lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.

2012-03-15 14:36:01 +00:00
parent 6300215b94
commit 957e80abbd
99 changed files with 6719 additions and 7240 deletions
--- a/modules/core/src/algorithm.cpp
+++ b/modules/core/src/algorithm.cpp
@@ -77,7 +77,7 @@ template<typename _KeyTp, typename _ValueTp> struct sorted_vector
                b = c;
        }
        
-        if( vec[a].first == key )
+        if( a < vec.size() && vec[a].first == key )
        {
            value = vec[a].second;
            return true;
@@ -176,6 +176,66 @@ string Algorithm::name() const
 {
    return info()->name();
 }
+  
+void Algorithm::set(const string& name, int value)
+{
+    info()->set(this, name.c_str(), ParamType<int>::type, &value);
+}
+
+void Algorithm::set(const string& name, double value)
+{
+    info()->set(this, name.c_str(), ParamType<double>::type, &value);
+}
+
+void Algorithm::set(const string& name, bool value)
+{
+    info()->set(this, name.c_str(), ParamType<bool>::type, &value);
+}
+
+void Algorithm::set(const string& name, const string& value)
+{
+    info()->set(this, name.c_str(), ParamType<string>::type, &value);
+}
+
+void Algorithm::set(const string& name, const Mat& value)
+{
+    info()->set(this, name.c_str(), ParamType<Mat>::type, &value);
+}
+
+void Algorithm::set(const string& name, const Ptr<Algorithm>& value)
+{
+    info()->set(this, name.c_str(), ParamType<Algorithm>::type, &value);
+}
+
+void Algorithm::set(const char* name, int value)
+{
+    info()->set(this, name, ParamType<int>::type, &value);
+}
+
+void Algorithm::set(const char* name, double value)
+{
+    info()->set(this, name, ParamType<double>::type, &value);
+}
+
+void Algorithm::set(const char* name, bool value)
+{
+    info()->set(this, name, ParamType<bool>::type, &value);
+}
+
+void Algorithm::set(const char* name, const string& value)
+{
+    info()->set(this, name, ParamType<string>::type, &value);
+}
+
+void Algorithm::set(const char* name, const Mat& value)
+{
+    info()->set(this, name, ParamType<Mat>::type, &value);
+}
+
+void Algorithm::set(const char* name, const Ptr<Algorithm>& value)
+{
+    info()->set(this, name, ParamType<Algorithm>::type, &value);
+}
    
 string Algorithm::paramHelp(const string& name) const
 {
@@ -261,25 +321,25 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const
        if( n.empty() )
            continue;
        if( p.type == Param::INT )
-            algo->set<int>(pname, (int)n);
+            algo->set(pname, (int)n);
        else if( p.type == Param::BOOLEAN )
-            algo->set<bool>(pname, (int)n != 0);
+            algo->set(pname, (int)n != 0);
        else if( p.type == Param::REAL )
-            algo->set<double>(pname, (double)n);
+            algo->set(pname, (double)n);
        else if( p.type == Param::STRING )
-            algo->set<string>(pname, (string)n);
+            algo->set(pname, (string)n);
        else if( p.type == Param::MAT )
        {
            Mat m;
            cv::read(fn, m);
-            algo->set<Mat>(pname, m);
+            algo->set(pname, m);
        }
        else if( p.type == Param::ALGORITHM )
        {
            Ptr<Algorithm> nestedAlgo = Algorithm::_create((string)n["name"]);
            CV_Assert( !nestedAlgo.empty() );
            nestedAlgo->read(n);
-            algo->set<Algorithm>(pname, nestedAlgo);
+            algo->set(pname, nestedAlgo);
        }
        else
            CV_Error( CV_StsUnsupportedFormat, "unknown/unsupported parameter type");
@@ -505,6 +565,16 @@ void AlgorithmInfo::addParam(const Algorithm* algo, const char* name,
              (Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
 }

