附加相片处理算法


class cv::xphoto::GrayworldWB
Gray-world white balance algorithm. 更多...
class cv::xphoto::LearningBasedWB
More sophisticated learning-based automatic white balance algorithm. 更多...
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. 更多...
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. 更多...
class cv::xphoto::WhiteBalancer
The base class for auto white balance algorithms. 更多...

枚举

enum cv::xphoto::Bm3dSteps {
cv::xphoto::BM3D_STEPALL = 0,
cv::xphoto::BM3D_STEP1 = 1,
cv::xphoto::BM3D_STEP2 = 2
}
BM3D algorithm steps. 更多...
enum cv::xphoto::InpaintTypes {
cv::xphoto::INPAINT_SHIFTMAP = 0,
cv::xphoto::INPAINT_FSR_BEST = 1,
cv::xphoto::INPAINT_FSR_FAST = 2
}
Various inpainting algorithms. 更多...
enum cv::xphoto::TransformTypes { cv::xphoto::HAAR = 0 }
BM3D transform types. 更多...

函数

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. 更多...
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. 更多...
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. 更多...
Ptr < GrayworldWB > cv::xphoto::createGrayworldWB ()
Creates an instance of GrayworldWB . 更多...
Ptr < LearningBasedWB > cv::xphoto::createLearningBasedWB (const String &path_to_model= String ())
Creates an instance of LearningBasedWB . 更多...
Ptr < SimpleWB > cv::xphoto::createSimpleWB ()
Creates an instance of SimpleWB . 更多...
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 对象。 更多...
void cv::xphoto::dctDenoising (const Mat &src, Mat &dst, const double sigma, const int psize=16)
The function implements simple dct-based denoising. 更多...
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. 更多...
void cv::xphoto::oilPainting ( InputArray src, OutputArray dst, int size, int dynRatio, int code)
oilPainting See the book [33] for details. 更多...
void cv::xphoto::oilPainting ( InputArray src, OutputArray dst, int size, int dynRatio)
oilPainting See the book [33] for details. 更多...
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

详细描述

枚举类型文档编制

Bm3dSteps

#include < opencv2/xphoto/bm3d_image_denoising.hpp >

BM3D algorithm steps.

枚举器
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

#include < opencv2/xphoto/inpainting.hpp >

Various inpainting algorithms.

另请参阅
inpaint
枚举器
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

INPAINT_FSR_BEST
Python: cv.xphoto.INPAINT_FSR_BEST

Performs Frequency Selective Reconstruction (FSR). One of the two quality profiles BEST and FAST can be chosen, depending on the time available for reconstruction. See [82] and [201] for details.

The algorithm may be utilized for the following areas of application:

  1. Error Concealment (Inpainting). The sampling mask indicates the missing pixels of the distorted input image to be reconstructed.
  2. Non-Regular Sampling. For more information on how to choose a good sampling mask, please review [89] and [88] .

1-channel grayscale or 3-channel BGR image are accepted.

Conventional accepted ranges:

  • 0-255 for CV_8U
  • 0-65535 for CV_16U
  • 0-1 for CV_32F/CV_64F.
INPAINT_FSR_FAST
Python: cv.xphoto.INPAINT_FSR_FAST

INPAINT_FSR_BEST .

TransformTypes

#include < opencv2/xphoto/bm3d_image_denoising.hpp >

BM3D transform types.

枚举器
HAAR
Python: cv.xphoto.HAAR

Un-normalized Haar transform

函数文档编制

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.

另请参阅
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.

另请参阅
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 对象。

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
另请参阅
fastNlMeansDenoising

getContrast()

virtual float cv::xphoto::TonemapDurand::getContrast ( ) const
pure virtual
Python:
retval = cv.xphoto_TonemapDurand.getContrast( )

getSaturation()

virtual float cv::xphoto::TonemapDurand::getSaturation ( ) const
pure virtual
Python:
retval = cv.xphoto_TonemapDurand.getSaturation( )

getSigmaColor()

virtual float cv::xphoto::TonemapDurand::getSigmaColor ( ) const
pure virtual
Python:
retval = cv.xphoto_TonemapDurand.getSigmaColor( )

getSigmaSpace()

virtual float cv::xphoto::TonemapDurand::getSigmaSpace ( ) const
pure virtual
Python:
retval = cv.xphoto_TonemapDurand.getSigmaSpace( )

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 papers [95] (Shiftmap) or [82] and [201] (FSR) for details.

Parameters
src source image
  • INPAINT_SHIFTMAP : 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.
  • INPAINT_FSR_BEST INPAINT_FSR_FAST : 1-channel grayscale or 3-channel BGR image.
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 [33] 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 [33] 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 )

setSaturation()

virtual void cv::xphoto::TonemapDurand::setSaturation ( float saturation )
pure virtual
Python:
None = cv.xphoto_TonemapDurand.setSaturation( saturation )

setSigmaColor()

virtual void cv::xphoto::TonemapDurand::setSigmaColor ( float sigma_color )
pure virtual
Python:
None = cv.xphoto_TonemapDurand.setSigmaColor( sigma_color )

setSigmaSpace()

virtual void cv::xphoto::TonemapDurand::setSigmaSpace ( float sigma_space )
pure virtual
Python:
None = cv.xphoto_TonemapDurand.setSigmaSpace( sigma_space )