mirror of
https://github.com/opencv/opencv.git
synced 2024-12-11 22:59:16 +08:00
101 lines
3.3 KiB
C++
101 lines
3.3 KiB
C++
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html.
|
|
//
|
|
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
|
|
#ifndef OPENCV_DNN_NMS_INL_HPP
|
|
#define OPENCV_DNN_NMS_INL_HPP
|
|
|
|
#include <opencv2/dnn.hpp>
|
|
|
|
namespace cv {
|
|
namespace dnn {
|
|
|
|
namespace
|
|
{
|
|
|
|
template <typename T>
|
|
static inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
|
|
const std::pair<float, T>& pair2)
|
|
{
|
|
return pair1.first > pair2.first;
|
|
}
|
|
|
|
} // namespace
|
|
|
|
// 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.
|
|
inline 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(),
|
|
SortScorePairDescend<int>);
|
|
|
|
// Keep top_k scores if needed.
|
|
if (top_k > 0 && top_k < (int)score_index_vec.size())
|
|
{
|
|
score_index_vec.resize(top_k);
|
|
}
|
|
}
|
|
|
|
// 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 > 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, float (*computeOverlap)(const BoxType&, const BoxType&))
|
|
{
|
|
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();
|
|
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];
|
|
float overlap = computeOverlap(bboxes[idx], bboxes[kept_idx]);
|
|
keep = overlap <= adaptive_threshold;
|
|
}
|
|
if (keep)
|
|
indices.push_back(idx);
|
|
if (keep && eta < 1 && adaptive_threshold > 0.5) {
|
|
adaptive_threshold *= eta;
|
|
}
|
|
}
|
|
}
|
|
|
|
}// dnn
|
|
}// cv
|
|
|
|
#endif
|