Tag Archives: C++

Open source Structure-from-Motion and Multi-View Stereo tools with C++

Structure-from-Motion (SFM) is genuinely an interesting topic in computer vision, Basically making 3D structure from something 2D is absolutely mesmerizing 🙂

There two open source yet very robust tools for SFM, which sometimes compiling them might be complicated, here I will share my experience with you.

1)VisualSFM

Prerequisite:

1)Glew

Download the glew from SF at http://glew.sourceforge.net/. Do NOT get it from Github as to seems to have some problems.

cd build/cmake/ && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

cd build/cmake/ && mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

2)SiftGPU

Prerequisite:

Install DevIl Image library

sudo apt-get install libdevil-dev

1	sudo apt-get install libdevil-dev

git clone https://github.com/pitzer/SiftGPU

1	git clone https://github.com/pitzer/SiftGPU

open makefile and enable siftgpu_enable_cuda

siftgpu_enable_cuda=1

1	siftgpu_enable_cuda=1

make -j8

make -j8

now go to bin directory and libsiftgpu.so to your vsfm bin directory

VSFM

1) Download the <a href="http://ccwu.me/vsfm/download/VisualSFM_linux_64bit.zip">vsfm</a> program
	1-1)cd vsfm
	1-2)make

2) Download <a href="https://grail.cs.washington.edu/projects/mcba/pba_v1.0.5.zip">PBA</a>
	2-1)If you have no GPU rename makefile_no_gpu to makefie
	2-2)make
	2-3)rename bin/libpba_no_gpu.so to libpba.so and copy it in the vsfm/bin

3) Download <a href="https://github.com/pmoulon/CMVS-PMVS">CMVS-PMVS</a>
	3-1)cd CMVS-PMVS-master/program
	3-2)mkdir build && cd build
	3-3)cmake ../ && make -j8
	3-4)copy cmvs, genOption, pmvs2 from build/main into  vsfm/bin

4) copy libsiftgpu.so from previous section to vsfm/bin

5) Run:
  export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH
  ./VisualSFM

1) Download the <a href="http://ccwu.me/vsfm/download/VisualSFM_linux_64bit.zip">vsfm</a> program

1-1)cd vsfm

1-2)make

2) Download <a href="https://grail.cs.washington.edu/projects/mcba/pba_v1.0.5.zip">PBA</a>

2-1)If you have no GPU rename makefile_no_gpu to makefie

2-2)make

2-3)rename bin/libpba_no_gpu.so to libpba.so and copy it in the vsfm/bin

3) Download <a href="https://github.com/pmoulon/CMVS-PMVS">CMVS-PMVS</a>

3-1)cd CMVS-PMVS-master/program

3-2)mkdir build && cd build

3-3)cmake ../ && make -j8

3-4)copy cmvs, genOption, pmvs2 from build/main into vsfm/bin

4) copy libsiftgpu.so from previous section to vsfm/bin

5) Run:

export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH

./VisualSFM

you can see the some of my results here:

Note: if you don’t have cuda or Nvidia driver you can use your CPU but then you have to limit your file image size and sift binary should be available in your vsfm/bin directory.

Download http://www.cs.ubc.ca/~lowe/keypoints/siftDemoV4.zip and make it and copy the sift binary to vsfm/bin.

2)Colmap

The installation fo this one is almost straightforward except you need the latest version of Ceres Solver (do not use the one binary package from Ubuntu they are old and they won’t work). So download and make and install the Ceres Solver using the following:

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

1	cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Now in the colmap CMakeList.txt add the following line:

set(Ceres_DIR "$ENV{HOME}/usr/lib/cmake/Ceres")

1	set(Ceres_DIR "$ENV{HOME}/usr/lib/cmake/Ceres")

just before “find_package(Ceres REQUIRED)”

and now you can make and install it. You can see some of my results here:

In the next post, I will show you how can you do that in OpenCV.

