View Source Evision.XFeatures2D.HarrisLaplaceFeatureDetector (Evision v0.2.9)
Summary
Functions
Variant 1:
compute
Variant 1:
compute
Creates a new implementation instance.
Creates a new implementation instance.
defaultNorm
descriptorSize
descriptorType
Variant 1:
detect
Variant 1:
detect
detectAndCompute
detectAndCompute
empty
getCornThresh
getDefaultName
getDOGThresh
getMaxCorners
getNumLayers
getNumOctaves
Variant 1:
read
setCornThresh
setDOGThresh
setMaxCorners
setNumLayers
setNumOctaves
write
write
Types
@type t() :: %Evision.XFeatures2D.HarrisLaplaceFeatureDetector{ref: reference()}
Type that represents an XFeatures2D.HarrisLaplaceFeatureDetector
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec compute(t(), [Evision.Mat.maybe_mat_in()], [[Evision.KeyPoint.t()]]) :: {[[Evision.KeyPoint.t()]], [Evision.Mat.t()]} | {:error, String.t()}
@spec compute(t(), Evision.Mat.maybe_mat_in(), [Evision.KeyPoint.t()]) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}
Variant 1:
compute
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
images:
[Evision.Mat]
.Image set.
Return
keypoints:
[[Evision.KeyPoint]]
.Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors:
[Evision.Mat]
.Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
Has overloading in C++
Python prototype (for reference only):
compute(images, keypoints[, descriptors]) -> keypoints, descriptors
Variant 2:
Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
image:
Evision.Mat
.Image.
Return
keypoints:
[Evision.KeyPoint]
.Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors:
Evision.Mat.t()
.Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
Python prototype (for reference only):
compute(image, keypoints[, descriptors]) -> keypoints, descriptors
@spec compute( t(), [Evision.Mat.maybe_mat_in()], [[Evision.KeyPoint.t()]], [{atom(), term()}, ...] | nil ) :: {[[Evision.KeyPoint.t()]], [Evision.Mat.t()]} | {:error, String.t()}
@spec compute( t(), Evision.Mat.maybe_mat_in(), [Evision.KeyPoint.t()], [{atom(), term()}, ...] | nil ) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}
Variant 1:
compute
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
images:
[Evision.Mat]
.Image set.
Return
keypoints:
[[Evision.KeyPoint]]
.Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors:
[Evision.Mat]
.Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
Has overloading in C++
Python prototype (for reference only):
compute(images, keypoints[, descriptors]) -> keypoints, descriptors
Variant 2:
Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
image:
Evision.Mat
.Image.
Return
keypoints:
[Evision.KeyPoint]
.Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors:
Evision.Mat.t()
.Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
Python prototype (for reference only):
compute(image, keypoints[, descriptors]) -> keypoints, descriptors
Creates a new implementation instance.
Keyword Arguments
numOctaves:
integer()
.the number of octaves in the scale-space pyramid
corn_thresh:
float
.the threshold for the Harris cornerness measure
dOG_thresh:
float
.the threshold for the Difference-of-Gaussians scale selection
maxCorners:
integer()
.the maximum number of corners to consider
num_layers:
integer()
.the number of intermediate scales per octave
Return
- retval:
HarrisLaplaceFeatureDetector
Python prototype (for reference only):
create([, numOctaves[, corn_thresh[, DOG_thresh[, maxCorners[, num_layers]]]]]) -> retval
@spec create(Keyword.t()) :: any() | {:error, String.t()}
@spec create( [ corn_thresh: term(), dOG_thresh: term(), maxCorners: term(), numOctaves: term(), num_layers: term() ] | nil ) :: t() | {:error, String.t()}
Creates a new implementation instance.
Keyword Arguments
numOctaves:
integer()
.the number of octaves in the scale-space pyramid
corn_thresh:
float
.the threshold for the Harris cornerness measure
dOG_thresh:
float
.the threshold for the Difference-of-Gaussians scale selection
maxCorners:
integer()
.the maximum number of corners to consider
num_layers:
integer()
.the number of intermediate scales per octave
Return
- retval:
HarrisLaplaceFeatureDetector
Python prototype (for reference only):
create([, numOctaves[, corn_thresh[, DOG_thresh[, maxCorners[, num_layers]]]]]) -> retval
@spec defaultNorm(Keyword.t()) :: any() | {:error, String.t()}
@spec defaultNorm(t()) :: integer() | {:error, String.t()}
defaultNorm
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
defaultNorm() -> retval
@spec descriptorSize(Keyword.t()) :: any() | {:error, String.t()}
@spec descriptorSize(t()) :: integer() | {:error, String.t()}
descriptorSize
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
descriptorSize() -> retval
@spec descriptorType(Keyword.t()) :: any() | {:error, String.t()}
@spec descriptorType(t()) :: integer() | {:error, String.t()}
descriptorType
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
descriptorType() -> retval
@spec detect(t(), [Evision.Mat.maybe_mat_in()]) :: [[Evision.KeyPoint.t()]] | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in()) :: [Evision.KeyPoint.t()] | {:error, String.t()}
Variant 1:
detect
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
images:
[Evision.Mat]
.Image set.