+void AlgorithmInfo::addParam(const Algorithm* algo, const char* name,
+                             const bool& value, bool readOnly, 
+                             int (Algorithm::*getter)(),
+                             void (Algorithm::*setter)(int),
+                             const string& help)
+{
+    addParam_(algo, name, ParamType<bool>::type, &value, readOnly,
+              (Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
+}
+    
 void AlgorithmInfo::addParam(const Algorithm* algo, const char* name,
                             const double& value, bool readOnly, 
                             double (Algorithm::*getter)(),
--- a/modules/core/src/opengl_interop.cpp
+++ b/modules/core/src/opengl_interop.cpp
@@ -113,11 +113,13 @@ namespace

    const CvOpenGlFuncTab* g_glFuncTab = 0;

+#ifdef HAVE_CUDA
    const CvOpenGlFuncTab* glFuncTab()
    {
        static EmptyGlFuncTab empty;
        return g_glFuncTab ? g_glFuncTab : &empty;
    }
+#endif
 }

 CvOpenGlFuncTab::~CvOpenGlFuncTab()
--- a/modules/core/src/stat.cpp
+++ b/modules/core/src/stat.cpp
@@ -41,6 +41,7 @@
 //M*/

 #include "precomp.hpp"
+#include <climits>

 namespace cv
 {
@@ -1425,6 +1426,339 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
 }


