retinaface.cpp
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#include "retinaface.h"
// 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(float *loc,float *score,float *pre,vector<vector<float>> priors,vector<float> variances){
vector<float> input_size={640,640};
float resize_scale=1.0;
vector<Bbox> boxes;
for(int i=0;i<priors.size();++i){
float b1=priors[i][0]+loc[4*i]*variances[0]*priors[i][2];
float b2=priors[i][1]+loc[4*i+1]*variances[0]*priors[i][3];
float b3=priors[i][2]*exp(loc[4*i+2]*variances[1]);
float b4=priors[i][3]*exp(loc[4*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[10*i]*variances[0]*priors[i][2];
float l2=priors[i][1]+pre[10*i+1]*variances[0]*priors[i][3];
float l3=priors[i][0]+pre[10*i+2]*variances[0]*priors[i][2];
float l4=priors[i][1]+pre[10*i+3]*variances[0]*priors[i][3];
float l5=priors[i][0]+pre[10*i+4]*variances[0]*priors[i][2];
float l6=priors[i][1]+pre[10*i+5]*variances[0]*priors[i][3];
float l7=priors[i][0]+pre[10*i+6]*variances[0]*priors[i][2];
float l8=priors[i][1]+pre[10*i+7]*variances[0]*priors[i][3];
float l9=priors[i][0]+pre[10*i+8]*variances[0]*priors[i][2];
float l10=priors[i][1]+pre[10*i+9]*variances[0]*priors[i][3];
b1>0?b1:0;
b2>0?b2:0;
b3>640?640:b3;
b4>640?640:b4;
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[2*i+1],.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;
}
//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;
}
// 数据后处理
vector<Bbox> RetinaFace::bbox_process(vector<Bbox> bboxes,float frame_w,float frame_h){
vector<Bbox> result_bboxes;
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;
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(string image_path){
Mat image = cv::imread(image_path);
float w_r=float(input_size[0])/float(image.cols);
float h_r=float(input_size[1])/float(image.rows);
Mat input_data;
cv::resize(image,input_data,Size(input_size[0],input_size[1]));
input_data = input_data-mean;
input_data.convertTo(input_data, CV_32F);
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
int nPlaneSize = input_size[0] * input_size[1];
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
// //推理
net->runSession(session);
auto output0= net->getSessionOutput(session, "output0");
auto output1= net->getSessionOutput(session, "output1");
auto output2= net->getSessionOutput(session, "output2");
MNN::Tensor feat_tensor0(output0, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE);
output0->copyToHostTensor(&feat_tensor0);
output1->copyToHostTensor(&feat_tensor1);
output2->copyToHostTensor(&feat_tensor2);
auto loc = feat_tensor0.host<float>();
auto score = feat_tensor1.host<float>();
auto landm = feat_tensor2.host<float>();
vector<Bbox> result_boxes = decode(loc,score,landm,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(results,image.cols,image.rows);
return res_bboxes;
}else{
return results;
}
}
vector<Bbox> RetinaFace::detect_image(Mat image){
float w_r=float(input_size[0])/float(image.cols);
float h_r=float(input_size[1])/float(image.rows);
Mat input_data;
cv::resize(image,input_data,Size(input_size[0],input_size[1]));
input_data = input_data-mean;
input_data.convertTo(input_data, CV_32F);
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
int nPlaneSize = input_size[0] * input_size[1];
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
// //推理
net->runSession(session);
auto output0= net->getSessionOutput(session, "output0");
auto output1= net->getSessionOutput(session, "output1");
auto output2= net->getSessionOutput(session, "output2");
MNN::Tensor feat_tensor0(output0, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE);
output0->copyToHostTensor(&feat_tensor0);
output1->copyToHostTensor(&feat_tensor1);
output2->copyToHostTensor(&feat_tensor2);
auto loc = feat_tensor0.host<float>();
auto score = feat_tensor1.host<float>();
auto landm = feat_tensor2.host<float>();
vector<Bbox> result_boxes = decode(loc,score,landm,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(results,image.cols,image.rows);
return res_bboxes;
}else{
return results;
}
}