View Source Evision.DNN.TextDetectionModelDB (Evision v0.2.7)

Summary

Types

t()

Type that represents an DNN.TextDetectionModelDB struct.

Functions

getBinaryThreshold

getMaxCandidates

getPolygonThreshold

Given the @p input frame, create input blob, run net and return the output @p blobs.

Given the @p input frame, create input blob, run net and return the output @p blobs.

Set flag crop for frame.

Set mean value for frame.

Set preprocessing parameters for frame.

Set preprocessing parameters for frame.

Set scalefactor value for frame.

Set input size for frame.

Set flag swapRB for frame.

Set output names for frame.

Variant 1:

Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Types

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

Type that represents an DNN.TextDetectionModelDB struct.

  • ref. reference()

    The underlying erlang resource variable.

Functions

@spec detect(Keyword.t()) :: any() | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in()) ::
  [[{number(), number()}]] | {:error, String.t()}

detect

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • frame: Evision.Mat
Return
  • detections: [[Point]]

Has overloading in C++

Python prototype (for reference only):

detect(frame) -> detections
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detectTextRectangles(named_args)

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@spec detectTextRectangles(Keyword.t()) :: any() | {:error, String.t()}
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detectTextRectangles(self, frame)

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

Performs detection

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • frame: Evision.Mat.

    the input image

Return
  • detections: [{centre={x, y}, size={s1, s2}, angle}].

    array with detections' RotationRect results

  • confidences: [float].

    array with detection confidences

Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections. Each result is rotated rectangle. Note: Result may be inaccurate in case of strong perspective transformations.

Python prototype (for reference only):

detectTextRectangles(frame) -> detections, confidences
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enableWinograd(named_args)

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@spec enableWinograd(Keyword.t()) :: any() | {:error, String.t()}
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enableWinograd(self, useWinograd)

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@spec enableWinograd(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}

enableWinograd

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • useWinograd: bool
Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

enableWinograd(useWinograd) -> retval
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getBinaryThreshold(named_args)

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

getBinaryThreshold

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Return
  • retval: float

Python prototype (for reference only):

getBinaryThreshold() -> retval
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getMaxCandidates(named_args)

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

getMaxCandidates

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Return
  • retval: integer()

Python prototype (for reference only):

getMaxCandidates() -> retval
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getPolygonThreshold(named_args)

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

getPolygonThreshold

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Return
  • retval: float

Python prototype (for reference only):

getPolygonThreshold() -> retval
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getUnclipRatio(named_args)

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

getUnclipRatio

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Return
  • retval: double

Python prototype (for reference only):

getUnclipRatio() -> retval
@spec predict(Keyword.t()) :: any() | {:error, String.t()}
@spec predict(t(), Evision.Mat.maybe_mat_in()) ::
  [Evision.Mat.t()] | {:error, String.t()}

Given the @p input frame, create input blob, run net and return the output @p blobs.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • frame: Evision.Mat
Return
  • outs: [Evision.Mat].

    Allocated output blobs, which will store results of the computation.

Python prototype (for reference only):

predict(frame[, outs]) -> outs
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predict(self, frame, opts)

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

Given the @p input frame, create input blob, run net and return the output @p blobs.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • frame: Evision.Mat
Return
  • outs: [Evision.Mat].

    Allocated output blobs, which will store results of the computation.

Python prototype (for reference only):

predict(frame[, outs]) -> outs
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setBinaryThreshold(named_args)

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@spec setBinaryThreshold(Keyword.t()) :: any() | {:error, String.t()}
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setBinaryThreshold(self, binaryThreshold)

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

setBinaryThreshold

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • binaryThreshold: float
Return
  • retval: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

setBinaryThreshold(binaryThreshold) -> retval
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setInputCrop(named_args)

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@spec setInputCrop(Keyword.t()) :: any() | {:error, String.t()}
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setInputCrop(self, crop)

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@spec setInputCrop(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}

Set flag crop for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • crop: bool.

    Flag which indicates whether image will be cropped after resize or not.

Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setInputCrop(crop) -> retval
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setInputMean(named_args)

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@spec setInputMean(Keyword.t()) :: any() | {:error, String.t()}
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setInputMean(self, mean)

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@spec setInputMean(t(), Evision.scalar()) ::
  Evision.DNN.Model.t() | {:error, String.t()}

Set mean value for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • mean: Evision.scalar().

    Scalar with mean values which are subtracted from channels.

Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setInputMean(mean) -> retval
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setInputParams(named_args)

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@spec setInputParams(Keyword.t()) :: any() | {:error, String.t()}
@spec setInputParams(t()) :: Evision.DNN.Model.t() | {:error, String.t()}

Set preprocessing parameters for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Keyword Arguments
  • scale: double.

    Multiplier for frame values.

  • size: Size.

    New input size.

  • mean: Evision.scalar().

    Scalar with mean values which are subtracted from channels.

  • swapRB: bool.

    Flag which indicates that swap first and last channels.

  • crop: bool.

    Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

Python prototype (for reference only):

setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]]) -> None
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setInputParams(self, opts)

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@spec setInputParams(
  t(),
  [crop: term(), mean: term(), scale: term(), size: term(), swapRB: term()]
  | nil
) :: Evision.DNN.Model.t() | {:error, String.t()}

Set preprocessing parameters for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
Keyword Arguments
  • scale: double.

