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//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.features2d;
import java.lang.String;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.utils.Converters;
// C++: class javaGenericDescriptorMatcher
/**
* Abstract interface for extracting and matching a keypoint descriptor. There
* are also "DescriptorExtractor" and "DescriptorMatcher" for these purposes but
* their interfaces are intended for descriptors represented as vectors in a
* multidimensional space. GenericDescriptorMatcher is a more
* generic interface for descriptors. DescriptorMatcher and
* GenericDescriptorMatcher have two groups of match methods: for
* matching keypoints of an image with another image or with an image set.
*
* class GenericDescriptorMatcher
*
* // C++ code:
*
*
* public:
*
* GenericDescriptorMatcher();
*
* virtual ~GenericDescriptorMatcher();
*
* virtual void add(const vector& images,
*
* vector >& keypoints);
*
* const vector& getTrainImages() const;
*
* const vector >& getTrainKeypoints() const;
*
* virtual void clear();
*
* virtual void train() = 0;
*
* virtual bool isMaskSupported() = 0;
*
* void classify(const Mat& queryImage,
*
* vector& queryKeypoints,
*
* const Mat& trainImage,
*
* vector& trainKeypoints) const;
*
* void classify(const Mat& queryImage,
*
* vector& queryKeypoints);
*
* / *
*
* - Group of methods to match keypoints from an image pair.
*
- /
*
*
* void match(const Mat& queryImage, vector& queryKeypoints,
*
* const Mat& trainImage, vector& trainKeypoints,
*
* vector& matches, const Mat& mask=Mat()) const;
*
* void knnMatch(const Mat& queryImage, vector& queryKeypoints,
*
* const Mat& trainImage, vector& trainKeypoints,
*
* vector >& matches, int k,
*
* const Mat& mask=Mat(), bool compactResult=false) const;
*
* void radiusMatch(const Mat& queryImage, vector& queryKeypoints,
*
* const Mat& trainImage, vector& trainKeypoints,
*
* vector >& matches, float maxDistance,
*
* const Mat& mask=Mat(), bool compactResult=false) const;
*
* / *
*
* - Group of methods to match keypoints from one image to an image set.
*
- /
*
*
* void match(const Mat& queryImage, vector& queryKeypoints,
*
* vector& matches, const vector& masks=vector());
*
* void knnMatch(const Mat& queryImage, vector& queryKeypoints,
*
* vector >& matches, int k,
*
* const vector& masks=vector(), bool compactResult=false);
*
* void radiusMatch(const Mat& queryImage, vector& queryKeypoints,
*
* vector >& matches, float maxDistance,
*
* const vector& masks=vector(), bool compactResult=false);
*
* virtual void read(const FileNode&);
*
* virtual void write(FileStorage&) const;
*
* virtual Ptr clone(bool emptyTrainData=false) const
* = 0;
*
* protected:...
*
* };
*
* @see org.opencv.features2d.GenericDescriptorMatcher
*/
public class GenericDescriptorMatcher {
protected final long nativeObj;
protected GenericDescriptorMatcher(long addr) { nativeObj = addr; }
public static final int
ONEWAY = 1,
FERN = 2;
//
// C++: void javaGenericDescriptorMatcher::add(vector_Mat images, vector_vector_KeyPoint keypoints)
//
/**
* Adds images and their keypoints to the training collection stored in the
* class instance.
*
* @param images Image collection.
* @param keypoints Point collection. It is assumed that keypoints[i]
* are keypoints detected in the image images[i].
*
* @see org.opencv.features2d.GenericDescriptorMatcher.add
*/
public void add(List images, List keypoints)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
List keypoints_tmplm = new ArrayList((keypoints != null) ? keypoints.size() : 0);
Mat keypoints_mat = Converters.vector_vector_KeyPoint_to_Mat(keypoints, keypoints_tmplm);
add_0(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::classify(Mat queryImage, vector_KeyPoint& queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints)
//
/**
* Classifies keypoints from a query set.
*
* The method classifies each keypoint from a query set. The first variant of
* the method takes a train image and its keypoints as an input argument. The
* second variant uses the internally stored training collection that can be
* built using the GenericDescriptorMatcher.add method.
*
* The methods do the following:
*
* - Call the
GenericDescriptorMatcher.match method to find
* correspondence between the query set and the training set.
* - Set the
class_id field of each keypoint from the query
* set to class_id of the corresponding keypoint from the training
* set.
*
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints from a query image.
* @param trainImage Train image.
