Merge pull request #4018 from vpisarev:imgproc_video_fixes

modules/core/src/matrix.cpp

@@ -4714,8 +4714,8 @@ SparseMat::Hdr::Hdr( int _dims, const int* _sizes, int _type )
     refcount = 1;
 
     dims = _dims;
-    valueOffset = (int)alignSize(sizeof(SparseMat::Node) +
-        sizeof(int)*std::max(dims - CV_MAX_DIM, 0), CV_ELEM_SIZE1(_type));
+    valueOffset = (int)alignSize(sizeof(SparseMat::Node) - MAX_DIM*sizeof(int) +
+                                 dims*sizeof(int), CV_ELEM_SIZE1(_type));
     nodeSize = alignSize(valueOffset +
         CV_ELEM_SIZE(_type), (int)sizeof(size_t));
 
@@ -4816,7 +4816,8 @@ void SparseMat::copyTo( SparseMat& m ) const
 void SparseMat::copyTo( Mat& m ) const
 {
     CV_Assert( hdr );
-    m.create( dims(), hdr->size, type() );
+    int ndims = dims();
+    m.create( ndims, hdr->size, type() );
     m = Scalar(0);
 
     SparseMatConstIterator from = begin();
@@ -4825,7 +4826,7 @@ void SparseMat::copyTo( Mat& m ) const
     for( i = 0; i < N; i++, ++from )
     {
         const Node* n = from.node();
-        copyElem( from.ptr, m.ptr(n->idx), esz);
+        copyElem( from.ptr, (ndims > 1 ? m.ptr(n->idx) : m.ptr(n->idx[0])), esz);
     }
 }
 
@@ -5114,7 +5115,8 @@ uchar* SparseMat::newNode(const int* idx, size_t hashval)
     if( !hdr->freeList )
     {
         size_t i, nsz = hdr->nodeSize, psize = hdr->pool.size(),
-            newpsize = std::max(psize*2, 8*nsz);
+            newpsize = std::max(psize*3/2, 8*nsz);
+        newpsize = (newpsize/nsz)*nsz;
         hdr->pool.resize(newpsize);
         uchar* pool = &hdr->pool[0];
         hdr->freeList = std::max(psize, nsz);

modules/core/src/stat.cpp

@@ -504,54 +504,21 @@ static int countNonZero_(const T* src, int len )
     return nz;
 }
 
-#if CV_SSE2
-
-static const uchar * initPopcountTable()
-{
-    static uchar tab[256];
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        // we compute inverse popcount table,
-        // since we pass (img[x] == 0) mask as index in the table.
-        unsigned int j = 0u;
-#if CV_POPCNT
-        if (checkHardwareSupport(CV_CPU_POPCNT))
-        {
-            for( ; j < 256u; j++ )
-                tab[j] = (uchar)(8 - _mm_popcnt_u32(j));
-        }
-#endif
-        for( ; j < 256u; j++ )
-        {
-            int val = 0;
-            for( int mask = 1; mask < 256; mask += mask )
-                val += (j & mask) == 0;
-            tab[j] = (uchar)val;
-        }
-        initialized = true;
-    }
-
-    return tab;
-}
-
-#endif
-
 static int countNonZero8u( const uchar* src, int len )
 {
     int i=0, nz = 0;
 #if CV_SSE2
     if(USE_SSE2)//5x-6x
     {
-        __m128i pattern = _mm_setzero_si128 ();
-        static const uchar * tab = initPopcountTable();
+        __m128i v_zero = _mm_setzero_si128();
+        __m128i sum = _mm_setzero_si128();
 
