OpenCV for Unity 2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.

Static Public Member Functions  
static void  inpaint (Mat src, Mat inpaintMask, Mat dst, double inpaintRadius, int flags) 
Restores the selected region in an image using the region neighborhood.  
static void  fastNlMeansDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, float h, int templateWindowSize) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, float h) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, MatOfFloat h, int templateWindowSize) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoising (Mat src, Mat dst, MatOfFloat h) 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.  
static void  fastNlMeansDenoisingColored (Mat src, Mat dst, float h, float hColor, int templateWindowSize, int searchWindowSize) 
Modification of fastNlMeansDenoising function for colored images.  
static void  fastNlMeansDenoisingColored (Mat src, Mat dst, float h, float hColor, int templateWindowSize) 
Modification of fastNlMeansDenoising function for colored images.  
static void  fastNlMeansDenoisingColored (Mat src, Mat dst, float h, float hColor) 
Modification of fastNlMeansDenoising function for colored images.  
static void  fastNlMeansDenoisingColored (Mat src, Mat dst, float h) 
Modification of fastNlMeansDenoising function for colored images.  
static void  fastNlMeansDenoisingColored (Mat src, Mat dst) 
Modification of fastNlMeansDenoising function for colored images.  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h) 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).  
static void  fastNlMeansDenoisingColoredMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize) 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.  
static void  fastNlMeansDenoisingColoredMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize) 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.  
static void  fastNlMeansDenoisingColoredMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor) 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.  
static void  fastNlMeansDenoisingColoredMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h) 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.  
static void  fastNlMeansDenoisingColoredMulti (List< Mat > srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize) 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.  
static void  denoise_TVL1 (List< Mat > observations, Mat result, double lambda, int niters) 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.  
static void  denoise_TVL1 (List< Mat > observations, Mat result, double lambda) 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.  
static void  denoise_TVL1 (List< Mat > observations, Mat result) 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.  
static Tonemap  createTonemap (float gamma) 
Creates simple linear mapper with gamma correction.  
static Tonemap  createTonemap () 
Creates simple linear mapper with gamma correction.  
static TonemapDrago  createTonemapDrago (float gamma, float saturation, float bias) 
Creates TonemapDrago object.  
static TonemapDrago  createTonemapDrago (float gamma, float saturation) 
Creates TonemapDrago object.  
static TonemapDrago  createTonemapDrago (float gamma) 
Creates TonemapDrago object.  
static TonemapDrago  createTonemapDrago () 
Creates TonemapDrago object.  
static TonemapReinhard  createTonemapReinhard (float gamma, float intensity, float light_adapt, float color_adapt) 
Creates TonemapReinhard object.  
static TonemapReinhard  createTonemapReinhard (float gamma, float intensity, float light_adapt) 
Creates TonemapReinhard object.  
static TonemapReinhard  createTonemapReinhard (float gamma, float intensity) 
Creates TonemapReinhard object.  
static TonemapReinhard  createTonemapReinhard (float gamma) 
Creates TonemapReinhard object.  
static TonemapReinhard  createTonemapReinhard () 
Creates TonemapReinhard object.  
static TonemapMantiuk  createTonemapMantiuk (float gamma, float scale, float saturation) 
Creates TonemapMantiuk object.  
static TonemapMantiuk  createTonemapMantiuk (float gamma, float scale) 
Creates TonemapMantiuk object.  
static TonemapMantiuk  createTonemapMantiuk (float gamma) 
Creates TonemapMantiuk object.  
static TonemapMantiuk  createTonemapMantiuk () 
Creates TonemapMantiuk object.  
static AlignMTB  createAlignMTB (int max_bits, int exclude_range, bool cut) 
Creates AlignMTB object.  
static AlignMTB  createAlignMTB (int max_bits, int exclude_range) 
Creates AlignMTB object.  
static AlignMTB  createAlignMTB (int max_bits) 
Creates AlignMTB object.  
static AlignMTB  createAlignMTB () 
Creates AlignMTB object.  
static CalibrateDebevec  createCalibrateDebevec (int samples, float lambda, bool random) 
Creates CalibrateDebevec object.  
static CalibrateDebevec  createCalibrateDebevec (int samples, float lambda) 
Creates CalibrateDebevec object.  
static CalibrateDebevec  createCalibrateDebevec (int samples) 
Creates CalibrateDebevec object.  
static CalibrateDebevec  createCalibrateDebevec () 
Creates CalibrateDebevec object.  
static CalibrateRobertson  createCalibrateRobertson (int max_iter, float threshold) 
