实例介绍
【实例简介】
轻量级的MTCNN实现,无需caffe和tensorflow基本库支持,内部实现卷积运算,速度比普通mtcnn要快一倍。
【实例截图】【核心代码】
#include "mtcnn.h" Pnet::Pnet(){ Pthreshold = 0.6; nms_threshold = 0.5; firstFlag = true; this->rgb = new pBox; this->conv1_matrix = new pBox; this->conv1 = new pBox; this->maxPooling1 = new pBox; this->maxPooling_matrix = new pBox; this->conv2 = new pBox; this->conv3_matrix = new pBox; this->conv3 = new pBox; this->score_matrix = new pBox; this->score_ = new pBox; this->location_matrix = new pBox; this->location_ = new pBox; this->conv1_wb = new Weight; this->prelu_gmma1 = new pRelu; this->conv2_wb = new Weight; this->prelu_gmma2 = new pRelu; this->conv3_wb = new Weight; this->prelu_gmma3 = new pRelu; this->conv4c1_wb = new Weight; this->conv4c2_wb = new Weight; // w sc lc ks s p long conv1 = initConvAndFc(this->conv1_wb, 10, 3, 3, 1, 0); initpRelu(this->prelu_gmma1, 10); long conv2 = initConvAndFc(this->conv2_wb, 16, 10, 3, 1, 0); initpRelu(this->prelu_gmma2, 16); long conv3 = initConvAndFc(this->conv3_wb, 32, 16, 3, 1, 0); initpRelu(this->prelu_gmma3, 32); long conv4c1 = initConvAndFc(this->conv4c1_wb, 2, 32, 1, 1, 0); long conv4c2 = initConvAndFc(this->conv4c2_wb, 4, 32, 1, 1, 0); long dataNumber[13] = {conv1,10,10, conv2,16,16, conv3,32,32, conv4c1,2, conv4c2,4}; mydataFmt *pointTeam[13] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \ this->conv2_wb->pdata, this->conv2_wb->pbias, this->prelu_gmma2->pdata, \ this->conv3_wb->pdata, this->conv3_wb->pbias, this->prelu_gmma3->pdata, \ this->conv4c1_wb->pdata, this->conv4c1_wb->pbias, \ this->conv4c2_wb->pdata, this->conv4c2_wb->pbias \ }; string filename = "Pnet.txt"; readData(filename, dataNumber, pointTeam); } Pnet::~Pnet(){ freepBox(this->rgb); freepBox(this->conv1); freepBox(this->maxPooling1); freepBox(this->conv2); freepBox(this->conv3); freepBox(this->score_); freepBox(this->location_); freepBox(this->conv1_matrix); freeWeight(this->conv1_wb); freepRelu(this->prelu_gmma1); freepBox(this->maxPooling_matrix); freeWeight(this->conv2_wb); freepBox(this->conv3_matrix); freepRelu(this->prelu_gmma2); freeWeight(this->conv3_wb); freepBox(this->score_matrix); freepRelu(this->prelu_gmma3); freeWeight(this->conv4c1_wb); freepBox(this->location_matrix); freeWeight(this->conv4c2_wb); } void Pnet::run(Mat &image, float scale){ if(firstFlag){ image2MatrixInit(image, this->rgb); feature2MatrixInit(this->rgb, this->conv1_matrix, this->conv1_wb); convolutionInit(this->conv1_wb, this->rgb, this->conv1, this->conv1_matrix); maxPoolingInit(this->conv1, this->maxPooling1, 2, 2); feature2MatrixInit(this->maxPooling1, this->maxPooling_matrix, this->conv2_wb); convolutionInit(this->conv2_wb, this->maxPooling1, this->conv2, this->maxPooling_matrix); feature2MatrixInit(this->conv2, this->conv3_matrix, this->conv3_wb); convolutionInit(this->conv3_wb, this->conv2, this->conv3, this->conv3_matrix); feature2MatrixInit(this->conv3, this->score_matrix, this->conv4c1_wb); convolutionInit(this->conv4c1_wb, this->conv3, this->score_, this->score_matrix); feature2MatrixInit(this->conv3, this->location_matrix, this->conv4c2_wb); convolutionInit(this->conv4c2_wb, this->conv3, this->location_, this->location_matrix); firstFlag = false; } image2Matrix(image, this->rgb); feature2Matrix(this->rgb, this->conv1_matrix, this->conv1_wb); convolution(this->conv1_wb, this->rgb, this->conv1, this->conv1_matrix); prelu(this->conv1, this->conv1_wb->pbias, this->prelu_gmma1->pdata); //Pooling layer maxPooling(this->conv1, this->maxPooling1, 2, 2); feature2Matrix(this->maxPooling1, this->maxPooling_matrix, this->conv2_wb); convolution(this->conv2_wb, this->maxPooling1, this->conv2, this->maxPooling_matrix); prelu(this->conv2, this->conv2_wb->pbias, this->prelu_gmma2->pdata); //conv3 feature2Matrix(this->conv2, this->conv3_matrix, this->conv3_wb); convolution(this->conv3_wb, this->conv2, this->conv3, this->conv3_matrix); prelu(this->conv3, this->conv3_wb->pbias, this->prelu_gmma3->pdata); //conv4c1 score feature2Matrix(this->conv3, this->score_matrix, this->conv4c1_wb); convolution(this->conv4c1_wb, this->conv3, this->score_, this->score_matrix); addbias(this->score_, this->conv4c1_wb->pbias); softmax(this->score_); // pBoxShow(this->score_); //conv4c2 location feature2Matrix(this->conv3, this->location_matrix, this->conv4c2_wb); convolution(this->conv4c2_wb, this->conv3, this->location_, this->location_matrix); addbias(this->location_, this->conv4c2_wb->pbias); //softmax layer generateBbox(this->score_, this->location_, scale); } void Pnet::generateBbox(const struct pBox *score, const struct pBox *location, mydataFmt scale){ //for pooling int stride = 2; int cellsize = 12; int count = 0; //score p mydataFmt *p = score->pdata score->width*score->height; mydataFmt *plocal = location->pdata; struct Bbox bbox; struct orderScore order; for(int row=0;row<score->height;row ){ for(int col=0;col<score->width;col ){ if(*p>Pthreshold){ bbox.score = *p; order.score = *p; order.oriOrder = count; bbox.x1 = round((stride*row 1)/scale); bbox.y1 = round((stride*col 1)/scale); bbox.x2 = round((stride*row 1 cellsize)/scale); bbox.y2 = round((stride*col 1 cellsize)/scale); bbox.exist = true; bbox.area = (bbox.x2 - bbox.x1)*(bbox.y2 - bbox.y1); for(int channel=0;channel<4;channel ) bbox.regreCoord[channel]=*(plocal channel*location->width*location->height); boundingBox_.push_back(bbox); bboxScore_.push_back(order); count ; } p ; plocal ; } } } Rnet::Rnet(){ Rthreshold = 0.7; this->rgb = new pBox; this->conv1_matrix = new pBox; this->conv1_out = new pBox; this->pooling1_out = new pBox; this->conv2_matrix = new pBox; this->conv2_out = new pBox; this->pooling2_out = new pBox; this->conv3_matrix = new pBox; this->conv3_out = new pBox; this->fc4_out = new pBox; this->score_ = new pBox; this->location_ = new pBox; this->conv1_wb = new Weight; this->prelu_gmma1 = new pRelu; this->conv2_wb = new Weight; this->prelu_gmma2 = new pRelu; this->conv3_wb = new Weight; this->prelu_gmma3 = new pRelu; this->fc4_wb = new Weight; this->prelu_gmma4 = new