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

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Python prototype (for reference only):

clear() -> None

@spec create() :: t() | {:error, String.t()}

Returns the implementation of the CCheckerDetector.

Return

• retval: CCheckerDetector

Python prototype (for reference only):

create() -> retval

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

@spec create(Evision.DNN.Net.t()) :: t() | {:error, String.t()}

Set the net which will be used to find the approximate bounding boxes for the color charts. And returns the implementation of the CCheckerDetector.

Positional Arguments

• net: Evision.DNN.Net.t().

  the neural network, if the network in empty, then the function will return false.

Return

• retval: CCheckerDetector

It is not necessary to use this, but this usually results in better detection rate.

Python prototype (for reference only):

create(net) -> retval

@spec draw(t(), [Evision.MCC.Ptr_CChecker.t()], Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

Draws the checker to the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• checkers: [Ptr_CChecker].

  The checkers which will be drawn by this object.

Keyword Arguments

• color: Evision.scalar().

  The color by with which the squares of the checker will be drawn

• thickness: integer().

  The thickness with which the sqaures will be drawn

Return

• img: Evision.Mat.t().

  image in color space BGR

Python prototype (for reference only):

draw(checkers, img[, color[, thickness]]) -> img

@spec draw(
  t(),
  [Evision.MCC.Ptr_CChecker.t()],
  Evision.Mat.maybe_mat_in(),
  [color: term(), thickness: term()] | nil
) :: Evision.Mat.t() | {:error, String.t()}

Draws the checker to the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• checkers: [Ptr_CChecker].

  The checkers which will be drawn by this object.

Keyword Arguments

• color: Evision.scalar().

  The color by with which the squares of the checker will be drawn

• thickness: integer().

  The thickness with which the sqaures will be drawn

Return

• img: Evision.Mat.t().

  image in color space BGR

Python prototype (for reference only):

draw(checkers, img[, color[, thickness]]) -> img

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

@spec empty(t()) :: boolean() | {:error, String.t()}

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

@spec getBestColorChecker(t()) :: t() | {:error, String.t()}

Get the best color checker. By the best it means the one detected with the highest confidence.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: Evision.MCC.CCheckerDetector.t()

@return checker A single colorchecker, if atleast one colorchecker

             was detected, 'nullptr' otherwise.

Python prototype (for reference only):

getBestColorChecker() -> retval

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

@spec getColorChartType(t()) :: Evision.MCC.ColorChart.t() | {:error, String.t()}

getColorChartType

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: ColorChart

Python prototype (for reference only):

getColorChartType() -> retval

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

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

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

@spec getDetectionParams(t()) ::
  Evision.MCC.DetectorParametersMCC.t() | {:error, String.t()}

getDetectionParams

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: DetectorParametersMCC

Python prototype (for reference only):

getDetectionParams() -> retval

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

@spec getListColorChecker(t()) :: [Evision.MCC.CChecker.t()] | {:error, String.t()}

Get the list of all detected colorcheckers

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: [CChecker]

@return checkers vector of colorcheckers

Python prototype (for reference only):

getListColorChecker() -> retval

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

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

Gets the reference color for chart.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: Evision.Mat.t()

Python prototype (for reference only):

getRefColors() -> retval

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

@spec getUseDnnModel(t()) :: boolean() | {:error, String.t()}

getUseDnnModel

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

Return

• retval: bool

Python prototype (for reference only):

getUseDnnModel() -> retval

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

Find the ColorCharts in the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• image: Evision.Mat.

  image in color space BGR

Keyword Arguments

• nc: integer().

  number of charts in the image, if you don't know the exact then keeping this number high helps.

Return

• retval: bool

Differs from the above one only in the arguments. This version searches for the chart in the full image. The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker() @return true if atleast one chart is detected otherwise false

Python prototype (for reference only):

process(image[, nc]) -> retval

@spec process(t(), Evision.Mat.maybe_mat_in(), [{:nc, term()}] | nil) ::
  boolean() | {:error, String.t()}

Find the ColorCharts in the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• image: Evision.Mat.

  image in color space BGR

Keyword Arguments

• nc: integer().

  number of charts in the image, if you don't know the exact then keeping this number high helps.

Return

• retval: bool

Differs from the above one only in the arguments. This version searches for the chart in the full image. The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker() @return true if atleast one chart is detected otherwise false

Python prototype (for reference only):

process(image[, nc]) -> retval

@spec processWithROI(t(), Evision.Mat.maybe_mat_in(), [
  {number(), number(), number(), number()}
]) ::
  boolean() | {:error, String.t()}

Find the ColorCharts in the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• image: Evision.Mat.

  image in color space BGR

• regionsOfInterest: [Rect].

  regions of image to look for the chart, if it is empty, charts are looked for in the entire image

Keyword Arguments

• nc: integer().

  number of charts in the image, if you don't know the exact then keeping this number high helps.

Return

• retval: bool

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker() @return true if atleast one chart is detected otherwise false

Python prototype (for reference only):

processWithROI(image, regionsOfInterest[, nc]) -> retval

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

Find the ColorCharts in the given image.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• image: Evision.Mat.

  image in color space BGR

• regionsOfInterest: [Rect].

  regions of image to look for the chart, if it is empty, charts are looked for in the entire image

Keyword Arguments

• nc: integer().

  number of charts in the image, if you don't know the exact then keeping this number high helps.

Return

• retval: bool

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker() @return true if atleast one chart is detected otherwise false

Python prototype (for reference only):

processWithROI(image, regionsOfInterest[, nc]) -> retval

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec setColorChartType(t(), Evision.MCC.ColorChart.t()) :: t() | {:error, String.t()}

Sets the color chart type for MCC detection.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• chartType: ColorChart.

  ColorChart enum specifying the type of color chart to detect.

Python prototype (for reference only):

setColorChartType(chartType) -> None

@spec setDetectionParams(t(), Evision.MCC.DetectorParametersMCC.t()) ::
  t() | {:error, String.t()}

Sets the detection paramaters for mcc.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• params: DetectorParametersMCC.

  DetectorParametersMCC structure containing detection configuration parameters.

Python prototype (for reference only):

setDetectionParams(params) -> None

@spec setUseDnnModel(t(), boolean()) :: t() | {:error, String.t()}

Enables or disables the use of the neural network for detection.

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()

• useDnn: bool.

  Boolean flag to indicate whether to use neural network (true) or not (false).

Python prototype (for reference only):

setUseDnnModel(useDnn) -> None

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()
• fs: Evision.FileStorage

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.MCC.CCheckerDetector.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None