* @param trainKeypoints Keypoints from a train image.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.classify
*/
public void classify(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
classify_0(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::classify(Mat queryImage, vector_KeyPoint& queryKeypoints)
//
/**
* Classifies keypoints from a query set.
*
* The method classifies each keypoint from a query set. The first variant of
* the method takes a train image and its keypoints as an input argument. The
* second variant uses the internally stored training collection that can be
* built using the GenericDescriptorMatcher.add method.
*
* The methods do the following:
*
* - Call the
GenericDescriptorMatcher.match method to find
* correspondence between the query set and the training set.
* - Set the
class_id field of each keypoint from the query
* set to class_id of the corresponding keypoint from the training
* set.
*
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints from a query image.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.classify
*/
public void classify(Mat queryImage, MatOfKeyPoint queryKeypoints)
{
Mat queryKeypoints_mat = queryKeypoints;
classify_1(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::clear()
//
/**
* Clears a train collection (images and keypoints).
*
* @see org.opencv.features2d.GenericDescriptorMatcher.clear
*/
public void clear()
{
clear_0(nativeObj);
return;
}
//
// C++: javaGenericDescriptorMatcher* javaGenericDescriptorMatcher::jclone(bool emptyTrainData = false)
//
public GenericDescriptorMatcher clone(boolean emptyTrainData)
{
GenericDescriptorMatcher retVal = new GenericDescriptorMatcher(clone_0(nativeObj, emptyTrainData));
return retVal;
}
public GenericDescriptorMatcher clone()
{
GenericDescriptorMatcher retVal = new GenericDescriptorMatcher(clone_1(nativeObj));
return retVal;
}
//
// C++: static javaGenericDescriptorMatcher* javaGenericDescriptorMatcher::create(int matcherType)
//
public static GenericDescriptorMatcher create(int matcherType)
{
GenericDescriptorMatcher retVal = new GenericDescriptorMatcher(create_0(matcherType));
return retVal;
}
//
// C++: bool javaGenericDescriptorMatcher::empty()
//
public boolean empty()
{
boolean retVal = empty_0(nativeObj);
return retVal;
}
//
// C++: vector_Mat javaGenericDescriptorMatcher::getTrainImages()
//
/**
* Returns a train image collection.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.getTrainImages
*/
public List getTrainImages()
{
List retVal = new ArrayList();
Mat retValMat = new Mat(getTrainImages_0(nativeObj));
Converters.Mat_to_vector_Mat(retValMat, retVal);
return retVal;
}
//
// C++: vector_vector_KeyPoint javaGenericDescriptorMatcher::getTrainKeypoints()
//
/**
* Returns a train keypoints collection.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.getTrainKeypoints
*/
public List getTrainKeypoints()
{
List retVal = new ArrayList();
Mat retValMat = new Mat(getTrainKeypoints_0(nativeObj));
Converters.Mat_to_vector_vector_KeyPoint(retValMat, retVal);
return retVal;
}
//
// C++: bool javaGenericDescriptorMatcher::isMaskSupported()
//
/**
* Returns true if a generic descriptor matcher supports masking
* permissible matches.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.isMaskSupported
*/
public boolean isMaskSupported()
{
boolean retVal = isMaskSupported_0(nativeObj);
return retVal;
}
//
// C++: void javaGenericDescriptorMatcher::knnMatch(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_vector_DMatch& matches, int k, Mat mask = Mat(), bool compactResult = false)
//
/**
* Finds the k best matches for each query keypoint.
*
* The methods are extended variants of GenericDescriptorMatch.match.
* The parameters are similar, and the semantics is similar to DescriptorMatcher.knnMatch.
* But this class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param trainImage a trainImage
* @param trainKeypoints a trainKeypoints
* @param matches a matches
* @param k a k
* @param mask a mask
* @param compactResult a compactResult
*
* @see org.opencv.features2d.GenericDescriptorMatcher.knnMatch
*/
public void knnMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, List matches, int k, Mat mask, boolean compactResult)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = new Mat();
knnMatch_0(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, k, mask.nativeObj, compactResult);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
/**
* Finds the k best matches for each query keypoint.
*
* The methods are extended variants of GenericDescriptorMatch.match.
* The parameters are similar, and the semantics is similar to DescriptorMatcher.knnMatch.