+///////////////////////////////////// batch distance ///////////////////////////////////////
+
+namespace cv
+{
+
+template<typename _Tp, typename _Rt>
+void batchDistL1_(const _Tp* src1, const _Tp* src2, size_t step2,
+                  int nvecs, int len, _Rt* dist, const uchar* mask)
+{
+    step2 /= sizeof(src2[0]);
+    if( !mask )
+    {
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = normL1<_Tp, _Rt>(src1, src2 + step2*i, len);
+    }
+    else
+    {
+        _Rt val0 = std::numeric_limits<_Rt>::max();
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = mask[i] ? normL1<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
+    }
+}
+
+template<typename _Tp, typename _Rt>
+void batchDistL2Sqr_(const _Tp* src1, const _Tp* src2, size_t step2,
+                     int nvecs, int len, _Rt* dist, const uchar* mask)
+{
+    step2 /= sizeof(src2[0]);
+    if( !mask )
+    {
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len);
+    }
+    else
+    {
+        _Rt val0 = std::numeric_limits<_Rt>::max();
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = mask[i] ? normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
+    }
+}
+
+template<typename _Tp, typename _Rt>
+void batchDistL2_(const _Tp* src1, const _Tp* src2, size_t step2,
+                  int nvecs, int len, _Rt* dist, const uchar* mask)
+{
+    step2 /= sizeof(src2[0]);
+    if( !mask )
+    {
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len));
+    }
+    else
+    {
+        _Rt val0 = std::numeric_limits<_Rt>::max();
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = mask[i] ? std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len)) : val0;
+    }
+}
+
+static void batchDistHamming(const uchar* src1, const uchar* src2, size_t step2,
+                             int nvecs, int len, int* dist, const uchar* mask)
+{
+    step2 /= sizeof(src2[0]);
+    if( !mask )
+    {
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = normHamming(src1, src2 + step2*i, len);
+    }
+    else
+    {
+        int val0 = INT_MAX;
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = mask[i] ? normHamming(src1, src2 + step2*i, len) : val0;
+    }
+}
+
+static void batchDistHamming2(const uchar* src1, const uchar* src2, size_t step2,
+                              int nvecs, int len, int* dist, const uchar* mask)
+{
+    step2 /= sizeof(src2[0]);
+    if( !mask )
+    {
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = normHamming(src1, src2 + step2*i, len, 2);
+    }
+    else
+    {
+        int val0 = INT_MAX;
+        for( int i = 0; i < nvecs; i++ )
+            dist[i] = mask[i] ? normHamming(src1, src2 + step2*i, len, 2) : val0;
+    }
+}
+
+static void batchDistL1_8u32s(const uchar* src1, const uchar* src2, size_t step2,
+                               int nvecs, int len, int* dist, const uchar* mask)
+{
+    batchDistL1_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL1_8u32f(const uchar* src1, const uchar* src2, size_t step2,
+                               int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL1_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL2Sqr_8u32s(const uchar* src1, const uchar* src2, size_t step2,
+                                  int nvecs, int len, int* dist, const uchar* mask)
+{
+    batchDistL2Sqr_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL2Sqr_8u32f(const uchar* src1, const uchar* src2, size_t step2,
+                                  int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL2Sqr_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL2_8u32f(const uchar* src1, const uchar* src2, size_t step2,
+                               int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL2_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL1_32f(const float* src1, const float* src2, size_t step2,
+                             int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL1_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL2Sqr_32f(const float* src1, const float* src2, size_t step2,
+                                int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL2Sqr_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+static void batchDistL2_32f(const float* src1, const float* src2, size_t step2,
+                             int nvecs, int len, float* dist, const uchar* mask)
+{
+    batchDistL2_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
+}
+
+typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2,
+                              int nvecs, int len, uchar* dist, const uchar* mask);
+
+    
+struct BatchDistInvoker
+{
+    BatchDistInvoker( const Mat& _src1, const Mat& _src2,
+                      Mat& _dist, Mat& _nidx, int _K,
+                      const Mat& _mask, int _update,
+                      BatchDistFunc _func)
+    {
+        src1 = &_src1;
+        src2 = &_src2;
+        dist = &_dist;
+        nidx = &_nidx;
+        K = _K;
+        mask = &_mask;
+        update = _update;
+        func = _func;
+    }
+    
+    void operator()(const BlockedRange& range) const
+    {
+        AutoBuffer<int> buf(src2->rows);
+        int* bufptr = buf;
+        Cv32suf val0;
+        if( dist->type() == CV_32S )
+            val0.i = INT_MAX;
+        else
+            val0.f = FLT_MAX;
+        
+        for( int i = range.begin(); i < range.end(); i++ )
+        {
+            func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols,
+                 K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0);
+            
+            if( K > 0 )
+            {
+                int* nidxptr = nidx->ptr<int>(i);
