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Merge remote-tracking branch 'upstream/master'

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Rebase to lates upstream
This commit is contained in:
Tony 2014-04-13 10:39:32 +01:00
commit befdef9685
21 changed files with 1437 additions and 804 deletions
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2
cmake/OpenCVFindLibsGUI.cmake
View File

@ -82,7 +82,7 @@ endif(WITH_OPENGL)
if(APPLE)
if(WITH_CARBON)
set(HAVE_CARBON YES)
elseif(NOT IOS)
elseif(NOT IOS AND CMAKE_COMPILER_IS_CLANGCXX)
set(HAVE_COCOA YES)
endif()
endif()

2
cmake/OpenCVFindLibsVideo.cmake
View File

@ -273,7 +273,7 @@ endif()
if (NOT IOS)
if(WITH_QUICKTIME)
set(HAVE_QUICKTIME YES)
elseif(APPLE)
elseif(APPLE AND CMAKE_COMPILER_IS_CLANGCXX)
set(HAVE_QTKIT YES)
endif()
endif()

2
modules/core/src/arithm.cpp
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@ -448,11 +448,13 @@ template<typename T> struct OpNot
T operator()( T a, T ) const { return ~a; }
};
#if (ARITHM_USE_IPP == 1)
static inline void fixSteps(Size sz, size_t elemSize, size_t& step1, size_t& step2, size_t& step)
{
if( sz.height == 1 )
step1 = step2 = step = sz.width*elemSize;
}
#endif
static void add8u( const uchar* src1, size_t step1,
const uchar* src2, size_t step2,

1
modules/core/src/mathfuncs.cpp
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@ -46,7 +46,6 @@
namespace cv
{
static const int MAX_BLOCK_SIZE = 1024;
typedef void (*MathFunc)(const void* src, void* dst, int len);
static const float atan2_p1 = 0.9997878412794807f*(float)(180/CV_PI);

30
modules/core/src/stat.cpp
View File

@ -680,7 +680,8 @@ static bool ocl_countNonZero( InputArray _src, int & res )
int cv::countNonZero( InputArray _src )
{
CV_Assert( _src.channels() == 1 );
int type = _src.type(), cn = CV_MAT_CN(type);
CV_Assert( cn == 1 );
#ifdef HAVE_OPENCL
int res = -1;
@ -690,8 +691,33 @@ int cv::countNonZero( InputArray _src )
#endif
Mat src = _src.getMat();
CountNonZeroFunc func = getCountNonZeroTab(src.depth());
#if defined HAVE_IPP && !defined HAVE_IPP_ICV_ONLY
if (src.dims <= 2 || src.isContinuous())
{
IppiSize roiSize = { src.cols, src.rows };
Ipp32s count, srcstep = (Ipp32s)src.step;
IppStatus status = (IppStatus)-1;
if (src.isContinuous())
{
roiSize.width = (Ipp32s)src.total();
roiSize.height = 1;
srcstep = (Ipp32s)src.total() * CV_ELEM_SIZE(type);
}
int depth = CV_MAT_DEPTH(type);
if (depth == CV_8U)
status = ippiCountInRange_8u_C1R((const Ipp8u *)src.data, srcstep, roiSize, &count, 0, 0);
else if (depth == CV_32F)
status = ippiCountInRange_32f_C1R((const Ipp32f *)src.data, srcstep, roiSize, &count, 0, 0);
if (status >= 0)
return (Ipp32s)src.total() - count;
}
#endif
CountNonZeroFunc func = getCountNonZeroTab(src.depth());
CV_Assert( func != 0 );
const Mat* arrays[] = {&src, 0};

3
modules/core/test/test_countnonzero.cpp
View File

@ -52,9 +52,6 @@ using namespace std;
#define sign(a) a > 0 ? 1 : a == 0 ? 0 : -1
const int FLOAT_TYPE [2] = {CV_32F, CV_64F};
const int INT_TYPE [5] = {CV_8U, CV_8S, CV_16U, CV_16S, CV_32S};
#define MAX_WIDTH 100
#define MAX_HEIGHT 100

12
modules/features2d/include/opencv2/features2d.hpp
View File

@ -405,8 +405,16 @@ public:
protected:
virtual void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
void buildPattern();
uchar meanIntensity( InputArray image, InputArray integral, const float kp_x, const float kp_y,
const unsigned int scale, const unsigned int rot, const unsigned int point ) const;
template <typename imgType, typename iiType>
imgType meanIntensity( InputArray image, InputArray integral, const float kp_x, const float kp_y,
const unsigned int scale, const unsigned int rot, const unsigned int point ) const;
template <typename srcMatType, typename iiMatType>
void computeDescriptors( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
template <typename srcMatType>
void extractDescriptor(srcMatType *pointsValue, void ** ptr) const;
bool orientationNormalized; //true if the orientation is normalized, false otherwise
bool scaleNormalized; //true if the scale is normalized, false otherwise

