BFMatcher

match

radiusMatch

modules/core/include/opencv2/core/mat.hpp

@@ -113,6 +113,7 @@ public:
     virtual Mat getMat(int idx=-1) const;
     virtual UMat getUMat(int idx=-1) const;
     virtual void getMatVector(std::vector<Mat>& mv) const;
+    virtual void getUMatVector(std::vector<UMat>& umv) const;
     virtual cuda::GpuMat getGpuMat() const;
     virtual ogl::Buffer getOGlBuffer() const;
     void* getObj() const;
@@ -134,7 +135,7 @@ public:
     virtual size_t step(int i=-1) const;
     bool isMat() const;
     bool isUMat() const;
-    bool isMatVectot() const;
+    bool isMatVector() const;
     bool isUMatVector() const;
     bool isMatx();
 

modules/core/include/opencv2/core/mat.inl.hpp

@@ -110,7 +110,7 @@ inline _InputArray::~_InputArray() {}
 
 inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; }
 inline bool _InputArray::isUMat() const  { return kind() == _InputArray::UMAT; }
-inline bool _InputArray::isMatVectot() const { return kind() == _InputArray::STD_VECTOR_MAT; }
+inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; }
 inline bool _InputArray::isUMatVector() const  { return kind() == _InputArray::STD_VECTOR_UMAT; }
 inline bool _InputArray::isMatx()  { return kind() == _InputArray::MATX; }
 

modules/core/src/matrix.cpp

@@ -1324,6 +1324,42 @@ void _InputArray::getMatVector(std::vector<Mat>& mv) const
     CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
 }
 
+void _InputArray::getUMatVector(std::vector<UMat>& umv) const
+{
+    int k = kind();
+    int accessFlags = flags & ACCESS_MASK;
+
+    if( k == NONE )
+    {
+        umv.clear();
+        return;
+    }
+
+    if( k == STD_VECTOR_MAT )
+    {
+        const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
+        size_t i, n = v.size();
+        umv.resize(n);
+
+        for( i = 0; i < n; i++ )
+            umv[i] = v[i].getUMat(accessFlags);
+        return;
+    }
+
+    if( k == STD_VECTOR_UMAT )
+    {
+        const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
+        size_t i, n = v.size();
+        umv.resize(n);
+
+        for( i = 0; i < n; i++ )
+            umv[i] = v[i];
+        return;
+    }
+
+    CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
+}
+
 cuda::GpuMat _InputArray::getGpuMat() const
 {
     int k = kind();

modules/features2d/include/opencv2/features2d.hpp

@@ -998,7 +998,7 @@ public:
      * Add descriptors to train descriptor collection.
      * descriptors      Descriptors to add. Each descriptors[i] is a descriptors set from one image.
      */
-    CV_WRAP virtual void add( const std::vector<Mat>& descriptors );
+    CV_WRAP virtual void add( InputArray descriptors );
     /*
      * Get train descriptors collection.
      */
@@ -1034,29 +1034,29 @@ public:
      * Method train() is run in this methods.
      */
     // Find one best match for each query descriptor (if mask is empty).
-    CV_WRAP void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
-                CV_OUT std::vector<DMatch>& matches, const Mat& mask=Mat() ) const;
+    CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
+                CV_OUT std::vector<DMatch>& matches, InputArray mask=Mat() ) const;
     // Find k best matches for each query descriptor (in increasing order of distances).
     // compactResult is used when mask is not empty. If compactResult is false matches
     // vector will have the same size as queryDescriptors rows. If compactResult is true
     // matches vector will not contain matches for fully masked out query descriptors.
-    CV_WRAP void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
+    CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
                    CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
-                   const Mat& mask=Mat(), bool compactResult=false ) const;
+                   InputArray mask=Mat(), bool compactResult=false ) const;
     // Find best matches for each query descriptor which have distance less than
     // maxDistance (in increasing order of distances).
-    void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
+    void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
                       std::vector<std::vector<DMatch> >& matches, float maxDistance,
-                      const Mat& mask=Mat(), bool compactResult=false ) const;
+                      InputArray mask=Mat(), bool compactResult=false ) const;
     /*
      * Group of methods to match descriptors from one image to image set.
      * See description of similar methods for matching image pair above.
      */
-    CV_WRAP void match( const Mat& queryDescriptors, CV_OUT std::vector<DMatch>& matches,
+    CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
                 const std::vector<Mat>& masks=std::vector<Mat>() );
-    CV_WRAP void knnMatch( const Mat& queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
+    CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
            const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
-    void radiusMatch( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+    void radiusMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
                    const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
 
     // Reads matcher object from a file node
@@ -1101,10 +1101,10 @@ protected:
     // In fact the matching is implemented only by the following two methods. These methods suppose
     // that the class object has been trained already. Public match methods call these methods
     // after calling train().
-    virtual void knnMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-           const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false ) = 0;
-    virtual void radiusMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-           const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false ) = 0;
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false ) = 0;
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false ) = 0;
 
     static bool isPossibleMatch( const Mat& mask, int queryIdx, int trainIdx );
     static bool isMaskedOut( const std::vector<Mat>& masks, int queryIdx );
@@ -1114,6 +1114,7 @@ protected:
 
     // Collection of descriptors from train images.
     std::vector<Mat> trainDescCollection;
+    std::vector<UMat> utrainDescCollection;
 };
 
 /*
@@ -1137,10 +1138,16 @@ public:
 
