View Source Evision.DNN.TextDetectionModelDB (Evision v0.1.21)

Link to this section Summary

Types

t()

Type that represents an Evision.DNN.TextDetectionModelDB struct.

Functions

detectTextRectangles

getBinaryThreshold

getMaxCandidates

getPolygonThreshold

getUnclipRatio

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.

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.

Link to this section Types

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

Type that represents an Evision.DNN.TextDetectionModelDB struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

@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(self, frame)

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

detectTextRectangles

Positional Arguments
  • self: Evision.DNN.TextDetectionModelDB.t()
  • frame: Evision.Mat
Return
  • detections: [{centre={x, y}, size={s1, s2}, angle}]

Has overloading in C++

Python prototype (for reference only):

detectTextRectangles(frame) -> detections
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getBinaryThreshold(self)

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

getMaxCandidates

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

Python prototype (for reference only):

getMaxCandidates() -> retval
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getPolygonThreshold(self)

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@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
@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(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(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

Python prototype (for reference only):

setBinaryThreshold(binaryThreshold) -> retval
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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

Python prototype (for reference only):

setInputCrop(crop) -> retval
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setInputMean(self, mean)

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

Set mean value for frame.

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

  • mean: Scalar.

    Scalar with mean values which are subtracted from channels.

Return

Python prototype (for reference only):

setInputMean(mean) -> retval
@spec setInputParams(t()) :: :ok | {: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: 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(), [{atom(), term()}, ...] | nil) :: :ok | {: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: 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(self, scale)

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

Set scalefactor value for frame.

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

  • scale: double.

    Multiplier for frame values.

Return

Python prototype (for reference only):

setInputScale(scale) -> retval
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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

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: int.

    New input width.

  • height: int.

    New input height.

Return

Has overloading in C++

Python prototype (for reference only):

setInputSize(width, height) -> retval
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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

Python prototype (for reference only):

setInputSwapRB(swapRB) -> retval
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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: int
Return

Python prototype (for reference only):

setMaxCandidates(maxCandidates) -> retval
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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

Python prototype (for reference only):

setPolygonThreshold(polygonThreshold) -> retval
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setPreferableBackend(self, backendId)

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

setPreferableBackend

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

Python prototype (for reference only):

setPreferableBackend(backendId) -> retval
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setPreferableTarget(self, targetId)

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

setPreferableTarget

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

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval
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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

Python prototype (for reference only):

setUnclipRatio(unclipRatio) -> retval
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textDetectionModelDB(model)

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@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

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
Return

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(), [{atom(), 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

Python prototype (for reference only):

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