269
modules/features2d/src/freak.cpp
View File

@ -239,13 +239,129 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
((FREAK*)this)->buildPattern();
// Convert to gray if not already
Mat grayImage = image;
// if( image.channels() > 1 )
// cvtColor( image, grayImage, COLOR_BGR2GRAY );
// Use 32-bit integers if we won't overflow in the integral image
if ((image.depth() == CV_8U || image.depth() == CV_8S) &&
(image.rows * image.cols) < 8388608 ) // 8388608 = 2 ^ (32 - 8(bit depth) - 1(sign bit))
{
// Create the integral image appropriate for our type & usage
if (image.depth() == CV_8U)
computeDescriptors<uchar, int>(grayImage, keypoints, _descriptors);
else if (image.depth() == CV_8S)
computeDescriptors<char, int>(grayImage, keypoints, _descriptors);
else
CV_Error( Error::StsUnsupportedFormat, "" );
} else {
// Create the integral image appropriate for our type & usage
if ( image.depth() == CV_8U )
computeDescriptors<uchar, double>(grayImage, keypoints, _descriptors);
else if ( image.depth() == CV_8S )
computeDescriptors<char, double>(grayImage, keypoints, _descriptors);
else if ( image.depth() == CV_16U )
computeDescriptors<ushort, double>(grayImage, keypoints, _descriptors);
else if ( image.depth() == CV_16S )
computeDescriptors<short, double>(grayImage, keypoints, _descriptors);
else
CV_Error( Error::StsUnsupportedFormat, "" );
}
}
template <typename srcMatType>
void FREAK::extractDescriptor(srcMatType *pointsValue, void ** ptr) const
{
std::bitset<FREAK_NB_PAIRS>** ptrScalar = (std::bitset<FREAK_NB_PAIRS>**) ptr;
// extracting descriptor preserving the order of SSE version
int cnt = 0;
for( int n = 7; n < FREAK_NB_PAIRS; n += 128)
{
for( int m = 8; m--; )
{
int nm = n-m;
for(int kk = nm+15*8; kk >= nm; kk-=8, ++cnt)
{
(*ptrScalar)->set(kk, pointsValue[descriptionPairs[cnt].i] >= pointsValue[descriptionPairs[cnt].j]);
}
}
}
--(*ptrScalar);
}
#if CV_SSE2
template <>
void FREAK::extractDescriptor(uchar *pointsValue, void ** ptr) const
{
__m128i** ptrSSE = (__m128i**) ptr;
// note that comparisons order is modified in each block (but first 128 comparisons remain globally the same-->does not affect the 128,384 bits segmanted matching strategy)
int cnt = 0;
for( int n = FREAK_NB_PAIRS/128; n-- ; )
{
__m128i result128 = _mm_setzero_si128();
for( int m = 128/16; m--; cnt += 16 )
{
__m128i operand1 = _mm_set_epi8(pointsValue[descriptionPairs[cnt+0].i],
pointsValue[descriptionPairs[cnt+1].i],
pointsValue[descriptionPairs[cnt+2].i],
pointsValue[descriptionPairs[cnt+3].i],
pointsValue[descriptionPairs[cnt+4].i],
pointsValue[descriptionPairs[cnt+5].i],
pointsValue[descriptionPairs[cnt+6].i],
pointsValue[descriptionPairs[cnt+7].i],
pointsValue[descriptionPairs[cnt+8].i],
pointsValue[descriptionPairs[cnt+9].i],
pointsValue[descriptionPairs[cnt+10].i],
pointsValue[descriptionPairs[cnt+11].i],
pointsValue[descriptionPairs[cnt+12].i],
pointsValue[descriptionPairs[cnt+13].i],
pointsValue[descriptionPairs[cnt+14].i],
pointsValue[descriptionPairs[cnt+15].i]);
__m128i operand2 = _mm_set_epi8(pointsValue[descriptionPairs[cnt+0].j],
pointsValue[descriptionPairs[cnt+1].j],
pointsValue[descriptionPairs[cnt+2].j],
pointsValue[descriptionPairs[cnt+3].j],
pointsValue[descriptionPairs[cnt+4].j],
pointsValue[descriptionPairs[cnt+5].j],
pointsValue[descriptionPairs[cnt+6].j],
pointsValue[descriptionPairs[cnt+7].j],
pointsValue[descriptionPairs[cnt+8].j],
pointsValue[descriptionPairs[cnt+9].j],
pointsValue[descriptionPairs[cnt+10].j],
pointsValue[descriptionPairs[cnt+11].j],
pointsValue[descriptionPairs[cnt+12].j],
pointsValue[descriptionPairs[cnt+13].j],
pointsValue[descriptionPairs[cnt+14].j],
pointsValue[descriptionPairs[cnt+15].j]);
__m128i workReg = _mm_min_epu8(operand1, operand2); // emulated "not less than" for 8-bit UNSIGNED integers
workReg = _mm_cmpeq_epi8(workReg, operand2); // emulated "not less than" for 8-bit UNSIGNED integers
workReg = _mm_and_si128(_mm_set1_epi16(short(0x8080 >> m)), workReg); // merge the last 16 bits with the 128bits std::vector until full
result128 = _mm_or_si128(result128, workReg);
}
(**ptrSSE) = result128;
++(*ptrSSE);
}
(*ptrSSE) -= 8;
}
#endif
template <typename srcMatType, typename iiMatType>
void FREAK::computeDescriptors( InputArray _image, std::vector<KeyPoint>& keypoints, OutputArray _descriptors ) const {
Mat image = _image.getMat();
Mat imgIntegral;
integral(image, imgIntegral);
integral(image, imgIntegral, DataType<iiMatType>::type);
std::vector<int> kpScaleIdx(keypoints.size()); // used to save pattern scale index corresponding to each keypoints
const std::vector<int>::iterator ScaleIdxBegin = kpScaleIdx.begin(); // used in std::vector erase function
const std::vector<cv::KeyPoint>::iterator kpBegin = keypoints.begin(); // used in std::vector erase function
const float sizeCst = static_cast<float>(FREAK_NB_SCALES/(FREAK_LOG2* nOctaves));
uchar pointsValue[FREAK_NB_POINTS];
srcMatType pointsValue[FREAK_NB_POINTS];
int thetaIdx = 0;
int direction0;
int direction1;
@ -300,13 +416,10 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
_descriptors.create((int)keypoints.size(), FREAK_NB_PAIRS/8, CV_8U);
_descriptors.setTo(Scalar::all(0));
Mat descriptors = _descriptors.getMat();
#if CV_SSE2
__m128i* ptr= (__m128i*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
#else
std::bitset<FREAK_NB_PAIRS>* ptr = (std::bitset<FREAK_NB_PAIRS>*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
#endif
for( size_t k = keypoints.size(); k--; )
{
void *ptr = descriptors.data+(keypoints.size()-1)*descriptors.step[0];
for( size_t k = keypoints.size(); k--; ) {
// estimate orientation (gradient)
if( !orientationNormalized )
{
@ -316,9 +429,10 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
else
{
// get the points intensity value in the un-rotated pattern
for( int i = FREAK_NB_POINTS; i--; )
{
pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], 0, i);
for( int i = FREAK_NB_POINTS; i--; ) {
pointsValue[i] = meanIntensity<srcMatType, iiMatType>(image, imgIntegral,
keypoints[k].pt.x, keypoints[k].pt.y,
kpScaleIdx[k], 0, i);
}
direction0 = 0;
direction1 = 0;
@ -339,80 +453,14 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
thetaIdx -= FREAK_NB_ORIENTATION;
}
// extract descriptor at the computed orientation
for( int i = FREAK_NB_POINTS; i--; )
{
pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], thetaIdx, i);
for( int i = FREAK_NB_POINTS; i--; ) {
pointsValue[i] = meanIntensity<srcMatType, iiMatType>(image, imgIntegral,
