View Source Evision.DNN.DetectionModel (Evision v0.1.10)
Link to this section Summary
Types
Type that represents an Evision.DNN.DetectionModel
struct.
Functions
Given the @p input frame, create input blob, run net and return result detections.
Given the @p input frame, create input blob, run net and return result detections.
Variant 1:
Create model from deep learning network.
Create detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
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
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.
Link to this section Types
@type t() :: %Evision.DNN.DetectionModel{ref: reference()}
Type that represents an Evision.DNN.DetectionModel
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
@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
- frame:
Evision.Mat
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):
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
- frame:
Evision.Mat
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):
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
.Net object.
Return
Python prototype (for reference):
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
Python prototype (for reference):
DetectionModel(model[, config]) -> <dnn_DetectionModel object>
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
Python prototype (for reference):
DetectionModel(model[, config]) -> <dnn_DetectionModel object>
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
Return
- retval:
bool
Python prototype (for reference):
getNmsAcrossClasses() -> retval
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
value:
bool
.The new value for nmsAcrossClasses
Return
- retval:
Evision.DNN.DetectionModel
Python prototype (for reference):
setNmsAcrossClasses(value) -> retval