Additional photo processing algorithms

Classes
class	cv::xphoto::GrayworldWB
	Gray-world white balance algorithm. More...
class	cv::xphoto::LearningBasedWB
	More sophisticated learning-based automatic white balance algorithm. More...
class	cv::xphoto::SimpleWB
	A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range. For increased robustness it ignores the top and bottom \(p\%\) of pixel values. More...
class	cv::xphoto::TonemapDurand
	This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter and compresses contrast of the base layer thus preserving all the details. More...
class	cv::xphoto::WhiteBalancer
	The base class for auto white balance algorithms. More...

Enumerations
enum	cv::xphoto::Bm3dSteps {   cv::xphoto::BM3D_STEPALL = 0,   cv::xphoto::BM3D_STEP1 = 1,   cv::xphoto::BM3D_STEP2 = 2 }
	BM3D algorithm steps. More...
enum	cv::xphoto::InpaintTypes { cv::xphoto::INPAINT_SHIFTMAP = 0 }
	various inpainting algorithms More...
enum	cv::xphoto::TransformTypes { cv::xphoto::HAAR = 0 }
	BM3D transform types. More...

Functions
void	cv::xphoto::applyChannelGains (InputArray src, OutputArray dst, float gainB, float gainG, float gainR)
	Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms. More...
void	cv::xphoto::bm3dDenoising (InputArray src, InputOutputArray dstStep1, OutputArray dstStep2, float h=1, int templateWindowSize=4, int searchWindowSize=16, int blockMatchingStep1=2500, int blockMatchingStep2=400, int groupSize=8, int slidingStep=1, float beta=2.0f, int normType=cv::NORM_L2, int step=cv::xphoto::BM3D_STEPALL, int transformType=cv::xphoto::HAAR)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise. More...
void	cv::xphoto::bm3dDenoising (InputArray src, OutputArray dst, float h=1, int templateWindowSize=4, int searchWindowSize=16, int blockMatchingStep1=2500, int blockMatchingStep2=400, int groupSize=8, int slidingStep=1, float beta=2.0f, int normType=cv::NORM_L2, int step=cv::xphoto::BM3D_STEPALL, int transformType=cv::xphoto::HAAR)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise. More...
Ptr< GrayworldWB >	cv::xphoto::createGrayworldWB ()
	Creates an instance of GrayworldWB. More...
Ptr< LearningBasedWB >	cv::xphoto::createLearningBasedWB (const String &path_to_model=String())
	Creates an instance of LearningBasedWB. More...
Ptr< SimpleWB >	cv::xphoto::createSimpleWB ()
	Creates an instance of SimpleWB. More...
Ptr< TonemapDurand >	cv::xphoto::createTonemapDurand (float gamma=1.0f, float contrast=4.0f, float saturation=1.0f, float sigma_space=2.0f, float sigma_color=2.0f)
	Creates TonemapDurand object. More...
void	cv::xphoto::dctDenoising (const Mat &src, Mat &dst, const double sigma, const int psize=16)
	The function implements simple dct-based denoising. More...
virtual float	cv::xphoto::TonemapDurand::getContrast () const =0
virtual float	cv::xphoto::TonemapDurand::getSaturation () const =0
virtual float	cv::xphoto::TonemapDurand::getSigmaColor () const =0
virtual float	cv::xphoto::TonemapDurand::getSigmaSpace () const =0
void	cv::xphoto::inpaint (const Mat &src, const Mat &mask, Mat &dst, const int algorithmType)
	The function implements different single-image inpainting algorithms. More...
void	cv::xphoto::oilPainting (InputArray src, OutputArray dst, int size, int dynRatio, int code)
	oilPainting See the book [32] for details. More...
void	cv::xphoto::oilPainting (InputArray src, OutputArray dst, int size, int dynRatio)
	oilPainting See the book [32] for details. More...
virtual void	cv::xphoto::TonemapDurand::setContrast (float contrast)=0
virtual void	cv::xphoto::TonemapDurand::setSaturation (float saturation)=0
virtual void	cv::xphoto::TonemapDurand::setSigmaColor (float sigma_color)=0
virtual void	cv::xphoto::TonemapDurand::setSigmaSpace (float sigma_space)=0

Detailed Description

Enumeration Type Documentation

Bm3dSteps

enum cv::xphoto::Bm3dSteps

#include <opencv2/xphoto/bm3d_image_denoising.hpp>

BM3D algorithm steps.

Enumerator
BM3D_STEPALL  Python: cv.xphoto.BM3D_STEPALL	Execute all steps of the algorithm
BM3D_STEP1  Python: cv.xphoto.BM3D_STEP1	Execute only first step of the algorithm
BM3D_STEP2  Python: cv.xphoto.BM3D_STEP2	Execute only second step of the algorithm

InpaintTypes

enum cv::xphoto::InpaintTypes

#include <opencv2/xphoto/inpainting.hpp>

various inpainting algorithms

Enumerator
INPAINT_SHIFTMAP  Python: cv.xphoto.INPAINT_SHIFTMAP	This algorithm searches for dominant correspondences (transformations) of image patches and tries to seamlessly fill-in the area to be inpainted using this transformations

TransformTypes

enum cv::xphoto::TransformTypes

#include <opencv2/xphoto/bm3d_image_denoising.hpp>

BM3D transform types.

Enumerator
HAAR  Python: cv.xphoto.HAAR	Un-normalized Haar transform

Function Documentation

applyChannelGains()

void cv::xphoto::applyChannelGains	(	InputArray	src,
		OutputArray	dst,
		float	gainB,
		float	gainG,
		float	gainR
	)

Python:
	dst	=	cv.xphoto.applyChannelGains(	src, gainB, gainG, gainR[, dst]	)

#include <opencv2/xphoto/white_balance.hpp>

Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms.

Parameters

src	Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)
dst	Output image of the same size and type as src.
gainB	gain for the B channel
gainG	gain for the G channel
gainR	gain for the R channel

bm3dDenoising() [1/2]

void cv::xphoto::bm3dDenoising	(	InputArray	src,
		InputOutputArray	dstStep1,
		OutputArray	dstStep2,
		float	h = 1,
		int	templateWindowSize = 4,
		int	searchWindowSize = 16,
		int	blockMatchingStep1 = 2500,
		int	blockMatchingStep2 = 400,
		int	groupSize = 8,
		int	slidingStep = 1,
		float	beta = 2.0f,
		int	normType = cv::NORM_L2,
		int	step = cv::xphoto::BM3D_STEPALL,
		int	transformType = cv::xphoto::HAAR
	)

Python:
	dstStep1, dstStep2	=	cv.xphoto.bm3dDenoising(	src, dstStep1[, dstStep2[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]	)
	dst	=	cv.xphoto.bm3dDenoising(	src[, dst[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]	)

#include <opencv2/xphoto/bm3d_image_denoising.hpp>

Performs image denoising using the Block-Matching and 3D-filtering algorithm http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [2/2]

void cv::xphoto::bm3dDenoising	(	InputArray	src,
		OutputArray	dst,
		float	h = 1,
		int	templateWindowSize = 4,
		int	searchWindowSize = 16,
		int	blockMatchingStep1 = 2500,
		int	blockMatchingStep2 = 400,
		int	groupSize = 8,
		int	slidingStep = 1,
		float	beta = 2.0f,
		int	normType = cv::NORM_L2,
		int	step = cv::xphoto::BM3D_STEPALL,
		int	transformType = cv::xphoto::HAAR
	)

Python:
	dstStep1, dstStep2	=	cv.xphoto.bm3dDenoising(	src, dstStep1[, dstStep2[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]	)
	dst	=	cv.xphoto.bm3dDenoising(	src[, dst[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]	)

#include <opencv2/xphoto/bm3d_image_denoising.hpp>

Performs image denoising using the Block-Matching and 3D-filtering algorithm http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

createGrayworldWB()

Ptr<GrayworldWB> cv::xphoto::createGrayworldWB

(

)

Python:
	retval	=	cv.xphoto.createGrayworldWB(		)

#include <opencv2/xphoto/white_balance.hpp>

Creates an instance of GrayworldWB.

createLearningBasedWB()

Ptr<LearningBasedWB> cv::xphoto::createLearningBasedWB

(

const String &

path_to_model = String()

)

Python:
	retval	=	cv.xphoto.createLearningBasedWB(	[, path_to_model]	)

#include <opencv2/xphoto/white_balance.hpp>

Creates an instance of LearningBasedWB.

Parameters

path_to_model

Path to a .yml file with the model. If not specified, the default model is used

createSimpleWB()

Ptr<SimpleWB> cv::xphoto::createSimpleWB

(

)

Python:
	retval	=	cv.xphoto.createSimpleWB(		)

#include <opencv2/xphoto/white_balance.hpp>

Creates an instance of SimpleWB.

createTonemapDurand()

Ptr<TonemapDurand> cv::xphoto::createTonemapDurand	(	float	gamma = 1.0f,
		float	contrast = 4.0f,
		float	saturation = 1.0f,
		float	sigma_space = 2.0f,
		float	sigma_color = 2.0f
	)

Python:
	retval	=	cv.xphoto.createTonemapDurand(	[, gamma[, contrast[, saturation[, sigma_space[, sigma_color]]]]]	)

#include <opencv2/xphoto/tonemap.hpp>

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_space	bilateral filter sigma in color space
sigma_color	bilateral filter sigma in coordinate space

dctDenoising()

void cv::xphoto::dctDenoising	(	const Mat &	src,
		Mat &	dst,
		const double	sigma,
		const int	psize = 16
	)

Python:
	None	=	cv.xphoto.dctDenoising(	src, dst, sigma[, psize]	)

#include <opencv2/xphoto/dct_image_denoising.hpp>

The function implements simple dct-based denoising.

http://www.ipol.im/pub/art/2011/ys-dct/.

Parameters

src	source image
dst	destination image
sigma	expected noise standard deviation
psize	size of block side where dct is computed

See also

fastNlMeansDenoising

getContrast()

virtual float cv::xphoto::TonemapDurand::getContrast ( ) const

pure virtual

Python:
	retval	=	cv.xphoto_TonemapDurand.getContrast(		)

#include <opencv2/xphoto/tonemap.hpp>

getSaturation()

virtual float cv::xphoto::TonemapDurand::getSaturation ( ) const

pure virtual

Python:
	retval	=	cv.xphoto_TonemapDurand.getSaturation(		)

#include <opencv2/xphoto/tonemap.hpp>

getSigmaColor()

virtual float cv::xphoto::TonemapDurand::getSigmaColor ( ) const

pure virtual

Python:
	retval	=	cv.xphoto_TonemapDurand.getSigmaColor(		)

#include <opencv2/xphoto/tonemap.hpp>

getSigmaSpace()

virtual float cv::xphoto::TonemapDurand::getSigmaSpace ( ) const

pure virtual

Python:
	retval	=	cv.xphoto_TonemapDurand.getSigmaSpace(		)

#include <opencv2/xphoto/tonemap.hpp>

inpaint()

void cv::xphoto::inpaint	(	const Mat &	src,
		const Mat &	mask,
		Mat &	dst,
		const int	algorithmType
	)

Python:
	None	=	cv.xphoto.inpaint(	src, mask, dst, algorithmType	)

#include <opencv2/xphoto/inpainting.hpp>

The function implements different single-image inpainting algorithms.

See the original paper [86] for details.

Parameters

src	source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces.
mask	mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
dst	destination image
algorithmType	see xphoto::InpaintTypes

oilPainting() [1/2]

void cv::xphoto::oilPainting	(	InputArray	src,
		OutputArray	dst,
		int	size,
		int	dynRatio,
		int	code
	)

Python:
	dst	=	cv.xphoto.oilPainting(	src, size, dynRatio, code[, dst]	)
	dst	=	cv.xphoto.oilPainting(	src, size, dynRatio[, dst]	)

#include <opencv2/xphoto/oilpainting.hpp>

oilPainting See the book [32] for details.

Parameters

src	Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
dst	Output image of the same size and type as src.
size	neighbouring size is 2-size+1
dynRatio	image is divided by dynRatio before histogram processing
code	color space conversion code(see ColorConversionCodes). Histogram will used only first plane

oilPainting() [2/2]

void cv::xphoto::oilPainting	(	InputArray	src,
		OutputArray	dst,
		int	size,
		int	dynRatio
	)

Python:
	dst	=	cv.xphoto.oilPainting(	src, size, dynRatio, code[, dst]	)
	dst	=	cv.xphoto.oilPainting(	src, size, dynRatio[, dst]	)

#include <opencv2/xphoto/oilpainting.hpp>

oilPainting See the book [32] for details.

Parameters

src	Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
dst	Output image of the same size and type as src.
size	neighbouring size is 2-size+1
dynRatio	image is divided by dynRatio before histogram processing

setContrast()

virtual void cv::xphoto::TonemapDurand::setContrast ( float  contrast )

pure virtual

Python:
	None	=	cv.xphoto_TonemapDurand.setContrast(	contrast	)

#include <opencv2/xphoto/tonemap.hpp>

setSaturation()

virtual void cv::xphoto::TonemapDurand::setSaturation ( float  saturation )

pure virtual

Python:
	None	=	cv.xphoto_TonemapDurand.setSaturation(	saturation	)

#include <opencv2/xphoto/tonemap.hpp>

setSigmaColor()

virtual void cv::xphoto::TonemapDurand::setSigmaColor ( float  sigma_color )

pure virtual

Python:
	None	=	cv.xphoto_TonemapDurand.setSigmaColor(	sigma_color	)

#include <opencv2/xphoto/tonemap.hpp>

setSigmaSpace()

virtual void cv::xphoto::TonemapDurand::setSigmaSpace ( float  sigma_space )

pure virtual

Python:
	None	=	cv.xphoto_TonemapDurand.setSigmaSpace(	sigma_space	)

#include <opencv2/xphoto/tonemap.hpp>