Examples of Dynamic Programming with C++ and Matlab

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In this tutorial, I will give you examples of using dynamic programming for solving the following problems:

1)Minimum number of coins for summing X.

int c[]={1,3,4}; // value of coins
int k=3; //number of cpins that we have

/*
The d[20] array is for memorization, for instance if we computed f_coin_problem_memo(3),
we put this value in d[3] and we don't compute it again, we just use d[3] instead of f_coin_problem_memo(3)
*/
int d[20];
int f_coin_problem_memo(int x)
{
    if (x < 0) return 1e9;
    if (x == 0) return 0;
    if (d[x]) return d[x];
    int u = 1e9;
    int u_old = 1e9;
    for (int i = 0; i < k; i++)
    {
        u_old=u;
        u = std::min(u, f_coin_problem_memo(x-c[i]) +1);
    }
    d[x] = u;
    return d[x];
}

int c[]={1,3,4}; // value of coins

int k=3; //number of cpins that we have

The d[20] array is for memorization, for instance if we computed f_coin_problem_memo(3),

we put this value in d[3] and we don't compute it again, we just use d[3] instead of f_coin_problem_memo(3)

int d[20];

int f_coin_problem_memo(int x)

{

if (x < 0) return 1e9;

if (x == 0) return 0;

if (d[x]) return d[x];

int u = 1e9;

int u_old = 1e9;

for (int i = 0; i < k; i++)

{

u_old=u;

u = std::min(u, f_coin_problem_memo(x-c[i]) +1);

}

d[x] = u;

return d[x];

}

The minimum number of coins from the set {1,3,4} to sum up value of 12 is:
3

1 2	The minimum number of coins from the set {1,3,4} to sum up value of 12 is: 3

2)The most (least) costly path on a grid (dynamic time warping).

finding a path in an n × n grid from the upper-left corner to the lower-right corner. We are only allowed
 to move right or down.

1,1  1,2 .... 1,X
2,1  2,2 .... 2,X
.
.
.
Y,1  Y,2 .... Y,X


3 7 9 2 7
9 8 3 5 5
1 7 9 8 5
3 8 6 4 10
6 3 9 7 8

*/

int grid[5][5]={{3,7, 9, 2, 7},{9, 8, 3 ,5 ,5},{1, 7 ,9 ,8 ,5},{3, 8, 6 ,4 ,10},{6, 3 ,9 ,7 ,8}};
int memo[5][5]{{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1}};

std::vector<int>x_solution,y_solution;

int f_paths_in_grid(int y,int x)
{
    if((x==0)&& (y==0))
    {
        memo[y][x]=grid[0][0];
    }
    else if (x==0)
    {
        if(memo[y][x]<0)
        {
            memo[y][x] =f_paths_in_grid(y-1,x) +grid[y][x];
        }
    }
    else if(y==0)
    {
       if(memo[y][x]<0)
       {
            memo[y][x]=f_paths_in_grid(y,x-1) +grid[y][x];
       }
    }
   else if(memo[y][x]<0)
   {
        memo[y][x]=std::max(f_paths_in_grid(y,x-1)  ,f_paths_in_grid(y-1,x)  )+grid[y][x];
    }
    return  memo[y][x];
}

finding a path in an n × n grid from the upper-left corner to the lower-right corner. We are only allowed

to move right or down.

1,1 1,2 .... 1,X

2,1 2,2 .... 2,X

Y,1 Y,2 .... Y,X

3 7 9 2 7

9 8 3 5 5

1 7 9 8 5

3 8 6 4 10

6 3 9 7 8

int grid[5][5]={{3,7, 9, 2, 7},{9, 8, 3 ,5 ,5},{1, 7 ,9 ,8 ,5},{3, 8, 6 ,4 ,10},{6, 3 ,9 ,7 ,8}};

int memo[5][5]{{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1},{-1,-1,-1,-1,-1}};

std::vector<int>x_solution,y_solution;

int f_paths_in_grid(int y,int x)

{

if((x==0)&& (y==0))

{

memo[y][x]=grid[0][0];

}

else if (x==0)

{

if(memo[y][x]<0)

{

memo[y][x] =f_paths_in_grid(y-1,x) +grid[y][x];

