OpenCV+OpenCL stereo match 代码
之前配置cuda跟opencv 的混合编程,发现只要使用的东西多半还要用opencv的代码编译一次,加上cuda的编译太浪费时间了,我看了几个博客,觉的opencl这个可能会比较好整,就把opencv里面的opencl代码的部分编译了一下,这个比较少,用的时候也能直接检测出来i7 自带的集成显卡:
Device name:Intel(R) HD Graphics 4600
后面调试程序时候发现,2.4.4版本好像还没有直接能用的dll,2.4.10的build文件夹中就有可以直接调用的现成dll也不用编译了,很是方便!
参考文献:
http://blog.csdn.net/pengx17/article/details/7880642
参数设置:
ocl_stereo_match -l=view1.png -r=view5.png -m=BM -n=64 -o=output.jpg
// ocl_stereo_match.cpp : 定义控制台应用程序的入口点。
// #include "stdafx.h" #include <iostream>
#include <string>
#include <sstream>
#include <iomanip>
#include <stdexcept>
#include "opencv2/ocl/ocl.hpp"
#include "opencv2/highgui/highgui.hpp" #pragma comment(lib,"opencv_core2410d.lib")
#pragma comment(lib,"opencv_highgui2410d.lib")
#pragma comment(lib,"opencv_ocl2410d.lib")
#pragma comment(lib,"opencv_imgproc2410d.lib") using namespace cv;
using namespace std;
using namespace ocl; struct App
{
App(CommandLineParser& cmd);
void run();
void handleKey(char key);
void printParams() const; void workBegin()
{
work_begin = getTickCount();
}
void workEnd()
{
int64 d = getTickCount() - work_begin;
double f = getTickFrequency();
work_fps = f / d;
}
string method_str() const
{
switch (method)
{
case BM:
return "BM";
case BP:
return "BP";
case CSBP:
return "CSBP";
}
return "";
}
string text() const
{
stringstream ss;
ss << "(" << method_str() << ") FPS: " << setiosflags(ios::left)
<< setprecision(4) << work_fps;
return ss.str();
}
private:
bool running, write_once; Mat left_src, right_src;
Mat left, right;
oclMat d_left, d_right; StereoBM_OCL bm;
StereoBeliefPropagation bp;
StereoConstantSpaceBP csbp; int64 work_begin;
double work_fps; string l_img, r_img;
string out_img;
enum {BM, BP, CSBP} method;
int ndisp; // Max disparity + 1
enum {GPU, CPU} type;
}; int main(int argc, char** argv)
{
const char* keys =
"{ h | help | false | print help message }"
"{ l | left | | specify left image }"
"{ r | right | | specify right image }"
"{ m | method | BM | specify match method(BM/BP/CSBP) }"
"{ n | ndisp | 64 | specify number of disparity levels }"
"{ o | output | stereo_match_output.jpg | specify output path when input is images}"; CommandLineParser cmd(argc, argv, keys);
if (cmd.get<bool>("help"))
{
cout << "Available options:" << endl;
cmd.printParams();
return 0;
} try
{
App app(cmd);
cout << "Device name:" << cv::ocl::Context::getContext()->getDeviceInfo().deviceName << endl; app.run();
getchar();
}
catch (const exception& e)
{
cout << "error: " << e.what() << endl;
} return EXIT_SUCCESS;
} App::App(CommandLineParser& cmd)
: running(false),method(BM)
{
cout << "stereo_match_ocl sample\n";
cout << "\nControls:\n"
<< "\tesc - exit\n"
<< "\to - save output image once\n"
<< "\tp - print current parameters\n"
<< "\tg - convert source images into gray\n"
<< "\tm - change stereo match method\n"
<< "\ts - change Sobel prefiltering flag (for BM only)\n"
<< "\t1/q - increase/decrease maximum disparity\n"
<< "\t2/w - increase/decrease window size (for BM only)\n"
<< "\t3/e - increase/decrease iteration count (for BP and CSBP only)\n"
<< "\t4/r - increase/decrease level count (for BP and CSBP only)\n"; l_img = cmd.get<string>("l");
r_img = cmd.get<string>("r");
string mstr = cmd.get<string>("m");
if(mstr == "BM") method = BM;
else if(mstr == "BP") method = BP;
else if(mstr == "CSBP") method = CSBP;
else cout << "unknown method!\n";
ndisp = cmd.get<int>("n");
out_img = cmd.get<string>("o");
write_once = false;
} void App::run()
{
// Load images
cout<<l_img;
left_src = imread(l_img,1);//cvLoadImage(l_img.c_str());//
right_src = imread(r_img,1);//cvLoadImage(r_img.c_str());//
if (left_src.empty()) throw runtime_error("can't open file \"" + l_img + "\"");
if (right_src.empty()) throw runtime_error("can't open file \"" + r_img + "\""); cvtColor(left_src, left, CV_BGR2GRAY);
cvtColor(right_src, right, CV_BGR2GRAY); d_left.upload(left);
d_right.upload(right); imshow("left", left);
imshow("right", right); waitKey(0); // Set common parameters
bm.ndisp = ndisp;
bp.ndisp = ndisp;
csbp.ndisp = ndisp; cout << endl;
printParams(); running = true;
