faster_rcnn.rar_RCNN_faster_faster rcnn 程序_faster-rcnn_测试工程

#include "ObjectDetector.hpp" #include "caffe_reg.h" ObjectDetector::ObjectDetector(const std::string &model_file, const std::string &weights_file){ #ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU); #else Caffe::set_mode(Caffe::GPU); #endif net_.reset(new Net<float>(model_file, TEST)); net_->CopyTrainedLayersFrom(weights_file); this->class_num_ = net_->blob_by_name("cls_prob")->channels(); //求得类别数+1 } //对一张图片，进行检测，将结果保存进map数据结构里,分别表示每个类别对应的目标框，如果需要分数信息，则计算分数 map<int, vector<cv::Rect> > ObjectDetector::detect(const cv::Mat& image, map<int, vector<float> >* objectScore){ if (objectScore != NULL) //如果需要保存置信度 objectScore->clear(); float CONF_THRESH = 0.8; //置信度阈值 float NMS_THRESH = 0.3; //非极大值抑制阈值 int max_side = max(image.rows, image.cols); //分别求出图片宽和高的较大者 int min_side = min(image.rows, image.cols); float max_side_scale = float(max_side) / float(INPUT_SIZE_LONG); //分别求出缩放因子 float min_side_scale = float(min_side) / float(INPUT_SIZE_NARROW); float max_scale = max(max_side_scale, min_side_scale); float img_scale = float(1) / max_scale; int height = int(image.rows * img_scale); int width = int(image.cols * img_scale); int num_out; cv::Mat cv_resized; image.convertTo(cv_resized, CV_32FC3); cv::resize(cv_resized, cv_resized, cv::Size(width, height)); cv::Mat mean(height, width, cv_resized.type(), cv::Scalar(102.9801, 115.9465, 122.7717)); cv::Mat normalized; subtract(cv_resized, mean, normalized); float im_info[3]; im_info[0] = height; im_info[1] = width; im_info[2] = img_scale; shared_ptr<Blob<float> > input_layer = net_->blob_by_name("data"); input_layer->Reshape(1, normalized.channels(), height, width); 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(height, width, CV_32FC1, input_data); input_channels.push_back(channel); input_data += height * width; } cv::split(normalized, input_channels); net_->blob_by_name("im_info")->set_cpu_data(im_info); net_->Forward(); //进行网络前向传播 int num = net_->blob_by_name("rois")->num(); //产生的 ROI 个数,比如为 13949个ROI const float *rois_data = net_->blob_by_name("rois")->cpu_data(); //维度比如为：13949*5*1*1 int num1 = net_->blob_by_name("bbox_pred")->num(); //预测的矩形框 维度为 13949*84 cv::Mat rois_box(num, 4, CV_32FC1); for (int i = 0; i < num; ++i) { rois_box.at<float>(i, 0) = rois_data[i * 5 + 1] / img_scale; rois_box.at<float>(i, 1) = rois_data[i * 5 + 2] / img_scale; rois_box.at<float>(i, 2) = rois_data[i * 5 + 3] / img_scale; rois_box.at<float>(i, 3) = rois_data[i * 5 + 4] / img_scale; } shared_ptr<Blob<float> > bbox_delt_data = net_->blob_by_name("bbox_pred"); // 13949*84 shared_ptr<Blob<float> > score = net_->blob_by_name("cls_prob"); // 3949*21 map<int, vector<cv::Rect> > label_objs; //每个类别，对应的检测目标框 for (int i = 1; i < class_num_; ++i){ //对每个类，进行遍历 cv::Mat bbox_delt(num, 4, CV_32FC1); for (int j = 0; j < num; ++j){ bbox_delt.at<float>(j, 0) = bbox_delt_data->data_at(j, i * 4 + 0, 0, 0); bbox_delt.at<float>(j, 1) = bbox_delt_data->data_at(j, i * 4 + 1, 0, 0); bbox_delt.at<float>(j, 2) = bbox_delt_data->data_at(j, i * 4 + 2, 0, 0); bbox_delt.at<float>(j, 3) = bbox_delt_data->data_at(j, i * 4 + 3, 0, 0); } cv::Mat box_class = RPN::bbox_tranform_inv(rois_box, bbox_delt); vector<RPN::abox> aboxes; //对于 类别i，检测出的矩形框保存在这 for (int j = 0; j < box_class.rows; ++j){ if (box_class.at<float>(j, 0) < 0) box_class.at<float>(j, 0) = 0; if (box_class.at<float>(j, 0) > (image.cols - 1)) box_class.at<float>(j, 0) = image.cols - 1; if (box_class.at<float>(j, 2) < 0) box_class.at<float>(j, 2) = 0; if (box_class.at<float>(j, 2) > (image.cols - 1)) box_class.at<float>(j, 2) = image.cols - 1; if (box_class.at<float>(j, 1) < 0) box_class.at<float>(j, 1) = 0; if (box_class.at<float>(j, 1) > (image.rows - 1)) box_class.at<float>(j, 1) = image.rows - 1; if (box_class.at<float>(j, 3) < 0) box_class.at<float>(j, 3) = 0; if (box_class.at<float>(j, 3) > (image.rows - 1)) box_class.at<float>(j, 3) = image.rows - 1; RPN::abox tmp; tmp.x1 = box_class.at<float>(j, 0); tmp.y1 = box_class.at<float>(j, 1); tmp.x2 = box_class.at<float>(j, 2); tmp.y2 = box_class.at<float>(j, 3); tmp.score = score->data_at(j, i, 0, 0); aboxes.push_back(tmp); } std::sort(aboxes.rbegin(), aboxes.rend()); RPN::nms(aboxes, NMS_THRESH); //与非极大值抑制消除对于的矩形框 for (int k = 0; k < aboxes.size();){ if (aboxes[k].score < CONF_THRESH) aboxes.erase(aboxes.begin() + k); else k++; } //################ 将类别i的所有检测框，保存 vector<cv::Rect> rect(aboxes.size()); //对于类别i，检测出的矩形框 for (int ii = 0; ii<aboxes.size(); ++ii) rect[ii] = cv::Rect(cv::Point(aboxes[ii].x1, aboxes[ii].y1), cv::Point(aboxes[ii].x2, aboxes[ii].y2)); label_objs[i] = rect; //################ 将类别i的所有检测框的打分，保存 if (objectScore != NULL){ //################ 将类别i的所有检测框的打分，保存 vector<float> tmp(aboxes.size()); //对于 类别i，检测出的矩形框的得分 for (int ii = 0; ii<aboxes.size(); ++ii) tmp[ii] = aboxes[ii].score; objectScore->insert(pair<int, vector<float> >(i, tmp)); } } return label_objs; }