/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include "opencl_kernels.hpp" namespace cv { static void calcMinEigenVal( const Mat& _cov, Mat& _dst ) { int i, j; Size size = _cov.size(); #if CV_SSE volatile bool simd = checkHardwareSupport(CV_CPU_SSE); #endif if( _cov.isContinuous() && _dst.isContinuous() ) { size.width *= size.height; size.height = 1; } for( i = 0; i < size.height; i++ ) { const float* cov = (const float*)(_cov.data + _cov.step*i); float* dst = (float*)(_dst.data + _dst.step*i); j = 0; #if CV_SSE if( simd ) { __m128 half = _mm_set1_ps(0.5f); for( ; j <= size.width - 5; j += 4 ) { __m128 t0 = _mm_loadu_ps(cov + j*3); // a0 b0 c0 x __m128 t1 = _mm_loadu_ps(cov + j*3 + 3); // a1 b1 c1 x __m128 t2 = _mm_loadu_ps(cov + j*3 + 6); // a2 b2 c2 x __m128 t3 = _mm_loadu_ps(cov + j*3 + 9); // a3 b3 c3 x __m128 a, b, c, t; t = _mm_unpacklo_ps(t0, t1); // a0 a1 b0 b1 c = _mm_unpackhi_ps(t0, t1); // c0 c1 x x b = _mm_unpacklo_ps(t2, t3); // a2 a3 b2 b3 c = _mm_movelh_ps(c, _mm_unpackhi_ps(t2, t3)); // c0 c1 c2 c3 a = _mm_movelh_ps(t, b); b = _mm_movehl_ps(b, t); a = _mm_mul_ps(a, half); c = _mm_mul_ps(c, half); t = _mm_sub_ps(a, c); t = _mm_add_ps(_mm_mul_ps(t, t), _mm_mul_ps(b,b)); a = _mm_sub_ps(_mm_add_ps(a, c), _mm_sqrt_ps(t)); _mm_storeu_ps(dst + j, a); } } #endif for( ; j < size.width; j++ ) { float a = cov[j*3]*0.5f; float b = cov[j*3+1]; float c = cov[j*3+2]*0.5f; dst[j] = (float)((a + c) - std::sqrt((a - c)*(a - c) + b*b)); } } } static void calcHarris( const Mat& _cov, Mat& _dst, double k ) { int i, j; Size size = _cov.size(); #if CV_SSE volatile bool simd = checkHardwareSupport(CV_CPU_SSE); #endif if( _cov.isContinuous() && _dst.isContinuous() ) { size.width *= size.height; size.height = 1; } for( i = 0; i < size.height; i++ ) { const float* cov = (const float*)(_cov.data + _cov.step*i); float* dst = (float*)(_dst.data + _dst.step*i); j = 0; #if CV_SSE if( simd ) { __m128 k4 = _mm_set1_ps((float)k); for( ; j <= size.width - 5; j += 4 ) { __m128 t0 = _mm_loadu_ps(cov + j*3); // a0 b0 c0 x __m128 t1 = _mm_loadu_ps(cov + j*3 + 3); // a1 b1 c1 x __m128 t2 = _mm_loadu_ps(cov + j*3 + 6); // a2 b2 c2 x __m128 t3 = _mm_loadu_ps(cov + j*3 + 9); // a3 b3 c3 x __m128 a, b, c, t; t = _mm_unpacklo_ps(t0, t1); // a0 a1 b0 b1 c = _mm_unpackhi_ps(t0, t1); // c0 c1 x x b = _mm_unpacklo_ps(t2, t3); // a2 a3 b2 b3 c = _mm_movelh_ps(c, _mm_unpackhi_ps(t2, t3)); // c0 c1 c2 c3 a = _mm_movelh_ps(t, b); b = _mm_movehl_ps(b, t); t = _mm_add_ps(a, c); a = _mm_sub_ps(_mm_mul_ps(a, c), _mm_mul_ps(b, b)); t = _mm_mul_ps(_mm_mul_ps(k4, t), t); a = _mm_sub_ps(a, t); _mm_storeu_ps(dst + j, a); } } #endif for( ; j < size.width; j++ ) { float a = cov[j*3]; float b = cov[j*3+1]; float c = cov[j*3+2]; dst[j] = (float)(a*c - b*b - k*(a + c)*(a + c)); } } } static void eigen2x2( const float* cov, float* dst, int n ) { for( int j = 0; j < n; j++ ) { double a = cov[j*3]; double b = cov[j*3+1]; double