View Source Evision.VariationalRefinement (Evision v0.1.34)
Summary
Functions
@ref calc function overload to handle separate horizontal (u) and vertical (v) flow components (to avoid extra splits/merges)
Creates an instance of VariationalRefinement
Weight of the smoothness term
Weight of the color constancy term
Number of outer (fixed-point) iterations in the minimization procedure.
Weight of the gradient constancy term
Relaxation factor in SOR
Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system.
setAlpha
setDelta
setFixedPointIterations
setGamma
setOmega
setSorIterations
Types
@type t() :: %Evision.VariationalRefinement{ref: reference()}
Type that represents an VariationalRefinement
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec calcUV( t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in() ) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@ref calc function overload to handle separate horizontal (u) and vertical (v) flow components (to avoid extra splits/merges)
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- i0:
Evision.Mat.t()
- i1:
Evision.Mat.t()
Return
- flow_u:
Evision.Mat.t()
- flow_v:
Evision.Mat.t()
Python prototype (for reference only):
calcUV(I0, I1, flow_u, flow_v) -> flow_u, flow_v
Creates an instance of VariationalRefinement
Return
- retval:
Evision.VariationalRefinement.t()
Python prototype (for reference only):
create() -> retval
Weight of the smoothness term
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
float
@see setAlpha/2
Python prototype (for reference only):
getAlpha() -> retval
Weight of the color constancy term
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
float
@see setDelta/2
Python prototype (for reference only):
getDelta() -> retval
Number of outer (fixed-point) iterations in the minimization procedure.
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
int
@see setFixedPointIterations/2
Python prototype (for reference only):
getFixedPointIterations() -> retval
Weight of the gradient constancy term
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
float
@see setGamma/2
Python prototype (for reference only):
getGamma() -> retval
Relaxation factor in SOR
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
float
@see setOmega/2
Python prototype (for reference only):
getOmega() -> retval
Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system.
Positional Arguments
- self:
Evision.VariationalRefinement.t()
Return
- retval:
int
@see setSorIterations/2
Python prototype (for reference only):
getSorIterations() -> retval
setAlpha
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
float
@see getAlpha/1
Python prototype (for reference only):
setAlpha(val) -> None
setDelta
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
float
@see getDelta/1
Python prototype (for reference only):
setDelta(val) -> None
setFixedPointIterations
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
int
@see getFixedPointIterations/1
Python prototype (for reference only):
setFixedPointIterations(val) -> None
setGamma
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
float
@see getGamma/1
Python prototype (for reference only):
setGamma(val) -> None
setOmega
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
float
@see getOmega/1
Python prototype (for reference only):
setOmega(val) -> None
setSorIterations
Positional Arguments
- self:
Evision.VariationalRefinement.t()
- val:
int
@see getSorIterations/1
Python prototype (for reference only):
setSorIterations(val) -> None