前言
darknet是一个基于C写出的框架,yolov3速度快,定位准确率比较高,很适合落地到自己的工程中。那么如何调用编译好的darknet.so动态链接库呢?
正文
首先需要去darknet官网下载好darknet-master,然后进行编译,编译的时候最好选择GPU格式的,这样在后面监测的时候速度会非常快。
好啦,直接上代码吧~代码主要包含两个头文件和两个源文件。
imprcoess.h
#ifndef IMPROCESS_H
#define IMPROCESS_H
#include<opencv2/opencv.hpp>
void imgConvert(const cv::Mat& img, float* dst);
void imgResize(float* src, float* dst,int srcWidth,int srcHeight,int dstWidth,int dstHeight);
void resizeInner(float *src, float* dst,int srcWidth,int srcHeight,int dstWidth,int dstHeight);
#endif // IMPROCESS_H
imprcoess.cpp
#include<improcess.h>
void imgConvert(const cv::Mat& img, float* dst){
uchar *data = img.data;
int h = img.rows;
int w = img.cols;
int c = img.channels();
for(int k= 0; k < c; ++k){
for(int i = 0; i < h; ++i){
for(int j = 0; j < w; ++j){
dst[k*w*h+i*w+j] = data[(i*w + j)*c + k]/255.;
}
}
}
}
void imgResize(float *src, float* dst,int srcWidth,int srcHeight,int dstWidth,int dstHeight){
int new_w = srcWidth;
int new_h = srcHeight;
if (((float)dstWidth/srcWidth) < ((float)dstHeight/srcHeight)) {
new_w = dstWidth;
new_h = (srcHeight * dstWidth)/srcWidth;
} else {
new_h = dstHeight;
new_w = (srcWidth * dstHeight)/srcHeight;
}
float* ImgReInner;
size_t sizeInner=new_w*new_h*3*sizeof(float);
ImgReInner=(float*)malloc(sizeInner);
resizeInner(src,ImgReInner,srcWidth,srcHeight,new_w,new_h);
for(int i=0;i<dstWidth*dstHeight*3;i++){
dst[i]=0.5;
}
for(int k = 0; k < 3; ++k){
for(int y = 0; y < new_h; ++y){
for(int x = 0; x < new_w; ++x){
float val = ImgReInner[k*new_w*new_h+y*new_w+x];
dst[k*dstHeight*dstWidth + ((dstHeight-new_h)/2+y)*dstWidth + (dstWidth-new_w)/2+x]=val;
}
}
}
free(ImgReInner);
}
void resizeInner(float *src, float* dst,int srcWidth,int srcHeight,int dstWidth,int dstHeight){
float* part;
size_t sizePa=dstWidth*srcHeight*3*sizeof(float);
part=(float*)malloc(sizePa);
float w_scale = (float)(srcWidth - 1) / (dstWidth - 1);
float h_scale = (float)(srcHeight - 1) / (dstHeight - 1);
for(int k = 0; k < 3; ++k){
for(int r = 0; r < srcHeight; ++r){
for(int c = 0; c < dstWidth; ++c){
float val = 0;
if(c == dstWidth-1 || srcWidth == 1){
val=src[k*srcWidth*srcHeight+r*srcWidth+srcWidth-1];
} else {
float sx = c*w_scale;
int ix = (int) sx;
float dx = sx - ix;
val=(1 - dx) * src[k*srcWidth*srcHeight+r*srcWidth+ix] + dx * src[k*srcWidth*srcHeight+r*srcWidth+ix+1];
}
part[k*srcHeight*dstWidth + r*dstWidth + c]=val;
}
}
}
for(int k = 0; k < 3; ++k){
for(int r = 0; r < dstHeight; ++r){
float sy = r*h_scale;
int iy = (int) sy;
float dy = sy - iy;
for(int c = 0; c < dstWidth; ++c){
float val = (1-dy) * part[k*dstWidth*srcHeight+iy*dstWidth+c];
dst[k*dstWidth*dstHeight + r*dstWidth + c]=val;
}
if(r == dstHeight-1 || srcHeight == 1)
continue;
for(int c = 0; c < dstWidth; ++c){
float val = dy * part[k*dstWidth*srcHeight+(iy+1)*dstWidth+c];
dst[k*dstWidth*dstHeight + r*dstWidth + c]+=val;
}
}
}
free(part);
}
darknet.h就不再列出啦,有头文件的,直接copy到自己工程下就ok啦~~~
main.cpp,这里是使用opencv去调用usb摄像头来进行物体检测,模型是darknet官网上的模型,当然你可以换成自己训练的模型。
#include<iostream>
#include<opencv2/opencv.hpp>
#include<darknet.h>
#include<improcess.h>
using namespace std;
using namespace cv;
float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
float get_color(int c, int x, int max)
{
float ratio = ((float)x/max)*5;
int i = floor(ratio);
int j = ceil(ratio);
ratio -= i;
float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
return r;
}
int main()
{
string cfgfile = "/home/oliver/darknet-master/cfg/yolov3.cfg";//读取模型文件,请自行修改相应路径
string weightfile = "/home/oliver/darknet-master/yolov3.weights";
float thresh=0.5;//参数设置
float nms=0.35;
int classes=80;
network *net=load_network((char*)cfgfile.c_str(),(char*)weightfile.c_str(),0);//加载网络模型
set_batch_network(net, 1);