}

else if(y==0)

{

if(memo[y][x]<0)

{

memo[y][x]=f_paths_in_grid(y,x-1) +grid[y][x];

}

else if(memo[y][x]<0)

{

memo[y][x]=std::max(f_paths_in_grid(y,x-1) ,f_paths_in_grid(y-1,x) )+grid[y][x];

}

return memo[y][x];

}

maximum sum on a path from the upper-left corner to the lower-right corner is:
67
3|10|19|21|28|
---------------
12|20|23|28|33|
---------------
13|27|36|44|49|
---------------
16|35|42|48|59|
---------------
22|38|51|58|67|
---------------

maximum sum on a path from the upper-left corner to the lower-right corner is:

3|10|19|21|28|

---------------

12|20|23|28|33|

---------------

13|27|36|44|49|

---------------

16|35|42|48|59|

---------------

22|38|51|58|67|

---------------

3)Levenshtein edit distance.

int LevenshteinDistance(std::string s, std::string t )
{
  int cost;

  /* base case: empty strings */
  if (s.length() == 0) return t.length();
  if (t.length() == 0) return s.length();

  /* test if last characters of the strings match */
  if (s.at(s.length()-1)  == t.at(t.length()-1) )
      cost = 0;
  else
      cost = 1;

  /* return minimum of delete char from s, delete char from t, and delete char from both */

  return std::min(   std::min(LevenshteinDistance(s.substr(0, s.size()-1 ),t ) + 1,LevenshteinDistance(s  , t.substr(0, t.size()-1 )) + 1 )    ,  LevenshteinDistance(s.substr(0, s.size()-1 ), t.substr(0, t.size()-1 )) + cost );
}

int LevenshteinDistance(std::string s, std::string t )

{

int cost;

/* base case: empty strings */

if (s.length() == 0) return t.length();

if (t.length() == 0) return s.length();

/* test if last characters of the strings match */

if (s.at(s.length()-1) == t.at(t.length()-1) )

cost = 0;

else

cost = 1;

/* return minimum of delete char from s, delete char from t, and delete char from both */

return std::min( std::min(LevenshteinDistance(s.substr(0, s.size()-1 ),t ) + 1,LevenshteinDistance(s , t.substr(0, t.size()-1 )) + 1 ) , LevenshteinDistance(s.substr(0, s.size()-1 ), t.substr(0, t.size()-1 )) + cost );

}

The Levenshtein distance between saturday and sunday is:
3

1 2	The Levenshtein distance between saturday and sunday is: 3

4)Seam Carving. I have written a tutorial on that here and the recursive part is in the following lines:

for x=2:rows
    for y=1:cols  
        M(x+1,y+1)=score_matrix(x,y)+ min( [M(x-1+1,y-1 +1) , M(x-1+1,y+1), M(x-1+1,y+1+1) ] );
    end
end

for x=2:rows

for y=1:cols

M(x+1,y+1)=score_matrix(x,y)+ min( [M(x-1+1,y-1 +1) , M(x-1+1,y+1), M(x-1+1,y+1+1) ] );

end

The examples are taken from “Competitive Programmer’s Handbook” written by Antti Laaksonen.

Peak Signal-to-Noise Ratio (PSNR) in Image using OpenCV and Matlab

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Peak signal-to-noise ratio (PSNR) shows the ratio between the maximum possible power of a signal and the power of the same image with noise. PSNR is usually expressed in logarithmic decibel scale.

$ MSE =1/m*n \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} [ Image( i,j) -NoisyImage( i,j) ] ^2 $
$ PSNR =10* \log_{10} \Bigg(MAXI^2/MSE\Bigg) $

MSE is Mean Square Error and MAXI is the maximum possible pixel value of the image. For instance if the image is uint8, it will be 255.

ref = imread('pout.tif');
A = imnoise(ref,'salt & pepper', 0.02);
%%%%%%%%%%%%%%%%or we can apply gaussian noise%%%%%%%%%%%%%%%%
%A = imnoise(ref, 'gaussian', 0, 0.003);

[M,N]=size(ref);

