,
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// Do nms.
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float adaptive_threshold = nms_threshold;
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indices.clear();
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while (score_index_vec.size() != 0) {
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const int idx = score_index_vec.front().second;
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for (size_t i = 0; i < score_index_vec.size(); ++i) {
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const int idx = score_index_vec[i].second;
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bool keep = true;
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for (int k = 0; k < (int)indices.size() && keep; ++k) {
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const int kept_idx = indices[k];
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@ -101,7 +88,6 @@ inline void NMSFast_(const std::vector<BoxType>& bboxes,
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}
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if (keep)
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indices.push_back(idx);
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score_index_vec.erase(score_index_vec.begin());
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if (keep && eta < 1 && adaptive_threshold > 0.5) {
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adaptive_threshold *= eta;
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}
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@ -62,6 +62,8 @@ static inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
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return pair1.first > pair2.first;
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}
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static inline float caffe_box_overlap(const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b);
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} // namespace
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class DetectionOutputLayerImpl : public DetectionOutputLayer
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@ -309,7 +311,8 @@ public:
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LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(label);
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if (label_bboxes == decodeBBoxes.end())
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label));
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ApplyNMSFast(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK, indices[c]);
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NMSFast_(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK,
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indices[c], util::caffe_box_overlap);
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numDetections += indices[c].size();
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}
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if (_keepTopK > -1 && numDetections > (size_t)_keepTopK)
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@ -620,16 +623,6 @@ public:
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}
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}
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static void ApplyNMSFast(const std::vector<caffe::NormalizedBBox>& bboxes,
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const std::vector<float>& scores, const float score_threshold,
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const float nms_threshold, const float eta, const int top_k,
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std::vector<int>& indices)
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{
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NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, NMSOverlap<caffe::NormalizedBBox>());
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}
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// Compute the jaccard (intersection over union IoU) overlap between two bboxes.
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template<bool normalized>
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static float JaccardOverlap(const caffe::NormalizedBBox& bbox1,
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@ -675,8 +668,7 @@ public:
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}
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};
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template <>
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float NMSOverlap<caffe::NormalizedBBox>::operator() (const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b)
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float util::caffe_box_overlap(const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b)
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{
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return DetectionOutputLayerImpl::JaccardOverlap<true>(a, b);
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}
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@ -12,13 +12,22 @@ namespace cv
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{
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namespace dnn
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{
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CV__DNN_EXPERIMENTAL_NS_BEGIN
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static inline float rectOverlap(const Rect& a, const Rect& b)
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{
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return 1.f - static_cast<float>(jaccardDistance(a, b));
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}
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void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
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const float score_threshold, const float nms_threshold,
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const float eta, const int top_k, std::vector<int>& indices)
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std::vector<int>& indices, const float eta, const int top_k)
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{
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NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, NMSOverlap<Rect>());
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CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
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nms_threshold >= 0, eta > 0);
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NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
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}
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CV__DNN_EXPERIMENTAL_NS_END
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}// dnn
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}// cv
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