Creates CalibrateRobertson object.  
static CalibrateRobertson  createCalibrateRobertson (int max_iter) 
Creates CalibrateRobertson object.  
static CalibrateRobertson  createCalibrateRobertson () 
Creates CalibrateRobertson object.  
static MergeDebevec  createMergeDebevec () 
Creates MergeDebevec object.  
static MergeMertens  createMergeMertens (float contrast_weight, float saturation_weight, float exposure_weight) 
Creates MergeMertens object.  
static MergeMertens  createMergeMertens (float contrast_weight, float saturation_weight) 
Creates MergeMertens object.  
static MergeMertens  createMergeMertens (float contrast_weight) 
Creates MergeMertens object.  
static MergeMertens  createMergeMertens () 
Creates MergeMertens object.  
static MergeRobertson  createMergeRobertson () 
Creates MergeRobertson object.  
static void  decolor (Mat src, Mat grayscale, Mat color_boost) 
Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized blackandwhite photograph rendering, and in many single channel image processing applications [CL12] .  
static void  seamlessClone (Mat src, Mat dst, Mat mask, Point p, Mat blend, int flags) 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .  
static void  colorChange (Mat src, Mat mask, Mat dst, float red_mul, float green_mul, float blue_mul) 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.  
static void  colorChange (Mat src, Mat mask, Mat dst, float red_mul, float green_mul) 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.  
static void  colorChange (Mat src, Mat mask, Mat dst, float red_mul) 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.  
static void  colorChange (Mat src, Mat mask, Mat dst) 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.  
static void  illuminationChange (Mat src, Mat mask, Mat dst, float alpha, float beta) 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.  
static void  illuminationChange (Mat src, Mat mask, Mat dst, float alpha) 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.  
static void  illuminationChange (Mat src, Mat mask, Mat dst) 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.  
static void  textureFlattening (Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold, int kernel_size) 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.  
static void  textureFlattening (Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold) 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.  
static void  textureFlattening (Mat src, Mat mask, Mat dst, float low_threshold) 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.  
static void  textureFlattening (Mat src, Mat mask, Mat dst) 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.  
static void  edgePreservingFilter (Mat src, Mat dst, int flags, float sigma_s, float sigma_r) 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .  
static void  edgePreservingFilter (Mat src, Mat dst, int flags, float sigma_s) 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .  
static void  edgePreservingFilter (Mat src, Mat dst, int flags) 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .  
static void  edgePreservingFilter (Mat src, Mat dst) 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .  
static void  detailEnhance (Mat src, Mat dst, float sigma_s, float sigma_r) 
This filter enhances the details of a particular image.  
static void  detailEnhance (Mat src, Mat dst, float sigma_s) 
This filter enhances the details of a particular image.  
static void  detailEnhance (Mat src, Mat dst) 
This filter enhances the details of a particular image.  
static void  pencilSketch (Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r, float shade_factor) 
Pencillike nonphotorealistic line drawing.  
static void  pencilSketch (Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r) 
Pencillike nonphotorealistic line drawing.  
static void  pencilSketch (Mat src, Mat dst1, Mat dst2, float sigma_s) 
Pencillike nonphotorealistic line drawing.  
static void  pencilSketch (Mat src, Mat dst1, Mat dst2) 
Pencillike nonphotorealistic line drawing.  
static void  stylization (Mat src, Mat dst, float sigma_s, float sigma_r) 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.  
static void  stylization (Mat src, Mat dst, float sigma_s) 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.  
static void  stylization (Mat src, Mat dst) 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.  
static void  seamlessClone (Mat src, Mat dst, Mat mask, in Vec2d p, Mat blend, int flags) 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .  
static void  seamlessClone (Mat src, Mat dst, Mat mask, in(double x, double y) p, Mat blend, int flags) 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .  
Static Public Attributes  
const int  INPAINT_NS = 0 
const int  INPAINT_TELEA = 1 
const int  LDR_SIZE = 256 
const int  NORMAL_CLONE = 1 
const int  MIXED_CLONE = 2 
const int  MONOCHROME_TRANSFER = 3 
const int  RECURS_FILTER = 1 
const int  NORMCONV_FILTER = 2 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src . 
red_mul  Rchannel multiply factor. 
green_mul  Gchannel multiply factor. 
blue_mul  Bchannel multiply factor. 
Multiplication factor is between .5 to 2.5.

static 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src . 
red_mul  Rchannel multiply factor. 
green_mul  Gchannel multiply factor. 
blue_mul  Bchannel multiply factor. 
Multiplication factor is between .5 to 2.5.

static 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src . 
red_mul  Rchannel multiply factor. 
green_mul  Gchannel multiply factor. 
blue_mul  Bchannel multiply factor. 
Multiplication factor is between .5 to 2.5.

static 
Given an original color image, two differently colored versions of this image can be mixed seamlessly.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src . 
red_mul  Rchannel multiply factor. 
green_mul  Gchannel multiply factor. 
blue_mul  Bchannel multiply factor. 
Multiplication factor is between .5 to 2.5.

static 
Creates AlignMTB object.
max_bits  logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively). 
exclude_range  range for exclusion bitmap that is constructed to suppress noise around the median value. 
cut  if true cuts images, otherwise fills the new regions with zeros. 

static 
Creates AlignMTB object.
max_bits  logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively). 
exclude_range  range for exclusion bitmap that is constructed to suppress noise around the median value. 
cut  if true cuts images, otherwise fills the new regions with zeros. 

static 
Creates AlignMTB object.
max_bits  logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively). 
exclude_range  range for exclusion bitmap that is constructed to suppress noise around the median value. 
cut  if true cuts images, otherwise fills the new regions with zeros. 

static 
Creates AlignMTB object.
max_bits  logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively). 
exclude_range  range for exclusion bitmap that is constructed to suppress noise around the median value. 
cut  if true cuts images, otherwise fills the new regions with zeros. 

static 
Creates CalibrateDebevec object.
samples  number of pixel locations to use 
lambda  smoothness term weight. Greater values produce smoother results, but can alter the response. 
random  if true sample pixel locations are chosen at random, otherwise they form a rectangular grid. 

static 
Creates CalibrateDebevec object.
samples  number of pixel locations to use 
lambda  smoothness term weight. Greater values produce smoother results, but can alter the response. 
random  if true sample pixel locations are chosen at random, otherwise they form a rectangular grid. 

static 
Creates CalibrateDebevec object.
samples  number of pixel locations to use 
lambda  smoothness term weight. Greater values produce smoother results, but can alter the response. 
random  if true sample pixel locations are chosen at random, otherwise they form a rectangular grid. 

static 
Creates CalibrateDebevec object.
samples  number of pixel locations to use 
lambda  smoothness term weight. Greater values produce smoother results, but can alter the response. 
random  if true sample pixel locations are chosen at random, otherwise they form a rectangular grid. 

static 
Creates CalibrateRobertson object.
max_iter  maximal number of GaussSeidel solver iterations. 
threshold  target difference between results of two successive steps of the minimization. 

static 
Creates CalibrateRobertson object.
max_iter  maximal number of GaussSeidel solver iterations. 
threshold  target difference between results of two successive steps of the minimization. 

static 
Creates CalibrateRobertson object.
max_iter  maximal number of GaussSeidel solver iterations. 
threshold  target difference between results of two successive steps of the minimization. 

