View Source Evision.ORB (Evision v0.1.7)

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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
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

Detects keypoints and computes the descriptors

Positional Arguments
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

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

Python prototype (for reference):

Positional Arguments
  • fs: Ptr<FileStorage>
Keyword Arguments

Python prototype (for reference):

Functions

Raising version of create/0.

Raising version of create/1.

Raising version of defaultNorm/1.

Raising version of detect/2.

Raising version of empty/1.

Raising version of getNLevels/1.

Raising version of getPatchSize/1.

Raising version of getScoreType/1.

Raising version of getWTA_K/1.

Raising version of read/2.

Raising version of setWTA_K/2.

Raising version of write/2.

Raising version of write/3.

Link to this section cv

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compute(self, images, keypoints)

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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
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
Link to this function

compute(self, images, keypoints, opts)

View Source

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
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
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
Link to this function

detect(self, images, opts)

View Source

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
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
Link to this function

detectAndCompute(self, image, mask)

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Positional Arguments
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

Detects keypoints and computes the descriptors

Python prototype (for reference):

detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
Link to this function

detectAndCompute(self, image, mask, opts)

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Positional Arguments
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

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

Python prototype (for reference):

read(fileName) -> None
Link to this function

setEdgeThreshold(self, edgeThreshold)

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Positional Arguments
  • edgeThreshold: int

Python prototype (for reference):

setEdgeThreshold(edgeThreshold) -> None
Link to this function

setFastThreshold(self, fastThreshold)

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Positional Arguments
  • fastThreshold: int

Python prototype (for reference):

setFastThreshold(fastThreshold) -> None
Link to this function

setFirstLevel(self, firstLevel)

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Positional Arguments
  • firstLevel: int

Python prototype (for reference):

setFirstLevel(firstLevel) -> None
Link to this function

setMaxFeatures(self, maxFeatures)

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Positional Arguments
  • maxFeatures: int

Python prototype (for reference):

setMaxFeatures(maxFeatures) -> None
Link to this function

setNLevels(self, nlevels)

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Positional Arguments
  • nlevels: int

Python prototype (for reference):

setNLevels(nlevels) -> None
Link to this function

setPatchSize(self, patchSize)

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Positional Arguments
  • patchSize: int

Python prototype (for reference):

setPatchSize(patchSize) -> None
Link to this function

setScaleFactor(self, scaleFactor)

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Positional Arguments
  • scaleFactor: double

Python prototype (for reference):

setScaleFactor(scaleFactor) -> None
Link to this function

setScoreType(self, scoreType)

View Source
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

Python prototype (for reference):

write(fs[, name]) -> None

Variant 2:

Positional Arguments

Python prototype (for reference):

write(fileName) -> None
Positional Arguments
  • fs: Ptr<FileStorage>
Keyword Arguments

Python prototype (for reference):

write(fs[, name]) -> None

Link to this section Functions

Link to this function

compute!(self, images, keypoints)

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Raising version of compute/3.

Link to this function

compute!(self, images, keypoints, opts)

View Source

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.

Link to this function

detect!(self, images, opts)

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Raising version of detect/3.

Link to this function

detectAndCompute!(self, image, mask)

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Raising version of detectAndCompute/3.

Link to this function

detectAndCompute!(self, image, mask, opts)

View Source

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.

Link to this function

setEdgeThreshold!(self, edgeThreshold)

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Raising version of setEdgeThreshold/2.

Link to this function

setFastThreshold!(self, fastThreshold)

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Raising version of setFastThreshold/2.

Link to this function

setFirstLevel!(self, firstLevel)

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Raising version of setFirstLevel/2.

Link to this function

setMaxFeatures!(self, maxFeatures)

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Raising version of setMaxFeatures/2.

Link to this function

setNLevels!(self, nlevels)

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Raising version of setNLevels/2.

Link to this function

setPatchSize!(self, patchSize)

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Raising version of setPatchSize/2.

Link to this function

setScaleFactor!(self, scaleFactor)

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Raising version of setScaleFactor/2.

Link to this function

setScoreType!(self, scoreType)

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Raising version of setScoreType/2.

Raising version of setWTA_K/2.

Raising version of write/2.

Raising version of write/3.