Package org.opencv.xfeatures2d
Class BoostDesc
- java.lang.Object
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- org.opencv.core.Algorithm
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- org.opencv.features.Feature2D
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- org.opencv.xfeatures2d.BoostDesc
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public class BoostDesc extends Feature2D
Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in CITE: Trzcinski13a and CITE: Trzcinski13b. desc type of descriptor to use, BoostDesc::BINBOOST_256 is default (256 bit long dimension) Available types are: BoostDesc::BGM, BoostDesc::BGM_HARD, BoostDesc::BGM_BILINEAR, BoostDesc::LBGM, BoostDesc::BINBOOST_64, BoostDesc::BINBOOST_128, BoostDesc::BINBOOST_256 use_orientation sample patterns using keypoints orientation, enabled by default scale_factor adjust the sampling window of detected keypoints 6.25f is default and fits for KAZE, SURF detected keypoints window ratio 6.75f should be the scale for SIFT detected keypoints window ratio 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio 0.75f should be the scale for ORB keypoints ratio 1.50f was the default in original implementation Note: BGM is the base descriptor where each binary dimension is computed as the output of a single weak learner. BGM_HARD and BGM_BILINEAR refers to same BGM but use different type of gradient binning. In the BGM_HARD that use ASSIGN_HARD binning type the gradient is assigned to the nearest orientation bin. In the BGM_BILINEAR that use ASSIGN_BILINEAR binning type the gradient is assigned to the two neighbouring bins. In the BGM and all other modes that use ASSIGN_SOFT binning type the gradient is assigned to 8 nearest bins according to the cosine value between the gradient angle and the bin center. LBGM (alias FP-Boost) is the floating point extension where each dimension is computed as a linear combination of the weak learner responses. BINBOOST and subvariants are the binary extensions of LBGM where each bit is computed as a thresholded linear combination of a set of weak learners. BoostDesc header files (boostdesc_*.i) was exported from original binaries with export-boostdesc.py script from samples subfolder.
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Constructor Summary
Constructors Modifier Constructor Description protected
BoostDesc(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static BoostDesc
__fromPtr__(long addr)
static BoostDesc
create()
static BoostDesc
create(int desc)
static BoostDesc
create(int desc, boolean use_scale_orientation)
static BoostDesc
create(int desc, boolean use_scale_orientation, float scale_factor)
protected void
finalize()
java.lang.String
getDefaultName()
Returns the algorithm string identifier.float
getScaleFactor()
boolean
getUseScaleOrientation()
void
setScaleFactor(float scale_factor)
void
setUseScaleOrientation(boolean use_scale_orientation)
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Methods inherited from class org.opencv.features.Feature2D
compute, compute, defaultNorm, descriptorSize, descriptorType, detect, detect, detect, detect, detectAndCompute, detectAndCompute, empty, read, write
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Methods inherited from class org.opencv.core.Algorithm
clear, getNativeObjAddr, save
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Method Detail
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__fromPtr__
public static BoostDesc __fromPtr__(long addr)
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create
public static BoostDesc create(int desc, boolean use_scale_orientation, float scale_factor)
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create
public static BoostDesc create(int desc, boolean use_scale_orientation)
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create
public static BoostDesc create(int desc)
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create
public static BoostDesc create()
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getDefaultName
public java.lang.String getDefaultName()
Description copied from class:Algorithm
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.- Overrides:
getDefaultName
in classFeature2D
- Returns:
- automatically generated
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setUseScaleOrientation
public void setUseScaleOrientation(boolean use_scale_orientation)
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getUseScaleOrientation
public boolean getUseScaleOrientation()
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setScaleFactor
public void setScaleFactor(float scale_factor)
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getScaleFactor
public float getScaleFactor()
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