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

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

Positional Arguments

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

Keyword Arguments

• confThreshold: float.

  A threshold used to filter boxes by confidences.

• nmsThreshold: float.

  A threshold used in non maximum suppression.

Return

• classIds: [int].

  Class indexes in result detection.

• confidences: [float].

  A set of corresponding confidences.

• boxes: [Rect].

  A set of bounding boxes.

Python prototype (for reference only):

detect(frame[, confThreshold[, nmsThreshold]]) -> classIds, confidences, boxes

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

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

Positional Arguments

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

Keyword Arguments

• confThreshold: float.

  A threshold used to filter boxes by confidences.

• nmsThreshold: float.

  A threshold used in non maximum suppression.

Return

• classIds: [int].

  Class indexes in result detection.

• confidences: [float].

  A set of corresponding confidences.

• boxes: [Rect].

  A set of bounding boxes.

Python prototype (for reference only):

detect(frame[, confThreshold[, nmsThreshold]]) -> classIds, confidences, boxes

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

@spec detectionModel(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.DetectionModel.t()

Python prototype (for reference only):

DetectionModel(network) -> <dnn_DetectionModel object>

Variant 2:

Create 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

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

Python prototype (for reference only):

DetectionModel(model[, config]) -> <dnn_DetectionModel object>

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

Create 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

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

Python prototype (for reference only):

DetectionModel(model[, config]) -> <dnn_DetectionModel object>

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

enableWinograd

Positional Arguments

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

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

enableWinograd(useWinograd) -> retval

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

Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class

Positional Arguments

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

Return

• retval: bool

Python prototype (for reference only):

getNmsAcrossClasses() -> 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.DetectionModel.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

@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.DetectionModel.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

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

Set flag crop for frame.

Positional Arguments

• self: Evision.DNN.DetectionModel.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(),
  {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.DetectionModel.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.DetectionModel.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

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

Set preprocessing parameters for frame.

Positional Arguments

• self: Evision.DNN.DetectionModel.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

@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.DetectionModel.t()

• scale: 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.DetectionModel.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.DetectionModel.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

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

Set flag swapRB for frame.

Positional Arguments

• self: Evision.DNN.DetectionModel.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 setNmsAcrossClasses(t(), boolean()) :: t() | {:error, String.t()}

nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.

Positional Arguments

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

• value: bool.

  The new value for nmsAcrossClasses

Return

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

Python prototype (for reference only):

setNmsAcrossClasses(value) -> retval

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

setPreferableBackend

Positional Arguments

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

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableBackend(backendId) -> retval

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

setPreferableTarget

Positional Arguments

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

Return

• retval: Evision.DNN.Model.t()

Python prototype (for reference only):

setPreferableTarget(targetId) -> retval