         for (; i<=len-16; i+=16)
         {
             __m128i r0 = _mm_loadu_si128((const __m128i*)(src+i));
-            int val = _mm_movemask_epi8(_mm_cmpeq_epi8(r0, pattern));
-            nz += tab[val & 255] + tab[val >> 8];
+            sum = _mm_add_epi32(sum, _mm_sad_epu8(_mm_sub_epi8(v_zero, _mm_cmpeq_epi8(r0, v_zero)), v_zero));
         }
+        nz = i - _mm_cvtsi128_si32(_mm_add_epi32(sum, _mm_unpackhi_epi64(sum, sum)));
     }
 #elif CV_NEON
     int len0 = len & -16, blockSize1 = (1 << 8) - 16, blockSize0 = blockSize1 << 6;
@@ -598,15 +565,15 @@ static int countNonZero16u( const ushort* src, int len )
     if (USE_SSE2)
     {
         __m128i v_zero = _mm_setzero_si128 ();
-        static const uchar * tab = initPopcountTable();
+        __m128i sum = _mm_setzero_si128();
 
         for ( ; i <= len - 8; i += 8)
         {
-            __m128i v_src = _mm_loadu_si128((const __m128i*)(src + i));
-            int val = _mm_movemask_epi8(_mm_packs_epi16(_mm_cmpeq_epi16(v_src, v_zero), v_zero));
-            nz += tab[val];
+            __m128i r0 = _mm_loadu_si128((const __m128i*)(src + i));
+            sum = _mm_add_epi32(sum, _mm_sad_epu8(_mm_sub_epi8(v_zero, _mm_cmpeq_epi16(r0, v_zero)), v_zero));
         }
 
+        nz = i - (_mm_cvtsi128_si32(_mm_add_epi32(sum, _mm_unpackhi_epi64(sum, sum))) >> 1);
         src += i;
     }
 #elif CV_NEON
@@ -649,20 +616,15 @@ static int countNonZero32s( const int* src, int len )
     if (USE_SSE2)
     {
         __m128i v_zero = _mm_setzero_si128 ();
-        static const uchar * tab = initPopcountTable();
+        __m128i sum = _mm_setzero_si128();
 
-        for ( ; i <= len - 8; i += 8)
+        for ( ; i <= len - 4; i += 4)
         {
-            __m128i v_src = _mm_loadu_si128((const __m128i*)(src + i));
-            __m128i v_dst0 = _mm_cmpeq_epi32(v_src, v_zero);
-
-            v_src = _mm_loadu_si128((const __m128i*)(src + i + 4));
-            __m128i v_dst1 = _mm_cmpeq_epi32(v_src, v_zero);
-
-            int val = _mm_movemask_epi8(_mm_packs_epi16(_mm_packs_epi32(v_dst0, v_dst1), v_zero));
-            nz += tab[val];
+            __m128i r0 = _mm_loadu_si128((const __m128i*)(src + i));
+            sum = _mm_add_epi32(sum, _mm_sad_epu8(_mm_sub_epi8(v_zero, _mm_cmpeq_epi32(r0, v_zero)), v_zero));
         }
 
+        nz = i - (_mm_cvtsi128_si32(_mm_add_epi32(sum, _mm_unpackhi_epi64(sum, sum))) >> 2);
         src += i;
     }
 #elif CV_NEON
@@ -706,19 +668,17 @@ static int countNonZero32f( const float* src, int len )
 #if CV_SSE2
     if (USE_SSE2)
     {
-        __m128i v_zero_i = _mm_setzero_si128();
         __m128 v_zero_f = _mm_setzero_ps();
-        static const uchar * tab = initPopcountTable();
+        __m128i v_zero = _mm_setzero_si128 ();
+        __m128i sum = _mm_setzero_si128();
 
-        for ( ; i <= len - 8; i += 8)
+        for ( ; i <= len - 4; i += 4)
         {
-            __m128i v_dst0 = _mm_castps_si128(_mm_cmpeq_ps(_mm_loadu_ps(src + i), v_zero_f));
-            __m128i v_dst1 = _mm_castps_si128(_mm_cmpeq_ps(_mm_loadu_ps(src + i + 4), v_zero_f));
-
-            int val = _mm_movemask_epi8(_mm_packs_epi16(_mm_packs_epi32(v_dst0, v_dst1), v_zero_i));
-            nz += tab[val];
+            __m128 r0 = _mm_loadu_ps(src + i);
+            sum = _mm_add_epi32(sum, _mm_sad_epu8(_mm_sub_epi8(v_zero, _mm_castps_si128(_mm_cmpeq_ps(r0, v_zero_f))), v_zero));
         }
 
+        nz = i - (_mm_cvtsi128_si32(_mm_add_epi32(sum, _mm_unpackhi_epi64(sum, sum))) >> 2);
         src += i;
     }
 #elif CV_NEON
@@ -758,32 +718,7 @@ static int countNonZero32f( const float* src, int len )
 
 static int countNonZero64f( const double* src, int len )
 {
-    int i = 0, nz = 0;
-#if CV_SSE2
-    if (USE_SSE2)
-    {
-        __m128i v_zero_i = _mm_setzero_si128();
-        __m128d v_zero_d = _mm_setzero_pd();
-        static const uchar * tab = initPopcountTable();
-
-        for ( ; i <= len - 8; i += 8)
-        {
-            __m128i v_dst0 = _mm_castpd_si128(_mm_cmpeq_pd(_mm_loadu_pd(src + i), v_zero_d));
-            __m128i v_dst1 = _mm_castpd_si128(_mm_cmpeq_pd(_mm_loadu_pd(src + i + 2), v_zero_d));
-            __m128i v_dst2 = _mm_castpd_si128(_mm_cmpeq_pd(_mm_loadu_pd(src + i + 4), v_zero_d));
-            __m128i v_dst3 = _mm_castpd_si128(_mm_cmpeq_pd(_mm_loadu_pd(src + i + 6), v_zero_d));
-
-            v_dst0 = _mm_packs_epi32(v_dst0, v_dst1);
-            v_dst1 = _mm_packs_epi32(v_dst2, v_dst3);
-
-            int val = _mm_movemask_epi8(_mm_packs_epi16(_mm_packs_epi32(v_dst0, v_dst1), v_zero_i));
-            nz += tab[val];
-        }
-
-        src += i;
-    }
-#endif
-    return nz + countNonZero_(src, len - i);
+    return countNonZero_(src, len);
 }
 
 typedef int (*CountNonZeroFunc)(const uchar*, int);

modules/core/test/test_mat.cpp

@@ -1248,3 +1248,27 @@ TEST(Core_SVD, orthogonality)
         ASSERT_LT(norm(mat_U, Mat::eye(2, 2, type), NORM_INF), 1e-5);
     }
 }
+
+
+TEST(Core_SparseMat, footprint)
+{
+    int n = 1000000;
+    int sz[] = { n, n };
+    SparseMat m(2, sz, CV_64F);
+
+    int nodeSize0 = (int)m.hdr->nodeSize;
+    double dataSize0 = ((double)m.hdr->pool.size() + (double)m.hdr->hashtab.size()*sizeof(size_t))*1e-6;
+    printf("before: node size=%d bytes, data size=%.0f Mbytes\n", nodeSize0, dataSize0);
+
+    for (int i = 0; i < n; i++)
+    {
+        m.ref<double>(i, i) = 1;
+    }
+
+    double dataSize1 = ((double)m.hdr->pool.size() + (double)m.hdr->hashtab.size()*sizeof(size_t))*1e-6;
+    double threshold = (n*nodeSize0*1.6 + n*2.*sizeof(size_t))*1e-6;
+    printf("after: data size=%.0f Mbytes, threshold=%.0f MBytes\n", dataSize1, threshold);
+
+    ASSERT_LE((int)m.hdr->nodeSize, 32);
+    ASSERT_LE(dataSize1, threshold);
+}

modules/hal/src/matrix.cpp

@@ -49,7 +49,7 @@ namespace cv { namespace hal {
 \****************************************************************************************/
 
 template<typename _Tp> static inline int
-LUImpl(_Tp* A, size_t astep, int m, _Tp* b, size_t bstep, int n)
+LUImpl(_Tp* A, size_t astep, int m, _Tp* b, size_t bstep, int n, _Tp eps)
 {
     int i, j, k, p = 1;
     astep /= sizeof(A[0]);
@@ -63,7 +63,7 @@ LUImpl(_Tp* A, size_t astep, int m, _Tp* b, size_t bstep, int n)
             if( std::abs(A[j*astep + i]) > std::abs(A[k*astep + i]) )
                 k = j;
 
-        if( std::abs(A[k*astep + i]) < std::numeric_limits<_Tp>::epsilon() )
+        if( std::abs(A[k*astep + i]) < eps )
             return 0;
 
         if( k != i )
@@ -111,13 +111,13 @@ LUImpl(_Tp* A, size_t astep, int m, _Tp* b, size_t bstep, int n)
 
 int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n)
 {
-    return LUImpl(A, astep, m, b, bstep, n);
+    return LUImpl(A, astep, m, b, bstep, n, FLT_EPSILON*10);
 }
 
 
 int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n)
 {
-    return LUImpl(A, astep, m, b, bstep, n);
+    return LUImpl(A, astep, m, b, bstep, n, DBL_EPSILON*100);
 }
 
 

modules/imgproc/src/filter.cpp

@@ -3455,7 +3455,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
         bool symmetrical = (this->symmetryType & KERNEL_SYMMETRICAL) != 0;
         bool is_1_2_1 = ky[0] == 2 && ky[1] == 1;
         bool is_1_m2_1 = ky[0] == -2 && ky[1] == 1;
-        bool is_m1_0_1 = ky[1] == 1 || ky[1] == -1;
+        bool is_m1_0_1 = ky[0] == 0 && (ky[1] == 1 || ky[1] == -1);
         ST f0 = ky[0], f1 = ky[1];
         ST _delta = this->delta;
         CastOp castOp = this->castOp0;
@@ -3486,13 +3486,12 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                         D[i+2] = castOp(s0);
                         D[i+3] = castOp(s1);
                     }
-                    #else
+                    #endif
                     for( ; i < width; i ++ )
                     {
                         ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta;
                         D[i] = castOp(s0);
                     }
-                    #endif
                 }
                 else if( is_1_m2_1 )
                 {
@@ -3509,17 +3508,16 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                         D[i+2] = castOp(s0);
                         D[i+3] = castOp(s1);
                     }
-                    #else
+                    #endif
                     for( ; i < width; i ++ )
                     {
                         ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta;
                         D[i] = castOp(s0);
                     }
-                    #endif
                 }
                 else
                 {
-                   #if CV_ENABLE_UNROLLED
+                    #if CV_ENABLE_UNROLLED
                     for( ; i <= width - 4; i += 4 )
                     {
                         ST s0 = (S0[i] + S2[i])*f1 + S1[i]*f0 + _delta;
@@ -3532,16 +3530,13 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                         D[i+2] = castOp(s0);
                         D[i+3] = castOp(s1);
                     }
-                    #else
+                    #endif
                     for( ; i < width; i ++ )
                     {
                         ST s0 = (S0[i] + S2[i])*f1 + S1[i]*f0 + _delta;
                         D[i] = castOp(s0);
                     }
-                    #endif
                 }
-                for( ; i < width; i++ )
-                    D[i] = castOp((S0[i] + S2[i])*f1 + S1[i]*f0 + _delta);
             }
             else
             {
@@ -3549,7 +3544,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                 {
                     if( f1 < 0 )
                         std::swap(S0, S2);
-                   #if CV_ENABLE_UNROLLED
+                    #if CV_ENABLE_UNROLLED
                     for( ; i <= width - 4; i += 4 )
                     {
                         ST s0 = S2[i] - S0[i] + _delta;
@@ -3562,19 +3557,18 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                         D[i+2] = castOp(s0);
                         D[i+3] = castOp(s1);
                     }
-                    #else
+                    #endif
                     for( ; i < width; i ++ )
                     {
                         ST s0 = S2[i] - S0[i] + _delta;
                         D[i] = castOp(s0);
                     }
-                    #endif
                     if( f1 < 0 )
                         std::swap(S0, S2);
                 }
                 else
                 {
-                   #if CV_ENABLE_UNROLLED
+                    #if CV_ENABLE_UNROLLED
                     for( ; i <= width - 4; i += 4 )
                     {
                         ST s0 = (S2[i] - S0[i])*f1 + _delta;
@@ -3588,10 +3582,9 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
                         D[i+3] = castOp(s1);
                     }
                     #endif
+                    for( ; i < width; i++ )
+                        D[i] = castOp((S2[i] - S0[i])*f1 + _delta);
                 }
-
-                for( ; i < width; i++ )
-                    D[i] = castOp((S2[i] - S0[i])*f1 + _delta);
             }
         }
     }

modules/imgproc/src/imgwarp.cpp

@@ -3805,20 +3805,20 @@ static void remapBilinear( const Mat& _src, Mat& _dst, const Mat& _xy,
     typedef typename CastOp::rtype T;
     typedef typename CastOp::type1 WT;
     Size ssize = _src.size(), dsize = _dst.size();
-    int cn = _src.channels();
+    int k, cn = _src.channels();
     const AT* wtab = (const AT*)_wtab;
     const T* S0 = _src.ptr<T>();
     size_t sstep = _src.step/sizeof(S0[0]);
-    Scalar_<T> cval(saturate_cast<T>(_borderValue[0]),
-        saturate_cast<T>(_borderValue[1]),
-        saturate_cast<T>(_borderValue[2]),
-        saturate_cast<T>(_borderValue[3]));
+    T cval[CV_CN_MAX];
     int dx, dy;
     CastOp castOp;
     VecOp vecOp;
 
+    for( k = 0; k < cn; k++ )
+        cval[k] = saturate_cast<T>(_borderValue[k & 3]);
+
     unsigned width1 = std::max(ssize.width-1, 0), height1 = std::max(ssize.height-1, 0);
-    CV_Assert( cn <= 4 && ssize.area() > 0 );
+    CV_Assert( ssize.area() > 0 );
 #if CV_SSE2
     if( _src.type() == CV_8UC3 )
         width1 = std::max(ssize.width-2, 0);
@@ -3882,7 +3882,7 @@ static void remapBilinear( const Mat& _src, Mat& _dst, const Mat& _xy,
                         WT t2 = S[2]*w[0] + S[5]*w[1] + S[sstep+2]*w[2] + S[sstep+5]*w[3];
                         D[0] = castOp(t0); D[1] = castOp(t1); D[2] = castOp(t2);
                     }
-                else
+                else if( cn == 4 )
                     for( ; dx < X1; dx++, D += 4 )
                     {
                         int sx = XY[dx*2], sy = XY[dx*2+1];
@@ -3895,6 +3895,18 @@ static void remapBilinear( const Mat& _src, Mat& _dst, const Mat& _xy,
                         t1 = S[3]*w[0] + S[7]*w[1] + S[sstep+3]*w[2] + S[sstep+7]*w[3];
                         D[2] = castOp(t0); D[3] = castOp(t1);
                     }
+                else
+                    for( ; dx < X1; dx++, D += cn )
+                    {
+                        int sx = XY[dx*2], sy = XY[dx*2+1];
+                        const AT* w = wtab + FXY[dx]*4;
+                        const T* S = S0 + sy*sstep + sx*cn;
+                        for( k = 0; k < cn; k++ )
+                        {
+                            WT t0 = S[k]*w[0] + S[k+cn]*w[1] + S[sstep+k]*w[2] + S[sstep+k+cn]*w[3];
+                            D[k] = castOp(t0);
+                        }
+                    }
             }
             else
             {
@@ -3948,7 +3960,7 @@ static void remapBilinear( const Mat& _src, Mat& _dst, const Mat& _xy,
                 else
                     for( ; dx < X1; dx++, D += cn )
                     {
-                        int sx = XY[dx*2], sy = XY[dx*2+1], k;
+                        int sx = XY[dx*2], sy = XY[dx*2+1];
                         if( borderType == BORDER_CONSTANT &&
                             (sx >= ssize.width || sx+1 < 0 ||
                              sy >= ssize.height || sy+1 < 0) )

modules/imgproc/test/test_contours.cpp

@@ -40,6 +40,7 @@
 //M*/
 
 #include "test_precomp.hpp"
+#include "opencv2/highgui.hpp"
 
 using namespace cv;
 using namespace std;
@@ -429,4 +430,64 @@ TEST(Core_Drawing, polylines)
     int cnt = countNonZero(img);
     ASSERT_EQ(cnt, 21);
 }
+
+//rotate/flip a quadrant appropriately
+static void rot(int n, int *x, int *y, int rx, int ry)
+{
+    if (ry == 0) {
+        if (rx == 1) {
+            *x = n-1 - *x;
+            *y = n-1 - *y;
+        }
+
+        //Swap x and y
+        int t  = *x;
+        *x = *y;
+        *y = t;
+    }
+}
+
+static void d2xy(int n, int d, int *x, int *y)
+{
+    int rx, ry, s, t=d;
+    *x = *y = 0;
+    for (s=1; s<n; s*=2)
+    {
+        rx = 1 & (t/2);
+        ry = 1 & (t ^ rx);
+        rot(s, x, y, rx, ry);
+        *x += s * rx;
+        *y += s * ry;
+        t /= 4;
+    }
+}
+
+TEST(Imgproc_FindContours, hilbert)
+{
+    int n = 64, n2 = n*n, scale = 10, w = (n + 2)*scale;
+    Point ofs(scale, scale);
+    Mat img(w, w, CV_8U);
+    img.setTo(Scalar::all(0));
+
+    Point p(0,0);
+    for( int i = 0; i < n2; i++ )
+    {
+        Point q(0,0);
+        d2xy(n2, i, &q.x, &q.y);
+        line(img, p*scale + ofs, q*scale + ofs, Scalar::all(255));
+        p = q;
+    }
+    dilate(img, img, Mat());
+    vector<vector<Point> > contours;
+    findContours(img, contours, noArray(), RETR_LIST, CHAIN_APPROX_SIMPLE);
+    printf("ncontours = %d, contour[0].npoints=%d\n", (int)contours.size(), (int)contours[0].size());
+    img.setTo(Scalar::all(0));
+
+    drawContours(img, contours, 0, Scalar::all(255), 1);
+    //imshow("hilbert", img);
+    //waitKey();
+    ASSERT_EQ(1, (int)contours.size());
+    ASSERT_EQ(9832, (int)contours[0].size());
+}
+
 /* End of file. */

modules/imgproc/test/test_filter.cpp

@@ -1918,3 +1918,37 @@ TEST(Imgproc_Blur, borderTypes)
     EXPECT_EQ(expected_dst.size(), dst.size());
     EXPECT_DOUBLE_EQ(0.0, cvtest::norm(expected_dst, dst, NORM_INF));
 }
+
+TEST(Imgproc_Morphology, iterated)
+{
+    RNG& rng = theRNG();
+    for( int iter = 0; iter < 30; iter++ )
+    {
+        int width = rng.uniform(5, 33);
+        int height = rng.uniform(5, 33);
+        int cn = rng.uniform(1, 5);
+        int iterations = rng.uniform(1, 11);
+        int op = rng.uniform(0, 2);
+        Mat src(height, width, CV_8UC(cn)), dst0, dst1, dst2;
+
+        randu(src, 0, 256);
+        if( op == 0 )
+            dilate(src, dst0, Mat(), Point(-1,-1), iterations);
+        else
+            erode(src, dst0, Mat(), Point(-1,-1), iterations);
+
+        for( int i = 0; i < iterations; i++ )
+            if( op == 0 )
+                dilate(i == 0 ? src : dst1, dst1, Mat(), Point(-1,-1), 1);
+            else
+                erode(i == 0 ? src : dst1, dst1, Mat(), Point(-1,-1), 1);
+
+        Mat kern = getStructuringElement(MORPH_RECT, Size(3,3));
+        if( op == 0 )
+            dilate(src, dst2, kern, Point(-1,-1), iterations);
+        else
+            erode(src, dst2, kern, Point(-1,-1), iterations);
+        ASSERT_EQ(0.0, norm(dst0, dst1, NORM_INF));
+        ASSERT_EQ(0.0, norm(dst0, dst2, NORM_INF));
+    }
+}

modules/imgproc/test/test_imgwarp.cpp

@@ -1632,4 +1632,64 @@ TEST(Resize, Area_half)
     }
 }
 
+TEST(Imgproc_Warp, multichannel)
+{
+    RNG& rng = theRNG();
+    for( int iter = 0; iter < 30; iter++ )
+    {
+        int width = rng.uniform(3, 333);
+        int height = rng.uniform(3, 333);
+        int cn = rng.uniform(1, 10);
+        Mat src(height, width, CV_8UC(cn)), dst;
+        //randu(src, 0, 256);
+        src.setTo(0.);
+
+        Mat rot = getRotationMatrix2D(Point2f(0.f, 0.f), 1, 1);
+        warpAffine(src, dst, rot, src.size());
+        ASSERT_EQ(0.0, norm(dst, NORM_INF));
+        Mat rot2 = Mat::eye(3, 3, rot.type());
+        rot.copyTo(rot2.rowRange(0, 2));
+        warpPerspective(src, dst, rot2, src.size());
+        ASSERT_EQ(0.0, norm(dst, NORM_INF));
+    }
+}
+
+TEST(Imgproc_GetAffineTransform, singularity)
+{
+    Point2f A_sample[3];
+    A_sample[0] = Point2f(8.f, 9.f);
+    A_sample[1] = Point2f(40.f, 41.f);
+    A_sample[2] = Point2f(47.f, 48.f);
+    Point2f B_sample[3];
+    B_sample[0] = Point2f(7.37465f, 11.8295f);
+    B_sample[1] = Point2f(15.0113f, 12.8994f);
+    B_sample[2] = Point2f(38.9943f, 9.56297f);
+    Mat trans = getAffineTransform(A_sample, B_sample);
+    ASSERT_EQ(0.0, norm(trans, NORM_INF));
+}
+
+TEST(Imgproc_Remap, DISABLED_memleak)
+{
+    Mat src;
+    const int N = 400;
+    src.create(N, N, CV_8U);
+    randu(src, 0, 256);
+    Mat map_x, map_y, dst;
+    dst.create( src.size(), src.type() );
+    map_x.create( src.size(), CV_32FC1 );
+    map_y.create( src.size(), CV_32FC1 );
+    randu(map_x, 0., N+0.);
+    randu(map_y, 0., N+0.);
+
+    for( int iter = 0; iter < 10000; iter++ )
+    {
+        if(iter % 100 == 0)
+        {
+            putchar('.');
+            fflush(stdout);
+        }
+        remap(src, dst, map_x, map_y, CV_INTER_LINEAR);
+    }
+}
+
 /* End of file. */

modules/photo/src/calibrate.cpp

@@ -90,7 +90,9 @@ public:
 
             for(int i = 0, x = step_x / 2; i < x_points; i++, x += step_x) {
                 for(int j = 0, y = step_y / 2; j < y_points; j++, y += step_y) {
-                    sample_points.push_back(Point(x, y));
+                    if( 0 <= x && x < images[0].cols &&
+                        0 <= y && y < images[0].rows )
+                        sample_points.push_back(Point(x, y));
                 }
             }
         }

modules/photo/src/denoising.cpp

@@ -50,42 +50,50 @@ static void fastNlMeansDenoising_( const Mat& src, Mat& dst, const std::vector<f
                                    int templateWindowSize, int searchWindowSize)
 {
     int hn = (int)h.size();
+    double granularity = (double)std::max(1., (double)dst.total()/(1 << 17));
 
     switch (CV_MAT_CN(src.type())) {
         case 1:
             parallel_for_(cv::Range(0, src.rows),
                           FastNlMeansDenoisingInvoker<ST, IT, UIT, D, int>(
-                              src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                              src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                          granularity);
             break;
         case 2:
             if (hn == 1)
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         case 3:
             if (hn == 1)
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         case 4:
             if (hn == 1)
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, src.rows),
                               FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
-                                  src, dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  src, dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         default:
             CV_Error(Error::StsBadArg,
@@ -237,6 +245,7 @@ static void fastNlMeansDenoisingMulti_( const std::vector<Mat>& srcImgs, Mat& ds
                                         int templateWindowSize, int searchWindowSize)
 {
     int hn = (int)h.size();
+    double granularity = (double)std::max(1., (double)dst.total()/(1 << 16));
 
     switch (srcImgs[0].type())
     {
@@ -244,43 +253,50 @@ static void fastNlMeansDenoisingMulti_( const std::vector<Mat>& srcImgs, Mat& ds
             parallel_for_(cv::Range(0, srcImgs[0].rows),
                           FastNlMeansMultiDenoisingInvoker<uchar, IT, UIT, D, int>(
                               srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                              dst, templateWindowSize, searchWindowSize, &h[0]));
+                              dst, templateWindowSize, searchWindowSize, &h[0]),
+                          granularity);
             break;
         case CV_8UC2:
             if (hn == 1)
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         case CV_8UC3:
             if (hn == 1)
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         case CV_8UC4:
             if (hn == 1)
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             else
                 parallel_for_(cv::Range(0, srcImgs[0].rows),
                               FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
                                   srcImgs, imgToDenoiseIndex, temporalWindowSize,
-                                  dst, templateWindowSize, searchWindowSize, &h[0]));
+                                  dst, templateWindowSize, searchWindowSize, &h[0]),
+                              granularity);
             break;
         default:
             CV_Error(Error::StsBadArg,

modules/photo/src/tonemap.cpp

@@ -47,6 +47,12 @@
 namespace cv
 {
 
+inline void log_(const Mat& src, Mat& dst)
+{
+    max(src, Scalar::all(1e-4), dst);
+    log(dst, dst);
+}
+
 class TonemapImpl : public Tonemap
 {
 public:
@@ -122,7 +128,7 @@ public:
         Mat gray_img;
         cvtColor(img, gray_img, COLOR_RGB2GRAY);
         Mat log_img;
-        log(gray_img, log_img);
+        log_(gray_img, log_img);
         float mean = expf(static_cast<float>(sum(log_img)[0]) / log_img.total());
         gray_img /= mean;
         log_img.release();
@@ -205,7 +211,7 @@ public:
         Mat gray_img;
         cvtColor(img, gray_img, COLOR_RGB2GRAY);
         Mat log_img;
-        log(gray_img, log_img);
+        log_(gray_img, log_img);
         Mat map_img;
         bilateralFilter(log_img, map_img, -1, sigma_color, sigma_space);
 
@@ -289,7 +295,7 @@ public:
         Mat gray_img;
         cvtColor(img, gray_img, COLOR_RGB2GRAY);
         Mat log_img;
-        log(gray_img, log_img);
+        log_(gray_img, log_img);
 
         float log_mean = static_cast<float>(sum(log_img)[0] / log_img.total());
         double log_min, log_max;
@@ -383,7 +389,7 @@ public:
         Mat gray_img;
         cvtColor(img, gray_img, COLOR_RGB2GRAY);
         Mat log_img;
-        log(gray_img, log_img);
+        log_(gray_img, log_img);
 
         std::vector<Mat> x_contrast, y_contrast;
         getContrast(log_img, x_contrast, y_contrast);

modules/photo/test/test_denoising.cpp

@@ -156,3 +156,14 @@ TEST(Photo_White, issue_2646)
 
     ASSERT_EQ(0, nonWhitePixelsCount);
 }
+
+TEST(Photo_Denoising, speed)
+{
+    string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png";
+    Mat src = imread(imgname, 0), dst;
+
+    double t = (double)getTickCount();
+    fastNlMeansDenoising(src, dst, 5, 7, 21);
+    t = (double)getTickCount() - t;
+    printf("execution time: %gms\n", t*1000./getTickFrequency());
+}