VideoCapture capture(0);//读取视频,请自行修改相应路径
capture.set(CV_CAP_PROP_FRAME_WIDTH,1920);
capture.set(CV_CAP_PROP_FRAME_HEIGHT,1080);
Mat frame;
Mat rgbImg;
vector<string> classNamesVec;
ifstream classNamesFile("/home/oliver/darknet-master/data/coco.names");//标签文件coco有80类
if (classNamesFile.is_open()){
string className = "";
while (getline(classNamesFile, className))
classNamesVec.push_back(className);
}
bool stop=false;
while(!stop)
{
cout<<frame.size<<endl;
if (!capture.read(frame))
{
printf("fail to read.\n");
return 0;
}
cvtColor(frame, rgbImg, cv::COLOR_BGR2RGB);
float* srcImg;
size_t srcSize=rgbImg.rows*rgbImg.cols*3*sizeof(float);
srcImg=(float*)malloc(srcSize);
imgConvert(rgbImg,srcImg);//将图像转为yolo形式
float* resizeImg;
size_t resizeSize=net->w*net->h*3*sizeof(float);
resizeImg=(float*)malloc(resizeSize);
imgResize(srcImg,resizeImg,frame.cols,frame.rows,net->w,net->h);//缩放图像
network_predict(net,resizeImg);//网络推理
int nboxes=0;
detection *dets=get_network_boxes(net,rgbImg.cols,rgbImg.rows,thresh,0.5,0,1,&nboxes);
if(nms){
do_nms_sort(dets,nboxes,classes,nms);
}
vector<cv::Rect>boxes;
boxes.clear();
vector<int>classNames;
for (int i = 0; i < nboxes; i++){
bool flag=0;
int className;
for(int j=0;j<classes;j++){
if(dets[i].prob[j]>thresh){
if(!flag){
flag=1;
className=j;
}
}
}
if(flag)
{
int left = (dets[i].bbox.x - dets[i].bbox.w / 2.)*frame.cols;
int right = (dets[i].bbox.x + dets[i].bbox.w / 2.)*frame.cols;
int top = (dets[i].bbox.y - dets[i].bbox.h / 2.)*frame.rows;
int bot = (dets[i].bbox.y + dets[i].bbox.h / 2.)*frame.rows;
if (left < 0)
left = 0;
if (right > frame.cols - 1)
right = frame.cols - 1;
if (top < 0)
top = 0;
if (bot > frame.rows - 1)
bot = frame.rows - 1;
Rect box(left, top, fabs(left - right), fabs(top - bot));
boxes.push_back(box);
classNames.push_back(className);
}
}
free_detections(dets, nboxes);
for(int i=0;i<boxes.size();i++)
{
int offset = classNames[i]*123457 % 80;
float red = 255*get_color(2,offset,80);
float green = 255*get_color(1,offset,80);
float blue = 255*get_color(0,offset,80);
rectangle(frame,boxes[i],Scalar(blue,green,red),2);
String label = String(classNamesVec[classNames[i]]);
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
putText(frame, label, Point(boxes[i].x, boxes[i].y + labelSize.height),
FONT_HERSHEY_SIMPLEX, 1, Scalar(red, blue, green),2);
}
// Mat resize_img;
// resize(frame,resize_img,cv::Size(f_width,f_height),(0,0),(0,0),cv::INTER_LINEAR);
// cout<<frame.size<<endl;
namedWindow("video",0);
imshow("video",frame);
int c=waitKey(30);
if((char)c==27)
break;
else if(c>=0)
waitKey(0);
free(srcImg);
free(resizeImg);
}
free_network(net);
capture.release();
return 1;
}
QT下库的配置:
QT += core
QT -= gui
CONFIG += c++11
TARGET = yolov3_master
CONFIG += console
CONFIG -= app_bundle
TEMPLATE = app
SOURCES += main.cpp \
improcess.cpp
# The following define makes your compiler emit warnings if you use
# any feature of Qt which as been marked deprecated (the exact warnings
# depend on your compiler). Please consult the documentation of the
# deprecated API in order to know how to port your code away from it.
DEFINES += QT_DEPRECATED_WARNINGS
# You can also make your code fail to compile if you use deprecated APIs.
# In order to do so, uncomment the following line.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000 # disables all the APIs deprecated before Qt 6.0.0
HEADERS += \
darknet.h \
improcess.h
INCLUDEPATH += /usr/local/include\
/usr/local/include/opencv\
/usr/local/include/opencv2
LIBS +=/usr/local/lib/libopencv_*.so\
/home/oliver/darknet-master/libdarknet.so\
/usr/local/cuda-9.0/lib64/libcudart.so.9.0\
/usr/local/cuda-9.0/lib64/libcudnn.so.7\
/usr/local/cuda-9.0/lib64/libcurand.so.9.0\
/usr/local/cuda-9.0/lib64/libcublas.so.9.0