keypoints[k].pt.x, keypoints[k].pt.y,
kpScaleIdx[k], thetaIdx, i);
}
#if CV_SSE2
// note that comparisons order is modified in each block (but first 128 comparisons remain globally the same-->does not affect the 128,384 bits segmanted matching strategy)
int cnt = 0;
for( int n = FREAK_NB_PAIRS/128; n-- ; )
{
__m128i result128 = _mm_setzero_si128();
for( int m = 128/16; m--; cnt += 16 )
{
__m128i operand1 = _mm_set_epi8(
pointsValue[descriptionPairs[cnt+0].i],
pointsValue[descriptionPairs[cnt+1].i],
pointsValue[descriptionPairs[cnt+2].i],
pointsValue[descriptionPairs[cnt+3].i],
pointsValue[descriptionPairs[cnt+4].i],
pointsValue[descriptionPairs[cnt+5].i],
pointsValue[descriptionPairs[cnt+6].i],
pointsValue[descriptionPairs[cnt+7].i],
pointsValue[descriptionPairs[cnt+8].i],
pointsValue[descriptionPairs[cnt+9].i],
pointsValue[descriptionPairs[cnt+10].i],
pointsValue[descriptionPairs[cnt+11].i],
pointsValue[descriptionPairs[cnt+12].i],
pointsValue[descriptionPairs[cnt+13].i],
pointsValue[descriptionPairs[cnt+14].i],
pointsValue[descriptionPairs[cnt+15].i]);
__m128i operand2 = _mm_set_epi8(
pointsValue[descriptionPairs[cnt+0].j],
pointsValue[descriptionPairs[cnt+1].j],
pointsValue[descriptionPairs[cnt+2].j],
pointsValue[descriptionPairs[cnt+3].j],
pointsValue[descriptionPairs[cnt+4].j],
pointsValue[descriptionPairs[cnt+5].j],
pointsValue[descriptionPairs[cnt+6].j],
pointsValue[descriptionPairs[cnt+7].j],
pointsValue[descriptionPairs[cnt+8].j],
pointsValue[descriptionPairs[cnt+9].j],
pointsValue[descriptionPairs[cnt+10].j],
pointsValue[descriptionPairs[cnt+11].j],
pointsValue[descriptionPairs[cnt+12].j],
pointsValue[descriptionPairs[cnt+13].j],
pointsValue[descriptionPairs[cnt+14].j],
pointsValue[descriptionPairs[cnt+15].j]);
__m128i workReg = _mm_min_epu8(operand1, operand2); // emulated "not less than" for 8-bit UNSIGNED integers
workReg = _mm_cmpeq_epi8(workReg, operand2); // emulated "not less than" for 8-bit UNSIGNED integers
workReg = _mm_and_si128(_mm_set1_epi16(short(0x8080 >> m)), workReg); // merge the last 16 bits with the 128bits std::vector until full
result128 = _mm_or_si128(result128, workReg);
}
(*ptr) = result128;
++ptr;
}
ptr -= 8;
#else
// extracting descriptor preserving the order of SSE version
int cnt = 0;
for( int n = 7; n < FREAK_NB_PAIRS; n += 128)
{
for( int m = 8; m--; )
{
int nm = n-m;
for(int kk = nm+15*8; kk >= nm; kk-=8, ++cnt)
{
ptr->set(kk, pointsValue[descriptionPairs[cnt].i] >= pointsValue[descriptionPairs[cnt].j]);
}
}
}
--ptr;
#endif
// Extract descriptor
extractDescriptor<srcMatType>(pointsValue, &ptr);
}
}
else // extract all possible comparisons for selection
@ -434,7 +482,9 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
{
//get the points intensity value in the un-rotated pattern
for( int i = FREAK_NB_POINTS;i--; )
pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], 0, i);
pointsValue[i] = meanIntensity<srcMatType, iiMatType>(image, imgIntegral,
keypoints[k].pt.x,keypoints[k].pt.y,
kpScaleIdx[k], 0, i);
direction0 = 0;
direction1 = 0;
@ -456,10 +506,10 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
thetaIdx -= FREAK_NB_ORIENTATION;
}
// get the points intensity value in the rotated pattern
for( int i = FREAK_NB_POINTS; i--; )
{
pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,
keypoints[k].pt.y, kpScaleIdx[k], thetaIdx, i);
for( int i = FREAK_NB_POINTS; i--; ) {
pointsValue[i] = meanIntensity<srcMatType, iiMatType>(image, imgIntegral,
keypoints[k].pt.x, keypoints[k].pt.y,
kpScaleIdx[k], thetaIdx, i);
}
int cnt(0);
@ -478,13 +528,13 @@ void FREAK::computeImpl( InputArray _image, std::vector<KeyPoint>& keypoints, Ou
}
// simply take average on a square patch, not even gaussian approx
uchar FREAK::meanIntensity( InputArray _image, InputArray _integral,
const float kp_x,
const float kp_y,
const unsigned int scale,
const unsigned int rot,
const unsigned int point) const
{
template <typename imgType, typename iiType>
imgType FREAK::meanIntensity( InputArray _image, InputArray _integral,
const float kp_x,
const float kp_y,
const unsigned int scale,
const unsigned int rot,
const unsigned int point) const {
Mat image = _image.getMat(), integral = _integral.getMat();
// get point position in image
const PatternPoint& FreakPoint = patternLookup[scale*FREAK_NB_ORIENTATION*FREAK_NB_POINTS + rot*FREAK_NB_POINTS + point];
@ -492,7 +542,6 @@ uchar FREAK::meanIntensity( InputArray _image, InputArray _integral,
const float yf = FreakPoint.y+kp_y;
const int x = int(xf);
const int y = int(yf);
const int& imagecols = image.cols;
// get the sigma:
const float radius = FreakPoint.sigma;
@ -505,19 +554,15 @@ uchar FREAK::meanIntensity( InputArray _image, InputArray _integral,
const int r_y = static_cast<int>((yf-y)*1024);
const int r_x_1 = (1024-r_x);
const int r_y_1 = (1024-r_y);
uchar* ptr = image.data+x+y*imagecols;
unsigned int ret_val;
// linear interpolation:
ret_val = (r_x_1*r_y_1*int(*ptr));
ptr++;
ret_val += (r_x*r_y_1*int(*ptr));
ptr += imagecols;
ret_val += (r_x*r_y*int(*ptr));
ptr--;
ret_val += (r_x_1*r_y*int(*ptr));
ret_val = r_x_1*r_y_1*int(image.at<imgType>(y , x ))
+ r_x *r_y_1*int(image.at<imgType>(y , x+1))
+ r_x_1*r_y *int(image.at<imgType>(y+1, x ))
+ r_x *r_y *int(image.at<imgType>(y+1, x+1));
//return the rounded mean
ret_val += 2 * 1024 * 1024;
return static_cast<uchar>(ret_val / (4 * 1024 * 1024));
return static_cast<imgType>(ret_val / (4 * 1024 * 1024));
}
// expected case:
@ -527,15 +572,15 @@ uchar FREAK::meanIntensity( InputArray _image, InputArray _integral,
const int y_top = int(yf-radius+0.5);
const int x_right = int(xf+radius+1.5);//integral image is 1px wider
const int y_bottom = int(yf+radius+1.5);//integral image is 1px higher
int ret_val;
iiType ret_val;
ret_val = integral.at<int>(y_bottom,x_right);//bottom right corner
ret_val -= integral.at<int>(y_bottom,x_left);
ret_val += integral.at<int>(y_top,x_left);
ret_val -= integral.at<int>(y_top,x_right);
ret_val = integral.at<iiType>(y_bottom,x_right);//bottom right corner
ret_val -= integral.at<iiType>(y_bottom,x_left);
ret_val += integral.at<iiType>(y_top,x_left);
ret_val -= integral.at<iiType>(y_top,x_right);
ret_val = ret_val/( (x_right-x_left)* (y_bottom-y_top) );
//~ std::cout<<integral.step[1]<<std::endl;
return static_cast<uchar>(ret_val);
return static_cast<imgType>(ret_val);
}
// pair selection algorithm from a set of training images and corresponding keypoints

