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Merge pull request #4018 from vpisarev:imgproc_video_fixes

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This commit is contained in:
Vadim Pisarevsky 2015-05-15 16:15:01 +00:00
commit a85bb091c8
13 changed files with 294 additions and 138 deletions
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12
modules/core/src/matrix.cpp
View File

@ -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);

105
modules/core/src/stat.cpp
View File

@ -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);

24
modules/core/test/test_mat.cpp
View File

@ -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);
}

8
modules/hal/src/matrix.cpp
View File

@ -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);
}

19
modules/imgproc/src/filter.cpp
View File

@ -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 )
{
@ -3509,13 +3508,12 @@ 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
{
@ -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
{
@ -3562,13 +3557,12 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
D[i+2] = castOp(s0);
D[i+3] = castOp(s1);
}
#else
#endif
for( ; i < width; i ++ )
{
ST s0 = S2[i] - S0[i] + _delta;
D[i] = castOp(s0);
}
#endif
if( f1 < 0 )
std::swap(S0, S2);
}
@ -3588,13 +3582,12 @@ 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);
}
}
}
}
};
template<typename ST, typename DT> struct Cast

28
modules/imgproc/src/imgwarp.cpp
View File

@ -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) )

61
modules/imgproc/test/test_contours.cpp
View File

@ -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. */

34
modules/imgproc/test/test_filter.cpp
View File

@ -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));
}
}

60
modules/imgproc/test/test_imgwarp.cpp
View File

@ -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. */

2
modules/photo/src/calibrate.cpp
View File

@ -90,6 +90,8 @@ 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) {
if( 0 <= x && x < images[0].cols &&
0 <= y && y < images[0].rows )
sample_points.push_back(Point(x, y));
}
}

44
modules/photo/src/denoising.cpp
View File

@ -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,

14
modules/photo/src/tonemap.cpp
View File

@ -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);

11
modules/photo/test/test_denoising.cpp
View File

@ -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());
}