Package org.opencv.features2d
Class BOWTrainer
- java.lang.Object
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- org.opencv.features2d.BOWTrainer
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- Direct Known Subclasses:
BOWKMeansTrainer
public class BOWTrainer extends Object
Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
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Field Summary
Fields Modifier and Type Field Description protected long
nativeObj
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Constructor Summary
Constructors Modifier Constructor Description protected
BOWTrainer(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static BOWTrainer
__fromPtr__(long addr)
void
add(Mat descriptors)
Adds descriptors to a training set.void
clear()
Mat
cluster()
Mat
cluster(Mat descriptors)
Clusters train descriptors.int
descriptorsCount()
Returns the count of all descriptors stored in the training set.protected void
finalize()
List<Mat>
getDescriptors()
Returns a training set of descriptors.long
getNativeObjAddr()
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Method Detail
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getNativeObjAddr
public long getNativeObjAddr()
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__fromPtr__
public static BOWTrainer __fromPtr__(long addr)
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add
public void add(Mat descriptors)
Adds descriptors to a training set.- Parameters:
descriptors
- Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor. The training set is clustered using clustermethod to construct the vocabulary.
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getDescriptors
public List<Mat> getDescriptors()
Returns a training set of descriptors.- Returns:
- automatically generated
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descriptorsCount
public int descriptorsCount()
Returns the count of all descriptors stored in the training set.- Returns:
- automatically generated
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clear
public void clear()
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cluster
public Mat cluster()
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cluster
public Mat cluster(Mat descriptors)
Clusters train descriptors.- Parameters:
descriptors
- Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.- Returns:
- automatically generated
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