* But this class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param trainImage a trainImage
* @param trainKeypoints a trainKeypoints
* @param matches a matches
* @param k a k
*
* @see org.opencv.features2d.GenericDescriptorMatcher.knnMatch
*/
public void knnMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, List matches, int k)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = new Mat();
knnMatch_1(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, k);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
//
// C++: void javaGenericDescriptorMatcher::knnMatch(Mat queryImage, vector_KeyPoint queryKeypoints, vector_vector_DMatch& matches, int k, vector_Mat masks = vector(), bool compactResult = false)
//
/**
* Finds the k best matches for each query keypoint.
*
* The methods are extended variants of GenericDescriptorMatch.match.
* The parameters are similar, and the semantics is similar to DescriptorMatcher.knnMatch.
* But this class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param matches a matches
* @param k a k
* @param masks a masks
* @param compactResult a compactResult
*
* @see org.opencv.features2d.GenericDescriptorMatcher.knnMatch
*/
public void knnMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, List matches, int k, List masks, boolean compactResult)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = new Mat();
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
knnMatch_2(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, k, masks_mat.nativeObj, compactResult);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
/**
* Finds the k best matches for each query keypoint.
*
* The methods are extended variants of GenericDescriptorMatch.match.
* The parameters are similar, and the semantics is similar to DescriptorMatcher.knnMatch.
* But this class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param matches a matches
* @param k a k
*
* @see org.opencv.features2d.GenericDescriptorMatcher.knnMatch
*/
public void knnMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, List matches, int k)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = new Mat();
knnMatch_3(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, k);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
//
// C++: void javaGenericDescriptorMatcher::match(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_DMatch& matches, Mat mask = Mat())
//
/**
* Finds the best match in the training set for each keypoint from the query
* set.
*
* The methods find the best match for each query keypoint. In the first variant
* of the method, a train image and its keypoints are the input arguments. In
* the second variant, query keypoints are matched to the internally stored
* training collection that can be built using the GenericDescriptorMatcher.add
* method. Optional mask (or masks) can be passed to specify which query and
* training descriptors can be matched. Namely, queryKeypoints[i]
* can be matched with trainKeypoints[j] only if mask.at(i,j)
* is non-zero.
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints detected in queryImage.
* @param trainImage Train image. It is not added to a train image collection
* stored in the class object.
* @param trainKeypoints Keypoints detected in trainImage. They are
* not added to a train points collection stored in the class object.
* @param matches Matches. If a query descriptor (keypoint) is masked out in
* mask, match is added for this descriptor. So, matches
* size may be smaller than the query keypoints count.
* @param mask Mask specifying permissible matches between an input query and
* train keypoints.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.match
*/
public void match(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, MatOfDMatch matches, Mat mask)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = matches;
match_0(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, mask.nativeObj);
return;
}
/**
* Finds the best match in the training set for each keypoint from the query
* set.
*
* The methods find the best match for each query keypoint. In the first variant
* of the method, a train image and its keypoints are the input arguments. In
* the second variant, query keypoints are matched to the internally stored
* training collection that can be built using the GenericDescriptorMatcher.add
* method. Optional mask (or masks) can be passed to specify which query and
* training descriptors can be matched. Namely, queryKeypoints[i]
* can be matched with trainKeypoints[j] only if mask.at(i,j)
* is non-zero.
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints detected in queryImage.
* @param trainImage Train image. It is not added to a train image collection
* stored in the class object.
* @param trainKeypoints Keypoints detected in trainImage. They are
* not added to a train points collection stored in the class object.
* @param matches Matches. If a query descriptor (keypoint) is masked out in
* mask, match is added for this descriptor. So, matches
* size may be smaller than the query keypoints count.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.match
*/
public void match(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, MatOfDMatch matches)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = matches;
match_1(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::match(Mat queryImage, vector_KeyPoint queryKeypoints, vector_DMatch& matches, vector_Mat masks = vector())
//
/**
* Finds the best match in the training set for each keypoint from the query
* set.
*
* The methods find the best match for each query keypoint. In the first variant
* of the method, a train image and its keypoints are the input arguments. In
* the second variant, query keypoints are matched to the internally stored
* training collection that can be built using the GenericDescriptorMatcher.add
* method. Optional mask (or masks) can be passed to specify which query and
* training descriptors can be matched. Namely, queryKeypoints[i]
* can be matched with trainKeypoints[j] only if mask.at(i,j)
* is non-zero.
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints detected in queryImage.
* @param matches Matches. If a query descriptor (keypoint) is masked out in
* mask, match is added for this descriptor. So, matches
* size may be smaller than the query keypoints count.
