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?

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 <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:

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

    Remap_Tutorial_Result_0.jpg
  3. Turning it upside down:

    Remap_Tutorial_Result_1.jpg
  4. Reflecting it in the x direction:

    Remap_Tutorial_Result_2.jpg
  5. Reflecting it in both directions:

    Remap_Tutorial_Result_3.jpg