pRelu; this->score_wb = new Weight; this->location_wb = new Weight; // // w sc lc ks s p long conv1 = initConvAndFc(this->conv1_wb, 28, 3, 3, 1, 0); initpRelu(this->prelu_gmma1, 28); long conv2 = initConvAndFc(this->conv2_wb, 48, 28, 3, 1, 0); initpRelu(this->prelu_gmma2, 48); long conv3 = initConvAndFc(this->conv3_wb, 64, 48, 2, 1, 0); initpRelu(this->prelu_gmma3, 64); long fc4 = initConvAndFc(this->fc4_wb, 128, 576, 1, 1, 0); initpRelu(this->prelu_gmma4, 128); long score = initConvAndFc(this->score_wb, 2, 128, 1, 1, 0); long location = initConvAndFc(this->location_wb, 4, 128, 1, 1, 0); long dataNumber[16] = {conv1,28,28, conv2,48,48, conv3,64,64, fc4,128,128, score,2, location,4}; mydataFmt *pointTeam[16] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \ this->conv2_wb->pdata, this->conv2_wb->pbias, this->prelu_gmma2->pdata, \ this->conv3_wb->pdata, this->conv3_wb->pbias, this->prelu_gmma3->pdata, \ this->fc4_wb->pdata, this->fc4_wb->pbias, this->prelu_gmma4->pdata, \ this->score_wb->pdata, this->score_wb->pbias, \ this->location_wb->pdata, this->location_wb->pbias \ }; string filename = "Rnet.txt"; readData(filename, dataNumber, pointTeam); //Init the network RnetImage2MatrixInit(rgb); feature2MatrixInit(this->rgb, this->conv1_matrix, this->conv1_wb); convolutionInit(this->conv1_wb, this->rgb, this->conv1_out, this->conv1_matrix); maxPoolingInit(this->conv1_out, this->pooling1_out, 3, 2); feature2MatrixInit(this->pooling1_out, this->conv2_matrix, this->conv2_wb); convolutionInit(this->conv2_wb, this->pooling1_out, this->conv2_out, this->conv2_matrix); maxPoolingInit(this->conv2_out, this->pooling2_out, 3, 2); feature2MatrixInit(this->pooling2_out, this->conv3_matrix, this->conv3_wb); convolutionInit(this->conv3_wb, this->pooling2_out, this->conv3_out, this->conv3_matrix); fullconnectInit(this->fc4_wb, this->fc4_out); fullconnectInit(this->score_wb, this->score_); fullconnectInit(this->location_wb, this->location_); } Rnet::~Rnet(){ freepBox(this->rgb); freepBox(this->conv1_matrix); freepBox(this->conv1_out); freepBox(this->pooling1_out); freepBox(this->conv2_matrix); freepBox(this->conv2_out); freepBox(this->pooling2_out); freepBox(this->conv3_matrix); freepBox(this->conv3_out); freepBox(this->fc4_out); freepBox(this->score_); freepBox(this->location_); freeWeight(this->conv1_wb); freepRelu(this->prelu_gmma1); freeWeight(this->conv2_wb); freepRelu(this->prelu_gmma2); freeWeight(this->conv3_wb); freepRelu(this->prelu_gmma3); freeWeight(this->fc4_wb); freepRelu(this->prelu_gmma4); freeWeight(this->score_wb); freeWeight(this->location_wb); } void Rnet::RnetImage2MatrixInit(struct pBox *pbox){ pbox->channel = 3; pbox->height = 24; pbox->width = 24; pbox->pdata = (mydataFmt *)malloc(pbox->channel*pbox->height*pbox->width*sizeof(mydataFmt)); if(pbox->pdata==NULL)cout<<"the image2MatrixInit is failed!!"<<endl; memset(pbox->pdata, 0, pbox->channel*pbox->height*pbox->width*sizeof(mydataFmt)); } void Rnet::run(Mat &image){ image2Matrix(image, this->rgb); feature2Matrix(this->rgb, this->conv1_matrix, this->conv1_wb); convolution(this->conv1_wb, this->rgb, this->conv1_out, this->conv1_matrix); prelu(this->conv1_out, this->conv1_wb->pbias, this->prelu_gmma1->pdata); maxPooling(this->conv1_out, this->pooling1_out, 3, 2); feature2Matrix(this->pooling1_out, this->conv2_matrix, this->conv2_wb); convolution(this->conv2_wb, this->pooling1_out, this->conv2_out, this->conv2_matrix); prelu(this->conv2_out, this->conv2_wb->pbias, this->prelu_gmma2->pdata); maxPooling(this->conv2_out, this->pooling2_out, 3, 2); //conv3 feature2Matrix(this->pooling2_out, this->conv3_matrix, this->conv3_wb); convolution(this->conv3_wb, this->pooling2_out, this->conv3_out, this->conv3_matrix); prelu(this->conv3_out, this->conv3_wb->pbias, this->prelu_gmma3->pdata); //flatten fullconnect(this->fc4_wb, this->conv3_out, this->fc4_out); prelu(this->fc4_out, this->fc4_wb->pbias, this->prelu_gmma4->pdata); //conv51 score fullconnect(this->score_wb, this->fc4_out, this->score_); addbias(this->score_, this->score_wb->pbias); softmax(this->score_); //conv5_2 location fullconnect(this->location_wb, this->fc4_out, this->location_); addbias(this->location_, this->location_wb->pbias); // pBoxShow(location_); } Onet::Onet(){ Othreshold = 0.8; this->rgb = new pBox; this->conv1_matrix = new pBox; this->conv1_out = new pBox; this->pooling1_out = new pBox; this->conv2_matrix = new pBox; this->conv2_out = new pBox; this->pooling2_out = new pBox; this->conv3_matrix = new pBox; this->conv3_out = new pBox; this->pooling3_out = new pBox; this->conv4_matrix = new pBox; this->conv4_out = new pBox; this->fc5_out = new pBox; this->score_ = new pBox; this->location_ = new pBox; this->keyPoint_ = new pBox; this->conv1_wb = new Weight; this->prelu_gmma1 = new pRelu; this->conv2_wb = new Weight; this->prelu_gmma2 = new pRelu; this->conv3_wb = new Weight; this->prelu_gmma3 = new pRelu; this->conv4_wb = new Weight; this->prelu_gmma4 = new pRelu; this->fc5_wb = new Weight; this->prelu_gmma5 = new pRelu; this->score_wb = new Weight; this->location_wb = new Weight; this->keyPoint_wb = new Weight; // // w sc lc ks s p long conv1 = initConvAndFc(this->conv1_wb, 32, 3, 3, 1, 0); initpRelu(this->prelu_gmma1, 32); long conv2 = initConvAndFc(this->conv2_wb, 64, 32, 3, 1, 0); initpRelu(this->prelu_gmma2, 64); long conv3 = initConvAndFc(this->conv3_wb, 64, 64, 3, 1, 0); initpRelu(this->prelu_gmma3, 64); long conv4 = initConvAndFc(this->conv4_wb, 128, 64, 2, 1, 0); initpRelu(this->prelu_gmma4, 128); long fc5 = initConvAndFc(this->fc5_wb, 256, 1152, 1, 1, 0); initpRelu(this->prelu_gmma5, 256); long score = initConvAndFc(this->score_wb, 2, 256, 1, 1, 0); long location = initConvAndFc(this->location_wb, 4, 256, 1, 1, 0); long keyPoint = initConvAndFc(this->keyPoint_wb, 10, 256, 1, 1, 0); long dataNumber[21] = {conv1,32,32, conv2,64,64, conv3,64,64, conv4,128,128, fc5,256,256, score,2, location,4, keyPoint,10}; mydataFmt *pointTeam[21] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \ this->conv2_wb->pdata, this->conv2_wb->pbias, this->prelu_gmma2->pdata, \ this->conv3_wb->pdata, this->conv3_wb->pbias, this->prelu_gmma3->pdata, \ this->conv4_wb->pdata, this->conv4_wb->pbias, this->prelu_gmma4->pdata, \ this->fc5_wb->pdata, this->fc5_wb->pbias, this->prelu_gmma5->pdata, \ this->score_wb->pdata, this->score_wb->pbias, \ this->location_wb->pdata, this->location_wb->pbias, \ this->keyPoint_wb->pdata, this->keyPoint_wb->pbias \ }; string filename = "Onet.txt"; readData(filename, dataNumber, pointTeam); //Init the network OnetImage2MatrixInit(rgb); feature2MatrixInit(this->rgb, this->conv1_matrix, this->conv1_wb); convolutionInit(this->conv1_wb, this->rgb, this->conv1_out, this->conv1_matrix); maxPoolingInit(this->conv1_out, this->pooling1_out, 3, 2); feature2MatrixInit(this->pooling1_out, this->conv2_matrix, this->conv2_wb); convolutionInit(this->conv2_wb, this->pooling1_out, this->conv2_out, this->conv2_matrix); maxPoolingInit(this->conv2_out, this->pooling2_out, 3, 2); feature2MatrixInit(this->pooling2_out, this->conv3_matrix, this->conv3_wb); convolutionInit(this->conv3_wb, this->pooling2_out, this->conv3_out, this->conv3_matrix); maxPoolingInit(this->conv3_out, this->pooling3_out, 2, 2); feature2MatrixInit(this->pooling3_out, this->conv4_matrix, this->conv4_wb); convolutionInit(this->conv4_wb, this->pooling3_out, this->conv4_out, this->conv4_matrix); fullconnectInit(this->fc5_wb, this->fc5_out); fullconnectInit(this->score_wb, this->score_); fullconnectInit(this->location_wb, this->location_); fullconnectInit(this->keyPoint_wb, this->keyPoint_); } Onet::~Onet(){ freepBox(this->rgb); freepBox(this->conv1_matrix); freepBox(this->conv1_out); freepBox(this->pooling1_out); freepBox(this->conv2_matrix); freepBox(this->conv2_out); freepBox(this->pooling2_out); freepBox(this->conv3_matrix); freepBox(this->conv3_out); freepBox(this->pooling3_out); freepBox(this->conv4_matrix); freepBox(this->conv4_out); freepBox(this->fc5_out); freepBox(this->score_); freepBox(this->location_); freepBox(this->keyPoint_); freeWeight(this->conv1_wb); freepRelu(this->prelu_gmma1); freeWeight(this->conv2_wb); freepRelu(this->prelu_gmma2); freeWeight(this->conv3_wb); freepRelu(this->prelu_gmma3); freeWeight(this->conv4_wb); freepRelu(this->prelu_gmma4); freeWeight(this->fc5_wb); freepRelu(this->prelu_gmma5); freeWeight(this->score_wb); freeWeight(this->location_wb); freeWeight(this->keyPoint_wb); } void Onet::OnetImage2MatrixInit(struct pBox *pbox){ pbox->channel = 3; pbox->height = 48; pbox->width = 48; pbox->pdata = (mydataFmt *)malloc(pbox->channel*pbox->height*pbox->width*sizeof(mydataFmt)); if(pbox->pdata==NULL)cout<<"the image2MatrixInit is failed!!"<<endl; memset(pbox->pdata, 0, pbox->channel*pbox->height*pbox->width*sizeof(mydataFmt)); } void Onet::run(Mat &image){ image2Matrix(image, this->rgb); feature2Matrix(this->rgb, this->conv1_matrix, this->conv1_wb); convolution(this->conv1_wb, this->rgb, this->conv1_out, this->conv1_matrix); prelu(this->conv1_out, this->conv1_wb->pbias, this->prelu_gmma1->pdata); //Pooling layer maxPooling(this->conv1_out, this->pooling1_out, 3, 2); feature2Matrix(this->pooling1_out, this->conv2_matrix, this->conv2_wb); convolution(this->conv2_wb, this->pooling1_out, this->conv2_out, this->conv2_matrix); prelu(this->conv2_out, this->conv2_wb->pbias, this->prelu_gmma2->pdata); maxPooling(this->conv2_out, this->pooling2_out, 3, 2); //conv3 feature2Matrix(this->pooling2_out, this->conv3_matrix, this->conv3_wb); convolution(this->conv3_wb, this->pooling2_out, this->conv3_out, this->conv3_matrix); prelu(this->conv3_out, this->conv3_wb->pbias, this->prelu_gmma3->pdata); maxPooling(this->conv3_out, this->pooling3_out, 2, 2); //conv4 feature2Matrix(this->pooling3_out, this->conv4_matrix, this->conv4_wb); convolution(this->conv4_wb, this->pooling3_out, this->conv4_out, this->conv4_matrix); prelu(this->conv4_out, this->conv4_wb->pbias, this->prelu_gmma4->pdata); fullconnect(this->fc5_wb, this->conv4_out, this->fc5_out); prelu(this->fc5_out, this->fc5_wb->pbias, this->prelu_gmma5->pdata); //conv6_1 score fullconnect(this->score_wb, this->fc5_out, this->score_); addbias(this->score_, this->score_wb->pbias); softmax(this->score_); // pBoxShow(this->score_); //conv6_2 location fullconnect(this->location_wb, this->fc5_out, this->location_); addbias(this->location_, this->location_wb->pbias); // pBoxShow(location_); //conv6_2 location fullconnect(this->keyPoint_wb, this->fc5_out, this->keyPoint_); addbias(this->keyPoint_, this->keyPoint_wb->pbias); // pBoxShow(keyPoint_); } mtcnn::mtcnn(int row, int col){ nms_threshold[0] = 0.7; nms_threshold[1] = 0.7; nms_threshold[2] = 0.7; float minl = row>col?row:col; int MIN_DET_SIZE = 12; int minsize = 60; float m = (float)MIN_DET_SIZE/minsize; minl *= m; float factor = 0.709; int factor_count = 0; while(minl>MIN_DET_SIZE){ if(factor_count>0)m = m*factor; scales_.push_back(m); minl *= factor; factor_count ; } float minside = row<col ? row : col; int count = 0; for (vector<float>::iterator it = scales_.begin(); it != scales_.end(); it ){ if (*it > 1){ cout << "the minsize is too small" << endl; while (1); } if (*it < (MIN_DET_SIZE / minside)){ scales_.resize(count); break; } count ; } simpleFace_ = new Pnet[scales_.size()]; } mtcnn::~mtcnn(){ delete []simpleFace_; } void mtcnn::findFace(Mat &image){ struct orderScore order; int count = 0; for (size_t i = 0; i < scales_.size(); i ) { int changedH = (int)ceil(image.rows*scales_.at(i)); int changedW = (int)ceil(image.cols*scales_.at(i)); resize(image, reImage, Size(changedW, changedH), 0, 0, cv::INTER_LINEAR); simpleFace_[i].run(reImage, scales_.at(i)); nms(simpleFace_[i].boundingBox_, simpleFace_[i].bboxScore_, simpleFace_[i].nms_threshold); for(vector<struct Bbox>::iterator it=simpleFace_[i].boundingBox_.begin(); it!