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windows下实现c++版faster-rcnn

發(fā)布時(shí)間：2024/9/21 c/c++ 32 豆豆

生活随笔收集整理的這篇文章主要介紹了 windows下实现c++版faster-rcnn 小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

本文主要參考：http://blog.csdn.net/oYangZi12/article/details/53290426?locationNum=5&fps=1?
1.下載微軟提供的caffe(https://github.com/Microsoft/caffe)并編譯，Pre-Build Steps?
Copy .\windows\CommonSettings.props.example to .\windows\CommonSettings.props 選擇cpu或者gpu模式。?
2.在caffe-master中l(wèi)ibcaffe的相應(yīng)位置加入roi-pooling層對(duì)應(yīng)的cpp，hpp，cu文件重新編譯caffe)。因roi-pooling層已經(jīng)在微軟版本的caffe中存在，只需要找到并添加到libcaffe模塊下。caffe-master\include\caffe\layers\roi_pooling_layer.hpp，caffe-master\include\caffe\layers\smooth_l1_loss_layer.hpp添加至caffe-master\include\caffe\layers路徑下，將caffe-master\src\caffe\layers\roi_pooling_layer.cpp，caffe-master\src\caffe\layers\smooth_l1_loss_layer添加至caffe-master\src\caffe\layers路徑下。?
3.重新build（生成各模塊），并對(duì)整個(gè)caffe工程進(jìn)行build.build成功則roi_pooling_layer層添加成功。?
4.caffe-master工程下添加Faster_rcnn模塊，這里直接將Classification模塊中classification.cpp替換掉，Classification重命名成Detect,添加Faster_rcnn.h,Faster_rcnn.cpp和Detect.cpp,添加路徑為..\caffe-master\examples\cpp_classification\?
5.Faster_rcnn.h代碼如下：

#pragma once #include <gflags\gflags.h> #include <glog\logging.h> #include <cstring> #include <map> #include <string> #include <vector> #include "boost\algorithm\string.hpp" #include "caffe\caffe.hpp" #include "caffe\util\signal_handler.h" #include <opencv2\opencv.hpp> using namespace cv; using caffe::Blob; using caffe::Caffe; using caffe::Net; using caffe::Layer; using caffe::Solver; using caffe::shared_ptr; using caffe::string; using caffe::Timer; using caffe::vector; using std::ostringstream; struct config{int maxsize;int target_size;int feat_stride;int anchor[9][4];int test_min_box_size;int per_nms_topN;int after_nms_topN;float overlap;config(){maxsize = 1000;target_size = 600;feat_stride = 16;int tmp[9][4] = {{-83,-39,100,56},{-175,-87,192,104},{-359,-183,376,200},{-55,-55,72,72},{-119,-119,136,136},{-247,-247,264,264},{-35,-79,52,96},{-79,-167,96,184},{-167,-343,184,360}};memcpy(anchor, tmp, 9 * 4 * sizeof(int));test_min_box_size = 16;per_nms_topN = 6000;after_nms_topN = 300;overlap = 0.7;} }; struct abox {float x1;float y1;float x2;float y2;float score; }; class Faster_rcnn { public:Faster_rcnn(Mat);~Faster_rcnn();bool init();Mat gettarget(Mat); public:config conf;private:Mat im, m_src;Size input_geometry_;shared_ptr<Net<float> > rpn_net, faster_rcnn_net;double im_scale;Size feature_map_size; private:bool loadnet();bool imgtoblob();vector<abox> forward();bool rpn_converttoboxs();void prep_im_size();Mat proposal_local_anchor();Mat bbox_tranform_inv(Mat, Mat, string);Mat get_rpn_score(Blob<float>*, int w, int h);void m_sort(Mat&, Mat&);void boxes_filter(vector<abox>&, int, vector<abox>, vector<int>);void filter_boxs(Mat&, Mat&, vector<abox>&);void nms(vector<abox>, double overlap, vector<int>& vPick, int &nPick);void testdetection(vector<abox>&);};