Keyword Arguments
masks:
[Evision.Mat]
.Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].
Return
keypoints:
[[Evision.KeyPoint]]
.The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
Has overloading in C++
Python prototype (for reference only):
detect(images[, masks]) -> keypoints
Variant 2:
Detects keypoints in an image (first variant) or image set (second variant).
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
image:
Evision.Mat
.Image.
Keyword Arguments
mask:
Evision.Mat
.Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.
Return
keypoints:
[Evision.KeyPoint]
.The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
Python prototype (for reference only):
detect(image[, mask]) -> keypoints
@spec detect(t(), [Evision.Mat.maybe_mat_in()], [{:masks, term()}] | nil) :: [[Evision.KeyPoint.t()]] | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{:mask, term()}] | nil) :: [Evision.KeyPoint.t()] | {:error, String.t()}
Variant 1:
detect
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
images:
[Evision.Mat]
.Image set.
Keyword Arguments
masks:
[Evision.Mat]
.Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].
Return
keypoints:
[[Evision.KeyPoint]]
.The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
Has overloading in C++
Python prototype (for reference only):
detect(images[, masks]) -> keypoints
Variant 2:
Detects keypoints in an image (first variant) or image set (second variant).
Positional Arguments
self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
image:
Evision.Mat
.Image.
Keyword Arguments
mask:
Evision.Mat
.Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.
Return
keypoints:
[Evision.KeyPoint]
.The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
Python prototype (for reference only):
detect(image[, mask]) -> keypoints
@spec detectAndCompute(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}
detectAndCompute
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat.t()
.
Detects keypoints and computes the descriptors
Python prototype (for reference only):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
@spec detectAndCompute( t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{:useProvidedKeypoints, term()}] | nil ) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}
detectAndCompute
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat.t()
.
Detects keypoints and computes the descriptors
Python prototype (for reference only):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
@spec empty(Keyword.t()) :: any() | {:error, String.t()}
@spec empty(t()) :: boolean() | {:error, String.t()}
empty
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
@spec getCornThresh(Keyword.t()) :: any() | {:error, String.t()}
@spec getCornThresh(t()) :: number() | {:error, String.t()}
getCornThresh
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
float
Python prototype (for reference only):
getCornThresh() -> retval
@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}
@spec getDefaultName(t()) :: binary() | {:error, String.t()}
getDefaultName
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
String
Python prototype (for reference only):
getDefaultName() -> retval
@spec getDOGThresh(Keyword.t()) :: any() | {:error, String.t()}
@spec getDOGThresh(t()) :: number() | {:error, String.t()}
getDOGThresh
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
float
Python prototype (for reference only):
getDOGThresh() -> retval
@spec getMaxCorners(Keyword.t()) :: any() | {:error, String.t()}
@spec getMaxCorners(t()) :: integer() | {:error, String.t()}
getMaxCorners
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
getMaxCorners() -> retval
@spec getNumLayers(Keyword.t()) :: any() | {:error, String.t()}
@spec getNumLayers(t()) :: integer() | {:error, String.t()}
getNumLayers
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
getNumLayers() -> retval
@spec getNumOctaves(Keyword.t()) :: any() | {:error, String.t()}
@spec getNumOctaves(t()) :: integer() | {:error, String.t()}
getNumOctaves
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
- retval:
integer()
Python prototype (for reference only):
getNumOctaves() -> retval
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}
@spec read(t(), binary()) :: t() | {:error, String.t()}
Variant 1:
read
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- arg1:
Evision.FileNode
Python prototype (for reference only):
read(arg1) -> None
Variant 2:
read
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- fileName:
String
Python prototype (for reference only):
read(fileName) -> None
setCornThresh
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- cornthresh:
float
Python prototype (for reference only):
setCornThresh(corn_thresh_) -> None
setDOGThresh
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- dOGthresh:
float
Python prototype (for reference only):
setDOGThresh(DOG_thresh_) -> None
setMaxCorners
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- maxCorners_:
integer()
Python prototype (for reference only):
setMaxCorners(maxCorners_) -> None
setNumLayers
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- numlayers:
integer()
Python prototype (for reference only):
setNumLayers(num_layers_) -> None
setNumOctaves
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- numOctaves_:
integer()
Python prototype (for reference only):
setNumOctaves(numOctaves_) -> None
write
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- fileName:
String
Python prototype (for reference only):
write(fileName) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}
write
Positional Arguments
- self:
Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
- fs:
Evision.FileStorage
- name:
String
Python prototype (for reference only):
write(fs, name) -> None