95
modules/features2d/src/stardetector.cpp
View File

@ -44,20 +44,24 @@
namespace cv
{
static void
computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT )
template <typename inMatType, typename outMatType> static void
computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT,
int iiType )
{
CV_Assert( matI.type() == CV_8U );
int x, y, rows = matI.rows, cols = matI.cols;
matS.create(rows + 1, cols + 1, CV_32S);
matT.create(rows + 1, cols + 1, CV_32S);
_FT.create(rows + 1, cols + 1, CV_32S);
matS.create(rows + 1, cols + 1, iiType );
matT.create(rows + 1, cols + 1, iiType );
_FT.create(rows + 1, cols + 1, iiType );
const uchar* I = matI.ptr<uchar>();
int *S = matS.ptr<int>(), *T = matT.ptr<int>(), *FT = _FT.ptr<int>();
int istep = (int)matI.step, step = (int)(matS.step/sizeof(S[0]));
const inMatType* I = matI.ptr<inMatType>();
outMatType *S = matS.ptr<outMatType>();
outMatType *T = matT.ptr<outMatType>();
outMatType *FT = _FT.ptr<outMatType>();
int istep = (int)(matI.step/matI.elemSize());
int step = (int)(matS.step/matS.elemSize());
for( x = 0; x <= cols; x++ )
S[x] = T[x] = FT[x] = 0;
@ -95,14 +99,9 @@ computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT )
}
}
struct StarFeature
{
int area;
int* p[8];
};
static int
StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int maxSize )
template <typename iiMatType> static int
StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes,
int maxSize, int iiType )
{
const int MAX_PATTERN = 17;
static const int sizes0[] = {1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128, -1};
@ -116,16 +115,21 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
__m128 sizes1_4[MAX_PATTERN];
union { int i; float f; } absmask;
absmask.i = 0x7fffffff;
volatile bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2);
volatile bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2) && iiType == CV_32S;
#endif
struct StarFeature
{
int area;
iiMatType* p[8];
};
StarFeature f[MAX_PATTERN];
Mat sum, tilted, flatTilted;
int y, rows = img.rows, cols = img.cols;
int border, npatterns=0, maxIdx=0;
CV_Assert( img.type() == CV_8UC1 );
responses.create( img.size(), CV_32F );
sizes.create( img.size(), CV_16S );
@ -139,7 +143,18 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
npatterns += (pairs[npatterns-1][0] >= 0);
maxIdx = pairs[npatterns-1][0];
computeIntegralImages( img, sum, tilted, flatTilted );
// Create the integral image appropriate for our type & usage
if ( img.type() == CV_8U )
computeIntegralImages<uchar, iiMatType>( img, sum, tilted, flatTilted, iiType );
else if ( img.type() == CV_8S )
computeIntegralImages<char, iiMatType>( img, sum, tilted, flatTilted, iiType );
else if ( img.type() == CV_16U )
computeIntegralImages<ushort, iiMatType>( img, sum, tilted, flatTilted, iiType );
else if ( img.type() == CV_16S )
computeIntegralImages<short, iiMatType>( img, sum, tilted, flatTilted, iiType );
else
CV_Error( Error::StsUnsupportedFormat, "" );
int step = (int)(sum.step/sum.elemSize());
for(int i = 0; i <= maxIdx; i++ )
@ -148,15 +163,15 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
int ur_area = (2*ur_size + 1)*(2*ur_size + 1);
int t_area = t_size*t_size + (t_size + 1)*(t_size + 1);
f[i].p[0] = sum.ptr<int>() + (ur_size + 1)*step + ur_size + 1;
f[i].p[1] = sum.ptr<int>() - ur_size*step + ur_size + 1;
f[i].p[2] = sum.ptr<int>() + (ur_size + 1)*step - ur_size;
f[i].p[3] = sum.ptr<int>() - ur_size*step - ur_size;
f[i].p[0] = sum.ptr<iiMatType>() + (ur_size + 1)*step + ur_size + 1;
f[i].p[1] = sum.ptr<iiMatType>() - ur_size*step + ur_size + 1;
f[i].p[2] = sum.ptr<iiMatType>() + (ur_size + 1)*step - ur_size;
f[i].p[3] = sum.ptr<iiMatType>() - ur_size*step - ur_size;
f[i].p[4] = tilted.ptr<int>() + (t_size + 1)*step + 1;
f[i].p[5] = flatTilted.ptr<int>() - t_size;
f[i].p[6] = flatTilted.ptr<int>() + t_size + 1;
f[i].p[7] = tilted.ptr<int>() - t_size*step + 1;
f[i].p[4] = tilted.ptr<iiMatType>() + (t_size + 1)*step + 1;
f[i].p[5] = flatTilted.ptr<iiMatType>() - t_size;
f[i].p[6] = flatTilted.ptr<iiMatType>() + t_size + 1;
f[i].p[7] = tilted.ptr<iiMatType>() - t_size*step + 1;
f[i].area = ur_area + t_area;
sizes1[i] = sizes0[i];
@ -227,7 +242,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
for(int i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
const iiMatType** p = (const iiMatType**)&f[i].p[0];
__m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)),
_mm_loadu_si128((const __m128i*)(p[1]+ofs)));
__m128i r1 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[3]+ofs)),
@ -269,9 +284,9 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
for(int i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] +
p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs];
const iiMatType** p = (const iiMatType**)&f[i].p[0];
vals[i] = (int)(p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] +
p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs]);
}
for(int i = 0; i < npatterns; i++ )
{
@ -429,7 +444,7 @@ StarDetector::StarDetector(int _maxSize, int _responseThreshold,
void StarDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) const
{
Mat image = _image.getMat(), mask = _mask.getMat(), grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, COLOR_BGR2GRAY );
if( image.channels() > 1 ) cvtColor( image, grayImage, COLOR_BGR2GRAY );
(*this)(grayImage, keypoints);
KeyPointsFilter::runByPixelsMask( keypoints, mask );
@ -438,7 +453,15 @@ void StarDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoin
void StarDetector::operator()(const Mat& img, std::vector<KeyPoint>& keypoints) const
{
Mat responses, sizes;
int border = StarDetectorComputeResponses( img, responses, sizes, maxSize );
int border;
// Use 32-bit integers if we won't overflow in the integral image
if ((img.depth() == CV_8U || img.depth() == CV_8S) &&
(img.rows * img.cols) < 8388608 ) // 8388608 = 2 ^ (32 - 8(bit depth) - 1(sign bit))
border = StarDetectorComputeResponses<int>( img, responses, sizes, maxSize, CV_32S );
else
border = StarDetectorComputeResponses<double>( img, responses, sizes, maxSize, CV_64F );
keypoints.clear();
if( border >= 0 )
StarDetectorSuppressNonmax( responses, sizes, keypoints, border,

