filter_2d.markdown

Making your own linear filters! {#tutorial_filter_2d}
===============================

Goal
----

In this tutorial you will learn how to:

-   Use the OpenCV function @ref cv::filter2D to create your own linear filters.

Theory
------

@note The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.

### Convolution

In a very general sense, convolution is an operation between every part of an image and an operator
(kernel).

### What is a kernel?

A kernel is essentially a fixed size array of numerical coefficeints along with an *anchor point* in
that array, which is tipically located at the center.

![](images/filter_2d_tutorial_kernel_theory.png)

### How does convolution with a kernel work?

Assume you want to know the resulting value of a particular location in the image. The value of the
convolution is calculated in the following way:

-#  Place the kernel anchor on top of a determined pixel, with the rest of the kernel overlaying the
    corresponding local pixels in the image.
-#  Multiply the kernel coefficients by the corresponding image pixel values and sum the result.
-#  Place the result to the location of the *anchor* in the input image.
-#  Repeat the process for all pixels by scanning the kernel over the entire image.

Expressing the procedure above in the form of an equation we would have:

\f[H(x,y) = \sum_{i=0}^{M_{i} - 1} \sum_{j=0}^{M_{j}-1} I(x+i - a_{i}, y + j - a_{j})K(i,j)\f]

Fortunately, OpenCV provides you with the function @ref cv::filter2D so you do not have to code all
these operations.

Code
----

-#  **What does this program do?**
    -   Loads an image
    -   Performs a *normalized box filter*. For instance, for a kernel of size \f$size = 3\f$, the
        kernel would be:

        \f[K = \dfrac{1}{3 \cdot 3} \begin{bmatrix}
        1 & 1 & 1  \\
        1 & 1 & 1  \\
        1 & 1 & 1
        \end{bmatrix}\f]

        The program will perform the filter operation with kernels of sizes 3, 5, 7, 9 and 11.

    -   The filter output (with each kernel) will be shown during 500 milliseconds

-#  The tutorial code's is shown lines below. You can also download it from
    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp)
@code{.cpp}
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>

using namespace cv;

/* @function main */
int main ( int argc, char** argv )
{
  /// Declare variables
  Mat src, dst;

  Mat kernel;
  Point anchor;
  double delta;
  int ddepth;
  int kernel_size;
  char* window_name = "filter2D Demo";

  int c;

  /// Load an image
  src = imread( argv[1] );

  if( !src.data )
  { return -1; }

  /// Create window
  namedWindow( window_name, WINDOW_AUTOSIZE );

  /// Initialize arguments for the filter
  anchor = Point( -1, -1 );
  delta = 0;
  ddepth = -1;

  /// Loop - Will filter the image with different kernel sizes each 0.5 seconds
  int ind = 0;
  while( true )
    {
      c = waitKey(500);
      /// Press 'ESC' to exit the program
      if( (char)c == 27 )
        { break; }

      /// Update kernel size for a normalized box filter
      kernel_size = 3 + 2*( ind%5 );
      kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size);

      /// Apply filter
      filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
      imshow( window_name, dst );
      ind++;
    }

  return 0;
}
@endcode
Explanation
-----------

-#  Load an image
    @code{.cpp}
    src = imread( argv[1] );

    if( !src.data )
      { return -1; }
    @endcode
-#  Create a window to display the result
    @code{.cpp}
    namedWindow( window_name, WINDOW_AUTOSIZE );
    @endcode
-#  Initialize the arguments for the linear filter
    @code{.cpp}
    anchor = Point( -1, -1 );
    delta = 0;
    ddepth = -1;
    @endcode
-#  Perform an infinite loop updating the kernel size and applying our linear filter to the input
    image. Let's analyze that more in detail:
-#  First we define the kernel our filter is going to use. Here it is:
    @code{.cpp}
    kernel_size = 3 + 2*( ind%5 );
    kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size);
    @endcode
    The first line is to update the *kernel_size* to odd values in the range: \f$[3,11]\f$. The second
    line actually builds the kernel by setting its value to a matrix filled with \f$1's\f$ and
    normalizing it by dividing it between the number of elements.

-#  After setting the kernel, we can generate the filter by using the function @ref cv::filter2D :
    @code{.cpp}
    filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
    @endcode
    The arguments denote:

    -#  *src*: Source image
    -#  *dst*: Destination image
    -#  *ddepth*: The depth of *dst*. A negative value (such as \f$-1\f$) indicates that the depth is
        the same as the source.
    -#  *kernel*: The kernel to be scanned through the image
    -#  *anchor*: The position of the anchor relative to its kernel. The location *Point(-1, -1)*
        indicates the center by default.
    -#  *delta*: A value to be added to each pixel during the convolution. By default it is \f$0\f$
    -#  *BORDER_DEFAULT*: We let this value by default (more details in the following tutorial)

-#  Our program will effectuate a *while* loop, each 500 ms the kernel size of our filter will be
    updated in the range indicated.

Results
-------

-#  After compiling the code above, you can execute it giving as argument the path of an image. The
    result should be a window that shows an image blurred by a normalized filter. Each 0.5 seconds
    the kernel size should change, as can be seen in the series of snapshots below:

    ![](images/filter_2d_tutorial_result.jpg)