+                // since positive float's can be compared just like int's,
+                // we handle both CV_32S and CV_32F cases with a single branch
+                int* distptr = (int*)dist->ptr(i);
+                
+                int k, k0, k0_, j;
+                for( k0 = 0; k0 < K; k0++ )
+                    if( nidxptr[k0] < 0 )
+                        break;
+                k0_ = std::max(k0, 1);
+                
+                for( j = 0; j < src2->rows; j++ )
+                {
+                    int d = bufptr[j];
+                    if( d < distptr[k0_-1] )
+                    {
+                        for( k = std::min(k0-1, K-2); k >= 0 && distptr[k] > d; k-- )
+                        {
+                            nidxptr[k+1] = nidxptr[k];
+                            distptr[k+1] = distptr[k];
+                        }
+                        nidxptr[k+1] = j + update;
+                        distptr[k+1] = d;
+                        k0_ = k0 = std::min(k0 + 1, K);
+                    }
+                }
+            }
+        }
+    }
+    
+    const Mat *src1;
+    const Mat *src2;
+    Mat *dist;
+    Mat *nidx;
+    const Mat *mask;
+    int K;
+    int update;
+    BatchDistFunc func;
+};
+    
+}
+    
+void cv::batchDistance( InputArray _src1, InputArray _src2,
+                        OutputArray _dist, int dtype, OutputArray _nidx,
+                        int normType, int K, InputArray _mask,
+                        int update, bool crosscheck )
+{
+    Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
+    int type = src1.type();
+    CV_Assert( type == src2.type() && src1.cols == src2.cols &&
+               (type == CV_32F || type == CV_8U));
+    CV_Assert( _nidx.needed() == (K > 0) );
+    
+    if( dtype == -1 )
+    {
+        dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
+    }
+    CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F);
+
+    K = std::min(K, src2.rows);
+    
+    _dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype);
+    Mat dist = _dist.getMat(), nidx;
+    if( _nidx.needed() )
+    {
+        _nidx.create(dist.size(), CV_32S);
+        nidx = _nidx.getMat();
+    }
+    
+    if( update == 0 && K > 0 )
+    {
+        dist = Scalar::all(dtype == CV_32S ? (double)INT_MAX : (double)FLT_MAX);
+        nidx = Scalar::all(-1);
+    }
+    
+    if( crosscheck )
+    {
+        CV_Assert( K == 1 && update == 0 && mask.empty() );
+        Mat tdist, tidx;
+        batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
+        
+        // if an idx-th element from src1 appeared to be the nearest to i-th element of src2,
+        // we update the minimum mutual distance between idx-th element of src1 and the whole src2 set.
+        // As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest
+        // to i*-th element of src2 and i*-th element of src2 is the closest to idx-th element of src1.
+        // If nidx[idx] = -1, it means that there is no such ideal couple for it in src2.
+        // This O(N) procedure is called cross-check and it helps to eliminate some false matches.
+        if( dtype == CV_32S )
+        {
+            for( int i = 0; i < tdist.rows; i++ )
+            {
+                int idx = tidx.at<int>(i);
+                int d = tdist.at<int>(i), d0 = dist.at<int>(idx);
+                if( d < d0 )
+                {
+                    dist.at<int>(idx) = d0;
+                    nidx.at<int>(idx) = i + update;
+                }
+            }
+        }
+        else
+        {
+            for( int i = 0; i < tdist.rows; i++ )
+            {
+                int idx = tidx.at<int>(i);
+                float d = tdist.at<float>(i), d0 = dist.at<float>(idx);
+                if( d < d0 )
+                {
+                    dist.at<float>(idx) = d0;
+                    nidx.at<int>(idx) = i + update;
+                }
+            }
+        }
+        return;
+    }
+    
+    BatchDistFunc func = 0;
+    if( type == CV_8U )
+    {
+        if( normType == NORM_L1 && dtype == CV_32S )
+            func = (BatchDistFunc)batchDistL1_8u32s;
+        else if( normType == NORM_L1 && dtype == CV_32F )
+            func = (BatchDistFunc)batchDistL1_8u32f;
+        else if( normType == NORM_L2SQR && dtype == CV_32S )
+            func = (BatchDistFunc)batchDistL2Sqr_8u32s;
+        else if( normType == NORM_L2SQR && dtype == CV_32F )
+            func = (BatchDistFunc)batchDistL2Sqr_8u32f;
+        else if( normType == NORM_L2 && dtype == CV_32F )
+            func = (BatchDistFunc)batchDistL2_8u32f;
+        else if( normType == NORM_HAMMING && dtype == CV_32S )
+            func = (BatchDistFunc)batchDistHamming;
+        else if( normType == NORM_HAMMING2 && dtype == CV_32S )
+            func = (BatchDistFunc)batchDistHamming2;
+    }
+    else if( type == CV_32F && dtype == CV_32F )
+    {
+        if( normType == NORM_L1 )
+            func = (BatchDistFunc)batchDistL1_32f;
+        else if( normType == NORM_L2SQR )
+            func = (BatchDistFunc)batchDistL2Sqr_32f;
+        else if( normType == NORM_L2 )
+            func = (BatchDistFunc)batchDistL2_32f;
+    }
+    
+    if( func == 0 )
+        CV_Error_(CV_StsUnsupportedFormat,
+                  ("The combination of type=%d, dtype=%d and normType=%d is not supported",
+                   type, dtype, normType));
+    
+    parallel_for(BlockedRange(0, src1.rows),
+                 BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
+}
+
+
 CV_IMPL CvScalar cvSum( const CvArr* srcarr )
 {
    cv::Scalar sum = cv::sum(cv::cvarrToMat(srcarr, false, true, 1));