1
modules/flann/include/opencv2/flann/defines.h
View File

@ -107,6 +107,7 @@ enum flann_centers_init_t
FLANN_CENTERS_RANDOM = 0,
FLANN_CENTERS_GONZALES = 1,
FLANN_CENTERS_KMEANSPP = 2,
FLANN_CENTERS_GROUPWISE = 3,
// deprecated constants, should use the FLANN_CENTERS_* ones instead
CENTERS_RANDOM = 0,

81
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
View File

@ -257,6 +257,84 @@ private:
}
/**
* Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel):
* select the first point of the list as a candidate, then parse the points list. If another
* point is further than current candidate from the other centers, test if it is a good center
* of a local aggregation. If it is, replace current candidate by this point. And so on...
*
* Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points,
* this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex
* class that pick centers among existing points instead of computing the barycenters, there is a real
* improvement.
*
* Params:
* k = number of centers
* vecs = the dataset of points
* indices = indices in the dataset
* Returns:
*/
void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
const float kSpeedUpFactor = 1.3f;
int n = indices_length;
DistanceType* closestDistSq = new DistanceType[n];
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
centers[0] = dsindices[index];
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
}
// Choose each center
int centerCount;
for (centerCount = 1; centerCount < k; centerCount++) {
// Repeat several trials
double bestNewPot = -1;
int bestNewIndex = 0;
DistanceType furthest = 0;
for (index = 0; index < n; index++) {
// We will test only the potential of the points further than current candidate
if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) {
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) {
newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols)
, closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot <= bestNewPot)) {
bestNewPot = newPot;
bestNewIndex = index;
furthest = closestDistSq[index];
}
}
}
// Add the appropriate center
centers[centerCount] = dsindices[bestNewIndex];
for (int i = 0; i < n; i++) {
closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols)
, closestDistSq[i] );
}
}
centers_length = centerCount;
delete[] closestDistSq;
}
public:
@ -290,6 +368,9 @@ public:
else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp;
}
else if (centers_init_==FLANN_CENTERS_GROUPWISE) {
chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser;
}
else {
throw FLANNException("Unknown algorithm for choosing initial centers.");
}

