ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

程序员文章站 2022-09-17 08:11:18

直接使用clion打开opencv2.4.10.4项目打开opencv-2.4.10.4/CMakeLists.txt修改CUDA相关为OFF会遇到一个错误:Problems were encountered while collecting compiler information:cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or di...

直接使用clion打开opencv2.4.10.4源代码 debug模式编译

打开opencv-2.4.10.4/CMakeLists.txt

修改CUDA相关为OFF

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

会遇到一个错误:

Problems were encountered while collecting compiler information:
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/calib3d/perf_precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory
cc1plus: fatal error: /--/--/cmake-build-release/modules/core/precomp.hpp: No such file or directory

https://*.com/questions/55575471/why-error-occurs-when-using-clion-build-opencv-after-cmake-configuration-and-gen

将

ENABLE_PRECOMPILED_HEADERS

设置为OFF

删除之前编译出来的文件集 opencv-2.4.10.4/cmake-build-debug

重新编译

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

编译test模块还会遇到一个bug

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

解决方案:

https://blog.csdn.net/qq_32768743/article/details/74935818

以上我们就通过clion编译了oepncv

然后新建一个clion工程opencv_debug:

他的cmakelist.txt

cmake_minimum_required(VERSION 3.12)
project(opencv_debug)

set(CMAKE_CXX_STANDARD 14)


set(OpenCV_DIR ~/deepglint/install/opencv-2.4.10.4/cmake-build-debug)
find_package(OpenCV REQUIRED)

INCLUDE_DIRECTORIES(
        ${OpenCV_INCLUDE_DIRS}
)

add_executable(opencv_debug main.cpp)
target_link_libraries(opencv_debug ${OpenCV_LIBS})

将OpenCV_DIR设置为刚刚我们clion编译源代码的位置:

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

然后,新建一个例子:

#include <opencv2/core/core.hpp>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

int main()
{
    initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
    Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器
    Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器
    Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器
    if( detector.empty() || descriptor_extractor.empty() )
        cout<<"fail to create detector!";

    //读入图像
    Mat img1 = imread("box.png");
    Mat img2 = imread("box_in_scene.png");

    //特征点检测
    double t = getTickCount();//当前滴答数
    vector<KeyPoint> keypoints1,keypoints2;
    detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中
    detector->detect( img2, keypoints2 );
    cout<<"图像1特征点个数:"<<keypoints1.size()<<endl;
    cout<<"图像2特征点个数:"<<keypoints2.size()<<endl;

    //根据特征点计算特征描述子矩阵，即特征向量矩阵
    Mat descriptors1,descriptors2;
    descriptor_extractor->compute( img1, keypoints1, descriptors1 );
    descriptor_extractor->compute( img2, keypoints2, descriptors2 );
    t = ((double)getTickCount() - t)/getTickFrequency();
    cout<<"SIFT算法用时："<<t<<"秒"<<endl;


    cout<<"图像1特征描述矩阵大小："<<descriptors1.size()
        <<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;
    cout<<"图像2特征描述矩阵大小："<<descriptors2.size()
        <<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;

    //画出特征点
    Mat img_keypoints1,img_keypoints2;
    drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0);
    drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0);
    //imshow("Src1",img_keypoints1);
    //imshow("Src2",img_keypoints2);

    //特征匹配
    vector<DMatch> matches;//匹配结果
    descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵
    cout<<"Match个数："<<matches.size()<<endl;

    //计算匹配结果中距离的最大和最小值
	//距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近
    double max_dist = 0;
    double min_dist = 100;
    for(int i=0; i<matches.size(); i++)
    {
        double dist = matches[i].distance;
        if(dist < min_dist) min_dist = dist;
        if(dist > max_dist) max_dist = dist;
    }
    cout<<"最大距离："<<max_dist<<endl;
    cout<<"最小距离："<<min_dist<<endl;

    //筛选出较好的匹配点
    vector<DMatch> goodMatches;
    for(int i=0; i<matches.size(); i++)
    {
        if(matches[i].distance < 0.31 * max_dist)
        {
            goodMatches.push_back(matches[i]);
        }
    }
    cout<<"goodMatch个数："<<goodMatches.size()<<endl;

    //画出匹配结果
    Mat img_matches;
    //红色连接的是匹配的特征点对，绿色是未匹配的特征点
    drawMatches(img1,keypoints1,img2,keypoints2,goodMatches,img_matches,
                Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2);
    imwrite("MatchSIFT.png", img_matches);
//    imshow("MatchSIFT",img_matches);
//    waitKey();
    return 0;
}

为了验证我们的想法,更改clion中opencv源代码工程的代码:

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

然后重新点编译,这时候编译会很快,因为只改动了某个模块

运行opencv_debug,我们得到如下结果:

~/CLionProjects/opencv_debug/cmake-build-debug/opencv_debug
yangninghua
yangninghua
图像1特征点个数:604
图像2特征点个数:969
yangninghua
yangninghua
SIFT算法用时：1.06016秒
图像1特征描述矩阵大小：[128 x 604]，特征向量个数：604，维数：128
图像2特征描述矩阵大小：[128 x 969]，特征向量个数：969，维数：128
Match个数：604
最大距离：432.236
最小距离：66.2571
goodMatch个数：34

验证后我们, 后续我们就可以通过断点调试进入源代码

updatas:

其实使用任何方式编译都行,不太需要clion,只不过clion修改方便,比如,只要在cmakelist.txt中将 CMAKE_BUILD_TYPE=DEBUG改为debug就行

# ----------------------------------------------------------------------------
# OpenCV compiler and linker options
# ----------------------------------------------------------------------------
# In case of Makefiles if the user does not setup CMAKE_BUILD_TYPE, assume it's Release:
if(CMAKE_GENERATOR MATCHES "Makefiles|Ninja" AND "${CMAKE_BUILD_TYPE}" STREQUAL "")
  set(CMAKE_BUILD_TYPE Release)
endif()
set(CMAKE_BUILD_TYPE Debug)

然后设置安装位置:

# Following block can broke build in case of cross-compilng
# but CMAKE_CROSSCOMPILING variable will be set only on project(OpenCV) command
# so we will try to detect crosscompiling by presense of CMAKE_TOOLCHAIN_FILE
if(NOT CMAKE_TOOLCHAIN_FILE)
  # it _must_ go before project(OpenCV) in order to work
  if(WIN32)
    set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Installation Directory")
  else()
    set(CMAKE_INSTALL_PREFIX "/usr/local" CACHE PATH "Installation Directory")
  endif()
else(NOT CMAKE_TOOLCHAIN_FILE)
  #Android: set output folder to ${CMAKE_BINARY_DIR}
  set( LIBRARY_OUTPUT_PATH_ROOT ${CMAKE_BINARY_DIR} CACHE PATH "root for library output, set this to change where android libs are compiled to" )
  # any crosscompiling
  set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Installation Directory")
endif(NOT CMAKE_TOOLCHAIN_FILE)

set(CMAKE_INSTALL_PREFIX "~/deepglint/install/opencv-2.4.13.7/install")

然后就直接编译,安装,

mkdir build

cmake ..

make -j4

make install -j4

我将他安装在了:

~/deepglint/install/opencv-2.4.13.7/install

然后打开clion

新建项目

cmakelist.txt:

cmake_minimum_required(VERSION 3.12)
project(opencv_debug)

set(CMAKE_CXX_STANDARD 14)


set(OpenCV_DIR ~/deepglint/install/opencv-2.4.13.7/install/share/OpenCV)
find_package(OpenCV REQUIRED)

INCLUDE_DIRECTORIES(
        ${OpenCV_INCLUDE_DIRS}
)

add_executable(opencv_debug main.cpp)
target_link_libraries(opencv_debug ${OpenCV_LIBS})

随便一个例子:

#include <opencv2/core/core.hpp>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

int main()
{
    initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
    Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器
    Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器
    Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器
    if( detector.empty() || descriptor_extractor.empty() )
        cout<<"fail to create detector!";

    //读入图像
    Mat img1 = imread("box.png");
    Mat img2 = imread("box_in_scene.png");

    //特征点检测
    double t = getTickCount();//当前滴答数
    vector<KeyPoint> keypoints1,keypoints2;
    detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中
    detector->detect( img2, keypoints2 );
    cout<<"图像1特征点个数:"<<keypoints1.size()<<endl;
    cout<<"图像2特征点个数:"<<keypoints2.size()<<endl;

    //根据特征点计算特征描述子矩阵，即特征向量矩阵
    Mat descriptors1,descriptors2;
    descriptor_extractor->compute( img1, keypoints1, descriptors1 );
    descriptor_extractor->compute( img2, keypoints2, descriptors2 );
    t = ((double)getTickCount() - t)/getTickFrequency();
    cout<<"SIFT算法用时："<<t<<"秒"<<endl;


    cout<<"图像1特征描述矩阵大小："<<descriptors1.size()
        <<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;
    cout<<"图像2特征描述矩阵大小："<<descriptors2.size()
        <<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;

    //画出特征点
    Mat img_keypoints1,img_keypoints2;
    drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0);
    drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0);
    //imshow("Src1",img_keypoints1);
    //imshow("Src2",img_keypoints2);

    //特征匹配
    vector<DMatch> matches;//匹配结果
    descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵
    cout<<"Match个数："<<matches.size()<<endl;

    //计算匹配结果中距离的最大和最小值
	//距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近
    double max_dist = 0;
    double min_dist = 100;
    for(int i=0; i<matches.size(); i++)
    {
        double dist = matches[i].distance;
        if(dist < min_dist) min_dist = dist;
        if(dist > max_dist) max_dist = dist;
    }
    cout<<"最大距离："<<max_dist<<endl;
    cout<<"最小距离："<<min_dist<<endl;

    //筛选出较好的匹配点
    vector<DMatch> goodMatches;
    for(int i=0; i<matches.size(); i++)
    {
        if(matches[i].distance < 0.31 * max_dist)
        {
            goodMatches.push_back(matches[i]);
        }
    }
    cout<<"goodMatch个数："<<goodMatches.size()<<endl;

    //画出匹配结果
    Mat img_matches;
    //红色连接的是匹配的特征点对，绿色是未匹配的特征点
    drawMatches(img1,keypoints1,img2,keypoints2,goodMatches,img_matches,
                Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2);
    imwrite("MatchSIFT.png", img_matches);
//    imshow("MatchSIFT",img_matches);
//    waitKey();
    return 0;
}

通过调试器的步入就可以debug

ubuntu16.04-gcc6.5.0-clion-opencv2.4.10.4断点调试源代码

本文地址：https://blog.csdn.net/baidu_40840693/article/details/108716801

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