retinaface.cpp
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#include "retinaface.h"
#include<iostream>
// 生成anchors
vector<vector<float>> RetinaFace::priorBox(vector<float> image_size){
vector<int> tmp1={16,32};
vector<int> tmp2={64,128};
vector<int> tmp3={256,512};
vector<vector<int>> min_sizes_;
min_sizes_.push_back(tmp1);
min_sizes_.push_back(tmp2);
min_sizes_.push_back(tmp3);
vector<int> steps={8,16,32};
vector<vector<int>> feature_maps;
vector<vector<float>> anchors;
for(int &step:steps){
vector<int> tmp(2,0);
tmp[0]=ceil(image_size[0]/step);
tmp[1]=ceil(image_size[1]/step);
feature_maps.push_back(tmp);
}
for(int k=0;k<feature_maps.size();k++){
vector<int> min_sizes=min_sizes_[k];
for(int i=0;i<feature_maps[k][0];i++){
for(int j=0;j<feature_maps[k][1];j++){
for(int &min_size:min_sizes){
float s_kx=float(min_size)/float(image_size[1]);
float s_ky=float(min_size)/float(image_size[0]);
float dense_cx=float((float(j)+float(0.5))*steps[k])/float(image_size[1]);
float dense_cy=float((float(i)+float(0.5))*steps[k])/float(image_size[1]);
vector<float> tmp_anchor={dense_cx,dense_cy,s_kx,s_ky};
anchors.push_back(tmp_anchor);
}
}
}
}
return anchors;
}
// 解析bounding box 包含置信度
vector<Bbox> RetinaFace::decode(Mat loc,Mat score,Mat pre,vector<vector<float>> priors,vector<float> variances){
vector<Bbox> boxes;
for(int i=0;i<priors.size();++i){
float b1=priors[i][0]+loc.at<float>(i,0)*variances[0]*priors[i][2];
float b2=priors[i][1]+loc.at<float>(i,1)*variances[0]*priors[i][3];
float b3=priors[i][2]*exp(loc.at<float>(i,2)*variances[1]);
float b4=priors[i][3]*exp(loc.at<float>(i,3)*variances[1]);
b1=b1-b3/float(2);
b2=b2-b4/float(2);
b3=b3+b1;
b4=b4+b2;
float l1=priors[i][0]+pre.at<float>(i,0)*variances[0]*priors[i][2];
float l2=priors[i][1]+pre.at<float>(i,1)*variances[0]*priors[i][3];
float l3=priors[i][0]+pre.at<float>(i,2)*variances[0]*priors[i][2];
float l4=priors[i][1]+pre.at<float>(i,3)*variances[0]*priors[i][3];
float l5=priors[i][0]+pre.at<float>(i,4)*variances[0]*priors[i][2];
float l6=priors[i][1]+pre.at<float>(i,5)*variances[0]*priors[i][3];
float l7=priors[i][0]+pre.at<float>(i,6)*variances[0]*priors[i][2];
float l8=priors[i][1]+pre.at<float>(i,7)*variances[0]*priors[i][3];
float l9=priors[i][0]+pre.at<float>(i,8)*variances[0]*priors[i][2];
float l10=priors[i][1]+pre.at<float>(i,9)*variances[0]*priors[i][3];
Bbox tmp_box={.xmin=b1*input_size[0]/resize_scale,.ymin=b2*input_size[1]/resize_scale,.xmax=b3*input_size[0]/resize_scale,.ymax=b4*input_size[1]/resize_scale,
.score=score.at<float>(i,0),.x1=(l1*input_size[0])/resize_scale,.y1=l2*input_size[1]/resize_scale,.x2=l3*input_size[0]/resize_scale,.y2=l4*input_size[1]/resize_scale,
.x3=l5*input_size[0]/resize_scale,.y3=l6*input_size[1]/resize_scale,.x4=l7*input_size[0]/resize_scale,.y4=l8*input_size[1]/resize_scale,.x5=l9*input_size[0]/resize_scale,.y5=l10*input_size[1]/resize_scale};
boxes.push_back(tmp_box);
}
return boxes;
}
// // 解析landmarks
// vector<vector<float>> RetinaFace::decode_landm(vector<vector<float>> pre,vector<vector<float>> priors,vector<float> variances){
// vector<vector<float>> landms;
// for(int i=0;i<priors.size();i++){
// vector<float> tmp_landm={l1,l2,l3,l4,l5,l6,l7,l8,l9,l10};
// landms.push_back(tmp_landm);
// }
// return landms;
// }
//NMS
void RetinaFace::nms_cpu(std::vector<Bbox> &bboxes, float threshold){
if (bboxes.empty()){
return ;
}
// 1.之前需要按照score排序
std::sort(bboxes.begin(), bboxes.end(), [&](Bbox b1, Bbox b2){return b1.score>b2.score;});
// 2.先求出所有bbox自己的大小
std::vector<float> area(bboxes.size());
for (int i=0; i<bboxes.size(); ++i){
area[i] = (bboxes[i].xmax - bboxes[i].xmin + 1) * (bboxes[i].ymax - bboxes[i].ymin + 1);
}
// 3.循环
for (int i=0; i<bboxes.size(); ++i){
for (int j=i+1; j<bboxes.size(); ){
float left = std::max(bboxes[i].xmin, bboxes[j].xmin);
float right = std::min(bboxes[i].xmax, bboxes[j].xmax);
float top = std::max(bboxes[i].ymin, bboxes[j].ymin);
float bottom = std::min(bboxes[i].ymax, bboxes[j].ymax);
float width = std::max(right - left + 1, 0.f);
float height = std::max(bottom - top + 1, 0.f);
float u_area = height * width;
float iou = (u_area) / (area[i] + area[j] - u_area);
if (iou>=threshold){
bboxes.erase(bboxes.begin()+j);
area.erase(area.begin()+j);
}else{
++j;
}
}
}
}
// 根据阈值筛选
vector<Bbox> RetinaFace::select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r){
vector<Bbox> results;
for(Bbox &box:bboxes){
if (float(box.score)>=threshold){
box.xmin=box.xmin/w_r;
box.ymin=box.ymin/h_r;
box.xmax=box.xmax/w_r;
box.ymax=box.ymax/h_r;
box.x1=box.x1/w_r;
box.y1=box.y1/h_r;
box.x2=box.x2/w_r;
box.y2=box.y2/h_r;
box.x3=box.x3/w_r;
box.y3=box.y3/h_r;
box.x4=box.x4/w_r;
box.y4=box.y4/h_r;
box.x5=box.x5/w_r;
box.y5=box.y5/h_r;
results.push_back(box);
}
}
return results;
}
// vector转Bbox
// vector<Bbox> RetinaFace::vec2Bbox(vector<vector<float>> bbox,float w_r,float h_r){
// vector<Bbox> result_bboxes;
// for(auto &c:bbox){
// Bbox tmp_box={.xmin=c[0]/w_r,.ymin=c[1]/h_r,.xmax=c[2]/w_r,.ymax=c[3]/h_r};
// result_bboxes.push_back(tmp_box);
// }
// return result_bboxes;
// }
// 数据后处理
vector<Bbox> RetinaFace::bbox_process(bool is_bbox_process,vector<Bbox> bboxes,float frame_w,float frame_h){
vector<Bbox> result_bboxes;
if(is_bbox_process){
for(Bbox &bbox:bboxes){
Bbox new_bbox;
float face_w=bbox.xmax-bbox.xmin;
float face_h=bbox.ymax-bbox.ymin;