65
modules/imgproc/src/color.cpp
View File

@ -2716,6 +2716,8 @@ struct mRGBA2RGBA
#ifdef HAVE_OPENCL
#define DIVUP(total, grain) (((total) + (grain) - 1) / (grain))
static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
{
bool ok = false;
@ -2729,6 +2731,17 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
if (depth != CV_8U && depth != CV_16U && depth != CV_32F)
return false;
cv::String opts = format("-D depth=%d -D scn=%d ", depth, scn);
ocl::Device dev = ocl::Device::getDefault();
int pxPerWIy = 1;
if (dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU))
{
pxPerWIy = 4;
}
globalsize[1] = DIVUP(globalsize[1], pxPerWIy);
opts += format("-D PIX_PER_WI_Y=%d ", pxPerWIy);
switch (code)
{
case COLOR_BGR2BGRA: case COLOR_RGB2BGRA: case COLOR_BGRA2BGR:
@ -2738,7 +2751,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dcn = code == COLOR_BGR2BGRA || code == COLOR_RGB2BGRA || code == COLOR_BGRA2RGBA ? 4 : 3;
bool reverse = !(code == COLOR_BGR2BGRA || code == COLOR_BGRA2BGR);
k.create("RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=%d -D bidx=0 -D %s", depth, scn, dcn,
opts + format("-D dcn=%d -D bidx=0 -D %s", dcn,
reverse ? "REVERSE" : "ORDER"));
break;
}
@ -2752,7 +2765,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
int greenbits = code == COLOR_BGR5652BGR || code == COLOR_BGR5652RGB ||
code == COLOR_BGR5652BGRA || code == COLOR_BGR5652RGBA ? 6 : 5;
k.create("RGB5x52RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=2 -D dcn=%d -D bidx=%d -D greenbits=%d", depth, dcn, bidx, greenbits));
opts + format("-D dcn=%d -D bidx=%d -D greenbits=%d", dcn, bidx, greenbits));
break;
}
case COLOR_BGR2BGR565: case COLOR_BGR2BGR555: case COLOR_RGB2BGR565: case COLOR_RGB2BGR555:
@ -2765,7 +2778,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
code == COLOR_BGRA2BGR565 || code == COLOR_RGBA2BGR565 ? 6 : 5;
dcn = 2;
k.create("RGB2RGB5x5", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=2 -D bidx=%d -D greenbits=%d", depth, scn, bidx, greenbits));
opts + format("-D dcn=2 -D bidx=%d -D greenbits=%d", bidx, greenbits));
break;
}
case COLOR_BGR5652GRAY: case COLOR_BGR5552GRAY:
@ -2774,7 +2787,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dcn = 1;
int greenbits = code == COLOR_BGR5652GRAY ? 6 : 5;
k.create("BGR5x52Gray", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=2 -D dcn=1 -D bidx=0 -D greenbits=%d", depth, greenbits));
opts + format("-D dcn=1 -D bidx=0 -D greenbits=%d", greenbits));
break;
}
case COLOR_GRAY2BGR565: case COLOR_GRAY2BGR555:
@ -2783,7 +2796,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dcn = 2;
int greenbits = code == COLOR_GRAY2BGR565 ? 6 : 5;
k.create("Gray2BGR5x5", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=1 -D dcn=2 -D bidx=0 -D greenbits=%d", depth, greenbits));
opts + format("-D dcn=2 -D bidx=0 -D greenbits=%d", greenbits));
break;
}
case COLOR_BGR2GRAY: case COLOR_BGRA2GRAY:
@ -2793,8 +2806,8 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
bidx = code == COLOR_BGR2GRAY || code == COLOR_BGRA2GRAY ? 0 : 2;
dcn = 1;
k.create("RGB2Gray", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=1 -D bidx=%d -D STRIPE_SIZE=%d",
depth, scn, bidx, stripeSize));
opts + format("-D dcn=1 -D bidx=%d -D STRIPE_SIZE=%d",
bidx, stripeSize));
globalsize[0] = (src.cols + stripeSize-1)/stripeSize;
break;
}
@ -2804,7 +2817,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
CV_Assert(scn == 1);
dcn = code == COLOR_GRAY2BGRA ? 4 : 3;
k.create("Gray2RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D bidx=0 -D scn=1 -D dcn=%d", depth, dcn));
opts + format("-D bidx=0 -D dcn=%d", dcn));
break;
}
case COLOR_BGR2YUV:
@ -2814,7 +2827,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
bidx = code == COLOR_RGB2YUV ? 0 : 2;
dcn = 3;
k.create("RGB2YUV", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=3 -D bidx=%d", depth, scn, bidx));
opts + format("-D dcn=3 -D bidx=%d", bidx));
break;
}
case COLOR_YUV2BGR:
@ -2824,7 +2837,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
CV_Assert(dcn == 3 || dcn == 4);
bidx = code == COLOR_YUV2RGB ? 0 : 2;
k.create("YUV2RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=3 -D dcn=%d -D bidx=%d", depth, dcn, bidx));
opts + format("-D dcn=%d -D bidx=%d", dcn, bidx));
break;
}
case COLOR_YUV2RGB_NV12: case COLOR_YUV2BGR_NV12:
@ -2837,7 +2850,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dstSz = Size(sz.width, sz.height * 2 / 3);
k.create("YUV2RGB_NV12", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=0 -D scn=1 -D dcn=%d -D bidx=%d", dcn, bidx));
opts + format("-D dcn=%d -D bidx=%d", dcn, bidx));
break;
}
case COLOR_BGR2YCrCb:
@ -2847,7 +2860,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
bidx = code == COLOR_BGR2YCrCb ? 0 : 2;
dcn = 3;
k.create("RGB2YCrCb", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=3 -D bidx=%d", depth, scn, bidx));
opts + format("-D dcn=3 -D bidx=%d", bidx));
break;
}
case COLOR_YCrCb2BGR:
@ -2858,7 +2871,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
CV_Assert(scn == 3 && (dcn == 3 || dcn == 4));
bidx = code == COLOR_YCrCb2BGR ? 0 : 2;
k.create("YCrCb2RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=%d -D bidx=%d", depth, scn, dcn, bidx));
opts + format("-D dcn=%d -D bidx=%d", dcn, bidx));
break;
}
case COLOR_BGR2XYZ: case COLOR_RGB2XYZ:
@ -2904,7 +2917,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dst = _dst.getUMat();
k.create("RGB2XYZ", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=%d -D dcn=3 -D bidx=%d", depth, scn, bidx));
opts + format("-D dcn=3 -D bidx=%d", bidx));
if (k.empty())
return false;
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst), ocl::KernelArg::PtrReadOnly(c));
@ -2955,7 +2968,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dst = _dst.getUMat();
k.create("XYZ2RGB", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D scn=3 -D dcn=%d -D bidx=%d", depth, dcn, bidx));
opts + format("-D dcn=%d -D bidx=%d", dcn, bidx));
if (k.empty())
return false;
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst), ocl::KernelArg::PtrReadOnly(c));
@ -3010,8 +3023,9 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
_dst.create(dstSz, CV_8UC3);
dst = _dst.getUMat();
k.create("RGB2HSV", ocl::imgproc::cvtcolor_oclsrc, format("-D depth=%d -D hrange=%d -D bidx=%d -D dcn=3 -D scn=%d",
depth, hrange, bidx, scn));
k.create("RGB2HSV", ocl::imgproc::cvtcolor_oclsrc,
opts + format("-D hrange=%d -D bidx=%d -D dcn=3",
hrange, bidx));
if (k.empty())
return false;
@ -3023,7 +3037,8 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
}
else
k.create(kernelName.c_str(), ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D hscale=%ff -D bidx=%d -D scn=%d -D dcn=3", depth, hrange*(1.f/360.f), bidx, scn));
opts + format("-D hscale=%ff -D bidx=%d -D dcn=3",
hrange*(1.f/360.f), bidx));
break;
}
case COLOR_HSV2BGR: case COLOR_HSV2RGB: case COLOR_HSV2BGR_FULL: case COLOR_HSV2RGB_FULL:
@ -3041,8 +3056,8 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
String kernelName = String(is_hsv ? "HSV" : "HLS") + "2RGB";
k.create(kernelName.c_str(), ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D dcn=%d -D scn=3 -D bidx=%d -D hrange=%d -D hscale=%ff",
depth, dcn, bidx, hrange, 6.f/hrange));
opts + format("-D dcn=%d -D bidx=%d -D hrange=%d -D hscale=%ff",
dcn, bidx, hrange, 6.f/hrange));
break;
}
case COLOR_RGBA2mRGBA: case COLOR_mRGBA2RGBA:
@ -3051,7 +3066,7 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dcn = 4;
k.create(code == COLOR_RGBA2mRGBA ? "RGBA2mRGBA" : "mRGBA2RGBA", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D dcn=4 -D scn=4 -D bidx=3", depth));
opts + "-D dcn=4 -D bidx=3");
break;
}
case CV_BGR2Lab: case CV_RGB2Lab: case CV_LBGR2Lab: case CV_LRGB2Lab:
@ -3063,8 +3078,8 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
dcn = 3;
k.create("BGR2Lab", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D dcn=3 -D scn=%d -D bidx=%d%s",
depth, scn, bidx, srgb ? " -D SRGB" : ""));
opts + format("-D dcn=3 -D bidx=%d%s",
bidx, srgb ? " -D SRGB" : ""));
if (k.empty())
return false;
@ -3165,8 +3180,8 @@ static bool ocl_cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
bool srgb = code == CV_Lab2BGR || code == CV_Lab2RGB;
k.create("Lab2BGR", ocl::imgproc::cvtcolor_oclsrc,
format("-D depth=%d -D dcn=%d -D scn=3 -D bidx=%d%s",
depth, dcn, bidx, srgb ? " -D SRGB" : ""));
opts + format("-D dcn=%d -D bidx=%d%s",
dcn, bidx, srgb ? " -D SRGB" : ""));
if (k.empty())
return false;

