View Source Evision.GFTTDetector (Evision v0.1.12)
Link to this section Summary
Types
Type that represents an Evision.GFTTDetector
struct.
Functions
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
Keyword Arguments
- maxCorners:
int
. - qualityLevel:
double
. - minDistance:
double
. - blockSize:
int
. - useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
Keyword Arguments
- maxCorners:
int
. - qualityLevel:
double
. - minDistance:
double
. - blockSize:
int
. - useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
Positional Arguments
- maxCorners:
int
- qualityLevel:
double
- minDistance:
double
- blockSize:
int
- gradiantSize:
int
Keyword Arguments
- useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
Positional Arguments
- maxCorners:
int
- qualityLevel:
double
- minDistance:
double
- blockSize:
int
- gradiantSize:
int
Keyword Arguments
- useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Return
- retval:
bool
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
bool
Python prototype (for reference):
Return
- retval:
double
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
double
Python prototype (for reference):
Return
- retval:
double
Python prototype (for reference):
Positional Arguments
- blockSize:
int
Python prototype (for reference):
Positional Arguments
- val:
bool
Python prototype (for reference):
Positional Arguments
- k:
double
Python prototype (for reference):
Positional Arguments
- maxFeatures:
int
Python prototype (for reference):
Positional Arguments
- minDistance:
double
Python prototype (for reference):
Positional Arguments
- qlevel:
double
Python prototype (for reference):
Link to this section Types
@type t() :: %Evision.GFTTDetector{ref: reference()}
Type that represents an Evision.GFTTDetector
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section 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:
Positional Arguments
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):
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
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
.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):
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:
Positional Arguments
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):
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
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
.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):
compute(image, keypoints[, descriptors]) -> keypoints, descriptors
Keyword Arguments
- maxCorners:
int
. - qualityLevel:
double
. - minDistance:
double
. - blockSize:
int
. - useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
create([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> retval
Keyword Arguments
- maxCorners:
int
. - qualityLevel:
double
. - minDistance:
double
. - blockSize:
int
. - useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
create([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> retval
create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize)
View SourcePositional Arguments
- maxCorners:
int
- qualityLevel:
double
- minDistance:
double
- blockSize:
int
- gradiantSize:
int
Keyword Arguments
- useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]) -> retval
create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize, opts)
View Source@spec create( integer(), number(), number(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: t() | {:error, String.t()}
Positional Arguments
- maxCorners:
int
- qualityLevel:
double
- minDistance:
double
- blockSize:
int
- gradiantSize:
int
Keyword Arguments
- useHarrisDetector:
bool
. - k:
double
.
Return
- retval:
Evision.GFTTDetector
Python prototype (for reference):
create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]) -> retval
Return
- retval:
int
Python prototype (for reference):
defaultNorm() -> retval
Return
- retval:
int
Python prototype (for reference):
descriptorSize() -> retval
Return
- retval:
int
Python prototype (for reference):
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:
Positional Arguments
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):
detect(images[, masks]) -> keypoints
Variant 2:
Detects keypoints in an image (first variant) or image set (second variant).
Positional Arguments
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):
detect(image[, mask]) -> keypoints
@spec detect(t(), [Evision.Mat.maybe_mat_in()], [{atom(), term()}, ...] | nil) :: [[Evision.KeyPoint.t()]] | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: [Evision.KeyPoint.t()] | {:error, String.t()}
Variant 1:
Positional Arguments
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):
detect(images[, masks]) -> keypoints
Variant 2:
Detects keypoints in an image (first variant) or image set (second variant).
Positional Arguments
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):
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()}
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Python prototype (for reference):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
@spec detectAndCompute( t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[Evision.KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Python prototype (for reference):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
Return
- retval:
bool
Python prototype (for reference):
empty() -> retval
Return
- retval:
int
Python prototype (for reference):
getBlockSize() -> retval
Return
- retval:
String
Python prototype (for reference):
getDefaultName() -> retval
Return
- retval:
bool
Python prototype (for reference):
getHarrisDetector() -> retval
Return
- retval:
double
Python prototype (for reference):
getK() -> retval
Return
- retval:
int
Python prototype (for reference):
getMaxFeatures() -> retval
Return
- retval:
double
Python prototype (for reference):
getMinDistance() -> retval
Return
- retval:
double
Python prototype (for reference):
getQualityLevel() -> retval
@spec read(t(), Evision.FileNode.t()) :: :ok | {:error, String.t()}
@spec read(t(), binary()) :: :ok | {:error, String.t()}
Variant 1:
Positional Arguments
- arg1:
Evision.FileNode
Python prototype (for reference):
read(arg1) -> None
Variant 2:
Positional Arguments
- fileName:
String
Python prototype (for reference):
read(fileName) -> None
Positional Arguments
- blockSize:
int
Python prototype (for reference):
setBlockSize(blockSize) -> None
Positional Arguments
- val:
bool
Python prototype (for reference):
setHarrisDetector(val) -> None
Positional Arguments
- k:
double
Python prototype (for reference):
setK(k) -> None
Positional Arguments
- maxFeatures:
int
Python prototype (for reference):
setMaxFeatures(maxFeatures) -> None
Positional Arguments
- minDistance:
double
Python prototype (for reference):
setMinDistance(minDistance) -> None
Positional Arguments
- qlevel:
double
Python prototype (for reference):
setQualityLevel(qlevel) -> None
@spec write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}
@spec write(t(), binary()) :: :ok | {:error, String.t()}
Variant 1:
Positional Arguments
Keyword Arguments
- name:
String
.
Python prototype (for reference):
write(fs[, name]) -> None
Variant 2:
Positional Arguments
- fileName:
String
Python prototype (for reference):
write(fileName) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) :: :ok | {:error, String.t()}
Positional Arguments
Keyword Arguments
- name:
String
.
Python prototype (for reference):
write(fs[, name]) -> None