dnn: add a documentation for NMS, fix missing experimantal namespace

pull/9862/head
Vladislav Sovrasov 7 years ago
parent acedb4a579
commit c704942b8a
  1. 20
      modules/dnn/include/opencv2/dnn/dnn.hpp
  2. 24
      modules/dnn/include/opencv2/dnn/nms.inl.hpp
  3. 18
      modules/dnn/src/layers/detection_output_layer.cpp
  4. 13
      modules/dnn/src/nms.cpp

@ -734,18 +734,20 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst);
/** @brief
* @param bboxes
* @param scores
* @param score_threshold
* @param nms_threshold
* @param eta
* @param top_k
* @param indices
/** @brief Performs non maximum suppression given boxes and corresponding scores.
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @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_W void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
const float score_threshold, const float nms_threshold,
const float eta, const int top_k, CV_OUT std::vector<int>& indices);
CV_OUT std::vector<int>& indices,
const float eta = 1.f, const int top_k = 0);
//! @}

@ -48,25 +48,12 @@ inline void GetMaxScoreIndex(const std::vector<float>& scores, const float thres
SortScorePairDescend<int>);
// Keep top_k scores if needed.
if (top_k > -1 && top_k < (int)score_index_vec.size())
if (top_k > 0 && top_k < (int)score_index_vec.size())
{
score_index_vec.resize(top_k);
}
}
template <typename BoxType>
struct NMSOverlap
{
float operator() (const BoxType& a, const BoxType& b);
};
template <>
inline float NMSOverlap<Rect>::operator() (const Rect& a, const Rect& b)
{
float rectIntersectionArea = (float)(a & b).area();
return rectIntersectionArea / (a.area() + b.area() - rectIntersectionArea);
}
// Do non maximum suppression given bboxes and scores.
// Inspired by Piotr Dollar's NMS implementation in EdgeBox.
// https://goo.gl/jV3JYS
@ -74,13 +61,13 @@ inline float NMSOverlap<Rect>::operator() (const Rect& a, const Rect& b)
// scores: a set of corresponding confidences.
// score_threshold: a threshold used to filter detection results.
// nms_threshold: a threshold used in non maximum suppression.
// top_k: if not -1, keep at most top_k picked indices.
// top_k: if not > 0, keep at most top_k picked indices.
// indices: the kept indices of bboxes after nms.
template <typename BoxType>
inline void NMSFast_(const std::vector<BoxType>& bboxes,
const std::vector<float>& scores, const float score_threshold,
const float nms_threshold, const float eta, const int top_k,
std::vector<int>& indices, NMSOverlap<BoxType> computeOverlap)
std::vector<int>& indices, float (*computeOverlap)(const BoxType&, const BoxType&))
{
CV_Assert(bboxes.size() == scores.size());
@ -91,8 +78,8 @@ inline void NMSFast_(const std::vector<BoxType>& bboxes,
// Do nms.
float adaptive_threshold = nms_threshold;
indices.clear();
while (score_index_vec.size() != 0) {
const int idx = score_index_vec.front().second;
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) {
const int kept_idx = indices[k];
@ -101,7 +88,6 @@ inline void NMSFast_(const std::vector<BoxType>& bboxes,
}
if (keep)
indices.push_back(idx);
score_index_vec.erase(score_index_vec.begin());
if (keep && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}

@ -62,6 +62,8 @@ static inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
return pair1.first > pair2.first;
}
static inline float caffe_box_overlap(const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b);
} // namespace
class DetectionOutputLayerImpl : public DetectionOutputLayer
@ -309,7 +311,8 @@ public:
LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(label);
if (label_bboxes == decodeBBoxes.end())
CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label));
ApplyNMSFast(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK, indices[c]);
NMSFast_(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK,
indices[c], util::caffe_box_overlap);
numDetections += indices[c].size();
}
if (_keepTopK > -1 && numDetections > (size_t)_keepTopK)
@ -620,16 +623,6 @@ public:
}
}
static void ApplyNMSFast(const std::vector<caffe::NormalizedBBox>& bboxes,
const std::vector<float>& scores, const float score_threshold,
const float nms_threshold, const float eta, const int top_k,
std::vector<int>& indices)
{
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, NMSOverlap<caffe::NormalizedBBox>());
}
// Compute the jaccard (intersection over union IoU) overlap between two bboxes.
template<bool normalized>
static float JaccardOverlap(const caffe::NormalizedBBox& bbox1,
@ -675,8 +668,7 @@ public:
}
};
template <>
float NMSOverlap<caffe::NormalizedBBox>::operator() (const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b)
float util::caffe_box_overlap(const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b)
{
return DetectionOutputLayerImpl::JaccardOverlap<true>(a, b);
}

@ -12,13 +12,22 @@ namespace cv
{
namespace dnn
{
CV__DNN_EXPERIMENTAL_NS_BEGIN
static inline float rectOverlap(const Rect& a, const Rect& b)
{
return 1.f - static_cast<float>(jaccardDistance(a, b));
}
void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
const float score_threshold, const float nms_threshold,
const float eta, const int top_k, std::vector<int>& indices)
std::vector<int>& indices, const float eta, const int top_k)
{
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, NMSOverlap<Rect>());
CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
nms_threshold >= 0, eta > 0);
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
}
CV__DNN_EXPERIMENTAL_NS_END
}// dnn
}// cv

Loading…
Cancel
Save