70
modules/imgproc/src/histogram.cpp
View File

@ -1175,6 +1175,48 @@ calcHist_8u( std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
}
}
#if defined HAVE_IPP && !defined HAVE_IPP_ICV_ONLY
class IPPCalcHistInvoker :
public ParallelLoopBody
{
public:
IPPCalcHistInvoker(const Mat & _src, Mat & _hist, AutoBuffer<Ipp32s> & _levels, Ipp32s _histSize, Ipp32s _low, Ipp32s _high, bool * _ok) :
ParallelLoopBody(), src(&_src), hist(&_hist), levels(&_levels), histSize(_histSize), low(_low), high(_high), ok(_ok)
{
*ok = true;
}
virtual void operator() (const Range & range) const
{
Mat phist(hist->size(), hist->type(), Scalar::all(0));
IppStatus status = ippiHistogramEven_8u_C1R(
src->data + src->step * range.start, (int)src->step, ippiSize(src->cols, range.end - range.start),
(Ipp32s *)phist.data, (Ipp32s *)*levels, histSize, low, high);
if (status < 0)
{
*ok = false;
return;
}
for (int i = 0; i < histSize; ++i)
CV_XADD((int *)(hist->data + i * hist->step), *(int *)(phist.data + i * phist.step));
}
private:
const Mat * src;
Mat * hist;
AutoBuffer<Ipp32s> * levels;
Ipp32s histSize, low, high;
bool * ok;
const IPPCalcHistInvoker & operator = (const IPPCalcHistInvoker & );
};
#endif
}
void cv::calcHist( const Mat* images, int nimages, const int* channels,
@ -1190,6 +1232,32 @@ void cv::calcHist( const Mat* images, int nimages, const int* channels,
Mat hist = _hist.getMat(), ihist = hist;
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S;
#if defined HAVE_IPP && !defined HAVE_IPP_ICV_ONLY
if (nimages == 1 && images[0].type() == CV_8UC1 && dims == 1 && channels &&
channels[0] == 0 && mask.empty() && images[0].dims <= 2 &&
!accumulate && uniform)
{
ihist.setTo(Scalar::all(0));
AutoBuffer<Ipp32s> levels(histSize[0] + 1);
bool ok = true;
const Mat & src = images[0];
int nstripes = std::min<int>(8, src.total() / (1 << 16));
#ifdef HAVE_CONCURRENCY
nstripes = 1;
#endif
IPPCalcHistInvoker invoker(src, ihist, levels, histSize[0] + 1, (Ipp32s)ranges[0][0], (Ipp32s)ranges[0][1], &ok);
Range range(0, src.rows);
parallel_for_(range, invoker, nstripes);
if (ok)
{
ihist.convertTo(hist, CV_32F);
return;
}
}
#endif
if( !accumulate || histdata != hist.data )
hist = Scalar(0.);
else
@ -1477,7 +1545,7 @@ void cv::calcHist( InputArrayOfArrays images, const std::vector<int>& channels,
CV_OCL_RUN(images.total() == 1 && channels.size() == 1 && images.channels(0) == 1 &&
channels[0] == 0 && images.isUMatVector() && mask.empty() && !accumulate &&
histSize.size() == 1 && histSize[0] == BINS && ranges.size() == 2 &&
ranges[0] == 0 && ranges[1] == 256,
ranges[0] == 0 && ranges[1] == BINS,
ocl_calcHist(images, hist))
int i, dims = (int)histSize.size(), rsz = (int)ranges.size(), csz = (int)channels.size();

55
modules/imgproc/src/moments.cpp
View File

@ -466,6 +466,61 @@ cv::Moments cv::moments( InputArray _src, bool binary )
if( cn > 1 )
CV_Error( CV_StsBadArg, "Invalid image type (must be single-channel)" );
#if (IPP_VERSION_X100 >= 801)
if (!binary)
{
IppiSize roi = {mat.cols, mat.rows};
IppiMomentState_64f *moment;
// ippiMomentInitAlloc_64f, ippiMomentFree_64f are deprecated in 8.1, but there are not another way
// to initialize IppiMomentState_64f. When GetStateSize and Init functions will appear we have to
// change our code.
if (0 <= ippiMomentInitAlloc_64f(&moment, ippAlgHintAccurate))
{
IppStatus sts = (IppStatus)(-1);
if (depth == CV_8U)
sts = ippiMoments64f_8u_C1R((const Ipp8u *)mat.data, (int)mat.step, roi, moment);
else if( depth == CV_16U )
sts = ippiMoments64f_16u_C1R((const Ipp16u *)mat.data, (int)mat.step, roi, moment);
else if( depth == CV_32F )
sts = ippiMoments64f_32f_C1R((const Ipp32f *)mat.data, (int)mat.step, roi, moment);
if (0 <= sts)
{
IppiPoint point = {0, 0};
ippiGetSpatialMoment_64f(moment, 0, 0, 0, point, &m.m00);
ippiGetSpatialMoment_64f(moment, 1, 0, 0, point, &m.m10);
ippiGetSpatialMoment_64f(moment, 0, 1, 0, point, &m.m01);
ippiGetSpatialMoment_64f(moment, 2, 0, 0, point, &m.m20);
ippiGetSpatialMoment_64f(moment, 1, 1, 0, point, &m.m11);
ippiGetSpatialMoment_64f(moment, 0, 2, 0, point, &m.m02);
ippiGetSpatialMoment_64f(moment, 3, 0, 0, point, &m.m30);
ippiGetSpatialMoment_64f(moment, 2, 1, 0, point, &m.m21);
ippiGetSpatialMoment_64f(moment, 1, 2, 0, point, &m.m12);
ippiGetSpatialMoment_64f(moment, 0, 3, 0, point, &m.m03);
ippiGetCentralMoment_64f(moment, 2, 0, 0, &m.mu20);
ippiGetCentralMoment_64f(moment, 1, 1, 0, &m.mu11);
ippiGetCentralMoment_64f(moment, 0, 2, 0, &m.mu02);
ippiGetCentralMoment_64f(moment, 3, 0, 0, &m.mu30);
ippiGetCentralMoment_64f(moment, 2, 1, 0, &m.mu21);
ippiGetCentralMoment_64f(moment, 1, 2, 0, &m.mu12);
ippiGetCentralMoment_64f(moment, 0, 3, 0, &m.mu03);
ippiGetNormalizedCentralMoment_64f(moment, 2, 0, 0, &m.nu20);
ippiGetNormalizedCentralMoment_64f(moment, 1, 1, 0, &m.nu11);
ippiGetNormalizedCentralMoment_64f(moment, 0, 2, 0, &m.nu02);
ippiGetNormalizedCentralMoment_64f(moment, 3, 0, 0, &m.nu30);
ippiGetNormalizedCentralMoment_64f(moment, 2, 1, 0, &m.nu21);
ippiGetNormalizedCentralMoment_64f(moment, 1, 2, 0, &m.nu12);
ippiGetNormalizedCentralMoment_64f(moment, 0, 3, 0, &m.nu03);
ippiMomentFree_64f(moment);
return m;
}
ippiMomentFree_64f(moment);
}
}
#endif
if( binary || depth == CV_8U )
func = momentsInTile<uchar, int, int>;
else if( depth == CV_16U )

1423
modules/imgproc/src/opencl/cvtcolor.cl
View File

File diff suppressed because it is too large Load Diff

2
modules/imgproc/src/samplers.cpp
View File

@ -172,7 +172,7 @@ void getRectSubPix_Cn_(const _Tp* src, size_t src_step, Size src_size,
dst[j+1] = cast_op(s1);
}
for( j = 0; j < win_size.width; j++ )
for( ; j < win_size.width; j++ )
{
_WTp s0 = src[j]*a11 + src[j+cn]*a12 + src[j+src_step]*a21 + src[j+src_step+cn]*a22;
dst[j] = cast_op(s0);

