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,
    NORMCONV_FILTER = 2
}

Edge preserving filters. View details

Enumeration Type Documentation#

enum#

#include <opencv2/photo.hpp>

Edge preserving filters.

Enumerator:

`RECURS_FILTER`	Recursive Filtering.
`NORMCONV_FILTER`	Normalized Convolution Filtering.

Function Documentation#

detailEnhance()#

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

#include <opencv2/photo.hpp>

Python:

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

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 )

#include <opencv2/photo.hpp>

Python:

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

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications [112] .

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 )

#include <opencv2/photo.hpp>

Python:

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

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 )

#include <opencv2/photo.hpp>

Python:

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

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.