=simpleFace_[i].boundingBox_.end();it ){ if((*it).exist){ firstBbox_.push_back(*it); order.score = (*it).score; order.oriOrder = count; firstOrderScore_.push_back(order); count ; } } simpleFace_[i].bboxScore_.clear(); simpleFace_[i].boundingBox_.clear(); } //the first stage's nms if(count<1)return; nms(firstBbox_, firstOrderScore_, nms_threshold[0]); refineAndSquareBbox(firstBbox_, image.rows, image.cols); //second stage count = 0; for(vector<struct Bbox>::iterator it=firstBbox_.begin(); it!=firstBbox_.end();it ){ if((*it).exist){ Rect temp((*it).y1, (*it).x1, (*it).y2-(*it).y1, (*it).x2-(*it).x1); Mat secImage; resize(image(temp), secImage, Size(24, 24), 0, 0, cv::INTER_LINEAR); refineNet.run(secImage); if(*(refineNet.score_->pdata 1)>refineNet.Rthreshold){ memcpy(it->regreCoord, refineNet.location_->pdata, 4*sizeof(mydataFmt)); it->area = (it->x2 - it->x1)*(it->y2 - it->y1); it->score = *(refineNet.score_->pdata 1); secondBbox_.push_back(*it); order.score = it->score; order.oriOrder = count ; secondBboxScore_.push_back(order); } else{ (*it).exist=false; } } } if(count<1)return; nms(secondBbox_, secondBboxScore_, nms_threshold[1]); refineAndSquareBbox(secondBbox_, image.rows, image.cols); //third stage count = 0; for(vector<struct Bbox>::iterator it=secondBbox_.begin(); it!=secondBbox_.end();it ){ if((*it).exist){ Rect temp((*it).y1, (*it).x1, (*it).y2-(*it).y1, (*it).x2-(*it).x1); Mat thirdImage; resize(image(temp), thirdImage, Size(48, 48), 0, 0, cv::INTER_LINEAR); outNet.run(thirdImage); mydataFmt *pp=NULL; if(*(outNet.score_->pdata 1)>outNet.Othreshold){ memcpy(it->regreCoord, outNet.location_->pdata, 4*sizeof(mydataFmt)); it->area = (it->x2 - it->x1)*(it->y2 - it->y1); it->score = *(outNet.score_->pdata 1); pp = outNet.keyPoint_->pdata; for(int num=0;num<5;num ){ (it->ppoint)[num] = it->y1 (it->y2 - it->y1)*(*(pp num)); } for(int num=0;num<5;num ){ (it->ppoint)[num 5] = it->x1 (it->x2 - it->x1)*(*(pp num 5)); } thirdBbox_.push_back(*it); order.score = it->score; order.oriOrder = count ; thirdBboxScore_.push_back(order); } else{ it->exist=false; } } } if(count<1)return; refineAndSquareBbox(thirdBbox_, image.rows, image.cols); nms(thirdBbox_, thirdBboxScore_, nms_threshold[2], "Min"); for(vector<struct Bbox>::iterator it=thirdBbox_.begin(); it!=thirdBbox_.end();it ){ if((*it).exist){ rectangle(image, Point((*it).y1, (*it).x1), Point((*it).y2, (*it).x2), Scalar(0,0,255), 2,8,0); for(int num=0;num<5;num )circle(image,Point((int)*(it->ppoint num), (int)*(it->ppoint num 5)),3,Scalar(0,255,255), -1); } } firstBbox_.clear(); firstOrderScore_.clear(); secondBbox_.clear(); secondBboxScore_.clear(); thirdBbox_.clear(); thirdBboxScore_.clear(); }
小贴士
感谢您为本站写下的评论,您的评论对其它用户来说具有重要的参考价值,所以请认真填写。
- 类似“顶”、“沙发”之类没有营养的文字,对勤劳贡献的楼主来说是令人沮丧的反馈信息。
- 相信您也不想看到一排文字/表情墙,所以请不要反馈意义不大的重复字符,也请尽量不要纯表情的回复。
- 提问之前请再仔细看一遍楼主的说明,或许是您遗漏了。
- 请勿到处挖坑绊人、招贴广告。既占空间让人厌烦,又没人会搭理,于人于己都无利。
关于好例子网
本站旨在为广大IT学习爱好者提供一个非营利性互相学习交流分享平台。本站所有资源都可以被免费获取学习研究。本站资源来自网友分享,对搜索内容的合法性不具有预见性、识别性、控制性,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,平台无法对用户传输的作品、信息、内容的权属或合法性、安全性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论平台是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二与二十三条之规定,若资源存在侵权或相关问题请联系本站客服人员,点此联系我们。关于更多版权及免责申明参见 版权及免责申明
网友评论
我要评论