Non-Photorealistic Rendering

Detailed Description

Useful links:

http://www.inf.ufrgs.br/~eslgastal/DomainTransform

https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/

Enumerations
enum	{   cv::RECURS_FILTER = 1 ,   cv::NORMCONV_FILTER = 2 }
	Edge preserving filters. More...

Functions
void	cv::detailEnhance (InputArray src, OutputArray dst, float sigma_s=10, float sigma_r=0.15f)
	This filter enhances the details of a particular image.
void	cv::edgePreservingFilter (InputArray src, OutputArray dst, int flags=1, float sigma_s=60, float sigma_r=0.4f)
	Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications [100] .
void	cv::pencilSketch (InputArray src, OutputArray dst1, OutputArray dst2, float sigma_s=60, float sigma_r=0.07f, float shade_factor=0.02f)
	Pencil-like non-photorealistic line drawing.
void	cv::stylization (InputArray src, OutputArray dst, float sigma_s=60, float sigma_r=0.45f)
	Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.

Enumeration Type Documentation

anonymous enum

anonymous enum

#include <opencv2/photo.hpp>

Edge preserving filters.

Enumerator
RECURS_FILTER  Python: cv.RECURS_FILTER	Recursive Filtering.
NORMCONV_FILTER  Python: cv.NORMCONV_FILTER	Normalized Convolution Filtering.

Function Documentation

detailEnhance()

void cv::detailEnhance	(	InputArray	src,
		OutputArray	dst,
		float	sigma_s = 10,
		float	sigma_r = 0.15f
	)

Python:
	cv.detailEnhance(	src[, dst[, sigma_s[, sigma_r]]]	) ->	dst

#include <opencv2/photo.hpp>

This filter enhances the details of a particular image.

Parameters

src	Input 8-bit 3-channel image.
dst	Output image with the same size and type as src.
sigma_s	Range between 0 to 200.
sigma_r	Range between 0 to 1.

edgePreservingFilter()

void cv::edgePreservingFilter	(	InputArray	src,
		OutputArray	dst,
		int	flags = 1,
		float	sigma_s = 60,
		float	sigma_r = 0.4f
	)

Python:
	cv.edgePreservingFilter(	src[, dst[, flags[, sigma_s[, sigma_r]]]]	) ->	dst

#include <opencv2/photo.hpp>

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications [100] .

Parameters

src	Input 8-bit 3-channel image.
dst	Output 8-bit 3-channel image.
flags	Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
sigma_s	Range between 0 to 200.
sigma_r	Range between 0 to 1.

pencilSketch()

void cv::pencilSketch	(	InputArray	src,
		OutputArray	dst1,
		OutputArray	dst2,
		float	sigma_s = 60,
		float	sigma_r = 0.07f,
		float	shade_factor = 0.02f
	)

Python:
	cv.pencilSketch(	src[, dst1[, dst2[, sigma_s[, sigma_r[, shade_factor]]]]]	) ->	dst1, dst2

#include <opencv2/photo.hpp>

Pencil-like non-photorealistic line drawing.

Parameters

src	Input 8-bit 3-channel image.
dst1	Output 8-bit 1-channel image.
dst2	Output image with the same size and type as src.
sigma_s	Range between 0 to 200.
sigma_r	Range between 0 to 1.
shade_factor	Range between 0 to 0.1.

stylization()

void cv::stylization	(	InputArray	src,
		OutputArray	dst,
		float	sigma_s = 60,
		float	sigma_r = 0.45f
	)

Python:
	cv.stylization(	src[, dst[, sigma_s[, sigma_r]]]	) ->	dst

#include <opencv2/photo.hpp>

Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.

Parameters

src	Input 8-bit 3-channel image.
dst	Output image with the same size and type as src.
sigma_s	Range between 0 to 200.
sigma_r	Range between 0 to 1.