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

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Types

t()

Type that represents an Evision.DNN.TextRecognitionModel struct.

Functions

Get the decoding method

Get the vocabulary for recognition.

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.

Given the @p input frame, create input blob, run net and return recognition result

Given the @p input frame, create input blob, run net and return recognition result

Set the decoding method options for "CTC-prefix-beam-search" decode usage

Set the decoding method options for "CTC-prefix-beam-search" decode usage

Set the decoding method of translating the network output into string

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 the vocabulary for recognition.

Variant 1:

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Link to this section Types

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

Type that represents an Evision.DNN.TextRecognitionModel struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

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

Get the decoding method

Positional Arguments
  • self: Evision.DNN.TextRecognitionModel.t()
Return

@return the decoding method

Python prototype (for reference only):

getDecodeType() -> retval
@spec getVocabulary(t()) :: [binary()] | {:error, String.t()}

Get the vocabulary for recognition.

Positional Arguments
  • self: Evision.DNN.TextRecognitionModel.t()
Return
  • retval: std::vector<std::string>

@return vocabulary the associated vocabulary

Python prototype (for reference only):

getVocabulary() -> 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.TextRecognitionModel.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.TextRecognitionModel.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
@spec recognize(t(), Evision.Mat.maybe_mat_in()) :: binary() | {:error, String.t()}

Given the @p input frame, create input blob, run net and return recognition result

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

  • frame: Evision.Mat.

    The input image

Return

@return The text recognition result

Python prototype (for reference only):

recognize(frame) -> retval
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recognize(self, frame, roiRects)

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

Given the @p input frame, create input blob, run net and return recognition result

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

  • frame: Evision.Mat.

    The input image

  • roiRects: [Evision.Mat].

    List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs

Return
  • results: [string].

    A set of text recognition results.

Python prototype (for reference only):

recognize(frame, roiRects) -> results
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setDecodeOptsCTCPrefixBeamSearch(self, beamSize)

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

Set the decoding method options for "CTC-prefix-beam-search" decode usage

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

  • beamSize: int.

    Beam size for search

Keyword Arguments
  • vocPruneSize: int.

    Parameter to optimize big vocabulary search, only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune.

Return

Python prototype (for reference only):

setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
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setDecodeOptsCTCPrefixBeamSearch(self, beamSize, opts)

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@spec setDecodeOptsCTCPrefixBeamSearch(t(), integer(), [{atom(), term()}, ...] | nil) ::
  t() | {:error, String.t()}

Set the decoding method options for "CTC-prefix-beam-search" decode usage

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

  • beamSize: int.

    Beam size for search

Keyword Arguments
  • vocPruneSize: int.

    Parameter to optimize big vocabulary search, only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune.

Return

Python prototype (for reference only):

setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
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setDecodeType(self, decodeType)

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

Set the decoding method of translating the network output into string

Positional Arguments
  • self: Evision.DNN.TextRecognitionModel.t()
  • decodeType: string.The decoding method of translating the network output into string, currently supported type:
    • "CTC-greedy" greedy decoding for the output of CTC-based methods
    • "CTC-prefix-beam-search" Prefix beam search decoding for the output of CTC-based methods
Return

Python prototype (for reference only):

setDecodeType(decodeType) -> 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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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.TextRecognitionModel.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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setPreferableBackend(self, backendId)

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

setPreferableBackend

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

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval
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setVocabulary(self, vocabulary)

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@spec setVocabulary(t(), [binary()]) :: t() | {:error, String.t()}

Set the vocabulary for recognition.

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

  • vocabulary: [string].

    the associated vocabulary of the network.

Return

Python prototype (for reference only):

setVocabulary(vocabulary) -> retval
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textRecognitionModel(model)

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

Variant 1:

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Positional Arguments
  • model: string.

    Binary file contains trained weights

Keyword Arguments
  • config: string.

    Text file contains network configuration

Return

Python prototype (for reference only):

TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>

Variant 2:

Create Text Recognition model from deep learning network Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Positional Arguments
Return

Python prototype (for reference only):

TextRecognitionModel(network) -> <dnn_TextRecognitionModel object>
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textRecognitionModel(model, opts)

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

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Positional Arguments
  • model: string.

    Binary file contains trained weights

Keyword Arguments
  • config: string.

    Text file contains network configuration

Return

Python prototype (for reference only):

TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>