retinaface.h
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#ifndef RETINAFACE_H
#define RETINAFACE_H
#include<opencv2/opencv.hpp>
#include<MNN/Interpreter.hpp>
#include<MNN/ImageProcess.hpp>
#include<iostream>
using namespace MNN;
using namespace std;
using namespace cv;
struct Bbox{
float xmin;
float ymin;
float xmax;
float ymax;
float score;
float x1;
float y1;
float x2;
float y2;
float x3;
float y3;
float x4;
float y4;
float x5;
float y5;
};
class RetinaFace{
public:
float confidence_threshold = 0.5;
bool is_bbox_process=true;
private:
bool use_gpu=true;
vector<float> input_size={640,640};
vector<float> variances={0.1,0.2};
Scalar mean = Scalar(104.0f, 117.0f, 123.0f);
float keep_top_k = 100;
float nms_threshold = 0.4;
float resize_scale = 1.0;
std::shared_ptr<MNN::Interpreter> net;
Session *session = nullptr;
ScheduleConfig config;
vector<vector<float>> anchors;
private:
// 生成anchors
vector<vector<float>> priorBox(vector<float> image_size);
// 解析bounding box landmarks 包含置信度
vector<Bbox> decode(float *loc,float *score,float *pre,vector<vector<float>> priors,vector<float> variances);
// 解析landmarks
// vector<vector<float>> decode_landm(vector<vector<float>> pre,vector<vector<float>> priors,vector<float> variances);
//NMS
void nms_cpu(std::vector<Bbox> &bboxes, float threshold);
// 根据阈值筛选
vector<Bbox> select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r);
// 数据后处理
vector<Bbox> bbox_process(vector<Bbox> bboxes,float frame_w,float frame_h);
public:
RetinaFace(){};
RetinaFace(string model_path){
net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器
config.numThread = 8;
config.type = MNN_FORWARD_CPU;
session = net->createSession(config);//创建session
anchors=priorBox(input_size);
}
// 推理
vector<Bbox> detect(string image_path);
vector<Bbox> detect_image(Mat image);
};
#endif