View Source Evision.DNN.TextDetectionModelDB (Evision v0.1.15)

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Types

t()

Type that represents an Evision.DNN.TextDetectionModelDB struct.

Functions

Return
  • retval: float

Python prototype (for reference):

Return
  • retval: int

Python prototype (for reference):

Return
  • retval: float

Python prototype (for reference):

Return
  • retval: double

Python prototype (for reference):

Positional Arguments
  • binaryThreshold: float
Return

Python prototype (for reference):

Positional Arguments
  • maxCandidates: int
Return

Python prototype (for reference):

Positional Arguments
  • polygonThreshold: float
Return

Python prototype (for reference):

Positional Arguments
  • unclipRatio: double
Return

Python prototype (for reference):

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

Link to this function

getBinaryThreshold(self)

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@spec getBinaryThreshold(t()) :: number() | {:error, String.t()}
Return
  • retval: float

Python prototype (for reference):

getBinaryThreshold() -> retval
@spec getMaxCandidates(t()) :: integer() | {:error, String.t()}
Return
  • retval: int

Python prototype (for reference):

getMaxCandidates() -> retval
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getPolygonThreshold(self)

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@spec getPolygonThreshold(t()) :: number() | {:error, String.t()}
Return
  • retval: float

Python prototype (for reference):

getPolygonThreshold() -> retval
@spec getUnclipRatio(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

getUnclipRatio() -> retval
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setBinaryThreshold(self, binaryThreshold)

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@spec setBinaryThreshold(t(), number()) :: t() | {:error, String.t()}
Positional Arguments
  • binaryThreshold: float
Return

Python prototype (for reference):

setBinaryThreshold(binaryThreshold) -> retval
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setMaxCandidates(self, maxCandidates)

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@spec setMaxCandidates(t(), integer()) :: t() | {:error, String.t()}
Positional Arguments
  • maxCandidates: int
Return

Python prototype (for reference):

setMaxCandidates(maxCandidates) -> retval
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setPolygonThreshold(self, polygonThreshold)

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@spec setPolygonThreshold(t(), number()) :: t() | {:error, String.t()}
Positional Arguments
  • polygonThreshold: float
Return

Python prototype (for reference):

setPolygonThreshold(polygonThreshold) -> retval
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setUnclipRatio(self, unclipRatio)

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@spec setUnclipRatio(t(), number()) :: t() | {:error, String.t()}
Positional Arguments
  • unclipRatio: double
Return

Python prototype (for reference):

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):

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):

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):

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