Faster_rcnn.cpp代碼如下：

#include "Faster_rcnn.h" #include <opencv2\opencv.hpp>#include <algorithm> #include "caffe\common.hpp" #include "caffe\layers\input_layer.hpp" #include "caffe\layers\inner_product_layer.hpp" #include "caffe\layers\dropout_layer.hpp" #include "caffe\layers\conv_layer.hpp" #include "caffe\layers\relu_layer.hpp" #include "caffe\layers\reshape_layer.hpp" #include "caffe\layers\pooling_layer.hpp" #include "caffe\layers\lrn_layer.hpp" #include "caffe\layers\softmax_layer.hpp" #include "caffe\layers\roi_pooling_layer.hpp" // //namespace caffe //{ // extern INSTANTIATE_CLASS(InputLayer); // extern INSTANTIATE_CLASS(InnerProductLayer); // extern INSTANTIATE_CLASS(DropoutLayer); // extern INSTANTIATE_CLASS(ConvolutionLayer); // extern INSTANTIATE_CLASS(ROIPoolingLayer); // REGISTER_LAYER_CLASS(Convolution); // extern INSTANTIATE_CLASS(ReLULayer); // REGISTER_LAYER_CLASS(ReLU); // extern INSTANTIATE_CLASS(PoolingLayer); // REGISTER_LAYER_CLASS(Pooling); // extern INSTANTIATE_CLASS(LRNLayer); // REGISTER_LAYER_CLASS(LRN); // extern INSTANTIATE_CLASS(SoftmaxLayer); // REGISTER_LAYER_CLASS(Softmax); // extern INSTANTIATE_CLASS(ReshapeLayer); // //}cv::Scalar colortable[20] = { cv::Scalar(0, 0, 0), cv::Scalar(0, 0, 125), cv::Scalar(0, 125, 125), cv::Scalar(125, 125, 125), cv::Scalar(125, 0, 0), cv::Scalar(125, 125, 0), cv::Scalar(0, 125, 0), cv::Scalar(125, 0, 125), cv::Scalar(0, 0, 255), cv::Scalar(0, 255, 255), cv::Scalar(255, 255, 255), cv::Scalar(255, 0, 0), cv::Scalar(255, 255, 0), cv::Scalar(0, 255, 0), cv::Scalar(255, 0, 255), cv::Scalar(0, 255, 100), cv::Scalar(0, 0, 100), cv::Scalar(255, 0, 100), cv::Scalar(255, 255, 100), cv::Scalar(100, 100, 100) }; string classname[20] = { "aeroplane", "bike", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motobike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor" }; Faster_rcnn::Faster_rcnn(Mat src) {m_src = src; } Faster_rcnn::~Faster_rcnn() {//if (boxs != NULL) delete boxs; } bool Faster_rcnn::init() {//initCaffe::set_mode(Caffe::CPU);loadnet();return true; } bool Faster_rcnn::loadnet() {//load netrpn_net.reset(new Net<float>("F:\\fast rcnn\\fasterrcnn_vs2013_beta\\fasterrcnn_vs2013\\faster_rcnn_VOC0712_ZF\\proposal_test.prototxt", caffe::TEST));rpn_net->CopyTrainedLayersFrom("F:\\fast rcnn\\fasterrcnn_vs2013_beta\\fasterrcnn_vs2013\\faster_rcnn_VOC0712_ZF\\proposal_final");faster_rcnn_net.reset(new Net<float>("F:\\fast