6
modules/imgproc/src/sumpixels.cpp Normal file → Executable file
View File

@ -219,6 +219,8 @@ static void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep,
DEF_INTEGRAL_FUNC(8u32s, uchar, int, double)
DEF_INTEGRAL_FUNC(8u32f64f, uchar, float, double)
DEF_INTEGRAL_FUNC(8u64f64f, uchar, double, double)
DEF_INTEGRAL_FUNC(16u64f64f, ushort, double, double)
DEF_INTEGRAL_FUNC(16s64f64f, short, double, double)
DEF_INTEGRAL_FUNC(32f32f64f, float, float, double)
DEF_INTEGRAL_FUNC(32f64f64f, float, double, double)
DEF_INTEGRAL_FUNC(64f64f64f, double, double, double)
@ -411,6 +413,10 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output
func = (IntegralFunc)integral_8u32f32f;
else if( depth == CV_8U && sdepth == CV_64F && sqdepth == CV_64F )
func = (IntegralFunc)integral_8u64f64f;
else if( depth == CV_16U && sdepth == CV_64F && sqdepth == CV_64F )
func = (IntegralFunc)integral_16u64f64f;
else if( depth == CV_16S && sdepth == CV_64F && sqdepth == CV_64F )
func = (IntegralFunc)integral_16s64f64f;
else if( depth == CV_32F && sdepth == CV_32F && sqdepth == CV_64F )
func = (IntegralFunc)integral_32f32f64f;
else if( depth == CV_32F && sdepth == CV_32F && sqdepth == CV_32F )

88
modules/imgproc/src/templmatch.cpp
View File

@ -341,10 +341,93 @@ static bool ocl_matchTemplate( InputArray _img, InputArray _templ, OutputArray _
#endif
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
typedef IppStatus (CV_STDCALL * ippimatchTemplate)(const void*, int, IppiSize, const void*, int, IppiSize, Ipp32f* , int , IppEnum , Ipp8u*);
static bool ipp_crossCorr(const Mat& src, const Mat& tpl, Mat& dst)
{
if (src.channels()!= 1)
return false;
IppStatus status;
IppiSize srcRoiSize = {src.cols,src.rows};
IppiSize tplRoiSize = {tpl.cols,tpl.rows};
Ipp8u *pBuffer;
int bufSize=0;
int depth = src.depth();
ippimatchTemplate ippFunc =
depth==CV_8U ? (ippimatchTemplate)ippiCrossCorrNorm_8u32f_C1R:
depth==CV_32F? (ippimatchTemplate)ippiCrossCorrNorm_32f_C1R: 0;
if (ippFunc==0)
return false;
IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiNormNone | ippiROIValid);
status = ippiCrossCorrNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize);
if ( status < 0 )
return false;
pBuffer = ippsMalloc_8u( bufSize );
status = ippFunc(src.data, (int)src.step, srcRoiSize, tpl.data, (int)tpl.step, tplRoiSize, (Ipp32f*)dst.data, (int)dst.step, funCfg, pBuffer);
ippsFree( pBuffer );
return status >= 0;
}
static bool ipp_sqrDistance(const Mat& src, const Mat& tpl, Mat& dst)
{
if (src.channels()!= 1)
return false;
IppStatus status;
IppiSize srcRoiSize = {src.cols,src.rows};
IppiSize tplRoiSize = {tpl.cols,tpl.rows};
Ipp8u *pBuffer;
int bufSize=0;
int depth = src.depth();
ippimatchTemplate ippFunc =
depth==CV_8U ? (ippimatchTemplate)ippiSqrDistanceNorm_8u32f_C1R:
depth==CV_32F? (ippimatchTemplate)ippiSqrDistanceNorm_32f_C1R: 0;
if (ippFunc==0)
return false;
IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiNormNone | ippiROIValid);
status = ippiSqrDistanceNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize);
if ( status < 0 )
return false;
pBuffer = ippsMalloc_8u( bufSize );
status = ippFunc(src.data, (int)src.step, srcRoiSize, tpl.data, (int)tpl.step, tplRoiSize, (Ipp32f*)dst.data, (int)dst.step, funCfg, pBuffer);
ippsFree( pBuffer );
return status >= 0;
}
#endif
void crossCorr( const Mat& img, const Mat& _templ, Mat& corr,
Size corrsize, int ctype,
Point anchor, double delta, int borderType )
{
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
if (ipp_crossCorr(img, _templ, corr))
return;
#endif
const double blockScale = 4.5;
const int minBlockSize = 256;
std::vector<uchar> buf;
@ -560,6 +643,11 @@ void cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result,
return;
#endif
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
if (method == CV_TM_SQDIFF && ipp_sqrDistance(img, templ, result))
return;
#endif
int cn = img.channels();
crossCorr( img, templ, result, result.size(), result.type(), Point(0,0), 0, 0);

15
modules/nonfree/src/sift.cpp
View File

@ -111,21 +111,6 @@ namespace cv
/******************************* Defs and macros *****************************/
// default number of sampled intervals per octave
static const int SIFT_INTVLS = 3;
// default sigma for initial gaussian smoothing
static const float SIFT_SIGMA = 1.6f;
// default threshold on keypoint contrast |D(x)|
static const float SIFT_CONTR_THR = 0.04f;
// default threshold on keypoint ratio of principle curvatures
static const float SIFT_CURV_THR = 10.f;
// double image size before pyramid construction?
static const bool SIFT_IMG_DBL = true;
// default width of descriptor histogram array
static const int SIFT_DESCR_WIDTH = 4;

15
modules/video/src/simpleflow.cpp
View File

@ -66,21 +66,6 @@ inline static float dist(const Vec2f& p1, const Vec2f& p2) {
(p1[1] - p2[1]) * (p1[1] - p2[1]);
}
inline static float dist(const Point2f& p1, const Point2f& p2) {
return (p1.x - p2.x) * (p1.x - p2.x) +
(p1.y - p2.y) * (p1.y - p2.y);
}
inline static float dist(float x1, float y1, float x2, float y2) {
return (x1 - x2) * (x1 - x2) +
(y1 - y2) * (y1 - y2);
}
inline static int dist(int x1, int y1, int x2, int y2) {
return (x1 - x2) * (x1 - x2) +
(y1 - y2) * (y1 - y2);
}
template<class T>
inline static T min(T t1, T t2, T t3) {
return (t1 <= t2 && t1 <= t3) ? t1 : min(t2, t3);

4
samples/cpp/CMakeLists.txt
View File

@ -93,6 +93,10 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
ocv_list_filterout(cpp_samples "viz")
if(NOT HAVE_IPP_A)
ocv_list_filterout(cpp_samples "/ippasync/")
endif()
foreach(sample_filename ${cpp_samples})
get_filename_component(sample ${sample_filename} NAME_WE)
OPENCV_DEFINE_CPP_EXAMPLE(${sample} ${sample_filename})