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batched nms impl

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This commit is contained in:
fengyuentau 2022-11-24 10:20:04 +08:00
parent 6ca205a029
commit 9fded9ca53
3 changed files with 106 additions and 0 deletions
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21
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -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

55
modules/dnn/src/nms.cpp
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@ -58,6 +58,61 @@ void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>&
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rotatedRectIOU);
}
template<class Rect_t>
static inline void NMSBoxesBatchedImpl(const std::vector<Rect_t>& bboxes,
const std::vector<float>& scores, const std::vector<int>& class_ids,
const float score_threshold, const float nms_threshold,
std::vector<int>& indices, const float eta, const int top_k)
{
double x1, y1, x2, y2, max_coord = 0;
for (int i = 0; i < bboxes.size(); i++)
{
x1 = bboxes[i].x;
y1 = bboxes[i].y;
x2 = x1 + bboxes[i].width;
y2 = y1 + bboxes[i].height;
max_coord = std::max(x1, max_coord);
max_coord = std::max(y1, max_coord);
max_coord = std::max(x2, max_coord);
max_coord = std::max(y2, max_coord);
}
// calculate offset and add offset to each bbox
std::vector<Rect_t> bboxes_offset;
double offset;
for (int i = 0; i < bboxes.size(); i++)
{
offset = class_ids[i] * (max_coord + 1);
bboxes_offset.push_back(
Rect_t(bboxes[i].x + offset, bboxes[i].y + offset,
bboxes[i].width, bboxes[i].height)
);
}
NMSFast_(bboxes_offset, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
}
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,
std::vector<int>& indices, const float eta, const int top_k)
{
CV_Assert_N(bboxes.size() == scores.size(), scores.size() == class_ids.size(), nms_threshold >= 0, eta > 0);
NMSBoxesBatchedImpl(bboxes, scores, class_ids, score_threshold, nms_threshold, indices, eta, top_k);
}
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,
std::vector<int>& indices, const float eta, const int top_k)
{
CV_Assert_N(bboxes.size() == scores.size(), scores.size() == class_ids.size(), nms_threshold >= 0, eta > 0);
NMSBoxesBatchedImpl(bboxes, scores, class_ids, score_threshold, nms_threshold, indices, eta, top_k);
}
void softNMSBoxes(const std::vector<Rect>& bboxes,
const std::vector<float>& scores,
std::vector<float>& updated_scores,

30
modules/dnn/test/test_nms.cpp
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@ -37,6 +37,36 @@ TEST(NMS, Accuracy)
ASSERT_EQ(indices[i], ref_indices[i]);
}
TEST(BatchedNMS, Accuracy)
{
//reference results obtained using tf.image.non_max_suppression with iou_threshold=0.5
std::string dataPath = findDataFile("dnn/batched_nms_reference.yml");
FileStorage fs(dataPath, FileStorage::READ);
std::vector<Rect> bboxes;
std::vector<float> scores;
std::vector<int> idxs;
std::vector<int> ref_indices;
fs["boxes"] >> bboxes;
fs["probs"] >> scores;
fs["idxs"] >> idxs;
fs["output"] >> ref_indices;
const float nms_thresh = .5f;
const float score_thresh = .05f;
std::vector<int> indices;
cv::dnn::NMSBoxesBatched(bboxes, scores, idxs, score_thresh, nms_thresh, indices);
ASSERT_EQ(ref_indices.size(), indices.size());
std::sort(indices.begin(), indices.end());
std::sort(ref_indices.begin(), ref_indices.end());
for(size_t i = 0; i < indices.size(); i++)
ASSERT_EQ(indices[i], ref_indices[i]);
}
TEST(SoftNMS, Accuracy)
{
//reference results are obtained using TF v2.7 tf.image.non_max_suppression_with_scores