rcnn\\fasterrcnn_vs2013_beta\\fasterrcnn_vs2013\\faster_rcnn_VOC0712_ZF\\detection_test.prototxt", caffe::TEST));faster_rcnn_net->CopyTrainedLayersFrom("F:\\fast rcnn\\fasterrcnn_vs2013_beta\\fasterrcnn_vs2013\\faster_rcnn_VOC0712_ZF\\detection_final");/*rpn_net.reset(new Net<float>("faster_rcnn_VOC0712_ZF\\proposal_test.prototxt", caffe::TEST));rpn_net->CopyTrainedLayersFrom("faster_rcnn_VOC0712_ZF\\proposal_final");faster_rcnn_net.reset(new Net<float>("faster_rcnn_VOC0712_ZF\\detection_test.prototxt", caffe::TEST));faster_rcnn_net->CopyTrainedLayersFrom("faster_rcnn_VOC0712_ZF\\detection_final");*/return true; } bool Faster_rcnn::imgtoblob() {Mat sample_float;m_src.convertTo(sample_float, CV_32FC3);cv::Scalar channel_mean = cv::mean(sample_float);Mat mean = cv::Mat(m_src.rows, m_src.cols, sample_float.type(), channel_mean);Mat sample_normalized;subtract(sample_float, mean, sample_normalized);prep_im_size();resize(sample_normalized, sample_normalized, input_geometry_);Blob<float>* input_layer = rpn_net->input_blobs()[0];input_layer->Reshape(1, sample_normalized.channels(), sample_normalized.rows, sample_normalized.cols);rpn_net->Reshape();float* input_data = input_layer->mutable_cpu_data();vector<cv::Mat> input_channels;for (int i = 0; i < input_layer->channels(); ++i){cv::Mat channel(sample_normalized.rows, sample_normalized.cols, CV_32FC1, input_data);input_channels.push_back(channel);input_data += sample_normalized.rows * sample_normalized.cols;}cv::split(sample_normalized, input_channels);CHECK(reinterpret_cast<float*>(input_channels.at(0).data) == rpn_net->input_blobs()[0]->cpu_data()) << "Input channels are not wrapping the input layer of the network.";return true; } bool aboxcomp(abox& b1, abox& b2) {return b1.score > b2.score; } vector<abox> Faster_rcnn::forward() {//forwardconst vector<Blob<float>*>& result = rpn_net->Forward();Blob<float>* resule0 = result[0];Blob<float>* resule1 = result[1];Mat boxs_delta(resule0->num()*resule0->channels()*resule0->width()*resule0->height() / 4, 4, CV_32FC1);float* p = resule0->mutable_cpu_data();int num = 0;for (int i = 0; i < resule0->num()*resule0->channels()*resule0->width()*resule0->height() / 4; i++){for (int j = 0; j < 4; j++){boxs_delta.at<float>(i, j) = resule0->data_at(0, num%resule0->channels(),(num - num / resule0->channels() / resule0->height() * resule0->channels() * resule0->height()) / resule0->height(),num / resule0->channels() / resule0->height());num++;//int order = j + i * 4;//boxs_delta.at<float>(i, j) = resule0->data_at(0, (order % (resule0->height()*resule0->channels())) % resule0->channels(), (order % (resule0->height()*resule0->channels())) / resule0->channels(), order / (resule0->height()*resule0->channels()));}}//create anchorsfeature_map_size = Size(resule0->width(), resule0->height());//prep_im_size();Mat