faster_rcnn c++版本的 caffe 封装，动态库（2）
摘要: 转载请注明出处，楼燚(yì)航的blog，http://www.cnblogs.com/louyihang-loves-baiyan/
github上的代码链接,求给星星:) https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
在上一篇文章中，我们是将对caffe的调用隔离了出来，可以说相当于原来caffe源码下的tools中cpp文件使用相同，然后自己写了个CMakeLists.txt进行编译。这里是进一步将代码进行分离，封装成libfaster_rcnn.so文件进行使用。对于部分接口，我可能做了一些改动。
目录结构
├── CMakeLists.txt
├── lib
│ ├── CMakeLists.txt
│ ├── faster_rcnn.cpp
│ ├── faster_rcnn.hpp
├── main.cpp
├── pbs_cxx_faster_rcnn_demo.job
在这里main.cpp就是直接调用faster_rcnn.cpp的接口，他的内容也很简单,只是在之前的基础上，再加上libfaster_rcnn.so这个动态库文件
#include "faster_rcnn.hpp"
int main()
{
string model_file = "/home/lyh1/workspace/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/faster_rcnn_test.pt";
string weights_file = "/home/lyh1/workspace/py-faster-rcnn/output/default/yuanzhang_car/vgg_cnn_m_1024_fast_rcnn_stage2_iter_40000.caffemodel";
int GPUID=0;
Caffe::SetDevice(GPUID);
Caffe::set_mode(Caffe::GPU);
Detector det = Detector(model_file, weights_file);
det.Detect("/home/lyh1/workspace/py-faster-rcnn/data/demo/car.jpg");
return 0;
}
可以看到这里只是include了faster_rcnn.hpp头文件，其对应的CMakeLists.txt文件如下：
#This part is used for compile faster_rcnn_demo.cpp
cmake_minimum_required (VERSION 2.8)
project (main_demo)
add_executable(main main.cpp)
include_directories ( "${PROJECT_SOURCE_DIR}/../caffe-fast-rcnn/include"
"${PROJECT_SOURCE_DIR}/../lib/nms"
"${PROJECT_SOURCE_DIR}/lib"
/share/apps/local/include
/usr/local/include
/opt/python/include/python2.7
/share/apps/opt/intel/mkl/include
/usr/local/cuda/include )
target_link_libraries(main /home/lyh1/workspace/py-faster-rcnn/faster_cxx_lib/lib/libfaster_rcnn.so
/home/lyh1/workspace/py-faster-rcnn/caffe-fast-rcnn/build/lib/libcaffe.so
/home/lyh1/workspace/py-faster-rcnn/lib/nms/gpu_nms.so
/share/apps/local/lib/libopencv_highgui.so
/share/apps/local/lib/libopencv_core.so
/share/apps/local/lib/libopencv_imgproc.so
/share/apps/local/lib/libopencv_imgcodecs.so
/share/apps/local/lib/libglog.so
/share/apps/local/lib/libboost_system.so
/share/apps/local/lib/libboost_python.so
/share/apps/local/lib/libglog.so
/opt/rh/python27/root/usr/lib64/libpython2.7.so
)
对于faster_rcnn.hpp和faster_rcnn.cpp ，我们需要将他们编译成动态库，下面是他们对应的CMakeLists.txt，在文件中，可以看到跟上面这个区别是用了add_library语句，并且加入了SHARED关键字，SHARED代表动态库。其次，在编译动态库的过程中，是不需要链接的，但是我们知道这个库是依赖别的很多个库的，所以在最后形成可执行文件也就是上面这个CMakeLists.txt，我们需要添加这个动态库所依赖的那些动态库，至此就OK了。编译的话，非常傻瓜cmake .然后在执行make即可。
cmake_minimum_required (VERSION 2.8)
SET (SRC_LIST faster_rcnn.cpp)
include_directories ( "${PROJECT_SOURCE_DIR}/../../caffe-fast-rcnn/include"
"${PROJECT_SOURCE_DIR}/../../lib/nms"
/share/apps/local/include
/usr/local/include
/opt/python/include/python2.7
/share/apps/opt/intel/mkl/include
/usr/local/cuda/include )
add_library(faster_rcnn SHARED ${SRC_LIST})
首先将原来的cpp文件中的声明提取出来，比较简单，就是hpp文件对应cpp文件。如下：
#ifndef FASTER_RCNN_HPP
#define FASTER_RCNN_HPP
#include <stdio.h> // for snprintf
#include <string>
#include <vector>
#include <math.h>
#include <fstream>
#include <boost/python.hpp>
#include "caffe/caffe.hpp"
#include "gpu_nms.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace caffe;
using namespace std;
#define max(a, b) (((a)>(b)) ? (a) :(b))
#define min(a, b) (((a)<(b)) ? (a) :(b))
//background and car
const int class_num=2;
/*
* === Class ======================================================================
* Name: Detector
* Description: FasterRCNN CXX Detector
* =====================================================================================
*/
class Detector {
public:
Detector(const string& model_file, const string& weights_file);
void Detect(const string& im_name);
void bbox_transform_inv(const int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width);
void vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH);
void boxes_sort(int num, const float* pred, float* sorted_pred);
private:
shared_ptr<Net<float> > net_;
Detector(){}
};
//Using for box sort
struct Info
{
float score;
const float* head;
};
bool compare(const Info& Info1, const Info& Info2)
{
return Info1.score > Info2.score;
}
#endif
相应的cpp文件
#include <stdio.h> // for snprintf
#include <string>
#include <vector>
#include <math.h>
#include <fstream>
#include <boost/python.hpp>
#include "caffe/caffe.hpp"
#include "gpu_nms.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "faster_rcnn.hpp"
using namespace caffe;
using namespace std;
/*
* === FUNCTION ======================================================================
* Name: Detector
* Description: Load the model file and weights file
* =====================================================================================
*/
//load modelfile and weights
Detector::Detector(const string& model_file, const string& weights_file)
{
net_ = shared_ptr<Net<float> >(new Net<float>(model_file, caffe::TEST));
net_->CopyTrainedLayersFrom(weights_file);
}
/*
* === FUNCTION ======================================================================
* Name: Detect
* Description: Perform detection operation
* Warning the max input size should less than 1000*600
* =====================================================================================
*/
//perform detection operation
//input image max size 1000*600
void Detector::Detect(const string& im_name)
{
float CONF_THRESH = 0.8;
float NMS_THRESH = 0.3;
const int max_input_side=1000;
const int min_input_side=600;
cv::Mat cv_img = cv::imread(im_name);
cv::Mat cv_new(cv_img.rows, cv_img.cols, CV_32FC3, cv::Scalar(0,0,0));
if(cv_img.empty())
{
std::cout<<"Can not get the image file !"<<endl;
return ;
}
int max_side = max(cv_img.rows, cv_img.cols);
int min_side = min(cv_img.rows, cv_img.cols);
float max_side_scale = float(max_side) / float(max_input_side);
float min_side_scale = float(min_side) /float( min_input_side);
float max_scale=max(max_side_scale, min_side_scale);
float img_scale = 1;
if(max_scale > 1)
{
img_scale = float(1) / max_scale;
}
int height = int(cv_img.rows * img_scale);
int width = int(cv_img.cols * img_scale);
int num_out;
cv::Mat cv_resized;
std::cout<<"imagename "<<im_name<<endl;
float im_info[3];
float data_buf[height*width*3];
float *boxes = NULL;
float *pred = NULL;
float *pred_per_class = NULL;
float *sorted_pred_cls = NULL;
int *keep = NULL;
const float* bbox_delt;
const float* rois;
const float* pred_cls;
int num;
for (int h = 0; h < cv_img.rows; ++h )
{
for (int w = 0; w < cv_img.cols; ++w)
{
cv_new.at<cv::Vec3f>(cv::Point(w, h))[0] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[0])-float(102.9801);
cv_new.at<cv::Vec3f>(cv::Point(w, h))[1] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[1])-float(115.9465);
cv_new.at<cv::Vec3f>(cv::Point(w, h))[2] = float(cv_img.at<cv::Vec3b>(cv::Point(w, h))[2])-float(122.7717);
}
}
cv::resize(cv_new, cv_resized, cv::Size(width, height));
im_info[0] = cv_resized.rows;
im_info[1] = cv_resized.cols;
im_info[2] = img_scale;
for (int h = 0; h < height; ++h )
{
for (int w = 0; w < width; ++w)
{
data_buf[(0*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[0]);
data_buf[(1*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[1]);
data_buf[(2*height+h)*width+w] = float(cv_resized.at<cv::Vec3f>(cv::Point(w, h))[2]);
}
}
net_->blob_by_name("data")->Reshape(1, 3, height, width);
net_->blob_by_name("data")->set_cpu_data(data_buf);
