View Source Evision.DNN.KeypointsModel (Evision v0.1.12)

Link to this section Summary

Types

t()

Type that represents an Evision.DNN.KeypointsModel struct.

Functions

Given the @p input frame, create input blob, run net

Given the @p input frame, create input blob, run net

Variant 1:

Create model from deep learning network.

Create keypoints 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.KeypointsModel{ref: reference()}

Type that represents an Evision.DNN.KeypointsModel struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

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

Given the @p input frame, create input blob, run net

Positional Arguments
Keyword Arguments
  • thresh: float.

    minimum confidence threshold to select a keypoint

Return
  • retval: std::vector<Point2f>

@returns a vector holding the x and y coordinates of each detected keypoint

Python prototype (for reference):

estimate(frame[, thresh]) -> retval
Link to this function

estimate(self, frame, opts)

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

Given the @p input frame, create input blob, run net

Positional Arguments
Keyword Arguments
  • thresh: float.

    minimum confidence threshold to select a keypoint

Return
  • retval: std::vector<Point2f>

@returns a vector holding the x and y coordinates of each detected keypoint

Python prototype (for reference):

estimate(frame[, thresh]) -> retval
@spec keypointsModel(Evision.DNN.Net.t()) :: t() | {:error, String.t()}
@spec keypointsModel(binary()) :: t() | {:error, String.t()}

Variant 1:

Create model from deep learning network.

Positional Arguments
Return

Python prototype (for reference):

KeypointsModel(network) -> <dnn_KeypointsModel object>

Variant 2:

Create keypoints 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):

KeypointsModel(model[, config]) -> <dnn_KeypointsModel object>
Link to this function

keypointsModel(model, opts)

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

Create keypoints 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):

KeypointsModel(model[, config]) -> <dnn_KeypointsModel object>