anchors = proposal_local_anchor();Mat pre_box = bbox_tranform_inv(anchors, boxs_delta, "rpn");//Mat score(resule0->width(), resule0->height(), CV_32FC1);Mat score = get_rpn_score(resule1, resule0->width(), resule0->height());vector<abox> aboxes;filter_boxs(pre_box, score, aboxes);std::sort(aboxes.begin(), aboxes.end(), aboxcomp);//m_sort(pre_box,score);vector<int> vPick(aboxes.size());int nPick;/有cuda版,待加入,此處為cpu版///nms(aboxes, conf.overlap, vPick, nPick);vector<abox> aboxes_;boxes_filter(aboxes_, nPick, aboxes, vPick);return aboxes_; } void Faster_rcnn::nms(vector<abox> input_boxes, double overlap, vector<int> &vPick, int &nPick) {int nSample = min(int(input_boxes.size()), conf.per_nms_topN);vector<double> vArea(nSample);for (int i = 0; i < nSample; ++i){vArea[i] = double(input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);}std::multimap<double, int> scores;for (int i = 0; i < nSample; ++i)scores.insert(std::pair<double, int>(input_boxes.at(i).score, i));nPick = 0;do{int last = scores.rbegin()->second;vPick[nPick] = last;nPick += 1;for (std::multimap<double, int>::iterator it = scores.begin(); it != scores.end();){int it_idx = it->second;double xx1 = max(input_boxes.at(last).x1, input_boxes.at(it_idx).x1);double yy1 = max(input_boxes.at(last).y1, input_boxes.at(it_idx).y1);double xx2 = min(input_boxes.at(last).x2, input_boxes.at(it_idx).x2);double yy2 = min(input_boxes.at(last).y2, input_boxes.at(it_idx).y2);double w = max(double(0.0), xx2 - xx1 + 1), h = max(double(0.0), yy2 - yy1 + 1);double ov = w*h / (vArea[last] + vArea[it_idx] - w*h);if (ov > overlap){it = scores.erase(it);}else{it++;}}} while (scores.size() != 0); } void Faster_rcnn::boxes_filter(vector<abox>& aboxes, int nPick, vector<abox> row, vector<int> vPick) {int n = min(nPick, conf.after_nms_topN);for (int i = 0; i < n; i++){aboxes.push_back(row[vPick[i]]);} } void Faster_rcnn::filter_boxs(Mat& pre_box, Mat& score, vector<abox>& aboxes) {aboxes.clear();for (int i = 0; i < pre_box.rows; i++){int widths = pre_box.at<float>(i, 2) - pre_box.at<float>(i, 0) + 1;int heights = pre_box.at<float>(i, 3) - pre_box.at<float>(i, 1) + 1;if (widths < conf.test_min_box_size || heights < conf.test_min_box_size){pre_box.at<float>(i, 0) = 0;pre_box.at<float>(i, 1) = 0;pre_box.at<float>(i, 2) = 0;pre_box.at<float>(i, 3) = 0;score.at<float>(i, 0) = 0;}abox tmp;tmp.x1 = pre_box.at<float>(i, 0);tmp.y1 = pre_box.at<float>(i, 1);tmp.x2 = pre_box.at<float>(i, 2);tmp.y2 = pre_box.at<float>(i, 3);tmp.score = score.at<float>(i, 0);aboxes.push_back(tmp);} } void Faster_rcnn::m_sort(Mat& pre_box, Mat& score) {for (int i = 0; i < pre_box.rows - 1; i++){for (int j = i + 1; j < pre_box.rows; j++){if (score.at<float>(i, 0) < score.at<float>(j, 0)){float tmp = score.at<float>(j, 0);score.at<float>(j, 0) = score.at<float>(i, 0);score.at<float>(i, 0) = tmp;float tmp0 = pre_box.at<float>(j, 0);float tmp1 = pre_box.at<float>(j, 1);float tmp2 = pre_box.at<float>(j, 2);float tmp3 = pre_box.at<float>(j, 3);pre_box.at<float>(j, 0) = pre_box.at<float>(i, 0);pre_box.at<float>(j, 1) = pre_box.at<float>(i, 1);pre_box.at<float>(j, 2) = pre_box.at<float>(i, 2);pre_box.at<float>(j, 3) = pre_box.at<float>(i, 3);pre_box.at<float>(i, 0) = tmp0;pre_box.at<float>(i, 1) = tmp1;pre_box.at<float>(i, 2) = tmp2;pre_box.at<float>(i, 3) = tmp3;}}} } Mat Faster_rcnn::get_rpn_score(Blob<float>* resule1, int w, int h) {//Blob<float> tmp;int channel = resule1->width()*resule1->height() / (w * h);Mat score(resule1->width()*resule1->height(), 1, CV_32FC1);//tmp.Reshape(1, resule1->width()*resule1->height() / (w * h),h,w);//float* p = tmp.mutable_cpu_data;int num = 0;for (int i = 0; i < w; i++){for (int j = 0; j < h; j++){for (int k = 0; k < channel; k++){score.at<float>(num, 0) = resule1->data_at(0, 1, h*k + j, i);num++;}}}return score; } Mat Faster_rcnn::bbox_tranform_inv(Mat anchors, Mat boxs_delta, string type) {if (type == "rpn"){Mat pre_box(anchors.rows, anchors.cols, CV_32FC1);for (int i = 0; i < anchors.rows; i++){double pred_ctr_x, pred_ctr_y, src_ctr_x, src_ctr_y;double dst_ctr_x, dst_ctr_y, dst_scl_x, dst_scl_y;double src_w, src_h, pred_w, pred_h;src_w = anchors.at<float>(i, 2) - anchors.at<float>(i, 0) + 1;src_h = anchors.at<float>(i, 3) - anchors.at<float>(i, 1) + 1;src_ctr_x = anchors.at<float>(i, 0) + 0.5 * (src_w - 1);src_ctr_y = anchors.at<float>(i, 1) + 0.5 * (src_h - 1);dst_ctr_x = boxs_delta.at<float>(i, 0);dst_ctr_y = boxs_delta.at<float>(i, 1);dst_scl_x = boxs_delta.at<float>(i, 2);dst_scl_y = boxs_delta.at<float>(i, 3);pred_ctr_x = dst_ctr_x*src_w + src_ctr_x;pred_ctr_y = dst_ctr_y*src_h + src_ctr_y;pred_w = exp(dst_scl_x) * src_w;pred_h = exp(dst_scl_y) * src_h;pre_box.at<float>(i, 0) = ((pred_ctr_x - 0.5*(pred_w - 1)) - 1) * (float)(m_src.cols) / (im.cols - 1) + 1;pre_box.at<float>(i, 1) = ((pred_ctr_y - 0.5*(pred_h - 1)) - 1) * (float)(m_src.rows) / (im.rows - 1) + 1;pre_box.at<float>(i, 2) = ((pred_ctr_x + 0.5*(pred_w - 1)) - 1) * (float)(m_src.cols) / (im.cols - 1) + 1;pre_box.at<float>(i, 3) = ((pred_ctr_y + 0.5*(pred_h - 1)) - 1) * (float)(m_src.rows) / (im.rows - 1) + 1;if (pre_box.at<float>(i, 0) < 0) pre_box.at<float>(i, 0) = 0;if (pre_box.at<float>(i, 0) > (m_src.cols - 1)) pre_box.at<float>(i, 0) = m_src.cols - 1;if (pre_box.at<float>(i, 2) < 0) pre_box.at<float>(i, 2) = 0;if (pre_box.at<float>(i, 2) > (m_src.cols - 1)) pre_box.at<float>(i, 2) = m_src.cols - 1;if (pre_box.at<float>(i, 1) < 0) pre_box.at<float>(i, 1) = 0;if (pre_box.at<float>(i, 