View Source Evision.CUDA.HoughCirclesDetector (Evision v0.1.38)
Summary
Functions
Clears the algorithm state
Variant 1:
Finds circles in a grayscale image using the Hough transform.
Variant 1:
Finds circles in a grayscale image using the Hough transform.
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
getCannyThreshold
getDefaultName
getDp
getMaxCircles
getMaxRadius
getMinDist
getMinRadius
getVotesThreshold
Reads algorithm parameters from a file storage
save
setCannyThreshold
setDp
setMaxCircles
setMaxRadius
setMinDist
setMinRadius
setVotesThreshold
Stores algorithm parameters in a file storage
write
Types
@type t() :: %Evision.CUDA.HoughCirclesDetector{ref: reference()}
Type that represents an CUDA.HoughCirclesDetector
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
Clears the algorithm state
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Python prototype (for reference only):
clear() -> None
@spec detect(t(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec detect(t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Finds circles in a grayscale image using the Hough transform.
Positional Arguments
self:
Evision.CUDA.HoughCirclesDetector.t()
src:
Evision.Mat.t()
.8-bit, single-channel grayscale input image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t()
.Stream for the asynchronous version.
Return
circles:
Evision.Mat.t()
.Output vector of found circles. Each vector is encoded as a 3-element floating-point vector \f$(x, y, radius)\f$ .
@sa HoughCircles
Python prototype (for reference only):
detect(src[, circles[, stream]]) -> circles
Variant 2:
Finds circles in a grayscale image using the Hough transform.
Positional Arguments
self:
Evision.CUDA.HoughCirclesDetector.t()
src:
Evision.CUDA.GpuMat.t()
.8-bit, single-channel grayscale input image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t()
.Stream for the asynchronous version.
Return
circles:
Evision.CUDA.GpuMat.t()
.Output vector of found circles. Each vector is encoded as a 3-element floating-point vector \f$(x, y, radius)\f$ .
@sa HoughCircles
Python prototype (for reference only):
detect(src[, circles[, stream]]) -> circles
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec detect(t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Finds circles in a grayscale image using the Hough transform.
Positional Arguments
self:
Evision.CUDA.HoughCirclesDetector.t()
src:
Evision.Mat.t()
.8-bit, single-channel grayscale input image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t()
.Stream for the asynchronous version.
Return
circles:
Evision.Mat.t()
.Output vector of found circles. Each vector is encoded as a 3-element floating-point vector \f$(x, y, radius)\f$ .
@sa HoughCircles
Python prototype (for reference only):
detect(src[, circles[, stream]]) -> circles
Variant 2:
Finds circles in a grayscale image using the Hough transform.
Positional Arguments
self:
Evision.CUDA.HoughCirclesDetector.t()
src:
Evision.CUDA.GpuMat.t()
.8-bit, single-channel grayscale input image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t()
.Stream for the asynchronous version.
Return
circles:
Evision.CUDA.GpuMat.t()
.Output vector of found circles. Each vector is encoded as a 3-element floating-point vector \f$(x, y, radius)\f$ .
@sa HoughCircles
Python prototype (for reference only):
detect(src[, circles[, stream]]) -> circles
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
getCannyThreshold
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
int
Python prototype (for reference only):
getCannyThreshold() -> retval
getDefaultName
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
String
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Python prototype (for reference only):
getDefaultName() -> retval
getDp
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
float
Python prototype (for reference only):
getDp() -> retval
getMaxCircles
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
int
Python prototype (for reference only):
getMaxCircles() -> retval
getMaxRadius
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
int
Python prototype (for reference only):
getMaxRadius() -> retval
getMinDist
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
float
Python prototype (for reference only):
getMinDist() -> retval
getMinRadius
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
int
Python prototype (for reference only):
getMinRadius() -> retval
getVotesThreshold
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
Return
- retval:
int
Python prototype (for reference only):
getVotesThreshold() -> retval
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}
Reads algorithm parameters from a file storage
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- fn_:
Evision.FileNode.t()
Python prototype (for reference only):
read(fn_) -> None
save
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Python prototype (for reference only):
save(filename) -> None
setCannyThreshold
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- cannyThreshold:
int
Python prototype (for reference only):
setCannyThreshold(cannyThreshold) -> None
setDp
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- dp:
float
Python prototype (for reference only):
setDp(dp) -> None
setMaxCircles
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- maxCircles:
int
Python prototype (for reference only):
setMaxCircles(maxCircles) -> None
setMaxRadius
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- maxRadius:
int
Python prototype (for reference only):
setMaxRadius(maxRadius) -> None
setMinDist
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- minDist:
float
Python prototype (for reference only):
setMinDist(minDist) -> None
setMinRadius
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- minRadius:
int
Python prototype (for reference only):
setMinRadius(minRadius) -> None
setVotesThreshold
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- votesThreshold:
int
Python prototype (for reference only):
setVotesThreshold(votesThreshold) -> None
@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}
Stores algorithm parameters in a file storage
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- fs:
Evision.FileStorage.t()
Python prototype (for reference only):
write(fs) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}
write
Positional Arguments
- self:
Evision.CUDA.HoughCirclesDetector.t()
- fs:
Evision.FileStorage.t()
- name:
String
Has overloading in C++
Python prototype (for reference only):
write(fs, name) -> None