c = cov[j*3+2]; double u = (a + c)*0.5; double v = std::sqrt((a - c)*(a - c)*0.25 + b*b); double l1 = u + v; double l2 = u - v; double x = b; double y = l1 - a; double e = fabs(x); if( e + fabs(y) < 1e-4 ) { y = b; x = l1 - c; e = fabs(x); if( e + fabs(y) < 1e-4 ) { e = 1./(e + fabs(y) + FLT_EPSILON); x *= e, y *= e; } } double d = 1./std::sqrt(x*x + y*y + DBL_EPSILON); dst[6*j] = (float)l1; dst[6*j + 2] = (float)(x*d); dst[6*j + 3] = (float)(y*d); x = b; y = l2 - a; e = fabs(x); if( e + fabs(y) < 1e-4 ) { y = b; x = l2 - c; e = fabs(x); if( e + fabs(y) < 1e-4 ) { e = 1./(e + fabs(y) + FLT_EPSILON); x *= e, y *= e; } } d = 1./std::sqrt(x*x + y*y + DBL_EPSILON); dst[6*j + 1] = (float)l2; dst[6*j + 4] = (float)(x*d); dst[6*j + 5] = (float)(y*d); } } static void calcEigenValsVecs( const Mat& _cov, Mat& _dst ) { Size size = _cov.size(); if( _cov.isContinuous() && _dst.isContinuous() ) { size.width *= size.height; size.height = 1; } for( int i = 0; i < size.height; i++ ) { const float* cov = (const float*)(_cov.data + _cov.step*i); float* dst = (float*)(_dst.data + _dst.step*i); eigen2x2(cov, dst, size.width); } } enum { MINEIGENVAL=0, HARRIS=1, EIGENVALSVECS=2 }; static void cornerEigenValsVecs( const Mat& src, Mat& eigenv, int block_size, int aperture_size, int op_type, double k=0., int borderType=BORDER_DEFAULT ) { #ifdef HAVE_TEGRA_OPTIMIZATION if (tegra::cornerEigenValsVecs(src, eigenv, block_size, aperture_size, op_type, k, borderType)) return; #endif int depth = src.depth(); double scale = (double)(1 << ((aperture_size > 0 ? aperture_size : 3) - 1)) * block_size; if( aperture_size < 0 ) scale *= 2.; if( depth == CV_8U ) scale *= 255.; scale = 1./scale; CV_Assert( src.type() == CV_8UC1 || src.type() == CV_32FC1 ); Mat Dx, Dy; if( aperture_size > 0 ) { Sobel( src, Dx, CV_32F, 1, 0, aperture_size, scale, 0, borderType ); Sobel( src, Dy, CV_32F, 0, 1, aperture_size, scale, 0, borderType ); } else { Scharr( src, Dx, CV_32F, 1, 0, scale, 0, borderType ); Scharr( src, Dy, CV_32F, 0, 1, scale, 0, borderType ); } Size size = src.size(); Mat cov( size, CV_32FC3 ); int i, j; for( i = 0; i < size.height; i++ ) { float* cov_data = (float*)(cov.data + i*cov.step); const float* dxdata = (const float*)(Dx.data + i*Dx.step); const float* dydata = (const float*)(Dy.data + i*Dy.step); for( j = 0; j < size.width; j++ ) { float dx = dxdata[j]; float dy = dydata[j]; cov_data[j*3] = dx*dx; cov_data[j*3+1] = dx*dy; cov_data[j*3+2] = dy*dy; } } boxFilter(cov, cov, cov.depth(), Size(block_size, block_size), Point(-1,-1), false, borderType ); if( op_type == MINEIGENVAL ) calcMinEigenVal( cov, eigenv ); else if( op_type == HARRIS ) calcHarris( cov, eigenv, k ); else if( op_type == EIGENVALSVECS ) calcEigenValsVecs( cov, eigenv ); } static bool ocl_cornerMinEigenValVecs(InputArray _src, OutputArray _dst, int block_size, int aperture_size, double k, int borderType, int op_type) { CV_Assert(op_type == HARRIS || op_type == MINEIGENVAL); if ( !(borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE || borderType == BORDER_REFLECT || borderType == BORDER_REFLECT_101) ) return false; int type = _src.type(), depth = CV_MAT_DEPTH(type); double scale = (double)(1 << ((aperture_size > 0 ? aperture_size : 3) - 1)) * block_size; if( aperture_size < 0 ) scale *= 2.0; if( depth == CV_8U ) scale *= 255.0; scale = 1.0 / scale; if ( !(type == CV_8UC1 || type == CV_32FC1) ) return false; UMat Dx, Dy; if (aperture_size > 0) { Sobel(_src, Dx, CV_32F, 1, 0, aperture_size, scale, 0, borderType); Sobel(_src, Dy, CV_32F, 0, 1, aperture_size, scale, 0, borderType); } else { Scharr(_src, Dx, CV_32F, 1, 0, scale, 0, borderType); Scharr(_src, Dy, CV_32F, 0, 1, scale, 0, borderType); } const char * const borderTypes[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT101" }; const char * const cornerType[] = { "CORNER_MINEIGENVAL", "CORNER_HARRIS", 0 }; ocl::Kernel cornelKernel("corner", ocl::imgproc::corner_oclsrc, format("-D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %s -D %s", block_size / 2, block_size / 2, block_size, block_size, borderTypes[borderType], cornerType[op_type])); if (cornelKernel.empty()) return false; _dst.createSameSize(_src, CV_32FC1); UMat dst = _dst.getUMat(); cornelKernel.args(ocl::KernelArg::ReadOnly(Dx), ocl::KernelArg::ReadOnly(Dy), ocl::KernelArg::WriteOnly(dst), (float)k); size_t blockSizeX = 256, blockSizeY = 1; size_t gSize = blockSizeX - block_size / 2 * 2; size_t globalSizeX = (Dx.cols) % gSize == 0 ? Dx.cols / gSize * blockSizeX : (Dx.cols / gSize + 1) * blockSizeX; size_t rows_per_thread = 2; size_t globalSizeY = ((Dx.rows + rows_per_thread - 1) / rows_per_thread) % blockSizeY == 0 ? ((Dx.rows + rows_per_thread - 1) / rows_per_thread) : (((Dx.rows + rows_per_thread - 1) / rows_per_thread) / blockSizeY + 1) * blockSizeY; size_t globalsize[2] = { globalSizeX, globalSizeY }, localsize[2] = { blockSizeX, blockSizeY }; return cornelKernel.run(2, globalsize, localsize, false); } } void cv::cornerMinEigenVal( InputArray _src, OutputArray _dst, int blockSize, int ksize, int borderType ) { if (ocl::useOpenCL() && _src.dims() <= 2 && _dst.isUMat() && ocl_cornerMinEigenValVecs(_src, _dst, blockSize, ksize, 0.0, borderType, MINEIGENVAL)) return; Mat src = _src.getMat(); _dst.create( src.size(), CV_32FC1 ); Mat dst = _dst.getMat(); cornerEigenValsVecs( src, dst, blockSize, ksize, MINEIGENVAL, 0, borderType ); } void cv::cornerHarris( InputArray _src, OutputArray _dst, int blockSize, int ksize, double k, int borderType ) { if (ocl::useOpenCL() && _src.dims() <= 2 && _dst.isUMat() && ocl_cornerMinEigenValVecs(_src, _dst, blockSize, ksize, k, borderType, HARRIS)) return; Mat src = _src.getMat(); _dst.create( src.size(), CV_32FC1 ); Mat dst = _dst.getMat(); cornerEigenValsVecs( src, dst, blockSize, ksize, HARRIS, k, borderType ); } void cv::cornerEigenValsAndVecs( InputArray _src, OutputArray _dst, int blockSize, int ksize, int borderType ) { Mat src = _src.getMat(); Size dsz = _dst.size(); int dtype = _dst.type(); if( dsz.height != src.rows || dsz.width*CV_MAT_CN(dtype) != src.cols*6 || CV_MAT_DEPTH(dtype) != CV_32F ) _dst.create( src.size(), CV_32FC(6) ); Mat dst = _dst.getMat(); cornerEigenValsVecs( src, dst, blockSize, ksize, EIGENVALSVECS, 0, borderType ); } void cv::preCornerDetect( InputArray _src, OutputArray _dst, int ksize, int borderType ) { Mat Dx, Dy, D2x, D2y, Dxy, src = _src.getMat(); CV_Assert( src.type() == CV_8UC1 || src.type() == CV_32FC1 ); _dst.create( src.size(), CV_32F ); Mat dst = _dst.getMat(); Sobel( src, Dx, CV_32F, 1, 0, ksize, 1, 0, borderType ); Sobel( src, Dy, CV_32F, 0, 1, ksize, 1, 0, borderType ); Sobel( src, D2x, CV_32F, 2, 0, ksize, 1, 0, borderType ); Sobel( src, D2y, CV_32F, 0, 2, ksize, 1, 0, borderType ); Sobel( src, Dxy, CV_32F, 1, 1, ksize, 1, 0, borderType ); double factor = 1 << (ksize - 1); if( src.depth() == CV_8U ) factor *= 255; factor = 1./(factor * factor * factor); Size size = src.size(); int i, j; for( i = 0; i < size.height; i++ ) { float* dstdata = (float*)(dst.data + i*dst.step); const float* dxdata = (const float*)(Dx.data + i*Dx.step); const float* dydata = (const float*)(Dy.data + i*Dy.step); const float* d2xdata = (const float*)(D2x.data + i*D2x.step); const float* d2ydata = (const float*)(D2y.data + i*D2y.step); const float* dxydata = (const float*)(Dxy.data + i*Dxy.step); for( j = 0; j < size.width; j++ ) { float dx = dxdata[j]; float dy = dydata[j]; dstdata[j] = (float)(factor*(dx*dx*d2ydata[j] + dy*dy*d2xdata[j] - 2*dx*dy*dxydata[j])); } } } CV_IMPL void cvCornerMinEigenVal( const CvArr* srcarr, CvArr* dstarr, int block_size, int aperture_size ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); CV_Assert( src.size() == dst.size() && dst.type() == CV_32FC1 ); cv::cornerMinEigenVal( src, dst, block_size, aperture_size, cv::BORDER_REPLICATE ); } CV_IMPL void cvCornerHarris( const CvArr* srcarr, CvArr* dstarr, int block_size, int aperture_size, double k ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); CV_Assert( src.size() == dst.size() && dst.type() == CV_32FC1 ); cv::cornerHarris( src, dst, block_size, aperture_size, k, cv::BORDER_REPLICATE ); } CV_IMPL void cvCornerEigenValsAndVecs( const void* srcarr, void* dstarr, int block_size, int aperture_size ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); CV_Assert( src.rows == dst.rows && src.cols*6 == dst.cols*dst.channels() && dst.depth() == CV_32F ); cv::cornerEigenValsAndVecs( src, dst, block_size, aperture_size, cv::BORDER_REPLICATE ); } CV_IMPL void cvPreCornerDetect( const void* srcarr, void* dstarr, int aperture_size ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); CV_Assert( src.size() == dst.size() && dst.type() == CV_32FC1 ); cv::preCornerDetect( src, dst, aperture_size, cv::BORDER_REPLICATE ); } /* End of file */