mnn_faceboxes.h
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//
// Created by DefTruth on 2021/11/20.
//
#ifndef LITE_AI_TOOLKIT_MNN_CV_MNN_FACEBOXES_H
#define LITE_AI_TOOLKIT_MNN_CV_MNN_FACEBOXES_H
#include "lite/mnn/core/mnn_core.h"
namespace mnncv
{
class LITE_EXPORTS MNNFaceBoxes : public BasicMNNHandler
{
public:
explicit MNNFaceBoxes(const std::string &_mnn_path, unsigned int _num_threads = 1); //
~MNNFaceBoxes() override = default;
private:
// nested classes
struct FaceBoxesAnchor
{
float cx;
float cy;
float s_kx;
float s_ky;
};
private:
const float mean_vals[3] = {104.f, 117.f, 123.f}; // bgr order
const float norm_vals[3] = {1.f, 1.f, 1.f};
const float variance[2] = {0.1f, 0.2f};
std::vector<int> steps = {32, 64, 128};
std::vector<std::vector<int>> min_sizes = {
{32, 64, 128},
{256},
{512}
};
enum NMS
{
HARD = 0, BLEND = 1, OFFSET = 2
};
static constexpr const unsigned int max_nms = 30000;
private:
void initialize_pretreat(); //
void transform(const cv::Mat &mat) override; //
void generate_anchors(const int target_height,
const int target_width,
std::vector<FaceBoxesAnchor> &anchors);
void generate_bboxes(std::vector<types::Boxf> &bbox_collection,
const std::map<std::string, MNN::Tensor *> &output_tensors,
float score_threshold, float img_height,
float img_width); // rescale & exclude
void nms(std::vector<types::Boxf> &input, std::vector<types::Boxf> &output,
float iou_threshold, unsigned int topk, unsigned int nms_type);
public:
void detect(const cv::Mat &mat, std::vector<types::Boxf> &detected_boxes,
float score_threshold = 0.7f, float iou_threshold = 0.3f,
unsigned int topk = 300, unsigned int nms_type = 0);
};
}
#endif //LITE_AI_TOOLKIT_MNN_CV_MNN_FACEBOXES_H