org.bytedeco.javacpp

Class opencv_xphoto

java.lang.Object

org.bytedeco.javacpp.presets.opencv_xphoto

org.bytedeco.javacpp.opencv_xphoto

All Implemented Interfaces:

org.bytedeco.javacpp.tools.InfoMapper

public class opencv_xphoto
extends opencv_xphoto

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	opencv_xphoto.GrayworldWB \brief Gray-world white balance algorithm
static class	opencv_xphoto.LearningBasedWB \brief More sophisticated learning-based automatic white balance algorithm.
static class	opencv_xphoto.SimpleWB \brief A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range.
static class	opencv_xphoto.WhiteBalancer \addtogroup xphoto \{

Field Summary

Fields

Modifier and Type	Field and Description
static int	BM3D_STEP1 enum cv::xphoto::Bm3dSteps
static int	BM3D_STEP2 enum cv::xphoto::Bm3dSteps
static int	BM3D_STEPALL enum cv::xphoto::Bm3dSteps
static int	HAAR enum cv::xphoto::TransformTypes
static int	INPAINT_SHIFTMAP enum cv::xphoto::InpaintTypes

Constructor Summary

Constructors

Constructor and Description
opencv_xphoto()

Method Summary

Modifier and Type	Method and Description
static void	applyChannelGains(opencv_core.GpuMat src, opencv_core.GpuMat dst, float gainB, float gainG, float gainR)
static void	applyChannelGains(opencv_core.Mat src, opencv_core.Mat dst, float gainB, float gainG, float gainR) \brief Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms.
static void	applyChannelGains(opencv_core.UMat src, opencv_core.UMat dst, float gainB, float gainG, float gainR)
static void	bm3dDenoising(opencv_core.GpuMat src, opencv_core.GpuMat dst)
static void	bm3dDenoising(opencv_core.GpuMat src, opencv_core.GpuMat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
static void	bm3dDenoising(opencv_core.GpuMat src, opencv_core.GpuMat dstStep1, opencv_core.GpuMat dstStep2)
static void	bm3dDenoising(opencv_core.GpuMat src, opencv_core.GpuMat dstStep1, opencv_core.GpuMat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
static void	bm3dDenoising(opencv_core.Mat src, opencv_core.Mat dst)
static void	bm3dDenoising(opencv_core.Mat src, opencv_core.Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType) \brief Performs image denoising using the Block-Matching and 3D-filtering algorithm with several computational optimizations.
static void	bm3dDenoising(opencv_core.Mat src, opencv_core.Mat dstStep1, opencv_core.Mat dstStep2)
static void	bm3dDenoising(opencv_core.Mat src, opencv_core.Mat dstStep1, opencv_core.Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType) \brief Performs image denoising using the Block-Matching and 3D-filtering algorithm with several computational optimizations.
static void	bm3dDenoising(opencv_core.UMat src, opencv_core.UMat dst)
static void	bm3dDenoising(opencv_core.UMat src, opencv_core.UMat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
static void	bm3dDenoising(opencv_core.UMat src, opencv_core.UMat dstStep1, opencv_core.UMat dstStep2)
static void	bm3dDenoising(opencv_core.UMat src, opencv_core.UMat dstStep1, opencv_core.UMat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
static opencv_xphoto.GrayworldWB	createGrayworldWB() \brief Creates an instance of GrayworldWB
static opencv_xphoto.LearningBasedWB	createLearningBasedWB()
static opencv_xphoto.LearningBasedWB	createLearningBasedWB(org.bytedeco.javacpp.BytePointer path_to_model) \brief Creates an instance of LearningBasedWB
static opencv_xphoto.LearningBasedWB	createLearningBasedWB(String path_to_model)
static opencv_xphoto.SimpleWB	createSimpleWB() \brief Creates an instance of SimpleWB
static void	dctDenoising(opencv_core.Mat src, opencv_core.Mat dst, double sigma)
static void	dctDenoising(opencv_core.Mat src, opencv_core.Mat dst, double sigma, int psize) \addtogroup xphoto \{
static void	inpaint(opencv_core.Mat src, opencv_core.Mat mask, opencv_core.Mat dst, int algorithmType) \brief The function implements different single-image inpainting algorithms.

Methods inherited from class org.bytedeco.javacpp.presets.opencv_xphoto

map

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

INPAINT_SHIFTMAP

public static final int INPAINT_SHIFTMAP

enum cv::xphoto::InpaintTypes

See Also:

Constant Field Values

HAAR

public static final int HAAR

enum cv::xphoto::TransformTypes

See Also:

Constant Field Values

BM3D_STEPALL

public static final int BM3D_STEPALL

enum cv::xphoto::Bm3dSteps

See Also:

Constant Field Values

BM3D_STEP1

public static final int BM3D_STEP1

enum cv::xphoto::Bm3dSteps

See Also:

Constant Field Values

BM3D_STEP2

public static final int BM3D_STEP2

enum cv::xphoto::Bm3dSteps

See Also:

Constant Field Values

Constructor Detail

opencv_xphoto

public opencv_xphoto()

Method Detail

inpaint

@Namespace(value="cv::xphoto")
public static void inpaint(@Const @ByRef
                                                          opencv_core.Mat src,
                                                          @Const @ByRef
                                                          opencv_core.Mat mask,
                                                          @ByRef
                                                          opencv_core.Mat dst,
                                                          int algorithmType)

\brief The function implements different single-image inpainting algorithms.

See the original paper \cite He2012 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

createSimpleWB

@Namespace(value="cv::xphoto")
 @opencv_core.Ptr
public static opencv_xphoto.SimpleWB createSimpleWB()

\brief Creates an instance of SimpleWB

createGrayworldWB

@Namespace(value="cv::xphoto")
 @opencv_core.Ptr
public static opencv_xphoto.GrayworldWB createGrayworldWB()

\brief Creates an instance of GrayworldWB

createLearningBasedWB

@Namespace(value="cv::xphoto")
 @opencv_core.Ptr
public static opencv_xphoto.LearningBasedWB createLearningBasedWB(@opencv_core.Str
                                                                                                                   org.bytedeco.javacpp.BytePointer path_to_model)

\brief 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

createLearningBasedWB

@Namespace(value="cv::xphoto")
 @opencv_core.Ptr
public static opencv_xphoto.LearningBasedWB createLearningBasedWB()

createLearningBasedWB

@Namespace(value="cv::xphoto")
 @opencv_core.Ptr
public static opencv_xphoto.LearningBasedWB createLearningBasedWB(@opencv_core.Str
                                                                                                                   String path_to_model)

applyChannelGains

@Namespace(value="cv::xphoto")
public static void applyChannelGains(@ByVal
                                                                    opencv_core.Mat src,
                                                                    @ByVal
                                                                    opencv_core.Mat dst,
                                                                    float gainB,
                                                                    float gainG,
                                                                    float gainR)

\brief 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

applyChannelGains

@Namespace(value="cv::xphoto")
public static void applyChannelGains(@ByVal
                                                                    opencv_core.UMat src,
                                                                    @ByVal
                                                                    opencv_core.UMat dst,
                                                                    float gainB,
                                                                    float gainG,
                                                                    float gainR)

applyChannelGains

@Namespace(value="cv::xphoto")
public static void applyChannelGains(@ByVal
                                                                    opencv_core.GpuMat src,
                                                                    @ByVal
                                                                    opencv_core.GpuMat dst,
                                                                    float gainB,
                                                                    float gainG,
                                                                    float gainR)

dctDenoising

@Namespace(value="cv::xphoto")
public static void dctDenoising(@Const @ByRef
                                                               opencv_core.Mat src,
                                                               @ByRef
                                                               opencv_core.Mat dst,
                                                               double sigma,
                                                               int psize)

\addtogroup xphoto \{

/** \brief The function implements simple dct-based denoising

.

Parameters:

src - source image

dst - destination image

sigma - expected noise standard deviation

psize - size of block side where dct is computed

\sa fastNlMeansDenoising

dctDenoising

@Namespace(value="cv::xphoto")
public static void dctDenoising(@Const @ByRef
                                                               opencv_core.Mat src,
                                                               @ByRef
                                                               opencv_core.Mat dst,
                                                               double sigma)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.Mat src,
                                                                @ByVal
                                                                opencv_core.Mat dstStep1,
                                                                @ByVal
                                                                opencv_core.Mat dstStep2,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

\brief Performs image denoising using the Block-Matching and 3D-filtering algorithm 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.

\sa fastNlMeansDenoising

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.Mat src,
                                                                @ByVal
                                                                opencv_core.Mat dstStep1,
                                                                @ByVal
                                                                opencv_core.Mat dstStep2)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.UMat src,
                                                                @ByVal
                                                                opencv_core.UMat dstStep1,
                                                                @ByVal
                                                                opencv_core.UMat dstStep2,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.UMat src,
                                                                @ByVal
                                                                opencv_core.UMat dstStep1,
                                                                @ByVal
                                                                opencv_core.UMat dstStep2)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.GpuMat src,
                                                                @ByVal
                                                                opencv_core.GpuMat dstStep1,
                                                                @ByVal
                                                                opencv_core.GpuMat dstStep2,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.GpuMat src,
                                                                @ByVal
                                                                opencv_core.GpuMat dstStep1,
                                                                @ByVal
                                                                opencv_core.GpuMat dstStep2)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.Mat src,
                                                                @ByVal
                                                                opencv_core.Mat dst,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

\brief Performs image denoising using the Block-Matching and 3D-filtering algorithm 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.

\sa fastNlMeansDenoising

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.Mat src,
                                                                @ByVal
                                                                opencv_core.Mat dst)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.UMat src,
                                                                @ByVal
                                                                opencv_core.UMat dst,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.UMat src,
                                                                @ByVal
                                                                opencv_core.UMat dst)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.GpuMat src,
                                                                @ByVal
                                                                opencv_core.GpuMat dst,
                                                                float h,
                                                                int templateWindowSize,
                                                                int searchWindowSize,
                                                                int blockMatchingStep1,
                                                                int blockMatchingStep2,
                                                                int groupSize,
                                                                int slidingStep,
                                                                float beta,
                                                                int normType,
                                                                int step,
                                                                int transformType)

bm3dDenoising

@Namespace(value="cv::xphoto")
public static void bm3dDenoising(@ByVal
                                                                opencv_core.GpuMat src,
                                                                @ByVal
                                                                opencv_core.GpuMat dst)