View Source Evision.FaceRecognizerSF (Evision v0.1.38)
Summary
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
Types
@type t() :: %Evision.FaceRecognizerSF{ref: reference()}
Type that represents an FaceRecognizerSF struct.
ref.
reference()The underlying erlang resource variable.
Functions
@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.t().input image
face_box:
Evision.Mat.t().the detection result used for indicate face in input image
Return
aligned_img:
Evision.Mat.t().output aligned image
Python prototype (for reference only):
alignCrop(src_img, face_box[, aligned_img]) -> aligned_img@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.t().input image
face_box:
Evision.Mat.t().the detection result used for indicate face in input image
Return
aligned_img:
Evision.Mat.t().output aligned image
Python prototype (for reference only):
alignCrop(src_img, face_box[, aligned_img]) -> aligned_imgCreates 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
- retval:
Evision.FaceRecognizerSF.t()
Python prototype (for reference only):
create(model, config[, backend_id[, target_id]]) -> retvalCreates 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
- retval:
Evision.FaceRecognizerSF.t()
Python prototype (for reference only):
create(model, config[, backend_id[, target_id]]) -> retval@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.t().input aligned image
Return
face_feature:
Evision.Mat.t().output face feature
Python prototype (for reference only):
feature(aligned_img[, face_feature]) -> face_feature@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.t().input aligned image
Return
face_feature:
Evision.Mat.t().output face feature
Python prototype (for reference only):
feature(aligned_img[, face_feature]) -> face_feature@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.t().the first input feature
face_feature2:
Evision.Mat.t().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@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.t().the first input feature
face_feature2:
Evision.Mat.t().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