static 
Creates MergeDebevec object.

static 
Creates MergeMertens object.
contrast_weight  contrast measure weight. See MergeMertens. 
saturation_weight  saturation measure weight 
exposure_weight  wellexposedness measure weight 

static 
Creates MergeMertens object.
contrast_weight  contrast measure weight. See MergeMertens. 
saturation_weight  saturation measure weight 
exposure_weight  wellexposedness measure weight 

static 
Creates MergeMertens object.
contrast_weight  contrast measure weight. See MergeMertens. 
saturation_weight  saturation measure weight 
exposure_weight  wellexposedness measure weight 

static 
Creates MergeMertens object.
contrast_weight  contrast measure weight. See MergeMertens. 
saturation_weight  saturation measure weight 
exposure_weight  wellexposedness measure weight 

static 
Creates MergeRobertson object.

static 
Creates simple linear mapper with gamma correction.
gamma  positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it. 

static 
Creates simple linear mapper with gamma correction.
gamma  positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it. 

static 
Creates TonemapDrago object.
gamma  gamma value for gamma correction. See createTonemap 
saturation  positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it. 
bias  value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85. 

static 
Creates TonemapDrago object.
gamma  gamma value for gamma correction. See createTonemap 
saturation  positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it. 
bias  value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85. 

static 
Creates TonemapDrago object.
gamma  gamma value for gamma correction. See createTonemap 
saturation  positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it. 
bias  value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85. 

static 
Creates TonemapDrago object.
gamma  gamma value for gamma correction. See createTonemap 
saturation  positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it. 
bias  value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85. 

static 
Creates TonemapMantiuk object.
gamma  gamma value for gamma correction. See createTonemap 
scale  contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results. 
saturation  saturation enhancement value. See createTonemapDrago 

static 
Creates TonemapMantiuk object.
gamma  gamma value for gamma correction. See createTonemap 
scale  contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results. 
saturation  saturation enhancement value. See createTonemapDrago 

static 
Creates TonemapMantiuk object.
gamma  gamma value for gamma correction. See createTonemap 
scale  contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results. 
saturation  saturation enhancement value. See createTonemapDrago 

static 
Creates TonemapMantiuk object.
gamma  gamma value for gamma correction. See createTonemap 
scale  contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results. 
saturation  saturation enhancement value. See createTonemapDrago 

static 
Creates TonemapReinhard object.
gamma  gamma value for gamma correction. See createTonemap 
intensity  result intensity in [8, 8] range. Greater intensity produces brighter results. 
light_adapt  light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. 
color_adapt  chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel. 

static 
Creates TonemapReinhard object.
gamma  gamma value for gamma correction. See createTonemap 
intensity  result intensity in [8, 8] range. Greater intensity produces brighter results. 
light_adapt  light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. 
color_adapt  chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel. 

static 
Creates TonemapReinhard object.
gamma  gamma value for gamma correction. See createTonemap 
intensity  result intensity in [8, 8] range. Greater intensity produces brighter results. 
light_adapt  light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. 
color_adapt  chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel. 

static 
Creates TonemapReinhard object.
gamma  gamma value for gamma correction. See createTonemap 
intensity  result intensity in [8, 8] range. Greater intensity produces brighter results. 
light_adapt  light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. 
color_adapt  chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel. 

static 
Creates TonemapReinhard object.
gamma  gamma value for gamma correction. See createTonemap 
intensity  result intensity in [8, 8] range. Greater intensity produces brighter results. 
light_adapt  light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. 
color_adapt  chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel. 

static 
Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized blackandwhite photograph rendering, and in many single channel image processing applications [CL12] .
src  Input 8bit 3channel image. 
grayscale  Output 8bit 1channel image. 
color_boost  Output 8bit 3channel image. 
This function is to be applied on color images.

static 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.
It should be noted, that this implementation was taken from the July 2013 blog entry [MA13] , which also contained (slightly more general) readytouse source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors.
Although the thorough discussion and justification of the algorithm involved may be found in [ChambolleEtAl], it might make sense to skim over it here, following [MA13] . To begin with, we consider the 1byte graylevel images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula
\[\left\\left\\nabla x\right\\right\ + \lambda\sum_i\left\\left\xf_i\right\\right\\]
\(\\\cdot\\\) here denotes \(L_2\)norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the PrimalDual algorithm comes into play.
observations  This array should contain one or more noised versions of the image that is to be restored. 
result  Here the denoised image will be stored. There is no need to do preallocation of storage space, as it will be automatically allocated, if necessary. 
lambda  Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed. 
niters  Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor. 

static 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.
It should be noted, that this implementation was taken from the July 2013 blog entry [MA13] , which also contained (slightly more general) readytouse source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors.
Although the thorough discussion and justification of the algorithm involved may be found in [ChambolleEtAl], it might make sense to skim over it here, following [MA13] . To begin with, we consider the 1byte graylevel images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula
\[\left\\left\\nabla x\right\\right\ + \lambda\sum_i\left\\left\xf_i\right\\right\\]
\(\\\cdot\\\) here denotes \(L_2\)norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the PrimalDual algorithm comes into play.
observations  This array should contain one or more noised versions of the image that is to be restored. 
result  Here the denoised image will be stored. There is no need to do preallocation of storage space, as it will be automatically allocated, if necessary. 
lambda  Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed. 
niters  Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor. 

static 
Primaldual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primaldual algorithm then can be used to perform denoising and this is exactly what is implemented.
It should be noted, that this implementation was taken from the July 2013 blog entry [MA13] , which also contained (slightly more general) readytouse source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors.
Although the thorough discussion and justification of the algorithm involved may be found in [ChambolleEtAl], it might make sense to skim over it here, following [MA13] . To begin with, we consider the 1byte graylevel images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula
\[\left\\left\\nabla x\right\\right\ + \lambda\sum_i\left\\left\xf_i\right\\right\\]
\(\\\cdot\\\) here denotes \(L_2\)norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the PrimalDual algorithm comes into play.
observations  This array should contain one or more noised versions of the image that is to be restored. 
result  Here the denoised image will be stored. There is no need to do preallocation of storage space, as it will be automatically allocated, if necessary. 
lambda  Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed. 
niters  Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor. 
This filter enhances the details of a particular image.
src  Input 8bit 3channel 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. 

static 
This filter enhances the details of a particular image.
src  Input 8bit 3channel 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. 

static 
This filter enhances the details of a particular image.
src  Input 8bit 3channel 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. 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .
src  Input 8bit 3channel image. 
dst  Output 8bit 3channel 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. 

static 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .
src  Input 8bit 3channel image. 
dst  Output 8bit 3channel 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. 

static 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .
src  Input 8bit 3channel image. 
dst  Output 8bit 3channel 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. 

static 
Filtering is the fundamental operation in image and video processing. Edgepreserving smoothing filters are used in many different applications [EM11] .
src  Input 8bit 3channel image. 
dst  Output 8bit 3channel 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. 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
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 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
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 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
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 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
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 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Perform image denoising using Nonlocal Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 
This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

static 
Modification of fastNlMeansDenoising function for colored images.
src  Input 8bit 3channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
hColor  The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors 
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

static 
Modification of fastNlMeansDenoising function for colored images.
src  Input 8bit 3channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
hColor  The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors 
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

static 
Modification of fastNlMeansDenoising function for colored images.
src  Input 8bit 3channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
hColor  The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors 
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

static 
Modification of fastNlMeansDenoising function for colored images.
src  Input 8bit 3channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
hColor  The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors 
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

static 
Modification of fastNlMeansDenoising function for colored images.
src  Input 8bit 3channel image. 
dst  Output image with the same size and type as src . 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
hColor  The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors 
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

static 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.
srcImgs  Input 8bit 3channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
hColor  The same as h but for color components. 
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

static 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.
srcImgs  Input 8bit 3channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
hColor  The same as h but for color components. 
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

static 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.
srcImgs  Input 8bit 3channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
hColor  The same as h but for color components. 
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

static 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.
srcImgs  Input 8bit 3channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
hColor  The same as h but for color components. 
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

static 
Modification of fastNlMeansDenoisingMulti function for colored images sequences.
srcImgs  Input 8bit 3channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
hColor  The same as h but for color components. 
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 

static 
Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See [Buades2005DenoisingIS] for more details (open access here).
srcImgs  Input 8bit or 16bit (only with NORM_L1) 1channel, 2channel, 3channel or 4channel images sequence. All images should have the same type and size. 
imgToDenoiseIndex  Target image to denoise index in srcImgs sequence 
temporalWindowSize  Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex  temporalWindowSize / 2 to imgToDenoiseIndex  temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image. 
dst  Output image with the same size and type as srcImgs images. 
templateWindowSize  Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels 
searchWindowSize  Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize  greater denoising time. Recommended value 21 pixels 
h  Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise 
normType  Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 

static 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
alpha  Value ranges between 02. 
beta  Value ranges between 02. 
This is useful to highlight underexposed foreground objects or to reduce specular reflections.

static 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
alpha  Value ranges between 02. 
beta  Value ranges between 02. 
This is useful to highlight underexposed foreground objects or to reduce specular reflections.

static 
Applying an appropriate nonlinear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
alpha  Value ranges between 02. 
beta  Value ranges between 02. 
This is useful to highlight underexposed foreground objects or to reduce specular reflections.

static 
Restores the selected region in an image using the region neighborhood.
src  Input 8bit, 16bit unsigned or 32bit float 1channel or 8bit 3channel image. 
inpaintMask  Inpainting mask, 8bit 1channel image. Nonzero pixels indicate the area that needs to be inpainted. 
dst  Output image with the same size and type as src . 
inpaintRadius  Radius of a circular neighborhood of each point inpainted that is considered by the algorithm. 
flags  Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA 
The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
Pencillike nonphotorealistic line drawing.
src  Input 8bit 3channel image. 
dst1  Output 8bit 1channel 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. 

static 
Pencillike nonphotorealistic line drawing.
src  Input 8bit 3channel image. 
dst1  Output 8bit 1channel 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. 

static 
Pencillike nonphotorealistic line drawing.
src  Input 8bit 3channel image. 
dst1  Output 8bit 1channel 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. 

static 
Pencillike nonphotorealistic line drawing.
src  Input 8bit 3channel image. 
dst1  Output 8bit 1channel 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. 

static 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .
src  Input 8bit 3channel image. 
dst  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
p  Point in dst image where object is placed. 
blend  Output image with the same size and type as dst. 
flags  Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER 

static 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .
src  Input 8bit 3channel image. 
dst  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
p  Point in dst image where object is placed. 
blend  Output image with the same size and type as dst. 
flags  Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER 

static 
Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03] .
src  Input 8bit 3channel image. 
dst  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
p  Point in dst image where object is placed. 
blend  Output image with the same size and type as dst. 
flags  Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.
src  Input 8bit 3channel 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. 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.
src  Input 8bit 3channel 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. 

static 
Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edgeaware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, highcontrast features.
src  Input 8bit 3channel 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. 

static 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
low_threshold  Range from 0 to 100. 
high_threshold  Value > 100. 
kernel_size  The size of the Sobel kernel to be used. 

static 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
low_threshold  Range from 0 to 100. 
high_threshold  Value > 100. 
kernel_size  The size of the Sobel kernel to be used. 

static 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
low_threshold  Range from 0 to 100. 
high_threshold  Value > 100. 
kernel_size  The size of the Sobel kernel to be used. 

static 
By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge Detector is used.
src  Input 8bit 3channel image. 
mask  Input 8bit 1 or 3channel image. 
dst  Output image with the same size and type as src. 
low_threshold  Range from 0 to 100. 
high_threshold  Value > 100. 
kernel_size  The size of the Sobel kernel to be used. 

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