View Source Evision.FaceRecognizerSF (Evision v0.1.21)

Link to this section Summary

Types

t()

Type that represents an Evision.FaceRecognizerSF struct.

Functions

Aligning image to put face on the standard position

Aligning image to put face on the standard position

Creates an instance of this class with given parameters

Creates an instance of this class with given parameters

Extracting face feature from aligned image

Extracting face feature from aligned image

Calculating the distance between two face features

Calculating the distance between two face features

Link to this section Types

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

Type that represents an Evision.FaceRecognizerSF struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Link to this function

alignCrop(self, src_img, face_box)

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

Aligning image to put face on the standard position

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • src_img: Evision.Mat.

    input image

  • face_box: Evision.Mat.

    the detection result used for indicate face in input image

Return

Python prototype (for reference only):

alignCrop(src_img, face_box[, aligned_img]) -> aligned_img
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alignCrop(self, src_img, face_box, opts)

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

Aligning image to put face on the standard position

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • src_img: Evision.Mat.

    input image

  • face_box: Evision.Mat.

    the detection result used for indicate face in input image

Return

Python prototype (for reference only):

alignCrop(src_img, face_box[, aligned_img]) -> aligned_img
@spec create(binary(), binary()) :: t() | {:error, String.t()}

Creates an instance of this class with given parameters

Positional Arguments
  • model: String.

    the path of the onnx model used for face recognition

  • config: String.

    the path to the config file for compability, which is not requested for ONNX models

Keyword Arguments
  • backend_id: int.

    the id of backend

  • target_id: int.

    the id of target device

Return

Python prototype (for reference only):

create(model, config[, backend_id[, target_id]]) -> retval
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create(model, config, opts)

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

Creates an instance of this class with given parameters

Positional Arguments
  • model: String.

    the path of the onnx model used for face recognition

  • config: String.

    the path to the config file for compability, which is not requested for ONNX models

Keyword Arguments
  • backend_id: int.

    the id of backend

  • target_id: int.

    the id of target device

Return

Python prototype (for reference only):

create(model, config[, backend_id[, target_id]]) -> retval
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feature(self, aligned_img)

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

Extracting face feature from aligned image

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • aligned_img: Evision.Mat.

    input aligned image

Return

Python prototype (for reference only):

feature(aligned_img[, face_feature]) -> face_feature
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feature(self, aligned_img, opts)

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

Extracting face feature from aligned image

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • aligned_img: Evision.Mat.

    input aligned image

Return

Python prototype (for reference only):

feature(aligned_img[, face_feature]) -> face_feature
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match(self, face_feature1, face_feature2)

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

Calculating the distance between two face features

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • face_feature1: Evision.Mat.

    the first input feature

  • face_feature2: Evision.Mat.

    the second input feature of the same size and the same type as face_feature1

Keyword Arguments
  • dis_type: int.

    defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2"

Return
  • retval: double

Python prototype (for reference only):

match(face_feature1, face_feature2[, dis_type]) -> retval
Link to this function

match(self, face_feature1, face_feature2, opts)

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

Calculating the distance between two face features

Positional Arguments
  • self: Evision.FaceRecognizerSF.t()

  • face_feature1: Evision.Mat.

    the first input feature

  • face_feature2: Evision.Mat.

    the second input feature of the same size and the same type as face_feature1

Keyword Arguments
  • dis_type: int.

    defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2"

Return
  • retval: double

Python prototype (for reference only):

match(face_feature1, face_feature2[, dis_type]) -> retval