Table of Contents

Class XPhotoInvoke

Namespace
Emgu.CV.XPhoto
Assembly
Emgu.CV.dll

Class that contains entry points for the XPhoto module.

public static class XPhotoInvoke
Inheritance
XPhotoInvoke
Inherited Members

Methods

ApplyChannelGains(IInputArray, IOutputArray, float, float, float)

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

public static void ApplyChannelGains(IInputArray src, IOutputArray dst, float gainB, float gainG, float gainR)

Parameters

src IInputArray

Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)

dst IOutputArray

Output image of the same size and type as src.

gainB float

Gain for the B channel

gainG float

Gain for the G channel

gainR float

Gain for the R channel

Bm3dDenoising(IInputArray, IInputOutputArray, IOutputArray, float, int, int, int, int, int, int, float, NormType, Bm3dSteps, TransformTypes)

Performs image denoising using the Block-Matching and 3D-filtering algorithm with several computational optimizations. Noise expected to be a gaussian white noise.

public static void Bm3dDenoising(IInputArray src, IInputOutputArray dstStep1, IOutputArray 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, NormType normType = NormType.L2, Bm3dSteps step = Bm3dSteps.All, TransformTypes transformType = TransformTypes.Haar)

Parameters

src IInputArray

Input 8-bit or 16-bit 1-channel image.

dstStep1 IInputOutputArray

Output image of the first step of BM3D with the same size and type as src.

dstStep2 IOutputArray

Output image of the second step of BM3D with the same size and type as src.

h float

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 int

Size in pixels of the template patch that is used for block-matching. Should be power of 2.

searchWindowSize int

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 int

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 int

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 int

Maximum size of the 3D group for collaborative filtering.

slidingStep int

Sliding step to process every next reference block.

beta float

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 NormType

Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.

step Bm3dSteps

Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.

transformType TransformTypes

Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Remarks

Bm3dDenoising(IInputArray, IOutputArray, float, int, int, int, int, int, int, float, NormType, Bm3dSteps, TransformTypes)

Performs image denoising using the Block-Matching and 3D-filtering algorithm with several computational optimizations. Noise expected to be a gaussian white noise.

public static void Bm3dDenoising(IInputArray src, IOutputArray 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, NormType normType = NormType.L2, Bm3dSteps step = Bm3dSteps.All, TransformTypes transformType = TransformTypes.Haar)

Parameters

src IInputArray

Input 8-bit or 16-bit 1-channel image.

dst IOutputArray

Output image with the same size and type as src.

h float

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 int

Size in pixels of the template patch that is used for block-matching. Should be power of 2.

searchWindowSize int

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 int

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 int

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 int

Maximum size of the 3D group for collaborative filtering.

slidingStep int

Sliding step to process every next reference block.

beta float

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 NormType

Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.

step Bm3dSteps

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 TransformTypes

Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Remarks

DctDenoising(Mat, Mat, double, int)

The function implements simple dct-based denoising, link: http://www.ipol.im/pub/art/2011/ys-dct/.

public static void DctDenoising(Mat src, Mat dst, double sigma, int psize = 16)

Parameters

src Mat

Source image

dst Mat

Destination image

sigma double

Expected noise standard deviation

psize int

Size of block side where dct is computed

Inpaint(Mat, Mat, Mat, InpaintType)

The function implements different single-image inpainting algorithms

public static void Inpaint(Mat src, Mat mask, Mat dst, XPhotoInvoke.InpaintType algorithmType)

Parameters

src Mat

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 Mat

mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted

dst Mat

destination image

algorithmType XPhotoInvoke.InpaintType

algorithm type

OilPainting(IInputArray, IOutputArray, int, int, ColorConversion)

Oil Painting effect

public static void OilPainting(IInputArray src, IOutputArray dst, int size, int dynRatio, ColorConversion code = ColorConversion.Bgr2Gray)

Parameters

src IInputArray

Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)

dst IOutputArray

Output image of the same size and type as src.

size int

Neighbouring size is 2-size+1

dynRatio int

Image is divided by dynRatio before histogram processing

code ColorConversion

Color space conversion code(see ColorConversionCodes). Histogram will used only first plane