View Source Evision.FaceDetectorYN (Evision v0.1.10)
Link to this section Summary
Types
Type that represents an Evision.FaceDetectorYN
struct.
Functions
Creates an instance of this class with given parameters
Creates an instance of this class with given parameters
A simple interface to detect face from given image
A simple interface to detect face from given image
Return
- retval:
Size
Python prototype (for reference):
Return
- retval:
float
Python prototype (for reference):
Return
- retval:
float
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model
Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value
Set the score threshold to filter out bounding boxes of score less than the given value
Set the number of bounding boxes preserved before NMS
Link to this section Types
@type t() :: %Evision.FaceDetectorYN{ref: reference()}
Type that represents an Evision.FaceDetectorYN
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
Creates an instance of this class with given parameters
Positional Arguments
model:
String
.the path to the requested model
config:
String
.the path to the config file for compability, which is not requested for ONNX models
input_size:
Size
.the size of the input image
Keyword Arguments
score_threshold:
float
.the threshold to filter out bounding boxes of score smaller than the given value
nms_threshold:
float
.the threshold to suppress bounding boxes of IoU bigger than the given value
top_k:
int
.keep top K bboxes before NMS
backend_id:
int
.the id of backend
target_id:
int
.the id of target device
Return
- retval:
Evision.FaceDetectorYN
Python prototype (for reference):
create(model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]) -> retval
@spec create(binary(), binary(), {number(), number()}, [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
Creates an instance of this class with given parameters
Positional Arguments
model:
String
.the path to the requested model
config:
String
.the path to the config file for compability, which is not requested for ONNX models
input_size:
Size
.the size of the input image
Keyword Arguments
score_threshold:
float
.the threshold to filter out bounding boxes of score smaller than the given value
nms_threshold:
float
.the threshold to suppress bounding boxes of IoU bigger than the given value
top_k:
int
.keep top K bboxes before NMS
backend_id:
int
.the id of backend
target_id:
int
.the id of target device
Return
- retval:
Evision.FaceDetectorYN
Python prototype (for reference):
create(model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]) -> retval
@spec detect(t(), Evision.Mat.maybe_mat_in()) :: {integer(), Evision.Mat.t()} | {:error, String.t()}
A simple interface to detect face from given image
Positional Arguments
image:
Evision.Mat
.an image to detect
Return
retval:
int
faces:
Evision.Mat
.detection results stored in a cv::Mat
Python prototype (for reference):
detect(image[, faces]) -> retval, faces
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {integer(), Evision.Mat.t()} | {:error, String.t()}
A simple interface to detect face from given image
Positional Arguments
image:
Evision.Mat
.an image to detect
Return
retval:
int
faces:
Evision.Mat
.detection results stored in a cv::Mat
Python prototype (for reference):
detect(image[, faces]) -> retval, faces
Return
- retval:
Size
Python prototype (for reference):
getInputSize() -> retval
Return
- retval:
float
Python prototype (for reference):
getNMSThreshold() -> retval
Return
- retval:
float
Python prototype (for reference):
getScoreThreshold() -> retval
Return
- retval:
int
Python prototype (for reference):
getTopK() -> retval
Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model
Positional Arguments
input_size:
Size
.the size of the input image
Python prototype (for reference):
setInputSize(input_size) -> None
Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value
Positional Arguments
nms_threshold:
float
.threshold for NMS operation
Python prototype (for reference):
setNMSThreshold(nms_threshold) -> None
Set the score threshold to filter out bounding boxes of score less than the given value
Positional Arguments
score_threshold:
float
.threshold for filtering out bounding boxes
Python prototype (for reference):
setScoreThreshold(score_threshold) -> None
Set the number of bounding boxes preserved before NMS
Positional Arguments
top_k:
int
.the number of bounding boxes to preserve from top rank based on score
Python prototype (for reference):
setTopK(top_k) -> None