    Multiplier for frame values.

  • size: Size.

    New input size.

  • mean: Evision.scalar().

    Scalar with mean values which are subtracted from channels.

  • swapRB: bool.

    Flag which indicates that swap first and last channels.

  • crop: bool.

    Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

Python prototype (for reference only):

setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]]) -> None
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setInputScale(named_args)

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@spec setInputScale(Keyword.t()) :: any() | {:error, String.t()}
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setInputScale(self, scale)

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@spec setInputScale(t(), Evision.scalar()) ::
  Evision.DNN.Model.t() | {:error, String.t()}

Set scalefactor value for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • scale: Evision.scalar().

    Multiplier for frame values.

Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setInputScale(scale) -> retval
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setInputSize(named_args)

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@spec setInputSize(Keyword.t()) :: any() | {:error, String.t()}
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setInputSize(self, size)

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@spec setInputSize(
  t(),
  {number(), number()}
) :: Evision.DNN.Model.t() | {:error, String.t()}

Set input size for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • size: Size.

    New input size.

Return
  • retval: Evision.DNN.Model.t()

Note: If shape of the new blob less than 0, then frame size not change.

Python prototype (for reference only):

setInputSize(size) -> retval
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setInputSize(self, width, height)

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@spec setInputSize(t(), integer(), integer()) ::
  Evision.DNN.Model.t() | {:error, String.t()}

setInputSize

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • width: integer().

    New input width.

  • height: integer().

    New input height.

Return
  • retval: Evision.DNN.Model.t()

Has overloading in C++

Python prototype (for reference only):

setInputSize(width, height) -> retval
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setInputSwapRB(named_args)

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@spec setInputSwapRB(Keyword.t()) :: any() | {:error, String.t()}
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setInputSwapRB(self, swapRB)

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@spec setInputSwapRB(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}

Set flag swapRB for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • swapRB: bool.

    Flag which indicates that swap first and last channels.

Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setInputSwapRB(swapRB) -> retval
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setMaxCandidates(named_args)

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@spec setMaxCandidates(Keyword.t()) :: any() | {:error, String.t()}
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setMaxCandidates(self, maxCandidates)

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

setMaxCandidates

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • maxCandidates: integer()
Return
  • retval: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

setMaxCandidates(maxCandidates) -> retval
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setOutputNames(named_args)

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@spec setOutputNames(Keyword.t()) :: any() | {:error, String.t()}
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setOutputNames(self, outNames)

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@spec setOutputNames(t(), [binary()]) :: Evision.DNN.Model.t() | {:error, String.t()}

Set output names for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()

  • outNames: [String].

    Names for output layers.

Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setOutputNames(outNames) -> retval
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setPolygonThreshold(named_args)

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@spec setPolygonThreshold(Keyword.t()) :: any() | {:error, String.t()}
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setPolygonThreshold(self, polygonThreshold)

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

setPolygonThreshold

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • polygonThreshold: float
Return
  • retval: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

setPolygonThreshold(polygonThreshold) -> retval
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setPreferableBackend(named_args)

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@spec setPreferableBackend(Keyword.t()) :: any() | {:error, String.t()}
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setPreferableBackend(self, backendId)

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@spec setPreferableBackend(t(), Evision.DNN.Backend.enum()) ::
  Evision.DNN.Model.t() | {:error, String.t()}

setPreferableBackend

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • backendId: dnn_Backend
Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableBackend(backendId) -> retval
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setPreferableTarget(named_args)

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@spec setPreferableTarget(Keyword.t()) :: any() | {:error, String.t()}
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setPreferableTarget(self, targetId)

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@spec setPreferableTarget(t(), Evision.DNN.Target.enum()) ::
  Evision.DNN.Model.t() | {:error, String.t()}

setPreferableTarget

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • targetId: dnn_Target
Return
  • retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval
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setUnclipRatio(named_args)

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@spec setUnclipRatio(Keyword.t()) :: any() | {:error, String.t()}
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setUnclipRatio(self, unclipRatio)

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

setUnclipRatio

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • unclipRatio: double
Return
  • retval: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

setUnclipRatio(unclipRatio) -> retval
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textDetectionModelDB(named_args)

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@spec textDetectionModelDB(Keyword.t()) :: any() | {:error, String.t()}
@spec textDetectionModelDB(binary()) :: t() | {:error, String.t()}
@spec textDetectionModelDB(Evision.DNN.Net.t()) :: t() | {:error, String.t()}

Variant 1:

Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Positional Arguments
  • model: string.

    Binary file contains trained weights.

Keyword Arguments
  • config: string.

    Text file contains network configuration.

Return
  • self: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>

Variant 2:

Create text detection algorithm from deep learning network.

Positional Arguments
  • network: Evision.DNN.Net.t().

    Net object.

Return
  • self: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

TextDetectionModel_DB(network) -> <dnn_TextDetectionModel_DB object>
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textDetectionModelDB(model, opts)

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@spec textDetectionModelDB(binary(), [{:config, term()}] | nil) ::
  t() | {:error, String.t()}

Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Positional Arguments
  • model: string.

    Binary file contains trained weights.

Keyword Arguments
  • config: string.

    Text file contains network configuration.

Return
  • self: Evision.DNN.TextDetectionModelDB.t()

Python prototype (for reference only):

TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>