1) > (m_src.rows - 1)) pre_box.at<float>(i, 1) = m_src.rows - 1;if (pre_box.at<float>(i, 3) < 0) pre_box.at<float>(i, 3) = 0;if (pre_box.at<float>(i, 3) > (m_src.rows - 1)) pre_box.at<float>(i, 3) = m_src.rows - 1;}return pre_box;}if (type == "rcnn"){Mat pre_box(boxs_delta.rows, boxs_delta.cols, CV_32FC1);for (int i = 0; i < boxs_delta.rows; i++){for (int j = 1; j < boxs_delta.cols / 4; j++){double pred_ctr_x, pred_ctr_y, src_ctr_x, src_ctr_y;double dst_ctr_x, dst_ctr_y, dst_scl_x, dst_scl_y;double src_w, src_h, pred_w, pred_h;src_w = anchors.at<float>(i, 2) - anchors.at<float>(i, 0) + 1;src_h = anchors.at<float>(i, 3) - anchors.at<float>(i, 1) + 1;src_ctr_x = anchors.at<float>(i, 0) + 0.5 * (src_w - 1);src_ctr_y = anchors.at<float>(i, 1) + 0.5 * (src_h - 1);dst_ctr_x = boxs_delta.at<float>(i, 4 * j + 0);dst_ctr_y = boxs_delta.at<float>(i, 4 * j + 1);dst_scl_x = boxs_delta.at<float>(i, 4 * j + 2);dst_scl_y = boxs_delta.at<float>(i, 4 * j + 3);pred_ctr_x = dst_ctr_x*src_w + src_ctr_x;pred_ctr_y = dst_ctr_y*src_h + src_ctr_y;pred_w = exp(dst_scl_x) * src_w;pred_h = exp(dst_scl_y) * src_h;pre_box.at<float>(i, 4 * (j - 1) + 0) = ((pred_ctr_x - 0.5*(pred_w - 1)) - 1);pre_box.at<float>(i, 4 * (j - 1) + 1) = ((pred_ctr_y - 0.5*(pred_h - 1)) - 1);pre_box.at<float>(i, 4 * (j - 1) + 2) = ((pred_ctr_x + 0.5*(pred_w - 1)) - 1);pre_box.at<float>(i, 4 * (j - 1) + 3) = ((pred_ctr_y + 0.5*(pred_h - 1)) - 1);if (pre_box.at<float>(i, 4 * (j - 1) + 0) < 0) pre_box.at<float>(i, 4 * (j - 1) + 0) = 0;if (pre_box.at<float>(i, 4 * (j - 1) + 0) > (m_src.cols - 1)) pre_box.at<float>(i, 4 * (j - 1) + 0) = m_src.cols - 1;if (pre_box.at<float>(i, 4 * (j - 1) + 2) < 0) pre_box.at<float>(i, 4 * (j - 1) + 2) = 0;if (pre_box.at<float>(i, 4 * (j - 1) + 2) > (m_src.cols - 1)) pre_box.at<float>(i, 4 * (j - 1) + 2) = m_src.cols - 1;if (pre_box.at<float>(i, 4 * (j - 1) + 1) < 0) pre_box.at<float>(i, 4 * (j - 1) + 1) = 0;if (pre_box.at<float>(i, 4 * (j - 1) + 1) > (m_src.rows - 1)) pre_box.at<float>(i, 4 * (j - 1) + 1) = m_src.rows - 1;if (pre_box.at<float>(i, 4 * (j - 1) + 3) < 0) pre_box.at<float>(i, 4 * (j - 1) + 3) = 0;if (pre_box.at<float>(i, 4 * (j - 1) + 3) > (m_src.rows - 1)) pre_box.at<float>(i, 4 * (j - 1) + 3) = m_src.rows - 1;}}return pre_box;}} Mat Faster_rcnn::proposal_local_anchor() {Blob<float> shift;Mat shitf_x(feature_map_size.height, feature_map_size.width, CV_32SC1);Mat shitf_y(feature_map_size.height, feature_map_size.width, CV_32SC1);for (size_t i = 0; i < feature_map_size.width; i++){for (size_t j = 0; j < feature_map_size.height; j++){shitf_x.at<int>(j, i) = i * conf.feat_stride;shitf_y.at<int>(j, i) = j * conf.feat_stride;}}shift.Reshape(9, feature_map_size.width*feature_map_size.height, 4, 1);float *p = shift.mutable_cpu_diff(), *a = shift.mutable_cpu_data();for (int i = 0; i < feature_map_size.width*feature_map_size.height; i++){for (int j = 0; j < 9; j++){{size_t num = i * 4 + j * 4 * feature_map_size.width*feature_map_size.height;p[num + 0] = -shitf_x.at<int>(i % shitf_x.rows, i / shitf_x.rows);p[num + 2] = -shitf_x.at<int>(i % shitf_x.rows, i / shitf_x.rows);p[num + 1] = -shitf_y.at<int>(i % shitf_y.rows, i / shitf_y.rows);p[num + 3] = -shitf_y.at<int>(i % shitf_y.rows, i / shitf_y.rows);a[num + 0] = conf.anchor[j][0];a[num + 1] = conf.anchor[j][1];a[num + 2] = conf.anchor[j][2];a[num + 3] = conf.anchor[j][3];}}}shift.Update();Mat anchors(9 * feature_map_size.width*feature_map_size.height, 4, CV_32FC1);size_t num = 0;for (int i = 0; i < anchors.cols; i++){for (int j = 0; j < anchors.rows; j++){anchors.at<float>(j, i) = shift.data_at(num%shift.num(),(num - num / (shift.num() * shift.channels())*shift.num() * shift.channels()) / shift.num(),num / (shift.num() * shift.channels()), 0);num++;}}/*for (int i = 0; i < 4; i++){for (int k = 0; k < feature_map_size.width*feature_map_size.height; k++){for (int j = 0; j < 9; j++){anchors.at<float>(num%anchors.rows, num / anchors.rows) = shift.data_at(j, k, i, 0);num++;}}}*/return anchors; } void Faster_rcnn::prep_im_size() {int im_size_min = min(m_src.cols, m_src.rows);int im_size_max = max(m_src.cols, m_src.rows);im_scale = double(conf.target_size) / im_size_min;if (round(im_scale*im_size_max) > conf.maxsize){im_scale = double(conf.maxsize) / im_size_max;}input_geometry_ = Size(round(m_src.cols * im_scale), round(m_src.rows * im_scale));resize(m_src, im, input_geometry_); } bool Faster_rcnn::rpn_converttoboxs() {return true; } Mat Faster_rcnn::gettarget(Mat src) {m_src = src;//img=>blobimgtoblob();vector<abox> abox = forward();testdetection(abox);return m_src; } void Faster_rcnn::testdetection(vector<abox>& aboxes) {float scales = im_scale;Mat anchors(aboxes.size(), 4, CV_32FC1);//int a= faster_rcnn_net->blob_by_name("data")->count();//vector<string> a = rpn_net->blob_names();// int b = rpn_net->blob_by_name("conv5")->count();faster_rcnn_net->blob_by_name("data")->CopyFrom(*rpn_net->blob_by_name("conv5").get(), false, true);Blob<float>* input_layer = faster_rcnn_net->input_blobs()[1];int sub_blob_num = aboxes.size();input_layer->Reshape(sub_blob_num, 5, 1, 1);float* input_data = input_layer->mutable_cpu_data();int num = 0;for (auto ite = aboxes.begin(); ite != aboxes.end(); ite++){anchors.at<float>(num, 0) = ite->x1;anchors.at<float>(num, 1) = ite->y1;anchors.at<float>(num, 2) = ite->x2;anchors.at<float>(num, 3) = ite->y2;ite->score = 0;ite->x1 = (ite->x1 - 1) * scales + 1 - 1;ite->y1 = (ite->y1 - 1) * scales + 1 - 1;ite->x2 = (ite->x2 - 1) * scales + 1 - 1;ite->y2 = (ite->y2 - 1) * scales + 1 - 1;input_data[num * 5 + 0] = ite->score;input_data[num * 5 + 1] = ite->x1;input_data[num * 5 + 2] = ite->y1;input_data[num * 5 + 3] = ite->x2;input_data[num * 5 + 4] = ite->y2;num++;}const vector<Blob<float>*>& result = faster_rcnn_net->Forward();Blob<float>* scores = result[1];Blob<float>* box_deltas = result[0];box_deltas->data_at(0, 3, 0, 0);Mat boxs_delta(box_deltas->num(), box_deltas->channels(), CV_32FC1);for (int i = 0; i < box_deltas->num(); i++){for (int j = 0; j < box_deltas->channels(); j++){boxs_delta.at<float>(i, j) = box_deltas->data_at(i, j, 0, 0);}}Mat score(scores->num(), scores->channels() - 1, CV_32FC1);for (int i = 0; i < scores->num(); i++){for (int j = 0 + 1; j < scores->channels(); j++){score.at<float>(i, j - 1) = scores->data_at(i, j, 0, 0);}}Mat pred_boxes = bbox_tranform_inv(anchors, boxs_delta, "rcnn");std::map<int, vector<Rect>> classer;20為類別數(shù)///for (int i = 0; i < 20; i++){vector<Rect> r;vector<abox> aboxes;for (int j = 0; j < pred_boxes.rows; j++){abox tmp;tmp.x1 = pred_boxes.at<float>(j, i * 4);tmp.y1 = pred_boxes.at<float>(j, i * 4 + 1);tmp.x2 = pred_boxes.at<float>(j, i * 4 + 2);tmp.y2 = pred_boxes.at<float>(j, i * 4 + 3);tmp.score = score.at<float>(j, i);aboxes.push_back(tmp);}vector<int> vPick(aboxes.size());int nPick;nms(aboxes, 0.3, vPick, nPick);for (int i = 0; i < nPick; i++){if (aboxes[vPick[i]].score > 0.6){Rect rt;rt.x = aboxes[vPick[i]].x1;rt.y = aboxes[vPick[i]].y1;rt.width = aboxes[vPick[i]].x2 - aboxes[vPick[i]].x1 + 1;rt.height = aboxes[vPick[i]].y2 - aboxes[vPick[i]].y1 + 1;r.push_back(rt);}}classer.insert(std::pair<int, vector<Rect>>(i, r));}for (int i = 0; i < 20; i++){if (!classer[i].empty()){//printf("%d\t%d\n", i, classer[i].size());for (auto ite = classer[i].begin(); ite != classer[i].end(); ite++){rectangle(m_src, *ite, colortable[i]);putText(m_src, classname[i], Point(ite->x, ite->y), CV_FONT_HERSHEY_SIMPLEX, 0.5, colortable[i], 2, 2);}}} }

Detect.cpp代碼如下：

#include "Faster_rcnn.h" //#include <opencv2\opencv.hpp>int main() {Mat im = imread("D:\\fast-rcnn-master\\data\\demo\\001551.jpg");Faster_rcnn detect(im);detect.init();while (1){Mat img = detect.gettarget(im);imshow("img", img);cvWaitKey(20);}return 0; }

6.將matlab版faster-rcnn的ZF網(wǎng)絡(luò)模型文件放到工程faster_rcnn_VOC0712_ZF文件夾下。https://github.com/ShaoqingRen/faster_rcnn?最后幾行給出了下載地址，下載地址為：https://pan.baidu.com/s/1hsFKmeK，下載完成放在自己的路徑下，并在Faster_rcnn.cpp文件bool Faster_rcnn::loadnet()函數(shù)中正確添加所需的模型文件路徑。?
7. build(生成）Detect模塊，如果是Debug模式，則可以在….\caffe-master\Build\x64\Debug\路徑下找到Detect.exe，?
cmd進(jìn)入到….\caffe-master\Build\x64\Debug\路徑，執(zhí)行?
..\x64\Debug>Detect.exe?
8.出來(lái)檢測(cè)結(jié)果：?
因圖片大小問(wèn)題不能上傳成功。

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