Remapping

Goal

In this tutorial you will learn how to:

a. Use the OpenCV function cv::remap to implement simple remapping routines.

Theory

What is remapping?

• It is the process of taking pixels from one place in the image and locating them in another position in a new image.
• To accomplish the mapping process, it might be necessary to do some interpolation for non-integer pixel locations, since there will not always be a one-to-one-pixel correspondence between source and destination images.

• We can express the remap for every pixel location \((x,y)\) as:

  \[g(x,y) = f ( h(x,y) )\]

  where \(g()\) is the remapped image, \(f()\) the source image and \(h(x,y)\) is the mapping function that operates on \((x,y)\).

• Let's think in a quick example. Imagine that we have an image \(I\) and, say, we want to do a remap such that:

  \[h(x,y) = (I.cols - x, y )\]

  What would happen? It is easily seen that the image would flip in the \(x\) direction. For instance, consider the input image:

  

  observe how the red circle changes positions with respect to x (considering \(x\) the horizontal direction):

  
• In OpenCV, the function cv::remap offers a simple remapping implementation.

Code

1. What does this program do?
   • Loads an image
   • Each second, apply 1 of 4 different remapping processes to the image and display them indefinitely in a window.
   • Wait for the user to exit the program
2. The tutorial code's is shown lines below. You can also download it from here

   
   #include "opencv2/imgcodecs.hpp"
   #include "opencv2/highgui.hpp"
   #include "opencv2/imgproc.hpp"
   #include <iostream>
   
   using namespace cv;
   
   Mat src, dst;
   Mat map_x, map_y;
   const char* remap_window = "Remap demo";
   int ind = 0;
   
   void update_map( void );
   
   int main(int argc, const char** argv)
   {
    CommandLineParser parser(argc, argv, "{@image |../data/chicky_512.png|input image name}");
    std::string filename = parser.get<std::string>(0);
    src = imread( filename, IMREAD_COLOR );
   
    dst.create( src.size(), src.type() );
    map_x.create( src.size(), CV_32FC1 );
    map_y.create( src.size(), CV_32FC1 );
   
    namedWindow( remap_window, WINDOW_AUTOSIZE );
   
    for(;;)
    {
    char c = (char)waitKey( 1000 );
   
    if( c == 27 )
    { break; }
   
    update_map();
    remap( src, dst, map_x, map_y, INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0) );
   
    // Display results
    imshow( remap_window, dst );
    }
    return 0;
   }
   
   void update_map( void )
   {
    ind = ind%4;
   
    for( int j = 0; j < src.rows; j++ )
    { for( int i = 0; i < src.cols; i++ )
    {
    switch( ind )
    {
    case 0:
    if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
    {
    map_x.at<float>(j,i) = 2*( i - src.cols*0.25f ) + 0.5f ;
    map_y.at<float>(j,i) = 2*( j - src.rows*0.25f ) + 0.5f ;
    }
    else
    { map_x.at<float>(j,i) = 0 ;
    map_y.at<float>(j,i) = 0 ;
    }
    break;
    case 1:
    map_x.at<float>(j,i) = (float)i ;
    map_y.at<float>(j,i) = (float)(src.rows - j) ;
    break;
    case 2:
    map_x.at<float>(j,i) = (float)(src.cols - i) ;
    map_y.at<float>(j,i) = (float)j ;
    break;
    case 3:
    map_x.at<float>(j,i) = (float)(src.cols - i) ;
    map_y.at<float>(j,i) = (float)(src.rows - j) ;
    break;
    } // end of switch
    }
    }
    ind++;
   }

Explanation

1. Create some variables we will use:

   Mat src, dst;
   Mat map_x, map_y;
   char* remap_window = "Remap demo";
   int ind = 0;

2. Load an image:

   src = imread( argv[1], 1 );

3. Create the destination image and the two mapping matrices (for x and y )

   dst.create( src.size(), src.type() );
   map_x.create( src.size(), CV_32FC1 );
   map_y.create( src.size(), CV_32FC1 );

4. Create a window to display results

   namedWindow( remap_window, WINDOW_AUTOSIZE );

5. Establish a loop. Each 1000 ms we update our mapping matrices (mat_x and mat_y) and apply them to our source image:

   while( true )
   {
    char c = (char)waitKey( 1000 );
   
    if( c == 27 )
    { break; }
   
    update_map();
    remap( src, dst, map_x, map_y, INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
   
    imshow( remap_window, dst );
   }

   The function that applies the remapping is cv::remap . We give the following arguments:

   • src: Source image
   • dst: Destination image of same size as src
   • map_x: The mapping function in the x direction. It is equivalent to the first component of \(h(i,j)\)
   • map_y: Same as above, but in y direction. Note that map_y and map_x are both of the same size as src
   • INTER_LINEAR: The type of interpolation to use for non-integer pixels. This is by default.
   • BORDER_CONSTANT: Default

   How do we update our mapping matrices mat_x and mat_y? Go on reading:

6. Updating the mapping matrices: We are going to perform 4 different mappings:
   1. Reduce the picture to half its size and will display it in the middle:

      \[h(i,j) = ( 2*i - src.cols/2 + 0.5, 2*j - src.rows/2 + 0.5)\]

      for all pairs \((i,j)\) such that: \(\dfrac{src.cols}{4}<i<\dfrac{3 \cdot src.cols}{4}\) and \(\dfrac{src.rows}{4}<j<\dfrac{3 \cdot src.rows}{4}\)
   2. Turn the image upside down: \(h( i, j ) = (i, src.rows - j)\)
   3. Reflect the image from left to right: \(h(i,j) = ( src.cols - i, j )\)
   4. Combination of b and c: \(h(i,j) = ( src.cols - i, src.rows - j )\)

This is expressed in the following snippet. Here, map_x represents the first coordinate of h(i,j) and map_y the second coordinate.

for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
 switch( ind )
 {
 case 0:
 if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
 {
 map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
 map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
 }
 else
 { map_x.at<float>(j,i) = 0 ;
 map_y.at<float>(j,i) = 0 ;
 }
 break;
 case 1:
 map_x.at<float>(j,i) = i ;
 map_y.at<float>(j,i) = src.rows - j ;
 break;
 case 2:
 map_x.at<float>(j,i) = src.cols - i ;
 map_y.at<float>(j,i) = j ;
 break;
 case 3:
 map_x.at<float>(j,i) = src.cols - i ;
 map_y.at<float>(j,i) = src.rows - j ;
 break;
 } // end of switch
}
 }
 ind++;
}

Result

1. After compiling the code above, you can execute it giving as argument an image path. For instance, by using the following image:

   

2. This is the result of reducing it to half the size and centering it:

   

3. Turning it upside down:

   

4. Reflecting it in the x direction:

   

5. Reflecting it in both directions: