View Source Evision.DNN.SegmentationModel (Evision v0.2.9)
Summary
Functions
enableWinograd
Given the @p input frame, create input blob, run net and return the output @p blobs.
Given the @p input frame, create input blob, run net and return the output @p blobs.
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 segmentation model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Set flag crop for frame.
Set mean value for frame.
Set preprocessing parameters for frame.
Set preprocessing parameters for frame.
Set scalefactor value for frame.
Set input size for frame.
setInputSize
Set flag swapRB for frame.
Set output names for frame.
setPreferableBackend
setPreferableTarget
Types
@type t() :: %Evision.DNN.SegmentationModel{ref: reference()}
Type that represents an DNN.SegmentationModel
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec enableWinograd(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}
enableWinograd
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- useWinograd:
bool
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
enableWinograd(useWinograd) -> retval
@spec predict(t(), Evision.Mat.maybe_mat_in()) :: [Evision.Mat.t()] | {:error, String.t()}
Given the @p input frame, create input blob, run net and return the output @p blobs.
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- frame:
Evision.Mat
Return
outs:
[Evision.Mat]
.Allocated output blobs, which will store results of the computation.
Python prototype (for reference only):
predict(frame[, outs]) -> outs
@spec predict(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: [Evision.Mat.t()] | {:error, String.t()}
Given the @p input frame, create input blob, run net and return the output @p blobs.
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- frame:
Evision.Mat
Return
outs:
[Evision.Mat]
.Allocated output blobs, which will store results of the computation.
Python prototype (for reference only):
predict(frame[, outs]) -> outs
@spec segment(t(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Given the @p input frame, create input blob, run net
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- frame:
Evision.Mat
Return
mask:
Evision.Mat.t()
.Allocated class prediction for each pixel
Python prototype (for reference only):
segment(frame[, mask]) -> mask
@spec segment(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Given the @p input frame, create input blob, run net
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- frame:
Evision.Mat
Return
mask:
Evision.Mat.t()
.Allocated class prediction for each pixel
Python prototype (for reference only):
segment(frame[, mask]) -> mask
@spec segmentationModel(Keyword.t()) :: any() | {:error, String.t()}
@spec segmentationModel(Evision.DNN.Net.t()) :: t() | {:error, String.t()}
@spec segmentationModel(binary()) :: t() | {:error, String.t()}
Variant 1:
Create model from deep learning network.
Positional Arguments
network:
Evision.DNN.Net.t()
.Net object.
Return
- self:
Evision.DNN.SegmentationModel.t()
Python prototype (for reference only):
SegmentationModel(network) -> <dnn_SegmentationModel object>
Variant 2:
Create segmentation 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
- self:
Evision.DNN.SegmentationModel.t()
Python prototype (for reference only):
SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>
Create segmentation 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
- self:
Evision.DNN.SegmentationModel.t()
Python prototype (for reference only):
SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>
@spec setInputCrop(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}
Set flag crop for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
crop:
bool
.Flag which indicates whether image will be cropped after resize or not.
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setInputCrop(crop) -> retval
@spec setInputMean(t(), Evision.scalar()) :: Evision.DNN.Model.t() | {:error, String.t()}
Set mean value for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
mean:
Evision.scalar()
.Scalar with mean values which are subtracted from channels.
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setInputMean(mean) -> retval
@spec setInputParams(Keyword.t()) :: any() | {:error, String.t()}
@spec setInputParams(t()) :: Evision.DNN.Model.t() | {:error, String.t()}
Set preprocessing parameters for frame.
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
Keyword Arguments
scale:
double
.Multiplier for frame values.
size:
Size
.New input size.
mean:
Evision.scalar()
.Scalar with mean values which are subtracted from channels.
swapRB:
bool
.Flag which indicates that swap first and last channels.
crop:
bool
.Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
Python prototype (for reference only):
setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]]) -> None
@spec setInputParams( t(), [crop: term(), mean: term(), scale: term(), size: term(), swapRB: term()] | nil ) :: Evision.DNN.Model.t() | {:error, String.t()}
Set preprocessing parameters for frame.
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
Keyword Arguments
scale:
double
.Multiplier for frame values.
size:
Size
.New input size.
mean:
Evision.scalar()
.Scalar with mean values which are subtracted from channels.
swapRB:
bool
.Flag which indicates that swap first and last channels.
crop:
bool
.Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
Python prototype (for reference only):
setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]]) -> None
@spec setInputScale(t(), Evision.scalar()) :: Evision.DNN.Model.t() | {:error, String.t()}
Set scalefactor value for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
scale:
Evision.scalar()
.Multiplier for frame values.
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setInputScale(scale) -> retval
@spec setInputSize( t(), {number(), number()} ) :: Evision.DNN.Model.t() | {:error, String.t()}
Set input size for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
size:
Size
.New input size.
Return
- retval:
Evision.DNN.Model.t()
Note: If shape of the new blob less than 0, then frame size not change.
Python prototype (for reference only):
setInputSize(size) -> retval
@spec setInputSize(t(), integer(), integer()) :: Evision.DNN.Model.t() | {:error, String.t()}
setInputSize
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
width:
integer()
.New input width.
height:
integer()
.New input height.
Return
- retval:
Evision.DNN.Model.t()
Has overloading in C++
Python prototype (for reference only):
setInputSize(width, height) -> retval
@spec setInputSwapRB(t(), boolean()) :: Evision.DNN.Model.t() | {:error, String.t()}
Set flag swapRB for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
swapRB:
bool
.Flag which indicates that swap first and last channels.
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setInputSwapRB(swapRB) -> retval
@spec setOutputNames(t(), [binary()]) :: Evision.DNN.Model.t() | {:error, String.t()}
Set output names for frame.
Positional Arguments
self:
Evision.DNN.SegmentationModel.t()
outNames:
[String]
.Names for output layers.
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setOutputNames(outNames) -> retval
@spec setPreferableBackend(t(), Evision.DNN.Backend.enum()) :: Evision.DNN.Model.t() | {:error, String.t()}
setPreferableBackend
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- backendId:
dnn_Backend
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setPreferableBackend(backendId) -> retval
@spec setPreferableTarget(t(), Evision.DNN.Target.enum()) :: Evision.DNN.Model.t() | {:error, String.t()}
setPreferableTarget
Positional Arguments
- self:
Evision.DNN.SegmentationModel.t()
- targetId:
dnn_Target
Return
- retval:
Evision.DNN.Model.t()
Python prototype (for reference only):
setPreferableTarget(targetId) -> retval