View Source Evision.DNN.TextDetectionModel (Evision v0.1.34)

Summary

Types

t()

Type that represents an DNN.TextDetectionModel struct.

Functions

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.

Types

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

Type that represents an DNN.TextDetectionModel struct.

  • ref. reference()

    The underlying erlang resource variable.

Functions

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

detect

Positional Arguments
  • self: Evision.DNN.TextDetectionModel.t()
  • frame: Evision.Mat.t()
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()) ::
  {[Evision.RotatedRect.t()], [number()]} | {:error, String.t()}

Performs detection

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

  • frame: Evision.Mat.t().

    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
@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.TextDetectionModel.t()
  • frame: Evision.Mat.t()
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.TextDetectionModel.t()
  • frame: Evision.Mat.t()
Return
  • outs: [Evision.Mat].

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

Python prototype (for reference only):

predict(frame[, outs]) -> outs
Link to this function

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.TextDetectionModel.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(self, mean)

View Source
@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.TextDetectionModel.t()

  • mean: Scalar.

    Scalar with mean values which are subtracted from channels.

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

Python prototype (for reference only):

setInputMean(mean) -> retval
@spec setInputParams(t()) :: t() | {:error, String.t()}

Set preprocessing parameters for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModel.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)

View Source
@spec setInputParams(t(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Set preprocessing parameters for frame.

Positional Arguments
  • self: Evision.DNN.TextDetectionModel.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()}
  | {number(), number()}
  | {number(), number(), number()}
  | {number(), number(), number(), number()}
) :: Evision.DNN.Model.t() | {:error, String.t()}

Set scalefactor value for frame.

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

  • scale: Scalar.

    Multiplier for frame values.

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

Python prototype (for reference only):

setInputScale(scale) -> retval
Link to this function

setInputSize(self, size)

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

Set input size for frame.

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

  • width: int.

    New input width.

  • height: int.

    New input height.

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

Has overloading in C++

Python prototype (for reference only):

setInputSize(width, height) -> retval
Link to this function

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.TextDetectionModel.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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setPreferableBackend(self, backendId)

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

setPreferableBackend

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

Python prototype (for reference only):

setPreferableBackend(backendId) -> retval
Link to this function

setPreferableTarget(self, targetId)

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

setPreferableTarget

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

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval