| OpenCV 3.4.1 Open Source Computer Vision

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: