openCV的sobel算子的深度学习卷积网络部分的C语言源码

参考的三种边缘检测算子其一，未完待续。。灰度或结构等信息的突变位置是图像的边缘，图像的边缘有幅度和方向属性，沿边缘方向像素变化缓慢，垂直边缘方向像素变化剧烈。因此，边缘上的变化能通过梯度计算出来。

同理:

#include "precomp.hpp"
#include "opencl_kernels_imgproc.hpp"#include "opencv2/core/openvx/ovx_defs.hpp"
#include "filter.hpp"
/****************************************************************************************\Sobel & Scharr Derivative Filters
\****************************************************************************************/namespace cv
{static void getScharrKernels( OutputArray _kx, OutputArray _ky,int dx, int dy, bool normalize, int ktype )
{const int ksize = 3;CV_Assert( ktype == CV_32F || ktype == CV_64F );_kx.create(ksize, 1, ktype, -1, true);_ky.create(ksize, 1, ktype, -1, true);Mat kx = _kx.getMat();Mat ky = _ky.getMat();CV_Assert( dx >= 0 && dy >= 0 && dx+dy == 1 );for( int k = 0; k < 2; k++ ){Mat* kernel = k == 0 ? &kx : &ky;int order = k == 0 ? dx : dy;int kerI[3];if( order == 0 )kerI[0] = 3, kerI[1] = 10, kerI[2] = 3;else if( order == 1 )kerI[0] = -1, kerI[1] = 0, kerI[2] = 1;Mat temp(kernel->rows, kernel->cols, CV_32S, &kerI[0]);double scale = !normalize || order == 1 ? 1. : 1./32;temp.convertTo(*kernel, ktype, scale);}
}static void getSobelKernels( OutputArray _kx, OutputArray _ky,int dx, int dy, int _ksize, bool normalize, int ktype )
{int i, j, ksizeX = _ksize, ksizeY = _ksize;if( ksizeX == 1 && dx > 0 )ksizeX = 3;if( ksizeY == 1 && dy > 0 )ksizeY = 3;CV_Assert( ktype == CV_32F || ktype == CV_64F );_kx.create(ksizeX, 1, ktype, -1, true);_ky.create(ksizeY, 1, ktype, -1, true);Mat kx = _kx.getMat();Mat ky = _ky.getMat();if( _ksize % 2 == 0 || _ksize > 31 )CV_Error( CV_StsOutOfRange, "The kernel size must be odd and not larger than 31" );std::vector<int> kerI(std::max(ksizeX, ksizeY) + 1);CV_Assert( dx >= 0 && dy >= 0 && dx+dy > 0 );for( int k = 0; k < 2; k++ ){Mat* kernel = k == 0 ? &kx : &ky;int order = k == 0 ? dx : dy;int ksize = k == 0 ? ksizeX : ksizeY;CV_Assert( ksize > order );if( ksize == 1 )kerI[0] = 1;else if( ksize == 3 ){if( order == 0 )kerI[0] = 1, kerI[1] = 2, kerI[2] = 1;else if( order == 1 )kerI[0] = -1, kerI[1] = 0, kerI[2] = 1;elsekerI[0] = 1, kerI[1] = -2, kerI[2] = 1;}else{int oldval, newval;kerI[0] = 1;for( i = 0; i < ksize; i++ )kerI[i+1] = 0;for( i = 0; i < ksize - order - 1; i++ ){oldval = kerI[0];for( j = 1; j <= ksize; j++ ){newval = kerI[j]+kerI[j-1];kerI[j-1] = oldval;oldval = newval;}}for( i = 0; i < order; i++ ){oldval = -kerI[0];for( j = 1; j <= ksize; j++ ){newval = kerI[j-1] - kerI[j];kerI[j-1] = oldval;oldval = newval;}}}Mat temp(kernel->rows, kernel->cols, CV_32S, &kerI[0]);double scale = !normalize ? 1. : 1./(1 << (ksize-order-1));temp.convertTo(*kernel, ktype, scale);}
}}void cv::getDerivKernels( OutputArray kx, OutputArray ky, int dx, int dy,int ksize, bool normalize, int ktype )
{if( ksize <= 0 )getScharrKernels( kx, ky, dx, dy, normalize, ktype );elsegetSobelKernels( kx, ky, dx, dy, ksize, normalize, ktype );
}cv::Ptr<cv::FilterEngine> cv::createDerivFilter(int srcType, int dstType,int dx, int dy, int ksize, int borderType )
{Mat kx, ky;getDerivKernels( kx, ky, dx, dy, ksize, false, CV_32F );return createSeparableLinearFilter(srcType, dstType,kx, ky, Point(-1,-1), 0, borderType );
}#ifdef HAVE_OPENVX
namespace cv
{namespace ovx {template <> inline bool skipSmallImages<VX_KERNEL_SOBEL_3x3>(int w, int h) { return w*h < 320 * 240; }}static bool openvx_sobel(InputArray _src, OutputArray _dst,int dx, int dy, int ksize,double scale, double delta, int borderType){if (_src.type() != CV_8UC1 || _dst.type() != CV_16SC1 ||ksize != 3 || scale != 1.0 || delta != 0.0 ||(dx | dy) != 1 || (dx + dy) != 1 ||_src.cols() < ksize || _src.rows() < ksize ||ovx::skipSmallImages<VX_KERNEL_SOBEL_3x3>(_src.cols(), _src.rows()))return false;Mat src = _src.getMat();Mat dst = _dst.getMat();if ((borderType & BORDER_ISOLATED) == 0 && src.isSubmatrix())return false; //Process isolated borders onlyvx_enum border;switch (borderType & ~BORDER_ISOLATED){case BORDER_CONSTANT:border = VX_BORDER_CONSTANT;break;case BORDER_REPLICATE:
//            border = VX_BORDER_REPLICATE;
//            break;default:return false;}try{ivx::Context ctx = ovx::getOpenVXContext();//if ((vx_size)ksize > ctx.convolutionMaxDimension())//    return false;Mat a;if (dst.data != src.data)a = src;elsesrc.copyTo(a);ivx::Imageia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_S16,ivx::Image::createAddressing(dst.cols, dst.rows, 2, (vx_int32)(dst.step)), dst.data);//ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments//since OpenVX standard says nothing about thread-safety for nowivx::border_t prevBorder = ctx.immediateBorder();ctx.setImmediateBorder(border, (vx_uint8)(0));if(dx)ivx::IVX_CHECK_STATUS(vxuSobel3x3(ctx, ia, ib, NULL));elseivx::IVX_CHECK_STATUS(vxuSobel3x3(ctx, ia, NULL, ib));ctx.setImmediateBorder(prevBorder);}catch (const ivx::RuntimeError & e){VX_DbgThrow(e.what());}catch (const ivx::WrapperError & e){VX_DbgThrow(e.what());}return true;}
}
#endif#if 0 //defined HAVE_IPP
namespace cv
{static bool ipp_Deriv(InputArray _src, OutputArray _dst, int dx, int dy, int ksize, double scale, double delta, int borderType)
{
#ifdef HAVE_IPP_IWCV_INSTRUMENT_REGION_IPP();::ipp::IwiSize size(_src.size().width, _src.size().height);IppDataType   srcType   = ippiGetDataType(_src.depth());IppDataType   dstType   = ippiGetDataType(_dst.depth());int           channels  = _src.channels();bool          useScale  = false;bool          useScharr = false;if(channels != _dst.channels() || channels > 1)return false;if(fabs(delta) > FLT_EPSILON || fabs(scale-1) > FLT_EPSILON)useScale = true;if(ksize <= 0){ksize     = 3;useScharr = true;}IppiMaskSize maskSize = ippiGetMaskSize(ksize, ksize);if((int)maskSize < 0)return false;#if IPP_VERSION_X100 <= 201703// Bug with mirror wrapif(borderType == BORDER_REFLECT_101 && (ksize/2+1 > size.width || ksize/2+1 > size.height))return false;
#endifIwiDerivativeType derivType = ippiGetDerivType(dx, dy, (useScharr)?false:true);if((int)derivType < 0)return false;// Acquire data and begin processingtry{Mat src = _src.getMat();Mat dst = _dst.getMat();::ipp::IwiImage iwSrc      = ippiGetImage(src);::ipp::IwiImage iwDst      = ippiGetImage(dst);::ipp::IwiImage iwSrcProc  = iwSrc;::ipp::IwiImage iwDstProc  = iwDst;::ipp::IwiBorderSize  borderSize(maskSize);::ipp::IwiBorderType  ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));if(!ippBorder)return false;if(srcType == ipp8u && dstType == ipp8u){iwDstProc.Alloc(iwDst.m_size, ipp16s, channels);useScale = true;}else if(srcType == ipp8u && dstType == ipp32f){iwSrc -= borderSize;iwSrcProc.Alloc(iwSrc.m_size, ipp32f, channels);CV_INSTRUMENT_FUN_IPP(::ipp::iwiScale, iwSrc, iwSrcProc, 1, 0, ::ipp::IwiScaleParams(ippAlgHintFast));iwSrcProc += borderSize;}if(useScharr)CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterScharr, iwSrcProc, iwDstProc, derivType, maskSize, ::ipp::IwDefault(), ippBorder);elseCV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterSobel, iwSrcProc, iwDstProc, derivType, maskSize, ::ipp::IwDefault(), ippBorder);if(useScale)CV_INSTRUMENT_FUN_IPP(::ipp::iwiScale, iwDstProc, iwDst, scale, delta, ::ipp::IwiScaleParams(ippAlgHintFast));}catch (const ::ipp::IwException &){return false;}return true;
#elseCV_UNUSED(_src); CV_UNUSED(_dst); CV_UNUSED(dx); CV_UNUSED(dy); CV_UNUSED(ksize); CV_UNUSED(scale); CV_UNUSED(delta); CV_UNUSED(borderType);return false;
#endif
}
}
#endif#ifdef HAVE_OPENCL
namespace cv
{
static bool ocl_sepFilter3x3_8UC1(InputArray _src, OutputArray _dst, int ddepth,InputArray _kernelX, InputArray _kernelY, double delta, int borderType)
{const ocl::Device & dev = ocl::Device::getDefault();int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);if ( !(dev.isIntel() && (type == CV_8UC1) && (ddepth == CV_8U) &&(_src.offset() == 0) && (_src.step() % 4 == 0) &&(_src.cols() % 16 == 0) && (_src.rows() % 2 == 0)) )return false;Mat kernelX = _kernelX.getMat().reshape(1, 1);if (kernelX.cols % 2 != 1)return false;Mat kernelY = _kernelY.getMat().reshape(1, 1);if (kernelY.cols % 2 != 1)return false;if (ddepth < 0)ddepth = sdepth;Size size = _src.size();size_t globalsize[2] = { 0, 0 };size_t localsize[2] = { 0, 0 };globalsize[0] = size.width / 16;globalsize[1] = size.height / 2;const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };char build_opts[1024];sprintf(build_opts, "-D %s %s%s", borderMap[borderType],ocl::kernelToStr(kernelX, CV_32F, "KERNEL_MATRIX_X").c_str(),ocl::kernelToStr(kernelY, CV_32F, "KERNEL_MATRIX_Y").c_str());ocl::Kernel kernel("sepFilter3x3_8UC1_cols16_rows2", cv::ocl::imgproc::sepFilter3x3_oclsrc, build_opts);if (kernel.empty())return false;UMat src = _src.getUMat();_dst.create(size, CV_MAKETYPE(ddepth, cn));if (!(_dst.offset() == 0 && _dst.step() % 4 == 0))return false;UMat dst = _dst.getUMat();int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));idxArg = kernel.set(idxArg, (int)src.step);idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dst));idxArg = kernel.set(idxArg, (int)dst.step);idxArg = kernel.set(idxArg, (int)dst.rows);idxArg = kernel.set(idxArg, (int)dst.cols);idxArg = kernel.set(idxArg, static_cast<float>(delta));return kernel.run(2, globalsize, (localsize[0] == 0) ? NULL : localsize, false);
}
}
#endifvoid cv::Sobel( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,int ksize, double scale, double delta, int borderType )
{CV_INSTRUMENT_REGION();CV_Assert(!_src.empty());int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);if (ddepth < 0)ddepth = sdepth;int dtype = CV_MAKE_TYPE(ddepth, cn);_dst.create( _src.size(), dtype );int ktype = std::max(CV_32F, std::max(ddepth, sdepth));Mat kx, ky;getDerivKernels( kx, ky, dx, dy, ksize, false, ktype );if( scale != 1 ){// usually the smoothing part is the slowest to compute,// so try to scale it instead of the faster differentiating partif( dx == 0 )kx *= scale;elseky *= scale;}CV_OCL_RUN(ocl::isOpenCLActivated() && _dst.isUMat() && _src.dims() <= 2 && ksize == 3 &&(size_t)_src.rows() > ky.total() && (size_t)_src.cols() > kx.total(),ocl_sepFilter3x3_8UC1(_src, _dst, ddepth, kx, ky, delta, borderType));CV_OCL_RUN(ocl::isOpenCLActivated() && _dst.isUMat() && _src.dims() <= 2 && (size_t)_src.rows() > kx.total() && (size_t)_src.cols() > kx.total(),ocl_sepFilter2D(_src, _dst, ddepth, kx, ky, Point(-1, -1), delta, borderType))Mat src = _src.getMat();Mat dst = _dst.getMat();Point ofs;Size wsz(src.cols, src.rows);if(!(borderType & BORDER_ISOLATED))src.locateROI( wsz, ofs );CALL_HAL(sobel, cv_hal_sobel, src.ptr(), src.step, dst.ptr(), dst.step, src.cols, src.rows, sdepth, ddepth, cn,ofs.x, ofs.y, wsz.width - src.cols - ofs.x, wsz.height - src.rows - ofs.y, dx, dy, ksize, scale, delta, borderType&~BORDER_ISOLATED);CV_OVX_RUN(true,openvx_sobel(src, dst, dx, dy, ksize, scale, delta, borderType))//CV_IPP_RUN_FAST(ipp_Deriv(src, dst, dx, dy, ksize, scale, delta, borderType));sepFilter2D(src, dst, ddepth, kx, ky, Point(-1, -1), delta, borderType );
}void cv::Scharr( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,double scale, double delta, int borderType )
{CV_INSTRUMENT_REGION();CV_Assert(!_src.empty());int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);if (ddepth < 0)ddepth = sdepth;int dtype = CV_MAKETYPE(ddepth, cn);_dst.create( _src.size(), dtype );int ktype = std::max(CV_32F, std::max(ddepth, sdepth));Mat kx, ky;getScharrKernels( kx, ky, dx, dy, false, ktype );if( scale != 1 ){// usually the smoothing part is the slowest to compute,// so try to scale it instead of the faster differentiating partif( dx == 0 )kx *= scale;elseky *= scale;}CV_OCL_RUN(ocl::isOpenCLActivated() && _dst.isUMat() && _src.dims() <= 2 &&(size_t)_src.rows() > ky.total() && (size_t)_src.cols() > kx.total(),ocl_sepFilter3x3_8UC1(_src, _dst, ddepth, kx, ky, delta, borderType));CV_OCL_RUN(ocl::isOpenCLActivated() && _dst.isUMat() && _src.dims() <= 2 &&(size_t)_src.rows() > kx.total() && (size_t)_src.cols() > kx.total(),ocl_sepFilter2D(_src, _dst, ddepth, kx, ky, Point(-1, -1), delta, borderType))Mat src = _src.getMat();Mat dst = _dst.getMat();Point ofs;Size wsz(src.cols, src.rows);if(!(borderType & BORDER_ISOLATED))src.locateROI( wsz, ofs );CALL_HAL(scharr, cv_hal_scharr, src.ptr(), src.step, dst.ptr(), dst.step, src.cols, src.rows, sdepth, ddepth, cn,ofs.x, ofs.y, wsz.width - src.cols - ofs.x, wsz.height - src.rows - ofs.y, dx, dy, scale, delta, borderType&~BORDER_ISOLATED);//CV_IPP_RUN_FAST(ipp_Deriv(src, dst, dx, dy, 0, scale, delta, borderType));sepFilter2D( src, dst, ddepth, kx, ky, Point(-1, -1), delta, borderType );
}#ifdef HAVE_OPENCLnamespace cv {#define LAPLACIAN_LOCAL_MEM(tileX, tileY, ksize, elsize) (((tileX) + 2 * (int)((ksize) / 2)) * (3 * (tileY) + 2 * (int)((ksize) / 2)) * elsize)static bool ocl_Laplacian5(InputArray _src, OutputArray _dst,const Mat & kd, const Mat & ks, double scale, double delta,int borderType, int depth, int ddepth)
{const size_t tileSizeX = 16;const size_t tileSizeYmin = 8;const ocl::Device dev = ocl::Device::getDefault();int stype = _src.type();int sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype), esz = CV_ELEM_SIZE(stype);bool doubleSupport = dev.doubleFPConfig() > 0;if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))return false;Mat kernelX = kd.reshape(1, 1);if (kernelX.cols % 2 != 1)return false;Mat kernelY = ks.reshape(1, 1);if (kernelY.cols % 2 != 1)return false;CV_Assert(kernelX.cols == kernelY.cols);size_t wgs = dev.maxWorkGroupSize();size_t lmsz = dev.localMemSize();size_t src_step = _src.step(), src_offset = _src.offset();const size_t tileSizeYmax = wgs / tileSizeX;CV_Assert(src_step != 0 && esz != 0);// workaround for NVIDIA: 3 channel vector type takes 4*elem_size in local memoryint loc_mem_cn = dev.vendorID() == ocl::Device::VENDOR_NVIDIA && cn == 3 ? 4 : cn;if (((src_offset % src_step) % esz == 0) &&((borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE) ||((borderType == BORDER_REFLECT || borderType == BORDER_WRAP || borderType == BORDER_REFLECT_101) &&(_src.cols() >= (int) (kernelX.cols + tileSizeX) && _src.rows() >= (int) (kernelY.cols + tileSizeYmax)))) &&(tileSizeX * tileSizeYmin <= wgs) &&(LAPLACIAN_LOCAL_MEM(tileSizeX, tileSizeYmin, kernelX.cols, loc_mem_cn * 4) <= lmsz)&& OCL_PERFORMANCE_CHECK(!dev.isAMD())  // TODO FIXIT 2018: Problem with AMDGPU on Linux (2482.3)){Size size = _src.size(), wholeSize;Point origin;int dtype = CV_MAKE_TYPE(ddepth, cn);int wdepth = CV_32F;size_t tileSizeY = tileSizeYmax;while ((tileSizeX * tileSizeY > wgs) || (LAPLACIAN_LOCAL_MEM(tileSizeX, tileSizeY, kernelX.cols, loc_mem_cn * 4) > lmsz)){tileSizeY /= 2;}size_t lt2[2] = { tileSizeX, tileSizeY};size_t gt2[2] = { lt2[0] * (1 + (size.width - 1) / lt2[0]), lt2[1] };char cvt[2][40];const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP","BORDER_REFLECT_101" };String opts = cv::format("-D BLK_X=%d -D BLK_Y=%d -D RADIUS=%d%s%s"" -D convertToWT=%s -D convertToDT=%s"" -D %s -D srcT1=%s -D dstT1=%s -D WT1=%s"" -D srcT=%s -D dstT=%s -D WT=%s"" -D CN=%d ",(int)lt2[0], (int)lt2[1], kernelX.cols / 2,ocl::kernelToStr(kernelX, wdepth, "KERNEL_MATRIX_X").c_str(),ocl::kernelToStr(kernelY, wdepth, "KERNEL_MATRIX_Y").c_str(),ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]),ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]),borderMap[borderType],ocl::typeToStr(sdepth), ocl::typeToStr(ddepth), ocl::typeToStr(wdepth),ocl::typeToStr(CV_MAKETYPE(sdepth, cn)),ocl::typeToStr(CV_MAKETYPE(ddepth, cn)),ocl::typeToStr(CV_MAKETYPE(wdepth, cn)),cn);ocl::Kernel k("laplacian", ocl::imgproc::laplacian5_oclsrc, opts);if (k.empty())return false;UMat src = _src.getUMat();_dst.create(size, dtype);UMat dst = _dst.getUMat();int src_offset_x = static_cast<int>((src_offset % src_step) / esz);int src_offset_y = static_cast<int>(src_offset / src_step);src.locateROI(wholeSize, origin);k.args(ocl::KernelArg::PtrReadOnly(src), (int)src_step, src_offset_x, src_offset_y,wholeSize.height, wholeSize.width, ocl::KernelArg::WriteOnly(dst),static_cast<float>(scale), static_cast<float>(delta));return k.run(2, gt2, lt2, false);}int iscale = cvRound(scale), idelta = cvRound(delta);bool floatCoeff = std::fabs(delta - idelta) > DBL_EPSILON || std::fabs(scale - iscale) > DBL_EPSILON;int wdepth = std::max(depth, floatCoeff ? CV_32F : CV_32S), kercn = 1;if (!doubleSupport && wdepth == CV_64F)return false;char cvt[2][40];ocl::Kernel k("sumConvert", ocl::imgproc::laplacian5_oclsrc,format("-D ONLY_SUM_CONVERT ""-D srcT=%s -D WT=%s -D dstT=%s -D coeffT=%s -D wdepth=%d ""-D convertToWT=%s -D convertToDT=%s%s",ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)),ocl::typeToStr(CV_MAKE_TYPE(wdepth, kercn)),ocl::typeToStr(CV_MAKE_TYPE(ddepth, kercn)),ocl::typeToStr(wdepth), wdepth,ocl::convertTypeStr(depth, wdepth, kercn, cvt[0]),ocl::convertTypeStr(wdepth, ddepth, kercn, cvt[1]),doubleSupport ? " -D DOUBLE_SUPPORT" : ""));if (k.empty())return false;UMat d2x, d2y;sepFilter2D(_src, d2x, depth, kd, ks, Point(-1, -1), 0, borderType);sepFilter2D(_src, d2y, depth, ks, kd, Point(-1, -1), 0, borderType);UMat dst = _dst.getUMat();ocl::KernelArg d2xarg = ocl::KernelArg::ReadOnlyNoSize(d2x),d2yarg = ocl::KernelArg::ReadOnlyNoSize(d2y),dstarg = ocl::KernelArg::WriteOnly(dst, cn, kercn);if (wdepth >= CV_32F)k.args(d2xarg, d2yarg, dstarg, (float)scale, (float)delta);elsek.args(d2xarg, d2yarg, dstarg, iscale, idelta);size_t globalsize[] = { (size_t)dst.cols * cn / kercn, (size_t)dst.rows };return k.run(2, globalsize, NULL, false);
}static bool ocl_Laplacian3_8UC1(InputArray _src, OutputArray _dst, int ddepth,InputArray _kernel, double delta, int borderType)
{const ocl::Device & dev = ocl::Device::getDefault();int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);if ( !(dev.isIntel() && (type == CV_8UC1) && (ddepth == CV_8U) &&(borderType != BORDER_WRAP) &&(_src.offset() == 0) && (_src.step() % 4 == 0) &&(_src.cols() % 16 == 0) && (_src.rows() % 2 == 0)) )return false;Mat kernel = _kernel.getMat().reshape(1, 1);if (ddepth < 0)ddepth = sdepth;Size size = _src.size();size_t globalsize[2] = { 0, 0 };size_t localsize[2] = { 0, 0 };globalsize[0] = size.width / 16;globalsize[1] = size.height / 2;const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };char build_opts[1024];sprintf(build_opts, "-D %s %s", borderMap[borderType],ocl::kernelToStr(kernel, CV_32F, "KERNEL_MATRIX").c_str());ocl::Kernel k("laplacian3_8UC1_cols16_rows2", cv::ocl::imgproc::laplacian3_oclsrc, build_opts);if (k.empty())return false;UMat src = _src.getUMat();_dst.create(size, CV_MAKETYPE(ddepth, cn));if (!(_dst.offset() == 0 && _dst.step() % 4 == 0))return false;UMat dst = _dst.getUMat();int idxArg = k.set(0, ocl::KernelArg::PtrReadOnly(src));idxArg = k.set(idxArg, (int)src.step);idxArg = k.set(idxArg, ocl::KernelArg::PtrWriteOnly(dst));idxArg = k.set(idxArg, (int)dst.step);idxArg = k.set(idxArg, (int)dst.rows);idxArg = k.set(idxArg, (int)dst.cols);idxArg = k.set(idxArg, static_cast<float>(delta));return k.run(2, globalsize, (localsize[0] == 0) ? NULL : localsize, false);
}}
#endif#if defined(HAVE_IPP)
namespace cv
{static bool ipp_Laplacian(InputArray _src, OutputArray _dst, int ksize, double scale, double delta, int borderType)
{
#ifdef HAVE_IPP_IWCV_INSTRUMENT_REGION_IPP();::ipp::IwiSize size(_src.size().width, _src.size().height);IppDataType   srcType   = ippiGetDataType(_src.depth());IppDataType   dstType   = ippiGetDataType(_dst.depth());int           channels  = _src.channels();bool          useScale  = false;if(channels != _dst.channels() || channels > 1)return false;if(fabs(delta) > FLT_EPSILON || fabs(scale-1) > FLT_EPSILON)useScale = true;IppiMaskSize maskSize = ippiGetMaskSize(ksize, ksize);if((int)maskSize < 0)return false;// Acquire data and begin processingtry{Mat src = _src.getMat();Mat dst = _dst.getMat();::ipp::IwiImage iwSrc      = ippiGetImage(src);::ipp::IwiImage iwDst      = ippiGetImage(dst);::ipp::IwiImage iwSrcProc  = iwSrc;::ipp::IwiImage iwDstProc  = iwDst;::ipp::IwiBorderSize  borderSize(maskSize);::ipp::IwiBorderType  ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));if(!ippBorder)return false;if(srcType == ipp8u && dstType == ipp8u){iwDstProc.Alloc(iwDst.m_size, ipp16s, channels);useScale = true;}else if(srcType == ipp8u && dstType == ipp32f){iwSrc -= borderSize;iwSrcProc.Alloc(iwSrc.m_size, ipp32f, channels);CV_INSTRUMENT_FUN_IPP(::ipp::iwiScale, iwSrc, iwSrcProc, 1, 0);iwSrcProc += borderSize;}CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterLaplacian, iwSrcProc, iwDstProc, maskSize, ::ipp::IwDefault(), ippBorder);if(useScale)CV_INSTRUMENT_FUN_IPP(::ipp::iwiScale, iwDstProc, iwDst, scale, delta);}catch (const ::ipp::IwException &){return false;}return true;
#elseCV_UNUSED(_src); CV_UNUSED(_dst); CV_UNUSED(ksize); CV_UNUSED(scale); CV_UNUSED(delta); CV_UNUSED(borderType);return false;
#endif
}
}
#endifvoid cv::Laplacian( InputArray _src, OutputArray _dst, int ddepth, int ksize,double scale, double delta, int borderType )
