face detection and aligh

从一个人脸数据集中获取人脸的五官位置

// caffe#include <caffe/caffe.hpp>#include <caffe/layers/memory_data_layer.hpp>// c++#include <string>#include <vector>#include <fstream>// opencv#include <opencv2/opencv.hpp>// boost#include "boost/make_shared.hpp"#include<iostream>#define CPU_ONLYusing namespace caffe;using namespace std;typedef struct FaceRect {  float x1;  float y1;  float x2;  float y2;  float score; /**< Larger score should mean higher confidence. */} FaceRect;typedef struct FacePts {  float x[5],y[5];} FacePts;typedef struct FaceInfo {  FaceRect bbox;  cv::Vec4f regression;  FacePts facePts;  double roll;  double pitch;  double yaw;} FaceInfo;class MTCNN { public:  MTCNN(const string& proto_model_dir);  void Detect(const cv::Mat& img, std::vector<FaceInfo> &faceInfo, int minSize, double* threshold, double factor); private:  bool CvMatToDatumSignalChannel(const cv::Mat& cv_mat, Datum* datum);  void Preprocess(const cv::Mat& img,                  std::vector<cv::Mat>* input_channels);  void WrapInputLayer(std::vector<cv::Mat>* input_channels,Blob<float>* input_layer,          const int height,const int width);  void SetMean();  void GenerateBoundingBox( Blob<float>* confidence,Blob<float>* reg,          float scale,float thresh,int image_width,int image_height);  void ClassifyFace(const std::vector<FaceInfo>& regressed_rects,cv::Mat &sample_single,        boost::shared_ptr<Net<float> >& net,double thresh,char netName);  void ClassifyFace_MulImage(const std::vector<FaceInfo> &regressed_rects, cv::Mat &sample_single,        boost::shared_ptr<Net<float> >& net, double thresh, char netName);  std::vector<FaceInfo> NonMaximumSuppression(std::vector<FaceInfo>& bboxes,float thresh,char methodType);  void Bbox2Square(std::vector<FaceInfo>& bboxes);  void Padding(int img_w, int img_h);  std::vector<FaceInfo> BoxRegress(std::vector<FaceInfo> &faceInfo_, int stage);  void RegressPoint(const std::vector<FaceInfo>& faceInfo); private:  boost::shared_ptr<Net<float> > PNet_;  boost::shared_ptr<Net<float> > RNet_;  boost::shared_ptr<Net<float> > ONet_;  // x1,y1,x2,t2 and score  std::vector<FaceInfo> condidate_rects_;  std::vector<FaceInfo> total_boxes_;  std::vector<FaceInfo> regressed_rects_;  std::vector<FaceInfo> regressed_pading_;  std::vector<cv::Mat> crop_img_;  int curr_feature_map_w_;  int curr_feature_map_h_;  int num_channels_;};// compare scorebool CompareBBox(const FaceInfo & a, const FaceInfo & b) {  return a.bbox.score > b.bbox.score;}// methodType : u is IoU(Intersection Over Union)// methodType : m is IoM(Intersection Over Maximum)std::vector<FaceInfo> MTCNN::NonMaximumSuppression(std::vector<FaceInfo>& bboxes,                float thresh,char methodType){  std::vector<FaceInfo> bboxes_nms;  std::sort(bboxes.begin(), bboxes.end(), CompareBBox);  int32_t select_idx = 0;  int32_t num_bbox = static_cast<int32_t>(bboxes.size());  std::vector<int32_t> mask_merged(num_bbox, 0);  bool all_merged = false;  while (!all_merged) {    while (select_idx < num_bbox && mask_merged[select_idx] == 1)      select_idx++;    if (select_idx == num_bbox) {      all_merged = true;      continue;    }    bboxes_nms.push_back(bboxes[select_idx]);    mask_merged[select_idx] = 1;    FaceRect select_bbox = bboxes[select_idx].bbox;    float area1 = static_cast<float>((select_bbox.x2-select_bbox.x1+1) * (select_bbox.y2-select_bbox.y1+1));    float x1 = static_cast<float>(select_bbox.x1);    float y1 = static_cast<float>(select_bbox.y1);    float x2 = static_cast<float>(select_bbox.x2);    float y2 = static_cast<float>(select_bbox.y2);    select_idx++;    for (int32_t i = select_idx; i < num_bbox; i++) {      if (mask_merged[i] == 1)        continue;      FaceRect& bbox_i = bboxes[i].bbox;      float x = std::max<float>(x1, static_cast<float>(bbox_i.x1));      float y = std::max<float>(y1, static_cast<float>(bbox_i.y1));      float w = std::min<float>(x2, static_cast<float>(bbox_i.x2)) - x + 1;      float h = std::min<float>(y2, static_cast<float>(bbox_i.y2)) - y + 1;      if (w <= 0 || h <= 0)        continue;      float area2 = static_cast<float>((bbox_i.x2-bbox_i.x1+1) * (bbox_i.y2-bbox_i.y1+1));      float area_intersect = w * h;      switch (methodType) {        case 'u':          if (static_cast<float>(area_intersect) / (area1 + area2 - area_intersect) > thresh)            mask_merged[i] = 1;          break;        case 'm':          if (static_cast<float>(area_intersect) / std::min(area1 , area2) > thresh)            mask_merged[i] = 1;          break;        default:          break;        }    }  }  return bboxes_nms;}void MTCNN::Bbox2Square(std::vector<FaceInfo>& bboxes){  for(int i=0;i<bboxes.size();i++){    float h = bboxes[i].bbox.x2 - bboxes[i].bbox.x1;    float w = bboxes[i].bbox.y2 - bboxes[i].bbox.y1;    float side = h>w ? h:w;    bboxes[i].bbox.x1 += (h-side)*0.5;    bboxes[i].bbox.y1 += (w-side)*0.5;    bboxes[i].bbox.x2 = (int)(bboxes[i].bbox.x1 + side);    bboxes[i].bbox.y2 = (int)(bboxes[i].bbox.y1 + side);    bboxes[i].bbox.x1 = (int)(bboxes[i].bbox.x1);    bboxes[i].bbox.y1 = (int)(bboxes[i].bbox.y1);  }}std::vector<FaceInfo> MTCNN::BoxRegress(std::vector<FaceInfo>& faceInfo,int stage){  std::vector<FaceInfo> bboxes;  for(int bboxId =0;bboxId<faceInfo.size();bboxId++){      FaceRect faceRect;      FaceInfo tempFaceInfo;      float regw = faceInfo[bboxId].bbox.y2 - faceInfo[bboxId].bbox.y1;      regw += (stage == 1)? 0:1;      float regh = faceInfo[bboxId].bbox.x2 - faceInfo[bboxId].bbox.x1;      regh += (stage == 1)? 0:1;      faceRect.y1 = faceInfo[bboxId].bbox.y1 + regw * faceInfo[bboxId].regression[0];      faceRect.x1 = faceInfo[bboxId].bbox.x1 + regh * faceInfo[bboxId].regression[1];      faceRect.y2 = faceInfo[bboxId].bbox.y2 + regw * faceInfo[bboxId].regression[2];      faceRect.x2 = faceInfo[bboxId].bbox.x2 + regh * faceInfo[bboxId].regression[3];      faceRect.score = faceInfo[bboxId].bbox.score;      tempFaceInfo.bbox = faceRect;      tempFaceInfo.regression = faceInfo[bboxId].regression;      if(stage == 3)        tempFaceInfo.facePts = faceInfo[bboxId].facePts;      bboxes.push_back(tempFaceInfo);  }  return bboxes;}// compute the padding coordinates (pad the bounding boxes to square)void MTCNN::Padding(int img_w,int img_h){  for(int i=0;i<regressed_rects_.size();i++){    FaceInfo tempFaceInfo;    tempFaceInfo = regressed_rects_[i];    tempFaceInfo.bbox.y2 = (regressed_rects_[i].bbox.y2 >= img_w) ? img_w : regressed_rects_[i].bbox.y2;    tempFaceInfo.bbox.x2 = (regressed_rects_[i].bbox.x2 >= img_h) ? img_h : regressed_rects_[i].bbox.x2;    tempFaceInfo.bbox.y1 = (regressed_rects_[i].bbox.y1 <1) ? 1 : regressed_rects_[i].bbox.y1;    tempFaceInfo.bbox.x1 = (regressed_rects_[i].bbox.x1 <1) ? 1 : regressed_rects_[i].bbox.x1;    regressed_pading_.push_back(tempFaceInfo);  }}void MTCNN::GenerateBoundingBox(Blob<float>* confidence,Blob<float>* reg,      float scale,float thresh,int image_width,int image_height){  int stride = 2;  int cellSize = 12;  int curr_feature_map_w_ = std::ceil((image_width - cellSize)*1.0/stride)+1;  int curr_feature_map_h_ = std::ceil((image_height - cellSize)*1.0/stride)+1;  //std::cout << "Feature_map_size:"<< curr_feature_map_w_ <<" "<<curr_feature_map_h_<<std::endl;  int regOffset = curr_feature_map_w_*curr_feature_map_h_;  // the first count numbers are confidence of face  int count = confidence->count()/2;  const float* confidence_data = confidence->cpu_data();  confidence_data += count;  const float* reg_data = reg->cpu_data();  condidate_rects_.clear();  for(int i=0;i<count;i++){    if(*(confidence_data+i)>=thresh){      int y = i / curr_feature_map_w_;      int x = i - curr_feature_map_w_ * y;      float xTop = (int)((x*stride+1)/scale);      float yTop = (int)((y*stride+1)/scale);      float xBot = (int)((x*stride+cellSize-1+1)/scale);      float yBot = (int)((y*stride+cellSize-1+1)/scale);      FaceRect faceRect;      faceRect.x1 = xTop;      faceRect.y1 = yTop;      faceRect.x2 = xBot;      faceRect.y2 = yBot;      faceRect.score  = *(confidence_data+i);      FaceInfo faceInfo;      faceInfo.bbox = faceRect;      faceInfo.regression = cv::Vec4f(reg_data[i+0*regOffset],reg_data[i+1*regOffset],reg_data[i+2*regOffset],reg_data[i+3*regOffset]);      condidate_rects_.push_back(faceInfo);    }  }}MTCNN::MTCNN(const std::string &proto_model_dir){#ifdef CPU_ONLY  Caffe::set_mode(Caffe::CPU);#else  Caffe::set_mode(Caffe::GPU);#endif  /* Load the network. */  PNet_.reset(new Net<float>((proto_model_dir+"/det1.prototxt"), TEST));  PNet_->CopyTrainedLayersFrom(proto_model_dir+"/det1.caffemodel");  CHECK_EQ(PNet_->num_inputs(), 1) << "Network should have exactly one input.";  CHECK_EQ(PNet_->num_outputs(),2) << "Network should have exactly two output, one"                                     " is bbox and another is confidence.";  #ifdef CPU_ONLY  RNet_.reset(new Net<float>((proto_model_dir+"/det2.prototxt"), TEST));  #else  RNet_.reset(new Net<float>((proto_model_dir+"/det2_input.prototxt"), TEST));  #endif  RNet_->CopyTrainedLayersFrom(proto_model_dir+"/det2.caffemodel");//  CHECK_EQ(RNet_->num_inputs(), 0) << "Network should have exactly one input.";//  CHECK_EQ(RNet_->num_outputs(),3) << "Network should have exactly two output, one"//                                     " is bbox and another is confidence.";  #ifdef CPU_ONLY  ONet_.reset(new Net<float>((proto_model_dir+"/det3.prototxt"), TEST));  #else  ONet_.reset(new Net<float>((proto_model_dir+"/det3_input.prototxt"), TEST));  #endif  ONet_->CopyTrainedLayersFrom(proto_model_dir+"/det3.caffemodel");//  CHECK_EQ(ONet_->num_inputs(), 1) << "Network should have exactly one input.";//  CHECK_EQ(ONet_->num_outputs(),3) << "Network should have exactly three output, one"//                                     " is bbox and another is confidence.";  Blob<float>* input_layer;  input_layer = PNet_->input_blobs()[0];  num_channels_ = input_layer->channels();  CHECK(num_channels_ == 3 || num_channels_ == 1) << "Input layer should have 1 or 3 channels.";}void MTCNN::WrapInputLayer(std::vector<cv::Mat>* input_channels,        Blob<float>* input_layer, const int height, const int width) {  float* input_data = input_layer->mutable_cpu_data();  for (int i = 0; i < input_layer->channels(); ++i) {    cv::Mat channel(height, width, CV_32FC1, input_data);    input_channels->push_back(channel);    input_data += width * height;  }}void MTCNN::ClassifyFace(const std::vector<FaceInfo>& regressed_rects,cv::Mat &sample_single,        boost::shared_ptr<Net<float> >& net,double thresh,char netName){  int numBox = regressed_rects.size();  Blob<float>* crop_input_layer = net->input_blobs()[0];  int input_channels = crop_input_layer->channels();  int input_width  = crop_input_layer->width();  int input_height = crop_input_layer->height();  crop_input_layer->Reshape(1, input_channels, input_width, input_height);  net->Reshape();  condidate_rects_.clear();  // load crop_img data to datum  for(int i=0;i<numBox;i++){    std::vector<cv::Mat> channels;    WrapInputLayer(&channels,net->input_blobs()[0],input_width,input_height);    int pad_top   = std::abs(regressed_pading_[i].bbox.x1 - regressed_rects[i].bbox.x1);    int pad_left  = std::abs(regressed_pading_[i].bbox.y1 - regressed_rects[i].bbox.y1);    int pad_right = std::abs(regressed_pading_[i].bbox.y2 - regressed_rects[i].bbox.y2);    int pad_bottom= std::abs(regressed_pading_[i].bbox.x2 - regressed_rects[i].bbox.x2);    cv::Mat crop_img = sample_single(cv::Range(regressed_pading_[i].bbox.y1-1,regressed_pading_[i].bbox.y2),                         cv::Range(regressed_pading_[i].bbox.x1-1,regressed_pading_[i].bbox.x2));    cv::copyMakeBorder(crop_img,crop_img,pad_left,pad_right,pad_top,pad_bottom,cv::BORDER_CONSTANT,cv::Scalar(0));    cv::resize(crop_img,crop_img,cv::Size(input_width,input_height),0,0,cv::INTER_AREA);    crop_img = (crop_img-127.5)*0.0078125;    cv::split(crop_img,channels);    CHECK(reinterpret_cast<float*>(channels.at(0).data) == net->input_blobs()[0]->cpu_data())          << "Input channels are not wrapping the input layer of the network.";    net->Forward();    int reg_id = 0;    int confidence_id = 1;    if(netName == 'o') confidence_id = 2;    const Blob<float>* reg = net->output_blobs()[reg_id];    const Blob<float>* confidence = net->output_blobs()[confidence_id];    // ONet points_offset != NULL    const Blob<float>* points_offset = net->output_blobs()[1];    const float* confidence_data = confidence->cpu_data() + confidence->count()/2;    const float* reg_data = reg->cpu_data();    const float* points_data;    if(netName == 'o') points_data = points_offset->cpu_data();    if(*(confidence_data) > thresh){      FaceRect faceRect;      faceRect.x1 = regressed_rects[i].bbox.x1;      faceRect.y1 = regressed_rects[i].bbox.y1;      faceRect.x2 = regressed_rects[i].bbox.x2;      faceRect.y2 = regressed_rects[i].bbox.y2 ;      faceRect.score  = *(confidence_data);      FaceInfo faceInfo;      faceInfo.bbox = faceRect;      faceInfo.regression = cv::Vec4f(reg_data[0],reg_data[1],reg_data[2],reg_data[3]);      // x x x x x y y y y y      if(netName == 'o'){        FacePts face_pts;        float w = faceRect.y2 - faceRect.y1 + 1;        float h = faceRect.x2 - faceRect.x1 + 1;        for(int j=0;j<5;j++){          face_pts.y[j] = faceRect.y1 + *(points_data+j) * h - 1;          face_pts.x[j] = faceRect.x1 + *(points_data+j+5) * w -1;        }        faceInfo.facePts = face_pts;      }      condidate_rects_.push_back(faceInfo);    }  }  regressed_pading_.clear();}// multi test image pass a forwardvoid MTCNN::ClassifyFace_MulImage(const std::vector<FaceInfo>& regressed_rects,cv::Mat &sample_single,        boost::shared_ptr<Net<float> >& net,double thresh,char netName){  condidate_rects_.clear();  int numBox = regressed_rects.size();  std::vector<Datum> datum_vector;  boost::shared_ptr<MemoryDataLayer<float> > mem_data_layer;  mem_data_layer = boost::static_pointer_cast<MemoryDataLayer<float> >(net->layers()[0]);  int input_width  = mem_data_layer->width();  int input_height = mem_data_layer->height();  // load crop_img data to datum  for(int i=0;i<numBox;i++){    int pad_top   = std::abs(regressed_pading_[i].bbox.x1 - regressed_rects[i].bbox.x1);    int pad_left  = std::abs(regressed_pading_[i].bbox.y1 - regressed_rects[i].bbox.y1);    int pad_right = std::abs(regressed_pading_[i].bbox.y2 - regressed_rects[i].bbox.y2);    int pad_bottom= std::abs(regressed_pading_[i].bbox.x2 - regressed_rects[i].bbox.x2);    cv::Mat crop_img = sample_single(cv::Range(regressed_pading_[i].bbox.y1-1,regressed_pading_[i].bbox.y2),                         cv::Range(regressed_pading_[i].bbox.x1-1,regressed_pading_[i].bbox.x2));    cv::copyMakeBorder(crop_img,crop_img,pad_left,pad_right,pad_top,pad_bottom,cv::BORDER_CONSTANT,cv::Scalar(0));    cv::resize(crop_img,crop_img,cv::Size(input_width,input_height),0,0,cv::INTER_AREA);    crop_img = (crop_img-127.5)*0.0078125;    Datum datum;    CvMatToDatumSignalChannel(crop_img,&datum);    datum_vector.push_back(datum);  }  regressed_pading_.clear();  /* extract the features and store */  mem_data_layer->set_batch_size(numBox);  mem_data_layer->AddDatumVector(datum_vector);  /* fire the network */  float no_use_loss = 0;  net->Forward(&no_use_loss);//  CHECK(reinterpret_cast<float*>(crop_img_set.at(0).data) == net->input_blobs()[0]->cpu_data())//          << "Input channels are not wrapping the input layer of the network.";  // return RNet/ONet result  std::string outPutLayerName = (netName == 'r' ? "conv5-2" : "conv6-2");  std::string pointsLayerName = "conv6-3";  const boost::shared_ptr<Blob<float> > reg = net->blob_by_name(outPutLayerName);  const boost::shared_ptr<Blob<float> > confidence = net->blob_by_name("prob1");  // ONet points_offset != NULL  const boost::shared_ptr<Blob<float> > points_offset = net->blob_by_name(pointsLayerName);  const float* confidence_data = confidence->cpu_data();  const float* reg_data = reg->cpu_data();  const float* points_data;  if(netName == 'o') points_data = points_offset->cpu_data();  for(int i=0;i<numBox;i++){    if(*(confidence_data+i*2+1) > thresh){      FaceRect faceRect;      faceRect.x1 = regressed_rects[i].bbox.x1;      faceRect.y1 = regressed_rects[i].bbox.y1;      