SE = (double(A) - double(ref)) .^ 2; % Squared Error
MSE=sum( sum( SE  ) ) /(M*N); %Mean Square Error
MAXI=255;
fprintf('\n PSNR is:')
10*log10((MAXI^2)/MSE)

figure,imshow(A, []);
figure,imshow(ref, []);
figure,imshow(SE, []);


%%%%%%%%%%%%%%%%% using Matlab API %%%%%%%%%%%%%%%%
%MSE = immse(A, ref)
[peaksnr, snr] = psnr(A, ref);
fprintf('\n The Peak-SNR value is %0.4f', peaksnr);
fprintf('\n The SNR value is %0.4f \n', snr);

ref = imread('pout.tif');

A = imnoise(ref,'salt & pepper', 0.02);

%%%%%%%%%%%%%%%%or we can apply gaussian noise%%%%%%%%%%%%%%%%

%A = imnoise(ref, 'gaussian', 0, 0.003);

[M,N]=size(ref);

SE = (double(A) - double(ref)) .^ 2; % Squared Error

MSE=sum( sum( SE ) ) /(M*N); %Mean Square Error

MAXI=255;

fprintf('\n PSNR is:')

10*log10((MAXI^2)/MSE)

figure,imshow(A, []);

figure,imshow(ref, []);

figure,imshow(SE, []);

%%%%%%%%%%%%%%%%% using Matlab API %%%%%%%%%%%%%%%%

%MSE = immse(A, ref)

[peaksnr, snr] = psnr(A, ref);

fprintf('\n The Peak-SNR value is %0.4f', peaksnr);

fprintf('\n The SNR value is %0.4f \n', snr);

double getPSNR(const Mat& I1, const Mat& I2)
{
 Mat s1;
 absdiff(I1, I2, s1);       // |I1 - I2|
 s1.convertTo(s1, CV_32F);  // cannot make a square on 8 bits
 s1 = s1.mul(s1);           // |I1 - I2|^2

 Scalar s = sum(s1);         // sum elements per channel

 double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels

 if( sse <= 1e-10) // for small values return zero
     return 0;
 else
 {
     double  mse =sse /(double)(I1.channels() * I1.total());
     double psnr = 10.0*log10((255*255)/mse);
     return psnr;
 }
}

double getPSNR(const Mat& I1, const Mat& I2)

{

Mat s1;

absdiff(I1, I2, s1); // |I1 - I2|

s1.convertTo(s1, CV_32F); // cannot make a square on 8 bits

s1 = s1.mul(s1); // |I1 - I2|^2

Scalar s = sum(s1); // sum elements per channel

double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels

if( sse <= 1e-10) // for small values return zero

return 0;

else

{

double mse =sse /(double)(I1.channels() * I1.total());

double psnr = 10.0*log10((255*255)/mse);

return psnr;

}

Ref [1],[2], [3], 4

get template parameter name and type with Boost demangle

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Have you ever tied to get the type of a variable by its name? for instance “int” or “string“?

The following code snippet shows how to use boost demangle to get the type:

#include <boost/units/detail/utility.hpp>
#include <boost/shared_ptr.hpp>
template
<typename T>
void foo(boost::shared_ptr<T> var)
{
    std::string var_type= boost::units::detail::demangle(typeid(*var.get()).name());



    if(var_type.find("int") !=std::string::npos )
    {
        std::cout<<"Type of var is int" <<std::endl;
    }else if(var_type.find("float") !=std::string::npos )
    {
        std::cout<<"Type of var is int" <<std::endl;
    }
    else
    {
        std::cout<<"Type of var is "<<var_type <<std::endl;
    }


}


int main()
{


    boost::shared_ptr<int > x_ptr(new int );
    *x_ptr=10;
    foo(x_ptr);

    boost::shared_ptr<float > y_ptr(new float );
    *y_ptr=10.3;
    foo(y_ptr);


    boost::shared_ptr<double > z_ptr(new double );
    *z_ptr=10.3;
    foo(z_ptr);

    return 0;
}

#include <boost/units/detail/utility.hpp>

#include <boost/shared_ptr.hpp>

template

void foo(boost::shared_ptr<T> var)

