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

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

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

Variant 1:

Create model from deep learning network.

Positional Arguments

• network: Evision.DNN.Net.t().

  Net object.

Return

• self: Evision.DNN.ClassificationModel.t()

Python prototype (for reference only):

ClassificationModel(network) -> <dnn_ClassificationModel object>

Variant 2:

Create classification 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.ClassificationModel.t()

Python prototype (for reference only):

ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>

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

Create classification 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.ClassificationModel.t()

Python prototype (for reference only):

ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>

@spec classify(t(), Evision.Mat.maybe_mat_in()) ::
  {integer(), number()} | {:error, String.t()}

classify

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()
• frame: Evision.Mat

Return

• classId: integer()
• conf: float

Has overloading in C++

Python prototype (for reference only):

classify(frame) -> classId, conf

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

enableWinograd

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()
• useWinograd: bool

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

enableWinograd(useWinograd) -> retval

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

@spec getEnableSoftmaxPostProcessing(t()) :: boolean() | {:error, String.t()}

Get enable/disable softmax post processing option.

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()

Return

• retval: bool

This option defaults to false, softmax post processing is not applied within the classify() function.

Python prototype (for reference only):

getEnableSoftmaxPostProcessing() -> 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.ClassificationModel.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 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.ClassificationModel.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 setEnableSoftmaxPostProcessing(t(), boolean()) :: t() | {:error, String.t()}

Set enable/disable softmax post processing option.

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()

• enable: bool.

  Set enable softmax post processing within the classify() function.

Return

• retval: Evision.DNN.ClassificationModel.t()

If this option is true, softmax is applied after forward inference within the classify() function to convert the confidences range to [0.0-1.0]. This function allows you to toggle this behavior. Please turn true when not contain softmax layer in model.

Python prototype (for reference only):

setEnableSoftmaxPostProcessing(enable) -> retval

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

Set flag crop for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.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

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

Set mean value for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.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

@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.ClassificationModel.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

@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.ClassificationModel.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

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

Set scalefactor value for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()

• scale: Evision.scalar().

  Multiplier for frame values.

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setInputScale(scale) -> retval

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

Set input size for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.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

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

setInputSize

Positional Arguments

• self: Evision.DNN.ClassificationModel.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

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

Set flag swapRB for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.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

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

Set output names for frame.

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()

• outNames: [String].

  Names for output layers.

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setOutputNames(outNames) -> retval

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

setPreferableBackend

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()
• backendId: dnn_Backend

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableBackend(backendId) -> retval

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

setPreferableTarget

Positional Arguments

• self: Evision.DNN.ClassificationModel.t()
• targetId: dnn_Target

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval