Non-Photorealistic Rendering

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. More...
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 [63] . More...
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. More...
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. More...

Detailed Description

Function Documentation

detailEnhance()

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

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

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.

Examples:

npr_demo.cpp.

edgePreservingFilter()

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

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

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications [63] .

Parameters

src	Input 8-bit 3-channel image.
dst	Output 8-bit 3-channel image.
flags	Edge preserving filters: RECURS_FILTER = 1 NORMCONV_FILTER = 2
sigma_s	Range between 0 to 200.
sigma_r	Range between 0 to 1.

Examples:

npr_demo.cpp.

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:
	dst1, dst2	=	cv.pencilSketch(	src[, dst1[, dst2[, sigma_s[, sigma_r[, shade_factor]]]]]	)

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.

Examples:

npr_demo.cpp.

stylization()

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

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

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.

Examples:

npr_demo.cpp.