[目标检测]windows下实现c++版faster-rcnn

本文主要参考：http://blog.csdn.net/oYangZi12/article/details/53290426?locationNum=5&fps=1
1.下载微软提供的caffe(https://github.com/Microsoft/caffe)并编译，Pre-Build Steps
Copy .\windows\CommonSettings.props.example to .\windows\CommonSettings.props 选择cpu或者gpu模式。
2.在caffe-master中libcaffe的相应位置加入roi-pooling层对应的cpp，hpp，cu文件重新编译caffe)。因roi-pooling层已经在微软版本的caffe中存在，只需要找到并添加到libcaffe模块下。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路径下，将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路径下。
3.重新build（生成各模块），并对整个caffe工程进行build.build成功则roi_pooling_layer层添加成功。
4.caffe-master工程下添加Faster_rcnn模块，这里直接将Classification模块中classification.cpp替换掉，Classification重命名成Detect,添加Faster_rcnn.h,Faster_rcnn.cpp和Detect.cpp,添加路径为..\caffe-master\examples\cpp_classification\
5.Faster_rcnn.h代码如下：

#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代码如下：

#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(){    //init    Caffe::set_mode(Caffe::CPU);    loadnet();    return true;}bool Faster_rcnn::loadnet(){    //load net    rpn_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(){    //forward    const 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 anchors    feature_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,此处为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=>blob    imgtoblob();    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为类别数///////    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代码如下：

#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.将matlab版faster-rcnn的ZF网络模型文件放到工程faster_rcnn_VOC0712_ZF文件夹下。https://github.com/ShaoqingRen/faster_rcnn 最后几行给出了下载地址，下载地址为：https://pan.baidu.com/s/1hsFKmeK，下载完成放在自己的路径下，并在Faster_rcnn.cpp文件bool Faster_rcnn::loadnet()函数中正确添加所需的模型文件路径。
7. build(生成）Detect模块，如果是Debug模式，则可以在….\caffe-master\Build\x64\Debug\路径下找到Detect.exe，
cmd进入到….\caffe-master\Build\x64\Debug\路径，执行
..\x64\Debug>Detect.exe
8.出来检测结果：
因图片大小问题不能上传成功。