* @param masks Set of masks. Each masks[i] specifies permissible
* matches between input query keypoints and stored train keypoints from the
* i-th image.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.match
*/
public void match(Mat queryImage, MatOfKeyPoint queryKeypoints, MatOfDMatch matches, List masks)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = matches;
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
match_2(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, masks_mat.nativeObj);
return;
}
/**
* Finds the best match in the training set for each keypoint from the query
* set.
*
* The methods find the best match for each query keypoint. In the first variant
* of the method, a train image and its keypoints are the input arguments. In
* the second variant, query keypoints are matched to the internally stored
* training collection that can be built using the GenericDescriptorMatcher.add
* method. Optional mask (or masks) can be passed to specify which query and
* training descriptors can be matched. Namely, queryKeypoints[i]
* can be matched with trainKeypoints[j] only if mask.at(i,j)
* is non-zero.
*
* @param queryImage Query image.
* @param queryKeypoints Keypoints detected in queryImage.
* @param matches Matches. If a query descriptor (keypoint) is masked out in
* mask, match is added for this descriptor. So, matches
* size may be smaller than the query keypoints count.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.match
*/
public void match(Mat queryImage, MatOfKeyPoint queryKeypoints, MatOfDMatch matches)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = matches;
match_3(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::radiusMatch(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_vector_DMatch& matches, float maxDistance, Mat mask = Mat(), bool compactResult = false)
//
/**
* For each query keypoint, finds the training keypoints not farther than the
* specified distance.
*
* The methods are similar to DescriptorMatcher.radius. But this
* class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param trainImage a trainImage
* @param trainKeypoints a trainKeypoints
* @param matches a matches
* @param maxDistance a maxDistance
* @param mask a mask
* @param compactResult a compactResult
*
* @see org.opencv.features2d.GenericDescriptorMatcher.radiusMatch
*/
public void radiusMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, List matches, float maxDistance, Mat mask, boolean compactResult)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = new Mat();
radiusMatch_0(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, maxDistance, mask.nativeObj, compactResult);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
/**
* For each query keypoint, finds the training keypoints not farther than the
* specified distance.
*
* The methods are similar to DescriptorMatcher.radius. But this
* class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param trainImage a trainImage
* @param trainKeypoints a trainKeypoints
* @param matches a matches
* @param maxDistance a maxDistance
*
* @see org.opencv.features2d.GenericDescriptorMatcher.radiusMatch
*/
public void radiusMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, List matches, float maxDistance)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat trainKeypoints_mat = trainKeypoints;
Mat matches_mat = new Mat();
radiusMatch_1(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, maxDistance);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
//
// C++: void javaGenericDescriptorMatcher::radiusMatch(Mat queryImage, vector_KeyPoint queryKeypoints, vector_vector_DMatch& matches, float maxDistance, vector_Mat masks = vector(), bool compactResult = false)
//
/**
* For each query keypoint, finds the training keypoints not farther than the
* specified distance.
*
* The methods are similar to DescriptorMatcher.radius. But this
* class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param matches a matches
* @param maxDistance a maxDistance
* @param masks a masks
* @param compactResult a compactResult
*
* @see org.opencv.features2d.GenericDescriptorMatcher.radiusMatch
*/
public void radiusMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, List matches, float maxDistance, List masks, boolean compactResult)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = new Mat();
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
radiusMatch_2(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, maxDistance, masks_mat.nativeObj, compactResult);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
/**
* For each query keypoint, finds the training keypoints not farther than the
* specified distance.
*
* The methods are similar to DescriptorMatcher.radius. But this
* class does not require explicitly computed keypoint descriptors.
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param matches a matches
* @param maxDistance a maxDistance
*
* @see org.opencv.features2d.GenericDescriptorMatcher.radiusMatch
*/
public void radiusMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, List matches, float maxDistance)
{
Mat queryKeypoints_mat = queryKeypoints;
Mat matches_mat = new Mat();
radiusMatch_3(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, maxDistance);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
return;
}
//
// C++: void javaGenericDescriptorMatcher::read(string fileName)
//
/**
* Reads a matcher object from a file node.
*
* @param fileName a fileName
*
* @see org.opencv.features2d.GenericDescriptorMatcher.read
*/
public void read(String fileName)
{
read_0(nativeObj, fileName);
return;
}
//
// C++: void javaGenericDescriptorMatcher::train()
//
/**
* Trains descriptor matcher
*
* Prepares descriptor matcher, for example, creates a tree-based structure, to
* extract descriptors or to optimize descriptors matching.
*
* @see org.opencv.features2d.GenericDescriptorMatcher.train
*/
public void train()
{
train_0(nativeObj);
return;
}
//
// C++: void javaGenericDescriptorMatcher::write(string fileName)
//
/**
* Writes a match object to a file storage.
*
* @param fileName a fileName
*
* @see org.opencv.features2d.GenericDescriptorMatcher.write
*/
public void write(String fileName)
{
write_0(nativeObj, fileName);
return;
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: void javaGenericDescriptorMatcher::add(vector_Mat images, vector_vector_KeyPoint keypoints)
private static native void add_0(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj);
// C++: void javaGenericDescriptorMatcher::classify(Mat queryImage, vector_KeyPoint& queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints)
private static native void classify_0(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj);
// C++: void javaGenericDescriptorMatcher::classify(Mat queryImage, vector_KeyPoint& queryKeypoints)
private static native void classify_1(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj);
// C++: void javaGenericDescriptorMatcher::clear()
private static native void clear_0(long nativeObj);
// C++: javaGenericDescriptorMatcher* javaGenericDescriptorMatcher::jclone(bool emptyTrainData = false)
private static native long clone_0(long nativeObj, boolean emptyTrainData);
private static native long clone_1(long nativeObj);
// C++: static javaGenericDescriptorMatcher* javaGenericDescriptorMatcher::create(int matcherType)
private static native long create_0(int matcherType);
// C++: bool javaGenericDescriptorMatcher::empty()
private static native boolean empty_0(long nativeObj);
// C++: vector_Mat javaGenericDescriptorMatcher::getTrainImages()
private static native long getTrainImages_0(long nativeObj);
// C++: vector_vector_KeyPoint javaGenericDescriptorMatcher::getTrainKeypoints()
private static native long getTrainKeypoints_0(long nativeObj);
// C++: bool javaGenericDescriptorMatcher::isMaskSupported()
private static native boolean isMaskSupported_0(long nativeObj);
// C++: void javaGenericDescriptorMatcher::knnMatch(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_vector_DMatch& matches, int k, Mat mask = Mat(), bool compactResult = false)
private static native void knnMatch_0(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj, int k, long mask_nativeObj, boolean compactResult);
private static native void knnMatch_1(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj, int k);
// C++: void javaGenericDescriptorMatcher::knnMatch(Mat queryImage, vector_KeyPoint queryKeypoints, vector_vector_DMatch& matches, int k, vector_Mat masks = vector(), bool compactResult = false)
private static native void knnMatch_2(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj, int k, long masks_mat_nativeObj, boolean compactResult);
private static native void knnMatch_3(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj, int k);
// C++: void javaGenericDescriptorMatcher::match(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_DMatch& matches, Mat mask = Mat())
private static native void match_0(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj, long mask_nativeObj);
private static native void match_1(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj);
// C++: void javaGenericDescriptorMatcher::match(Mat queryImage, vector_KeyPoint queryKeypoints, vector_DMatch& matches, vector_Mat masks = vector())
private static native void match_2(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj, long masks_mat_nativeObj);
private static native void match_3(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj);
// C++: void javaGenericDescriptorMatcher::radiusMatch(Mat queryImage, vector_KeyPoint queryKeypoints, Mat trainImage, vector_KeyPoint trainKeypoints, vector_vector_DMatch& matches, float maxDistance, Mat mask = Mat(), bool compactResult = false)
private static native void radiusMatch_0(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj, float maxDistance, long mask_nativeObj, boolean compactResult);
private static native void radiusMatch_1(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long trainImage_nativeObj, long trainKeypoints_mat_nativeObj, long matches_mat_nativeObj, float maxDistance);
// C++: void javaGenericDescriptorMatcher::radiusMatch(Mat queryImage, vector_KeyPoint queryKeypoints, vector_vector_DMatch& matches, float maxDistance, vector_Mat masks = vector(), bool compactResult = false)
private static native void radiusMatch_2(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj, float maxDistance, long masks_mat_nativeObj, boolean compactResult);
private static native void radiusMatch_3(long nativeObj, long queryImage_nativeObj, long queryKeypoints_mat_nativeObj, long matches_mat_nativeObj, float maxDistance);
// C++: void javaGenericDescriptorMatcher::read(string fileName)
private static native void read_0(long nativeObj, String fileName);
// C++: void javaGenericDescriptorMatcher::train()
private static native void train_0(long nativeObj);
// C++: void javaGenericDescriptorMatcher::write(string fileName)
private static native void write_0(long nativeObj, String fileName);
// native support for java finalize()
private static native void delete(long nativeObj);
}