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Merge pull request #9862 from sovrasov:dnn_nms

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This commit is contained in:
Vadim Pisarevsky
2017-10-27 11:19:57 +00:00
parent b4367c92f3 7e3e9144de
commit bc93775385
5 changed files with 199 additions and 70 deletions
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modules/dnn/src/layers/detection_output_layer.cpp
+10 -70
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@@ -45,6 +45,7 @@
#include <float.h>
#include <string>
#include <caffe.pb.h>
#include "../nms.inl.hpp"
namespace cv
{
@@ -61,6 +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
@@ -308,7 +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)
@@ -619,75 +623,6 @@ public:
}
}
// Do non maximum suppression given bboxes and scores.
// Inspired by Piotr Dollar's NMS implementation in EdgeBox.
// https://goo.gl/jV3JYS
// bboxes: a set of bounding boxes.
// 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.
// indices: the kept indices of bboxes after nms.
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)
{
CV_Assert(bboxes.size() == scores.size());
// Get top_k scores (with corresponding indices).
std::vector<std::pair<float, int> > score_index_vec;
GetMaxScoreIndex(scores, score_threshold, top_k, score_index_vec);
// Do nms.
float adaptive_threshold = nms_threshold;
indices.clear();
while (score_index_vec.size() != 0) {
const int idx = score_index_vec.front().second;
bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = JaccardOverlap<true>(bboxes[idx], bboxes[kept_idx]);
keep = overlap <= adaptive_threshold;
}
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;
}
}
}
// Get max scores with corresponding indices.
// scores: a set of scores.
// threshold: only consider scores higher than the threshold.
// top_k: if -1, keep all; otherwise, keep at most top_k.
// score_index_vec: store the sorted (score, index) pair.
static void GetMaxScoreIndex(const std::vector<float>& scores, const float threshold, const int top_k,
std::vector<std::pair<float, int> >& score_index_vec)
{
CV_DbgAssert(score_index_vec.empty());
// Generate index score pairs.
for (size_t i = 0; i < scores.size(); ++i)
{
if (scores[i] > threshold)
{
score_index_vec.push_back(std::make_pair(scores[i], i));
}
}
// Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
util::SortScorePairDescend<int>);
// Keep top_k scores if needed.
if (top_k > -1 && top_k < (int)score_index_vec.size())
{
score_index_vec.resize(top_k);
}
}
// Compute the jaccard (intersection over union IoU) overlap between two bboxes.
template<bool normalized>
static float JaccardOverlap(const caffe::NormalizedBBox& bbox1,
@@ -733,6 +668,11 @@ public:
}
};
float util::caffe_box_overlap(const caffe::NormalizedBBox& a, const caffe::NormalizedBBox& b)
{
return DetectionOutputLayerImpl::JaccardOverlap<true>(a, b);
}
const std::string DetectionOutputLayerImpl::_layerName = std::string("DetectionOutput");
Ptr<DetectionOutputLayer> DetectionOutputLayer::create(const LayerParams &params)