{

std::string var_type= boost::units::detail::demangle(typeid(*var.get()).name());

if(var_type.find("int") !=std::string::npos )

{

std::cout<<"Type of var is int" <<std::endl;

}else if(var_type.find("float") !=std::string::npos )

{

std::cout<<"Type of var is int" <<std::endl;

}

else

{

std::cout<<"Type of var is "<<var_type <<std::endl;

}

int main()

{

boost::shared_ptr<int > x_ptr(new int );

*x_ptr=10;

foo(x_ptr);

boost::shared_ptr<float > y_ptr(new float );

*y_ptr=10.3;

foo(y_ptr);

boost::shared_ptr<double > z_ptr(new double );

*z_ptr=10.3;

foo(z_ptr);

return 0;

}

Finding roll, pitch yaw from 3X3 rotation matrix with Eigen

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/*  http://planning.cs.uiuc.edu/node103.html

    |r11 r12 r13 |
    |r21 r22 r23 |
    |r31 r32 r33 |

    yaw: alpha=arctan(r21/r11)
    pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )
    roll: gamma=arctan(r32/r33)
*/
    double roll, pitch, yaw;
    roll=M_PI/3;
    pitch=M_PI/4;
    yaw=M_PI/6;
    Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());
    Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());
    Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

    Eigen::Quaternion<double> q =  yawAngle*pitchAngle *rollAngle;
    Eigen::Matrix3d rotationMatrix = q.matrix();



    std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;
    std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5  )  ) <<std::endl;
    std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

/* http://planning.cs.uiuc.edu/node103.html

|r11 r12 r13 |

|r21 r22 r23 |

|r31 r32 r33 |

yaw: alpha=arctan(r21/r11)

pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )

roll: gamma=arctan(r32/r33)

double roll, pitch, yaw;

roll=M_PI/3;

pitch=M_PI/4;

yaw=M_PI/6;

Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());

Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());

Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle;

Eigen::Matrix3d rotationMatrix = q.matrix();

std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;

std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5 ) ) <<std::endl;

std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

pkg-config and CMake

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The environmental variable:
PKG_CONFIG_PATH is the place that you should place all of your “.pc” files.

Example:
Type in the shell:

PKG_CONFIG_PATH=/home/behnam/workspace/flannBuild/lib/pkgconfig:${PKG_CONFIG_PATH}
export PKG_CONFIG_PATH
printenv PKG_CONFIG_PATH

PKG_CONFIG_PATH=/home/behnam/workspace/flannBuild/lib/pkgconfig:${PKG_CONFIG_PATH}

export PKG_CONFIG_PATH

printenv PKG_CONFIG_PATH

Then you can type:

pkg-config --cflags flann
pkg-config --libs flann
pkg-config --modversion flann

pkg-config --cflags flann

pkg-config --libs flann

pkg-config --modversion flann

Now how to print environmental variable in CMake file:

MESSAGE("HOME: " $ENV{HOME})

1	MESSAGE("HOME: " $ENV{HOME})

How to set environmental variable in CMake file:

SET(ENV{PKG_CONFIG_PATH} "/home/behnam/workspace/flannBuild/lib/pkgconfig:" $ENV{PKG_CONFIG_PATH})

1	SET(ENV{PKG_CONFIG_PATH} "/home/behnam/workspace/flannBuild/lib/pkgconfig:" $ENV{PKG_CONFIG_PATH})

After setting environmental variable PKG_CONFIG_PATH pointing to your .pc file you can call

find_package(PkgConfig)
pkg_check_modules(PC_FLANN flann)

1 2	find_package(PkgConfig) pkg_check_modules(PC_FLANN flann)

How to find CMake from arbitrary installed locations

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In my other tutorial, I showed you how to install your code in an arbitrary location in Unix/ Linux systems. In this tutorial, I’m gonna show you how to find them after installation. Here I have two examples: OpenCV, PCL point cloud

I can assume that you have compiled and installed them using the following command:

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

1	cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

1)PCL point cloud

put this line in your CMake file:

set(PCL_DIR "$ENV{HOME}/usr/share/pcl-1.8/")

1	set(PCL_DIR "$ENV{HOME}/usr/share/pcl-1.8/")

and check if everything is correct:

find_package(PCL 1.8.0 REQUIRED COMPONENTS common octree io kdtree search sample_consensus filters 2d features  registration geometry visualization outofcore surface keypoints ml segmentation recognition people tracking stereo)

MESSAGE("PCL_FOUND:" ${PCL_FOUND})
MESSAGE("PCL_INCLUDE_DIRS:" ${PCL_LIBRARY_DIRS})
MESSAGE("PCL_LIBRARIES:" ${PCL_LIBRARIES})
MESSAGE("PCL_LIBRARY_DIRS:" ${PCL_LIBRARY_DIRS})
MESSAGE("PCL_VERSION:" ${PCL_VERSION})
MESSAGE("PCL_COMPONENTS:" ${PCL_COMPONENTS})
MESSAGE("PCL_DEFINITIONS:" ${PCL_DEFINITIONS})

find_package(PCL 1.8.0 REQUIRED COMPONENTS common octree io kdtree search sample_consensus filters 2d features registration geometry visualization outofcore surface keypoints ml segmentation recognition people tracking stereo)

MESSAGE("PCL_FOUND:" ${PCL_FOUND})

MESSAGE("PCL_INCLUDE_DIRS:" ${PCL_LIBRARY_DIRS})

MESSAGE("PCL_LIBRARIES:" ${PCL_LIBRARIES})

MESSAGE("PCL_LIBRARY_DIRS:" ${PCL_LIBRARY_DIRS})

MESSAGE("PCL_VERSION:" ${PCL_VERSION})

MESSAGE("PCL_COMPONENTS:" ${PCL_COMPONENTS})

MESSAGE("PCL_DEFINITIONS:" ${PCL_DEFINITIONS})

2)OpenCV

put this line in your CMake file:

set(OpenCV_DIR "$ENV{HOME}/usr/share/OpenCV/")

1	set(OpenCV_DIR "$ENV{HOME}/usr/share/OpenCV/")

and check if everything is correct:

FIND_PACKAGE( OpenCV REQUIRED )

MESSAGE("OpenCV_DIR: " ${OpenCV_DIR})
MESSAGE("OpenCV_VERSION: " ${OpenCV_VERSION})
MESSAGE("OpenCV_INCLUDE_DIRS: " ${OpenCV_INCLUDE_DIRS})
MESSAGE("OpenCV_LIBS: " ${OpenCV_LIBS})

FIND_PACKAGE( OpenCV REQUIRED )

MESSAGE("OpenCV_DIR: " ${OpenCV_DIR})

MESSAGE("OpenCV_VERSION: " ${OpenCV_VERSION})

MESSAGE("OpenCV_INCLUDE_DIRS: " ${OpenCV_INCLUDE_DIRS})

MESSAGE("OpenCV_LIBS: " ${OpenCV_LIBS})

if you don’t want to make changes to your CMakeList.txt file you can send <>__DIR as CMake parameter. Example:

cmake -DCMAKE_BUILD_TYPE=Release -DOpenCV_DIR=~/usr/share/OpenCV

1	cmake -DCMAKE_BUILD_TYPE=Release -DOpenCV_DIR=~/usr/share/OpenCV

Installing programs into arbitrary location in Unix/ Linux systems

1 Reply

After you compiled and built your code, you should install it by calling sudo install to put headers and shared libraries into /usr/include/ and /usr/lib/ and configuration files into /usr/local and /usr/share

usually, I don’t like to touch my system binaries and I prefer to install my compiled code into my home directory so I can safely delete them anytime. If you using CMake use the following parameters to install everything in “/home/<user_name>/usr” instead of “/usr”

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

1	cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

if you are just using make, use the following:

make install DESTDIR=/home/<user_name>/usr

1	make install DESTDIR=/home/<user_name>/usr