View Source Evision.ORB (Evision v0.1.7)
Link to this section Summary
cv
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.
The ORB constructor
The ORB constructor
Python prototype (for reference):
Python prototype (for reference):
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:
[KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Variant 1:
Positional Arguments
- arg1:
FileNode
Python prototype (for reference):
Positional Arguments
- edgeThreshold:
int
Python prototype (for reference):
Positional Arguments
- fastThreshold:
int
Python prototype (for reference):
Positional Arguments
- firstLevel:
int
Python prototype (for reference):
Positional Arguments
- maxFeatures:
int
Python prototype (for reference):
Positional Arguments
- nlevels:
int
Python prototype (for reference):
Positional Arguments
- patchSize:
int
Python prototype (for reference):
Positional Arguments
- scaleFactor:
double
Python prototype (for reference):
Positional Arguments
- scoreType:
ORB_ScoreType
Python prototype (for reference):
Positional Arguments
- wta_k:
int
Python prototype (for reference):
Variant 1:
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Python prototype (for reference):
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Python prototype (for reference):
Functions
Raising version of compute/3
.
Raising version of compute/4
.
Raising version of create/1
.
Raising version of defaultNorm/1
.
Raising version of descriptorSize/1
.
Raising version of descriptorType/1
.
Raising version of detect/2
.
Raising version of detect/3
.
Raising version of detectAndCompute/3
.
Raising version of detectAndCompute/4
.
Raising version of empty/1
.
Raising version of getDefaultName/1
.
Raising version of getEdgeThreshold/1
.
Raising version of getFastThreshold/1
.
Raising version of getFirstLevel/1
.
Raising version of getMaxFeatures/1
.
Raising version of getNLevels/1
.
Raising version of getPatchSize/1
.
Raising version of getScaleFactor/1
.
Raising version of getScoreType/1
.
Raising version of getWTA_K/1
.
Raising version of read/2
.
Raising version of setEdgeThreshold/2
.
Raising version of setFastThreshold/2
.
Raising version of setFirstLevel/2
.
Raising version of setMaxFeatures/2
.
Raising version of setNLevels/2
.
Raising version of setPatchSize/2
.
Raising version of setScaleFactor/2
.
Raising version of setScoreType/2
.
Raising version of setWTA_K/2
.
Raising version of write/2
.
Raising version of write/3
.
Link to this section cv
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.Image set.
Return
keypoints:
[vector_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:
[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
Variant 1:
Positional Arguments
images:
[Evision.Mat]
.Image set.
Return
keypoints:
[vector_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:
[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
The ORB constructor
Keyword Arguments
nfeatures:
int
.The maximum number of features to retain.
scaleFactor:
float
.Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels:
int
.The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
edgeThreshold:
int
.This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel:
int
.The level of pyramid to put source image to. Previous layers are filled with upscaled source image.
wTA_K:
int
.The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType:
ORB_ScoreType
.The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize:
int
.size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold:
int
.the fast threshold
Python prototype (for reference):
create([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) -> retval
The ORB constructor
Keyword Arguments
nfeatures:
int
.The maximum number of features to retain.
scaleFactor:
float
.Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels:
int
.The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
edgeThreshold:
int
.This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel:
int
.The level of pyramid to put source image to. Previous layers are filled with upscaled source image.
wTA_K:
int
.The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType:
ORB_ScoreType
.The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize:
int
.size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold:
int
.the fast threshold
Python prototype (for reference):
create([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) -> retval
Python prototype (for reference):
defaultNorm() -> retval
Python prototype (for reference):
descriptorSize() -> retval
Python prototype (for reference):
descriptorType() -> retval
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:
[vector_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:
[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
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:
[vector_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:
[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
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Python prototype (for reference):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
Positional Arguments
- image:
Evision.Mat
- mask:
Evision.Mat
Keyword Arguments
- useProvidedKeypoints:
bool
.
Return
- keypoints:
[KeyPoint]
- descriptors:
Evision.Mat
.
Detects keypoints and computes the descriptors
Python prototype (for reference):
detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
Python prototype (for reference):
empty() -> retval
Python prototype (for reference):
getDefaultName() -> retval
Python prototype (for reference):
getEdgeThreshold() -> retval
Python prototype (for reference):
getFastThreshold() -> retval
Python prototype (for reference):
getFirstLevel() -> retval
Python prototype (for reference):
getMaxFeatures() -> retval
Python prototype (for reference):
getNLevels() -> retval
Python prototype (for reference):
getPatchSize() -> retval
Python prototype (for reference):
getScaleFactor() -> retval
Python prototype (for reference):
getScoreType() -> retval
Python prototype (for reference):
getWTA_K() -> retval
Variant 1:
Positional Arguments
- arg1:
FileNode
Python prototype (for reference):
read(arg1) -> None
Variant 2:
Positional Arguments
- fileName:
String
Python prototype (for reference):
read(fileName) -> None
Positional Arguments
- edgeThreshold:
int
Python prototype (for reference):
setEdgeThreshold(edgeThreshold) -> None
Positional Arguments
- fastThreshold:
int
Python prototype (for reference):
setFastThreshold(fastThreshold) -> None
Positional Arguments
- firstLevel:
int
Python prototype (for reference):
setFirstLevel(firstLevel) -> None
Positional Arguments
- maxFeatures:
int
Python prototype (for reference):
setMaxFeatures(maxFeatures) -> None
Positional Arguments
- nlevels:
int
Python prototype (for reference):
setNLevels(nlevels) -> None
Positional Arguments
- patchSize:
int
Python prototype (for reference):
setPatchSize(patchSize) -> None
Positional Arguments
- scaleFactor:
double
Python prototype (for reference):
setScaleFactor(scaleFactor) -> None
Positional Arguments
- scoreType:
ORB_ScoreType
Python prototype (for reference):
setScoreType(scoreType) -> None
Positional Arguments
- wta_k:
int
Python prototype (for reference):
setWTA_K(wta_k) -> None
Variant 1:
Positional Arguments
- fs:
Ptr<FileStorage>
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
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Python prototype (for reference):
write(fs[, name]) -> None
Link to this section Functions
Raising version of compute/3
.
Raising version of compute/4
.
Raising version of create/0
.
Raising version of create/1
.
Raising version of defaultNorm/1
.
Raising version of descriptorSize/1
.
Raising version of descriptorType/1
.
Raising version of detect/2
.
Raising version of detect/3
.
Raising version of detectAndCompute/3
.
Raising version of detectAndCompute/4
.
Raising version of empty/1
.
Raising version of getDefaultName/1
.
Raising version of getEdgeThreshold/1
.
Raising version of getFastThreshold/1
.
Raising version of getFirstLevel/1
.
Raising version of getMaxFeatures/1
.
Raising version of getNLevels/1
.
Raising version of getPatchSize/1
.
Raising version of getScaleFactor/1
.
Raising version of getScoreType/1
.
Raising version of getWTA_K/1
.
Raising version of read/2
.
Raising version of setEdgeThreshold/2
.
Raising version of setFastThreshold/2
.
Raising version of setFirstLevel/2
.
Raising version of setMaxFeatures/2
.
Raising version of setNLevels/2
.
Raising version of setPatchSize/2
.
Raising version of setScaleFactor/2
.
Raising version of setScoreType/2
.
Raising version of setWTA_K/2
.
Raising version of write/2
.
Raising version of write/3
.