new_bbox.xmin=bbox.xmin-face_w*0.15;
new_bbox.xmax=bbox.xmax+face_w*0.15;
new_bbox.ymin=bbox.ymin;
new_bbox.ymax=bbox.ymax+face_h*0.15;
new_bbox.xmin=new_bbox.xmin>0?new_bbox.xmin:0;
new_bbox.ymin=new_bbox.ymin>0?new_bbox.ymin:0;
new_bbox.xmax=new_bbox.xmax>frame_w?frame_w:new_bbox.xmax;
new_bbox.ymax=new_bbox.ymax>frame_h?frame_h:new_bbox.ymax;
new_bbox.score=bbox.score;
// cout<<bbox.x1<<endl;
new_bbox.x1=bbox.x1>0?bbox.x1:0;
new_bbox.y1=bbox.y1>0?bbox.y1:0;
new_bbox.x2=bbox.x2>0?bbox.x2:0;
new_bbox.y2=bbox.y2>0?bbox.y2:0;
new_bbox.x3=bbox.x3>0?bbox.x3:0;
new_bbox.y3=bbox.y3>0?bbox.y3:0;
new_bbox.x4=bbox.x4>0?bbox.x4:0;
new_bbox.y4=bbox.y4>0?bbox.y4:0;
new_bbox.x5=bbox.x5>0?bbox.x5:0;
new_bbox.y5=bbox.y5>0?bbox.y5:0;
result_bboxes.push_back(new_bbox);
}
}else{
for(Bbox &bbox:bboxes){
Bbox new_bbox;
float face_w=bbox.xmax-bbox.xmin;
float face_h=bbox.ymax-bbox.ymin;
new_bbox.xmin=bbox.xmin-face_w;
new_bbox.xmax=bbox.xmax+face_w;
new_bbox.ymin=bbox.ymin;
new_bbox.ymax=bbox.ymax+face_h;
new_bbox.xmin=new_bbox.xmin>0?new_bbox.xmin:0;
new_bbox.ymin=new_bbox.ymin>0?new_bbox.ymin:0;
new_bbox.xmax=new_bbox.xmax>frame_w?frame_w:new_bbox.xmax;
new_bbox.ymax=new_bbox.ymax>frame_h?frame_h:new_bbox.ymax;
new_bbox.score=bbox.score;
new_bbox.x1=bbox.x1>0?bbox.x1:0;
new_bbox.y1=bbox.y1>0?bbox.y1:0;
new_bbox.x2=bbox.x2>0?bbox.x2:0;
new_bbox.y2=bbox.y2>0?bbox.y2:0;
new_bbox.x3=bbox.x3>0?bbox.x3:0;
new_bbox.y3=bbox.y3>0?bbox.y3:0;
new_bbox.x4=bbox.x4>0?bbox.x4:0;
new_bbox.y4=bbox.y4>0?bbox.y4:0;
new_bbox.x5=bbox.x5>0?bbox.x5:0;
new_bbox.y5=bbox.y5>0?bbox.y5:0;
result_bboxes.push_back(new_bbox);
}
}
return result_bboxes;
}
// 推理
// vector<Bbox> RetinaFace::detect_image(Mat image){
vector<Bbox> RetinaFace::detect_image(Mat image){
float w_r=float(640)/float(image.cols);
float h_r=float(640)/float(image.rows);
cv::Mat blob = cv::dnn::blobFromImage(image,resize_scale,Size(input_size[0],input_size[1]),Scalar(mean_data[0],mean_data[1],mean_data[2])); // 由图片加载数据 这里还可以进行缩放、归一化等预处理
net.setInput(blob); // 设置模型输入
std::vector<cv::Mat> netOutputImg;
net.forward(netOutputImg,net.getUnconnectedOutLayersNames()); // 推理出结果
cv::Mat scores = netOutputImg[2].reshape(1,16800);
cv::Mat boxes = netOutputImg[0].reshape(1,16800);
cv::Mat landms = netOutputImg[1].reshape(1,16800);
vector<Bbox> result_boxes=decode(boxes,scores,landms,anchors,variances);
vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r);
nms_cpu(results,nms_threshold);
if(is_bbox_process){
vector<Bbox> res_bboxes=bbox_process(true,results,image.cols,image.rows);
return res_bboxes;
}else{
vector<Bbox> res_bboxes=bbox_process(false,results,image.cols,image.rows);
return res_bboxes;
}
}