     AlgorithmInfo* info() const;
 protected:
-    virtual void knnMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-           const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
-    virtual void radiusMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-           const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false );
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false );
+
+    bool ocl_knnMatch(InputArray query, InputArray train, std::vector< std::vector<DMatch> > &matches,
+        int k, int dstType, bool compactResult=false);
+    bool ocl_radiusMatch(InputArray query, InputArray train, std::vector< std::vector<DMatch> > &matches,
+        float maxDistance, int dstType, bool compactResult=false);
+    bool ocl_match(InputArray query, InputArray train, std::vector< std::vector<DMatch> > &matches, int dstType);
 
     int normType;
     bool crossCheck;
@@ -1175,10 +1182,10 @@ protected:
                                    const Mat& indices, const Mat& distances,
                                    std::vector<std::vector<DMatch> >& matches );
 
-    virtual void knnMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-                   const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
-    virtual void radiusMatchImpl( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-                   const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false );
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false );
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=std::vector<Mat>(), bool compactResult=false );
 
     Ptr<flann::IndexParams> indexParams;
     Ptr<flann::SearchParams> searchParams;

modules/features2d/src/precomp.hpp

@@ -48,6 +48,7 @@
 
 #include "opencv2/core/utility.hpp"
 #include "opencv2/core/private.hpp"
+#include "opencv2/core/ocl.hpp"
 
 #include <algorithm>
 

modules/features2d/test/ocl/test_brute_force_matcher.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Niko Li, newlife20080214@gmail.com
+//    Jia Haipeng, jiahaipeng95@gmail.com
+//    Zero Lin, Zero.Lin@amd.com
+//    Zhang Ying, zhangying913@gmail.com
+//    Yao Wang, bitwangyaoyao@gmail.com
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include "cvconfig.h"
+#include "opencv2/ts/ocl_test.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace cvtest {
+namespace ocl {
+PARAM_TEST_CASE(BruteForceMatcher, int, int)
+{
+    int distType;
+    int dim;
+
+    int queryDescCount;
+    int countFactor;
+
+    Mat query, train;
+    UMat uquery, utrain;
+
+    virtual void SetUp()
+    {
+        distType = GET_PARAM(0);
+        dim = GET_PARAM(1);
+
+        queryDescCount = 300; // must be even number because we split train data in some cases in two
+        countFactor = 4; // do not change it
+
+        cv::Mat queryBuf, trainBuf;
+
+        // Generate query descriptors randomly.
+        // Descriptor vector elements are integer values.
+        queryBuf.create(queryDescCount, dim, CV_32SC1);
+        rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
+        queryBuf.convertTo(queryBuf, CV_32FC1);
+
+        // Generate train decriptors as follows:
+        // copy each query descriptor to train set countFactor times
+        // and perturb some one element of the copied descriptors in
+        // in ascending order. General boundaries of the perturbation
+        // are (0.f, 1.f).
+        trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
+        float step = 1.f / countFactor;
+        for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
+        {
+            cv::Mat queryDescriptor = queryBuf.row(qIdx);
+            for (int c = 0; c < countFactor; c++)
+            {
+                int tIdx = qIdx * countFactor + c;
+                cv::Mat trainDescriptor = trainBuf.row(tIdx);
+                queryDescriptor.copyTo(trainDescriptor);
+                int elem = rng(dim);
+                float diff = rng.uniform(step * c, step * (c + 1));
+                trainDescriptor.at<float>(0, elem) += diff;
+            }
+        }
+
+        queryBuf.convertTo(query, CV_32F);
+        trainBuf.convertTo(train, CV_32F);
+        query.copyTo(uquery);
+        train.copyTo(utrain);
+    }
+};
+
+#ifdef ANDROID
+OCL_TEST_P(BruteForceMatcher, DISABLED_Match_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, Match_Single)
+#endif
+{
+    BFMatcher matcher(distType);
+
+    std::vector<cv::DMatch> matches;
+    matcher.match(uquery, utrain,  matches);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        cv::DMatch match = matches[i];
+        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
+            badCount++;
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+#ifdef ANDROID
+OCL_TEST_P(BruteForceMatcher, DISABLED_KnnMatch_2_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
+#endif
+{
+    const int knn = 2;
+
+    BFMatcher matcher(distType);
+
+    std::vector< std::vector<cv::DMatch> > matches;
+    matcher.knnMatch(uquery, utrain, matches, knn);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        if ((int)matches[i].size() != knn)
+            badCount++;
+        else
+        {
+            int localBadCount = 0;
+            for (int k = 0; k < knn; k++)
+            {
+                cv::DMatch match = matches[i][k];
+                if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
+                    localBadCount++;
+            }
+            badCount += localBadCount > 0 ? 1 : 0;
+        }
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+#ifdef ANDROID
+OCL_TEST_P(BruteForceMatcher, DISABLED_RadiusMatch_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, RadiusMatch_Single)
+#endif
+{
+    float radius = 1.f / countFactor;
+
+    BFMatcher matcher(distType);
+
+    std::vector< std::vector<cv::DMatch> > matches;
+    matcher.radiusMatch(uquery, utrain, matches, radius);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        if ((int)matches[i].size() != 1)
+        {
+            badCount++;
+        }
+        else
+        {
+            cv::DMatch match = matches[i][0];
+            if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
+                badCount++;
+        }
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+OCL_INSTANTIATE_TEST_CASE_P(Matcher, BruteForceMatcher, Combine( Values((int)NORM_L1, (int)NORM_L2),
+                                                                Values(57, 64, 83, 128, 179, 256, 304) ) );
+
+}//ocl
+}//cvtest
+
+#endif //HAVE_OPENCL