{CV_INSTRUMENT_REGION();CV_Assert(!_src.empty());int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);if (ddepth < 0)ddepth = sdepth;_dst.create( _src.size(), CV_MAKETYPE(ddepth, cn) );if( ksize == 1 || ksize == 3 ){float K[2][9] ={{ 0, 1, 0, 1, -4, 1, 0, 1, 0 },{ 2, 0, 2, 0, -8, 0, 2, 0, 2 }};Mat kernel(3, 3, CV_32F, K[ksize == 3]);if( scale != 1 )kernel *= scale;CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2,ocl_Laplacian3_8UC1(_src, _dst, ddepth, kernel, delta, borderType));}CV_IPP_RUN(!(cv::ocl::isOpenCLActivated() && _dst.isUMat()), ipp_Laplacian(_src, _dst, ksize, scale, delta, borderType));if( ksize == 1 || ksize == 3 ){float K[2][9] ={{ 0, 1, 0, 1, -4, 1, 0, 1, 0 },{ 2, 0, 2, 0, -8, 0, 2, 0, 2 }};Mat kernel(3, 3, CV_32F, K[ksize == 3]);if( scale != 1 )kernel *= scale;filter2D( _src, _dst, ddepth, kernel, Point(-1, -1), delta, borderType );}else{int ktype = std::max(CV_32F, std::max(ddepth, sdepth));int wdepth = sdepth == CV_8U && ksize <= 5 ? CV_16S : sdepth <= CV_32F ? CV_32F : CV_64F;int wtype = CV_MAKETYPE(wdepth, cn);Mat kd, ks;getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );CV_OCL_RUN(_dst.isUMat(),ocl_Laplacian5(_src, _dst, kd, ks, scale,delta, borderType, wdepth, ddepth))Mat src = _src.getMat(), dst = _dst.getMat();Point ofs;Size wsz(src.cols, src.rows);if(!(borderType&BORDER_ISOLATED))src.locateROI( wsz, ofs );borderType = (borderType&~BORDER_ISOLATED);const size_t STRIPE_SIZE = 1 << 14;Ptr<FilterEngine> fx = createSeparableLinearFilter(stype,wtype, kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );Ptr<FilterEngine> fy = createSeparableLinearFilter(stype,wtype, ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );int y = fx->start(src, wsz, ofs), dsty = 0, dy = 0;fy->start(src, wsz, ofs);const uchar* sptr = src.ptr() + src.step[0] * y;int dy0 = std::min(std::max((int)(STRIPE_SIZE/(CV_ELEM_SIZE(stype)*src.cols)), 1), src.rows);Mat d2x( dy0 + kd.rows - 1, src.cols, wtype );Mat d2y( dy0 + kd.rows - 1, src.cols, wtype );for( ; dsty < src.rows; sptr += dy0*src.step, dsty += dy ){fx->proceed( sptr, (int)src.step, dy0, d2x.ptr(), (int)d2x.step );dy = fy->proceed( sptr, (int)src.step, dy0, d2y.ptr(), (int)d2y.step );if( dy > 0 ){Mat dstripe = dst.rowRange(dsty, dsty + dy);d2x.rows = d2y.rows = dy; // modify the headers, which should workd2x += d2y;d2x.convertTo( dstripe, ddepth, scale, delta );}}}
}CV_IMPL void
cvSobel( const void* srcarr, void* dstarr, int dx, int dy, int aperture_size )
{cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);CV_Assert( src.size() == dst.size() && src.channels() == dst.channels() );cv::Sobel( src, dst, dst.depth(), dx, dy, aperture_size, 1, 0, cv::BORDER_REPLICATE );if( CV_IS_IMAGE(srcarr) && ((IplImage*)srcarr)->origin && dy % 2 != 0 )dst *= -1;
}CV_IMPL void
cvLaplace( const void* srcarr, void* dstarr, int aperture_size )
{cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);CV_Assert( src.size() == dst.size() && src.channels() == dst.channels() );cv::Laplacian( src, dst, dst.depth(), aperture_size, 1, 0, cv::BORDER_REPLICATE );
}/* End of file. */

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逐渐的走向开源是个好事。。

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openCV的sobel算子的深度学习卷积网络部分的C语言源码

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