while (running)
{
// Prepare disparity map of specified type
Mat disp;
oclMat d_disp;
workBegin();
switch (method)
{
case BM:
if (d_left.channels() > 1 || d_right.channels() > 1)
{
cout << "BM doesn't support color images\n";
cvtColor(left_src, left, CV_BGR2GRAY);
cvtColor(right_src, right, CV_BGR2GRAY);
cout << "image_channels: " << left.channels() << endl;
d_left.upload(left);
d_right.upload(right);
imshow("left", left);
imshow("right", right);
}
bm(d_left, d_right, d_disp);
break;
case BP:
bp(d_left, d_right, d_disp);
break;
case CSBP:
csbp(d_left, d_right, d_disp);
break;
} // Show results
d_disp.download(disp);
workEnd(); if (method != BM)
{
disp.convertTo(disp, 0);
}
putText(disp, text(), Point(5, 25), FONT_HERSHEY_SIMPLEX, 1.0, Scalar::all(255));
imshow("disparity", disp);
if(write_once)
{
imwrite(out_img, disp);
write_once = false;
}
handleKey((char)waitKey(3));
}
} void App::printParams() const
{
cout << "--- Parameters ---\n";
cout << "image_size: (" << left.cols << ", " << left.rows << ")\n";
cout << "image_channels: " << left.channels() << endl;
cout << "method: " << method_str() << endl
<< "ndisp: " << ndisp << endl;
switch (method)
{
case BM:
cout << "win_size: " << bm.winSize << endl;
cout << "prefilter_sobel: " << bm.preset << endl;
break;
case BP:
cout << "iter_count: " << bp.iters << endl;
cout << "level_count: " << bp.levels << endl;
break;
case CSBP:
cout << "iter_count: " << csbp.iters << endl;
cout << "level_count: " << csbp.levels << endl;
break;
}
cout << endl;
} void App::handleKey(char key)
{
switch (key)
{
case 27:
running = false;
break;
case 'p':
case 'P':
printParams();
break;
case 'g':
case 'G':
if (left.channels() == 1 && method != BM)
{
left = left_src;
right = right_src;
}
else
{
cvtColor(left_src, left, CV_BGR2GRAY);
cvtColor(right_src, right, CV_BGR2GRAY);
}
d_left.upload(left);
d_right.upload(right);
cout << "image_channels: " << left.channels() << endl;
imshow("left", left);
imshow("right", right);
break;
case 'm':
case 'M':
switch (method)
{
case BM:
method = BP;
break;
case BP:
method = CSBP;
break;
case CSBP:
method = BM;
break;
}
cout << "method: " << method_str() << endl;
break;
case 's':
case 'S':
if (method == BM)
{
switch (bm.preset)
{
case StereoBM_OCL::BASIC_PRESET:
bm.preset = StereoBM_OCL::PREFILTER_XSOBEL;
break;
case StereoBM_OCL::PREFILTER_XSOBEL:
bm.preset = StereoBM_OCL::BASIC_PRESET;
break;
}
cout << "prefilter_sobel: " << bm.preset << endl;
}
break;
case '1':
ndisp == 1 ? ndisp = 8 : ndisp += 8;
cout << "ndisp: " << ndisp << endl;
bm.ndisp = ndisp;
bp.ndisp = ndisp;
csbp.ndisp = ndisp;
break;
case 'q':
case 'Q':
ndisp = max(ndisp - 8, 1);
cout << "ndisp: " << ndisp << endl;
bm.ndisp = ndisp;
bp.ndisp = ndisp;
csbp.ndisp = ndisp;
break;
case '2':
if (method == BM)
{
bm.winSize = min(bm.winSize + 1, 51);
cout << "win_size: " << bm.winSize << endl;
}
break;
case 'w':
case 'W':
if (method == BM)
{
bm.winSize = max(bm.winSize - 1, 2);
cout << "win_size: " << bm.winSize << endl;
}
break;
case '3':
if (method == BP)
{
bp.iters += 1;
cout << "iter_count: " << bp.iters << endl;
}
else if (method == CSBP)
{
csbp.iters += 1;
cout << "iter_count: " << csbp.iters << endl;
}
break;
case 'e':
case 'E':
if (method == BP)
{
bp.iters = max(bp.iters - 1, 1);
cout << "iter_count: " << bp.iters << endl;
}
else if (method == CSBP)
{
csbp.iters = max(csbp.iters - 1, 1);
cout << "iter_count: " << csbp.iters << endl;
}
break;
case '4':
if (method == BP)
{
bp.levels += 1;
cout << "level_count: " << bp.levels << endl;
}
else if (method == CSBP)
{
csbp.levels += 1;
cout << "level_count: " << csbp.levels << endl;
}
break;
case 'r':
case 'R':
if (method == BP)
{
bp.levels = max(bp.levels - 1, 1);
cout << "level_count: " << bp.levels << endl;
}
else if (method == CSBP)
{
csbp.levels = max(csbp.levels - 1, 1);
cout << "level_count: " << csbp.levels << endl;
}
break;
case 'o':
case 'O':
write_once = true;
break;
}
}
OpenCV+OpenCL stereo match 代码的更多相关文章
- [转]Android通过NDK调用JNI,使用opencv做本地c++代码开发配置方法
原文地址:http://blog.csdn.net/watkinsong/article/details/9849973 有一种方式不需要自己配置所有的Sun JDK, Android SDK以及ND ...
