OPENCV Official Document Learning record (7)

Start today with the official Image Processing section.

Image filtering:

This paper mainly introduces the usage of four kinds of filter functions.

Example code:

1#include <opencv2\opencv.hpp>2#include <iostream>3#include <string>4 5 #pragmaComment (linker, "/subsystem:\" windows\ "/entry:\" Maincrtstartup\ "")6 7 intMax_kernel_length = to;8 9 using namespacestd;Ten using namespaceCV; One  A voidShow (std::stringname,mat img) - { - Namedwindow (name, cv_window_autosize); the imshow (Name, IMG); - } -  - intMainvoid) + { -Mat src = imread ("lena.jpg"); +     if(Src.empty ()) A         return-1; atShow ("SRC", SRC); -  -Mat DST =Src.clone (); -      for(inti =1; i < max_kernel_length; i = i +2) -     { -Blur (SRC, DST, Size (i, I), point (-1, -1)); in     } -Show ("Blur", DST); to  +      for(inti =1; i < max_kernel_length; i = i +2) -     { theGaussianblur (SRC, DST, Size (i, I),0,0); *     } $Show ("Gaussianblur", DST);Panax Notoginseng  -      for(inti =1; i < max_kernel_length; i = i +2) the     { + Medianblur (SRC, DST, i); A     } theShow ("Medianblur", DST); +  -      for(inti =1; i < max_kernel_length; i = i +2) $     { $Bilateralfilter (SRC, DST, I, I *2I2); -     } -Show ("Bilateralfilter", DST); the  - Waitkey ();Wuyi     return 0; the}

You can display the degree of transformation in the following ways:

1#include <opencv2\opencv.hpp>2#include <iostream>3#include <string>4 5 #pragmaComment (linker, "/subsystem:\" windows\ "/entry:\" Maincrtstartup\ "")6 7 intMax_kernel_length = to;8 9 using namespacestd;Ten using namespaceCV; One  A voidShow (std::stringname,mat img) - { - Namedwindow (name, cv_window_autosize); the imshow (Name, IMG); - } -  - intMainvoid) + { -Mat src = imread ("lena.jpg"); +     if(Src.empty ()) A         return-1; atShow ("SRC", SRC); -  -Mat DST =Src.clone (); -      for(inti =1; i < max_kernel_length; i = i +2) -     { -Blur (SRC, DST, Size (i, I), point (-1, -1)); inShow ("Blur", DST); -Waitkey ( -); to     } +  - Waitkey (); the     return 0; *}

Function Description:

Box Filter:

Blur (SRC, DST, Size (i, I), point (-1,-1));

Size (I,i) is the filter's Kernel,point ( -1,-1) is the filter anchor position, which is negative when represented by the kernel of the following filter (which prevents the image from shifting during the transformation process).

Gaussian Filter:

0 0);

The size is also kernel, with the following two parameters being the value of Sigma X, Y, and 0 for following kernel (because it cannot be 0 as the denominator).

Median Filter:

Medianblur (SRC, DST, i);

The parameter I represents the kernel value, because it is a square (two-dimensional) image, so it is good to use only one number, and it needs to be odd.

Bilateral Filter:

2 2);

The following three parameters indicate:

D, the range of diameters to be used for the transformation is that each pixel is calculated according to the surrounding pixels.

Color, colour gamut standard deviation;

Space, the standard deviation of the coordinate spaces.

Although the speed is not good, or upload the converted results Picture it:

The original image went to OpenCV's installation folder to find it.

Above.

OPENCV Official Document Learning record (7)