View Source Evision.XPhoto (Evision v0.2.9)

Summary

Types

t()

Type that represents an XPhoto struct.

Functions

applyChannelGains(src, gainB, gainG, gainR)

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

applyChannelGains(src, gainB, gainG, gainR, opts)

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

bm3dDenoising(named_args)

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

bm3dDenoising(src, opts)

Variant 1:

bm3dDenoising(src, dstStep1, opts)

createGrayworldWB()

Creates an instance of GrayworldWB

createGrayworldWB(named_args)

createLearningBasedWB()

Creates an instance of LearningBasedWB

createLearningBasedWB(named_args)

Creates an instance of LearningBasedWB

createSimpleWB()

Creates an instance of SimpleWB

createSimpleWB(named_args)

createTonemapDurand()

Creates TonemapDurand object

createTonemapDurand(named_args)

Creates TonemapDurand object

dctDenoising(named_args)

dctDenoising(src, dst, sigma)

The function implements simple dct-based denoising

dctDenoising(src, dst, sigma, opts)

The function implements simple dct-based denoising

inpaint(named_args)

inpaint(src, mask, dst, algorithmType)

The function implements different single-image inpainting algorithms.

oilPainting(named_args)

oilPainting(src, size, dynRatio)

oilPainting See the book @cite Holzmann1988 for details.

oilPainting(src, size, dynRatio, opts)

Variant 1:

oilPainting See the book @cite Holzmann1988 for details.

oilPainting(src, size, dynRatio, code, opts)

oilPainting See the book @cite Holzmann1988 for details.

Types

t()

@type t() :: %Evision.XPhoto{ref: reference()}

Type that represents an XPhoto struct.

ref. reference()
The underlying erlang resource variable.

Functions

applyChannelGains(named_args)

@spec applyChannelGains(Keyword.t()) :: any() | {:error, String.t()}

applyChannelGains(src, gainB, gainG, gainR)

@spec applyChannelGains(Evision.Mat.maybe_mat_in(), number(), number(), number()) ::
  Evision.Mat.t() | {:error, String.t()}

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

Positional Arguments

src: Evision.Mat.
Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)
gainB: float.
gain for the B channel
gainG: float.
gain for the G channel
gainR: float.
gain for the R channel

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

applyChannelGains(src, gainB, gainG, gainR[, dst]) -> dst

applyChannelGains(src, gainB, gainG, gainR, opts)

@spec applyChannelGains(
  Evision.Mat.maybe_mat_in(),
  number(),
  number(),
  number(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}

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

Positional Arguments

src: Evision.Mat.
Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)
gainB: float.
gain for the B channel
gainG: float.
gain for the G channel
gainR: float.
gain for the R channel

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

applyChannelGains(src, gainB, gainG, gainR[, dst]) -> dst

bm3dDenoising(named_args)

@spec bm3dDenoising(Keyword.t()) :: any() | {:error, String.t()}

@spec bm3dDenoising(Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

Positional Arguments

src: Evision.Mat.
Input 8-bit or 16-bit 1-channel image.

Keyword Arguments

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: integer().
Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize: integer().
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: integer().
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: integer().
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: integer().
Maximum size of the 3D group for collaborative filtering.
slidingStep: integer().
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: integer().
Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step: integer().
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: integer().
Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Return

dst: Evision.Mat.t().
Output image with the same size and type as src.

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

Python prototype (for reference only):

bm3dDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]) -> dst

bm3dDenoising(src, opts)

@spec bm3dDenoising(
  Evision.Mat.maybe_mat_in(),
  [
    beta: term(),
    blockMatchingStep1: term(),
    blockMatchingStep2: term(),
    groupSize: term(),
    h: term(),
    normType: term(),
    searchWindowSize: term(),
    slidingStep: term(),
    step: term(),
    templateWindowSize: term(),
    transformType: term()
  ]
  | nil
) :: Evision.Mat.t() | {:error, String.t()}

@spec bm3dDenoising(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}

Variant 1:

Positional Arguments

src: Evision.Mat.
Input 8-bit or 16-bit 1-channel image.

Keyword Arguments

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: integer().
Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize: integer().
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: integer().
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: integer().
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: integer().
Maximum size of the 3D group for collaborative filtering.
slidingStep: integer().
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: integer().
Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step: integer().
Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType: integer().
Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Return

dstStep1: Evision.Mat.t().
Output image of the first step of BM3D with the same size and type as src.
dstStep2: Evision.Mat.t().
Output image of the second step of BM3D with the same size and type as src.

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

Python prototype (for reference only):

bm3dDenoising(src, dstStep1[, dstStep2[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]) -> dstStep1, dstStep2

Variant 2:

Positional Arguments

src: Evision.Mat.
Input 8-bit or 16-bit 1-channel image.

Keyword Arguments

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: integer().
Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize: integer().
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: integer().
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: integer().
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: integer().
Maximum size of the 3D group for collaborative filtering.
slidingStep: integer().
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: integer().
Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step: integer().
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: integer().
Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Return

dst: Evision.Mat.t().
Output image with the same size and type as src.

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

Python prototype (for reference only):

bm3dDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]) -> dst

bm3dDenoising(src, dstStep1, opts)

@spec bm3dDenoising(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [
    beta: term(),
    blockMatchingStep1: term(),
    blockMatchingStep2: term(),
    groupSize: term(),
    h: term(),
    normType: term(),
    searchWindowSize: term(),
    slidingStep: term(),
    step: term(),
    templateWindowSize: term(),
    transformType: term()
  ]
  | nil
) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}

Positional Arguments

src: Evision.Mat.
Input 8-bit or 16-bit 1-channel image.

Keyword Arguments

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: integer().
Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize: integer().
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: integer().
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: integer().
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: integer().
Maximum size of the 3D group for collaborative filtering.
slidingStep: integer().
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: integer().
Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step: integer().
Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType: integer().
Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

Return

dstStep1: Evision.Mat.t().
Output image of the first step of BM3D with the same size and type as src.
dstStep2: Evision.Mat.t().
Output image of the second step of BM3D with the same size and type as src.

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

Python prototype (for reference only):

bm3dDenoising(src, dstStep1[, dstStep2[, h[, templateWindowSize[, searchWindowSize[, blockMatchingStep1[, blockMatchingStep2[, groupSize[, slidingStep[, beta[, normType[, step[, transformType]]]]]]]]]]]]) -> dstStep1, dstStep2

createGrayworldWB()

@spec createGrayworldWB() :: Evision.XPhoto.GrayworldWB.t() | {:error, String.t()}

Creates an instance of GrayworldWB

Return

retval: Evision.XPhoto.GrayworldWB.t()

Python prototype (for reference only):

createGrayworldWB() -> retval

createGrayworldWB(named_args)

@spec createGrayworldWB(Keyword.t()) :: any() | {:error, String.t()}

createLearningBasedWB()

@spec createLearningBasedWB() ::
  Evision.XPhoto.LearningBasedWB.t() | {:error, String.t()}

Creates an instance of LearningBasedWB

Keyword Arguments

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

Return

retval: Evision.XPhoto.LearningBasedWB.t()

Python prototype (for reference only):

createLearningBasedWB([, path_to_model]) -> retval

createLearningBasedWB(named_args)

@spec createLearningBasedWB(Keyword.t()) :: any() | {:error, String.t()}

@spec createLearningBasedWB([{:path_to_model, term()}] | nil) ::
  Evision.XPhoto.LearningBasedWB.t() | {:error, String.t()}

Creates an instance of LearningBasedWB

Keyword Arguments

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

Return

retval: Evision.XPhoto.LearningBasedWB.t()

Python prototype (for reference only):

createLearningBasedWB([, path_to_model]) -> retval

createSimpleWB()

@spec createSimpleWB() :: Evision.XPhoto.SimpleWB.t() | {:error, String.t()}

Creates an instance of SimpleWB

Return

retval: Evision.XPhoto.SimpleWB.t()

Python prototype (for reference only):

createSimpleWB() -> retval

createSimpleWB(named_args)

@spec createSimpleWB(Keyword.t()) :: any() | {:error, String.t()}

createTonemapDurand()

@spec createTonemapDurand() :: Evision.XPhoto.TonemapDurand.t() | {:error, String.t()}

Creates TonemapDurand object

Keyword Arguments

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

Return

retval: Evision.XPhoto.TonemapDurand.t()

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

Python prototype (for reference only):

createTonemapDurand([, gamma[, contrast[, saturation[, sigma_color[, sigma_space]]]]]) -> retval

createTonemapDurand(named_args)

