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faster-api.cpp
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faster-api.cpp
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#include "faster-api.h"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include <fstream>
using std::string;
using std::vector;
using namespace caffe;
using std::max;
using std::min;
cv::Mat bbox_tranform_inv(cv::Mat local_anchors, cv::Mat boxs_delta){
cv::Mat pre_box(local_anchors.rows, local_anchors.cols, CV_32FC1);
for (int i = 0; i < local_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 = local_anchors.at<float>(i, 2) - local_anchors.at<float>(i, 0) + 1;
src_h = local_anchors.at<float>(i, 3) - local_anchors.at<float>(i, 1) + 1;
src_ctr_x = local_anchors.at<float>(i, 0) + 0.5 * src_w;
src_ctr_y = local_anchors.at<float>(i, 1) + 0.5 * src_h;
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;
pre_box.at<float>(i, 1) = pred_ctr_y - 0.5*pred_h;
pre_box.at<float>(i, 2) = pred_ctr_x + 0.5*pred_w;
pre_box.at<float>(i, 3) = pred_ctr_y + 0.5*pred_h;
}
return pre_box;
}
void nms(std::vector<abox> &input_boxes, float nms_thresh){
std::vector<float>vArea(input_boxes.size());
for (int i = 0; i < input_boxes.size(); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);
}
for (int i = 0; i < input_boxes.size(); ++i)
{
for (int j = i + 1; j < input_boxes.size();)
{
float xx1 = std::max(input_boxes[i].x1, input_boxes[j].x1);
float yy1 = std::max(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = std::min(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = std::min(input_boxes[i].y2, input_boxes[j].y2);
float w = std::max(float(0), xx2 - xx1 + 1);
float h = std::max(float(0), yy2 - yy1 + 1);
float inter = w * h;
float ovr = inter / (vArea[i] + vArea[j] - inter);
if (ovr >= nms_thresh)
{
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else
{
j++;
}
}
}
}
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;
boost::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;
}
boost::shared_ptr<Blob<float> > bbox_delt_data = net_->blob_by_name("bbox_pred"); // 13949*84
boost::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 = bbox_tranform_inv(rois_box, bbox_delt);
vector<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;
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());
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;
}