net_->blob_by_name("im_info")->set_cpu_data(im_info);
net_->ForwardFrom(0);
bbox_delt = net_->blob_by_name("bbox_pred")->cpu_data();
num = net_->blob_by_name("rois")->num();
rois = net_->blob_by_name("rois")->cpu_data();
pred_cls = net_->blob_by_name("cls_prob")->cpu_data();
boxes = new float[num*4];
pred = new float[num*5*class_num];
pred_per_class = new float[num*5];
sorted_pred_cls = new float[num*5];
keep = new int[num];
for (int n = 0; n < num; n++)
{
for (int c = 0; c < 4; c++)
{
boxes[n*4+c] = rois[n*5+c+1] / img_scale;
}
}
bbox_transform_inv(num, bbox_delt, pred_cls, boxes, pred, cv_img.rows, cv_img.cols);
for (int i = 1; i < class_num; i ++)
{
for (int j = 0; j< num; j++)
{
for (int k=0; k<5; k++)
pred_per_class[j*5+k] = pred[(i*num+j)*5+k];
}
boxes_sort(num, pred_per_class, sorted_pred_cls);
_nms(keep, &num_out, sorted_pred_cls, num, 5, NMS_THRESH, 0);
//for visualize only
vis_detections(cv_img, keep, num_out, sorted_pred_cls, CONF_THRESH);
}
cv::imwrite("vis.jpg",cv_img);
delete []boxes;
delete []pred;
delete []pred_per_class;
delete []keep;
delete []sorted_pred_cls;
}
/*
* === FUNCTION ======================================================================
* Name: vis_detections
* Description: Visuallize the detection result
* =====================================================================================
*/
void Detector::vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH)
{
int i=0;
while(sorted_pred_cls[keep[i]*5+4]>CONF_THRESH && i < num_out)
{
if(i>=num_out)
return;
cv::rectangle(image,cv::Point(sorted_pred_cls[keep[i]*5+0], sorted_pred_cls[keep[i]*5+1]),cv::Point(sorted_pred_cls[keep[i]*5+2], sorted_pred_cls[keep[i]*5+3]),cv::Scalar(255,0,0));
i++;
}
}
/*
* === FUNCTION ======================================================================
* Name: boxes_sort
* Description: Sort the bounding box according score
* =====================================================================================
*/
void Detector::boxes_sort(const int num, const float* pred, float* sorted_pred)
{
vector<Info> my;
Info tmp;
for (int i = 0; i< num; i++)
{
tmp.score = pred[i*5 + 4];
tmp.head = pred + i*5;
my.push_back(tmp);
}
std::sort(my.begin(), my.end(), compare);
for (int i=0; i<num; i++)
{
for (int j=0; j<5; j++)
sorted_pred[i*5+j] = my[i].head[j];
}
}
/*
* === FUNCTION ======================================================================
* Name: bbox_transform_inv
* Description: Compute bounding box regression value
* =====================================================================================
*/
void Detector::bbox_transform_inv(int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width)
{
float width, height, ctr_x, ctr_y, dx, dy, dw, dh, pred_ctr_x, pred_ctr_y, pred_w, pred_h;
for(int i=0; i< num; i++)
{
width = boxes[i*4+2] - boxes[i*4+0] + 1.0;
height = boxes[i*4+3] - boxes[i*4+1] + 1.0;
ctr_x = boxes[i*4+0] + 0.5 * width;
ctr_y = boxes[i*4+1] + 0.5 * height;
for (int j=0; j< class_num; j++)
{
dx = box_deltas[(i*class_num+j)*4+0];
dy = box_deltas[(i*class_num+j)*4+1];
dw = box_deltas[(i*class_num+j)*4+2];
dh = box_deltas[(i*class_num+j)*4+3];
pred_ctr_x = ctr_x + width*dx;
pred_ctr_y = ctr_y + height*dy;
pred_w = width * exp(dw);
pred_h = height * exp(dh);
pred[(j*num+i)*5+0] = max(min(pred_ctr_x - 0.5* pred_w, img_width -1), 0);
pred[(j*num+i)*5+1] = max(min(pred_ctr_y - 0.5* pred_h, img_height -1), 0);
pred[(j*num+i)*5+2] = max(min(pred_ctr_x + 0.5* pred_w, img_width -1), 0);
pred[(j*num+i)*5+3] = max(min(pred_ctr_y + 0.5* pred_h, img_height -1), 0);
pred[(j*num+i)*5+4] = pred_cls[i*class_num+j];
}
}
}