- OpenCV/OpenCL/OpenGL区别
OpenCV/OpenCL/OpenGL区别: OpenGL(全写Open Graphics Library)是个定义了一个跨编程语言.跨平台的应用程序接口(API)的规格,它用于生成二维.三维图像. ...
- Android(安卓)开发通过NDK调用JNI,使用opencv做本地c++代码开发配置方法 边缘检测 范例代码
以前写过两个Android开发配置文档,使用NDK进行JNI开发,这样能够利用以前已经写好的C++代码. 前两篇博客地址: http://blog.csdn.net/watkinsong/articl ...
- 编译opencv有关cuda的代码
opencv3.2提供了cuda很好的支持,cuda的opencv接口,让用户想使用opencv那样去使用cuda,不用写cuda代码 一开始编译opencv有关cuda的代码,opencv 里sam ...
- OpenCV:OpenCV图像旋转的代码
OpenCV图像旋转的代码 cv::transpose( bfM, bfM ) 前提:使用两个矩阵Mat型进行下标操作是不行的,耗费的时间太长了.直接使用两个指针对拷贝才是王道.不知道和OpenCV比 ...
- OpenCV stereo matching 代码 matlab实现视差显示
转载请注明出处:http://blog.csdn.net/wangyaninglm/article/details/44151213, 来自:shiter编写程序的艺术 基础知识 计算机视觉是一门研究 ...
- 立体视觉-opencv中立体匹配相关代码
三种匹配算法比较 BM算法: 该算法代码: view plaincopy to clipboardprint? CvStereoBMState *BMState = cvCreateStereoBMS ...
- 混合高斯模型:opencv中MOG2的代码结构梳理
/* 头文件:OurGaussmix2.h */ #include "opencv2/core/core.hpp" #include <list> #include&q ...
- OpenCV检测人脸实例代码
下面是使用OpenCV通过在硬盘中读入图像来对其进行Haar人脸检测的代码. //包含头文件 #include <opencv2/core/core.hpp> #include " ...
随机推荐
- Compass 更智能的搜索引擎(2)--进阶
经过了Compass 更智能的搜索引擎(1)–入门的学习,想必对于Compass的使用有了更深的认识了吧.下面谈点更加切合实际开发的东西.那就是CRUD. 面向对象的分页 dao层实现 代码释义 优点 ...
- JAVA面向对象-----final关键字
JAVA面向对象-–final关键字 1:定义静态方法求圆的面积 2:定义静态方法求圆的周长 3:发现方法中有重复的代码,就是PI,圆周率. 1:如果需要提高计算精度,就需要修改每个方法中圆周率. 4 ...
- OpenCV相机标定
标签(空格分隔): Opencv 相机标定是图像处理的基础,虽然相机使用的是小孔成像模型,但是由于小孔的透光非常有限,所以需要使用透镜聚焦足够多的光线.在使用的过程中,需要知道相机的焦距.成像中心以及 ...
- 6.4、Android Studio的GPU Monitor
Android Monitor包含GPU Monitor,它将可视化的显示渲染窗体的时间.GPU Monitor可以帮助你: 1. 迅速查看UI窗体生成 2. 辨别是否渲染管道超出使用线程时间 在GP ...
- linux中的网络通信指令
1.write write命令通信是一对一的通信,即两个人之间的通信,如上图. 效果图 用法:write <用户名> 2.wall wall指令可将信息发送给每位同意接收公众信息的终端机用 ...
- Cytoscape源码下载地址和编译办法
开发环境:Windows2008 R2 64位+Jdk1.7+Maven3.2.3 前提条件:安装好JDK1.7到C:\Program Files\Java\jdk1.7.0_67,下载好Maven并 ...
- iOS中崩溃调试的使用和技巧总结 韩俊强的博客
每日更新关注:http://weibo.com/hanjunqiang 新浪微博 在iOS开发调试过程中以及上线之后,程序经常会出现崩溃的问题.简单的崩溃还好说,复杂的崩溃就需要我们通过解析Cras ...
- Uva - 816 - Abbott's Revenge
这个迷宫问题还是挺好玩的,多加了一个转向的问题,有些路口不同的进入方式会有不同的转向限制,这个会比较麻烦一点,所以定义结点结构体的时候需要加一个朝向dir.总体来说是一道BFS求最短路的问题.最后打印 ...
- Android官方命令深入分析之dmtracedump
dmtracedump是一个根据log文件生成图形化调用堆栈的工具(除了Traceview之外). dmtracedump的用法: dmtracedump [-ho] [-s sortable] [- ...
- XML解析之sax解析案例(一)读取contact.xml文件,完整输出文档内容
一.新建Demo2类: import java.io.File; import javax.xml.parsers.SAXParser; import javax.xml.parsers.SAXPar ...