faceRect.x2 = regressed_rects[i].bbox.x2;      faceRect.y2 = regressed_rects[i].bbox.y2 ;      faceRect.score  = *(confidence_data+i*2+1);      FaceInfo faceInfo;      faceInfo.bbox = faceRect;      faceInfo.regression = cv::Vec4f(reg_data[4*i+0],reg_data[4*i+1],reg_data[4*i+2],reg_data[4*i+3]);      // x x x x x y y y y y      if(netName == 'o'){        FacePts face_pts;        float w = faceRect.y2 - faceRect.y1 + 1;        float h = faceRect.x2 - faceRect.x1 + 1;        for(int j=0;j<5;j++){          face_pts.y[j] = faceRect.y1 + *(points_data+j+10*i) * h - 1;          face_pts.x[j] = faceRect.x1 + *(points_data+j+5+10*i) * w -1;        }        faceInfo.facePts = face_pts;      }      condidate_rects_.push_back(faceInfo);    }  }}bool MTCNN::CvMatToDatumSignalChannel(const cv::Mat& cv_mat, Datum* datum){  if (cv_mat.empty())    return false;  int channels = cv_mat.channels();  datum->set_channels(cv_mat.channels());  datum->set_height(cv_mat.rows);  datum->set_width(cv_mat.cols);  datum->set_label(0);  datum->clear_data();  datum->clear_float_data();  datum->set_encoded(false);  int datum_height = datum->height();  int datum_width  = datum->width();  if(channels == 3){    for(int c = 0;c < channels;c++){      for (int h = 0; h < datum_height; ++h){        for (int w = 0; w < datum_width; ++w){          const float* ptr = cv_mat.ptr<float>(h);          datum->add_float_data(ptr[w*channels+c]);        }      }    }  }  return true;}void MTCNN::Detect(const cv::Mat& image,std::vector<FaceInfo>& faceInfo,int minSize,double* threshold,double factor){  // 2~3ms  // invert to RGB color space and float type  cv::Mat sample_single,resized;  image.convertTo(sample_single,CV_32FC3);  cv::cvtColor(sample_single,sample_single,cv::COLOR_BGR2RGB);  sample_single = sample_single.t();  int height = image.rows;  int width  = image.cols;  int minWH = std::min(height,width);  int factor_count = 0;  double m = 12./minSize;  minWH *= m;  std::vector<double> scales;  while (minWH >=24)  {    scales.push_back(m * std::pow(factor,factor_count));    minWH *= factor;    ++factor_count;  }  // 11ms main consum  Blob<float>* input_layer = PNet_->input_blobs()[0];  for(int i=0;i<factor_count;i++)  {    double scale = scales[i];    int ws = std::ceil(height*scale);    int hs = std::ceil(width*scale);    // wrap image and normalization using INTER_AREA method    cv::resize(sample_single,resized,cv::Size(ws,hs),0,0,cv::INTER_AREA);    resized.convertTo(resized, CV_32FC3, 0.0078125,-127.5*0.0078125);    // input data    input_layer->Reshape(1, 3, hs, ws);    PNet_->Reshape();    std::vector<cv::Mat> input_channels;    WrapInputLayer(&input_channels,PNet_->input_blobs()[0],hs,ws);    cv::split(resized,input_channels);    // check data transform right    CHECK(reinterpret_cast<float*>(input_channels.at(0).data) == PNet_->input_blobs()[0]->cpu_data())        << "Input channels are not wrapping the input layer of the network.";    PNet_->Forward();    // return result    Blob<float>* reg = PNet_->output_blobs()[0];    //const float* reg_data = reg->cpu_data();    Blob<float>* confidence = PNet_->output_blobs()[1];    GenerateBoundingBox(confidence, reg, scale, threshold[0],ws,hs);    std::vector<FaceInfo> bboxes_nms = NonMaximumSuppression(condidate_rects_,0.5,'u');    total_boxes_.insert(total_boxes_.end(),bboxes_nms.begin(),bboxes_nms.end());  }  int numBox = total_boxes_.size();  if(numBox != 0){    total_boxes_ = NonMaximumSuppression(total_boxes_,0.7,'u');    regressed_rects_ = BoxRegress(total_boxes_,1);    total_boxes_.clear();    Bbox2Square(regressed_rects_);    Padding(width,height);    /// Second stage    #ifdef CPU_ONLY    ClassifyFace(regressed_rects_,sample_single,RNet_,threshold[1],'r');    #else    ClassifyFace_MulImage(regressed_rects_,sample_single,RNet_,threshold[1],'r');    #endif    condidate_rects_ = NonMaximumSuppression(condidate_rects_,0.7,'u');    regressed_rects_ = BoxRegress(condidate_rects_,2);    Bbox2Square(regressed_rects_);    Padding(width,height);    /// three stage    numBox = regressed_rects_.size();    if(numBox != 0){      #ifdef CPU_ONLY      ClassifyFace(regressed_rects_,sample_single,ONet_,threshold[2],'o');      #else      ClassifyFace_MulImage(regressed_rects_,sample_single,ONet_,threshold[2],'o');      #endif      regressed_rects_ = BoxRegress(condidate_rects_,3);      faceInfo = NonMaximumSuppression(regressed_rects_,0.7,'m');    }  }  regressed_pading_.clear();  regressed_rects_.clear();  condidate_rects_.clear();}cv::Mat getwarpAffineImg(cv::Mat &src,cv::Point2f leftEye,cv::Point2f rightEye){    //计算两眼中心点，按照此中心点进行旋转， 第31个为左眼坐标，36为右眼坐标   cv:: Point2f eyesCenter = cv::Point2f( (leftEye.x + rightEye.x) * 0.5f, (leftEye.y + rightEye.y) * 0.5f );        // 计算两个眼睛间的角度    double dy = (rightEye.y - leftEye.y);    double dx = (rightEye.x - leftEye.x);    double angle = atan2(dy, dx) * 180.0/CV_PI; // Convert from radians to degrees.        //由eyesCenter, andle, scale按照公式计算仿射变换矩阵，此时1.0表示不进行缩放   cv::Mat rot_mat = getRotationMatrix2D(eyesCenter, angle, 1.0);    // 进行仿射变换，变换后大小为src的大小    cv::Mat rot;    warpAffine(src, rot, rot_mat, src.size());    //imwrite("rot.jpg",rot);    return rot;}int main(int argc,char **argv){  double threshold[3] = {0.6,0.7,0.7};  double factor = 0.709;  int minSize = 40;  std::string proto_model_dir = "caffe/examples/MTSrc/MTmodel";//模型存放位置包括三个.prototxt和三个.model  std::string pic_root_dir = "pic/";//人脸数据集的位置  std::string ffp_root_dir = "./"   MTCNN detector(proto_model_dir);  //读取列表对每个图像进行处理  ifstream fin("dataset/lfw_all.list");//人脸数据集的列表  string img_path;  ofstream outfile;//保存图像及其信息的文件  outfile.open("result.txt");  while(getline(fin,img_path))  {      cv::Mat image0 = cv::imread(pic_root_dir + img_path);      cout<<img_path<<endl;      cv::Mat image;      cv::resize(image0,image,cv::Size(image0.cols,image0.rows));      std::vector<FaceInfo> faceInfo;      std::cout <<"Detect "<<image.rows<<"X"<<image.cols;      detector.Detect(image,faceInfo,minSize,threshold,factor);      //只画一张人脸      int face_num = 0;      if(faceInfo.size()>=1){           face_num = 1;      }else{          face_num = -1;      }       //描述五官的位置      cv::Mat warped_img;      for(int i=0;i<face_num;i++){        FacePts facePts = faceInfo[i].facePts;        //保存人脸的坐标信息        outfile<<img_path<<" ";        for(int j=0;j<5;j++){            outfile << facePts.y[j]<<" "<<facePts.x[j]<<" ";        }        outfile<<endl;           }      }  outfile.close();  printf("execute succeed\n");  return 1;}