@spec createTonemapDurand(Keyword.t()) :: any() | {:error, String.t()}

@spec createTonemapDurand(
  [
    contrast: term(),
    gamma: term(),
    saturation: term(),
    sigma_color: term(),
    sigma_space: term()
  ]
  | nil
) :: Evision.XPhoto.TonemapDurand.t() | {:error, String.t()}

Creates TonemapDurand object

Keyword Arguments

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

Return

retval: Evision.XPhoto.TonemapDurand.t()

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

Python prototype (for reference only):

createTonemapDurand([, gamma[, contrast[, saturation[, sigma_color[, sigma_space]]]]]) -> retval

dctDenoising(named_args)

@spec dctDenoising(Keyword.t()) :: any() | {:error, String.t()}

dctDenoising(src, dst, sigma)

@spec dctDenoising(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), number()) ::
  :ok | {:error, String.t()}

The function implements simple dct-based denoising

Positional Arguments

src: Evision.Mat.
source image
dst: Evision.Mat.
destination image
sigma: double.
expected noise standard deviation

Keyword Arguments

psize: integer().
size of block side where dct is computed

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

@sa fastNlMeansDenoising

Python prototype (for reference only):

dctDenoising(src, dst, sigma[, psize]) -> None

dctDenoising(src, dst, sigma, opts)

@spec dctDenoising(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  number(),
  [{:psize, term()}] | nil
) :: :ok | {:error, String.t()}

The function implements simple dct-based denoising

Positional Arguments

src: Evision.Mat.
source image
dst: Evision.Mat.
destination image
sigma: double.
expected noise standard deviation

Keyword Arguments

psize: integer().
size of block side where dct is computed

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

@sa fastNlMeansDenoising

Python prototype (for reference only):

dctDenoising(src, dst, sigma[, psize]) -> None

inpaint(named_args)

@spec inpaint(Keyword.t()) :: any() | {:error, String.t()}

inpaint(src, mask, dst, algorithmType)

@spec inpaint(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer()
) :: :ok | {:error, String.t()}

The function implements different single-image inpainting algorithms.

Positional Arguments

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

See the original papers @cite He2012 (Shiftmap) or @cite GenserPCS2018 and @cite SeilerTIP2015 (FSR) for details.

Python prototype (for reference only):

inpaint(src, mask, dst, algorithmType) -> None

oilPainting(named_args)

@spec oilPainting(Keyword.t()) :: any() | {:error, String.t()}

oilPainting(src, size, dynRatio)

@spec oilPainting(Evision.Mat.maybe_mat_in(), integer(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

oilPainting See the book @cite Holzmann1988 for details.

Positional Arguments

src: Evision.Mat.
Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
size: integer().
neighbouring size is 2-size+1
dynRatio: integer().
image is divided by dynRatio before histogram processing

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

oilPainting(src, size, dynRatio[, dst]) -> dst

oilPainting(src, size, dynRatio, opts)

@spec oilPainting(
  Evision.Mat.maybe_mat_in(),
  integer(),
  integer(),
  [{atom(), term()}, ...] | nil
) ::
  Evision.Mat.t() | {:error, String.t()}

@spec oilPainting(Evision.Mat.maybe_mat_in(), integer(), integer(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

Variant 1:

oilPainting See the book @cite Holzmann1988 for details.

Positional Arguments

src: Evision.Mat.
Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
size: integer().
neighbouring size is 2-size+1
dynRatio: integer().
image is divided by dynRatio before histogram processing
code: integer()

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

oilPainting(src, size, dynRatio, code[, dst]) -> dst

Variant 2:

oilPainting See the book @cite Holzmann1988 for details.

Positional Arguments

src: Evision.Mat.
Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
size: integer().
neighbouring size is 2-size+1
dynRatio: integer().
image is divided by dynRatio before histogram processing

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

oilPainting(src, size, dynRatio[, dst]) -> dst

oilPainting(src, size, dynRatio, code, opts)

@spec oilPainting(
  Evision.Mat.maybe_mat_in(),
  integer(),
  integer(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}

oilPainting See the book @cite Holzmann1988 for details.

Positional Arguments

src: Evision.Mat.
Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
size: integer().
neighbouring size is 2-size+1
dynRatio: integer().
image is divided by dynRatio before histogram processing
code: integer()

Return

dst: Evision.Mat.t().
Output image of the same size and type as src.

Python prototype (for reference only):

oilPainting(src, size, dynRatio, code[, dst]) -> dst