2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
680 lines
22 KiB
C++
680 lines
22 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#if !defined (HAVE_CUDA)
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cv::gpu::SCascade::SCascade(const double, const double, const int, const int) { throw_nogpu(); }
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cv::gpu::SCascade::~SCascade() { throw_nogpu(); }
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bool cv::gpu::SCascade::load(const FileNode&) { throw_nogpu(); return false;}
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void cv::gpu::SCascade::detect(InputArray, InputArray, OutputArray, Stream&) const { throw_nogpu(); }
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void cv::gpu::SCascade::read(const FileNode& fn) { Algorithm::read(fn); }
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cv::gpu::ChannelsProcessor::ChannelsProcessor() { throw_nogpu(); }
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cv::gpu::ChannelsProcessor::~ChannelsProcessor() { throw_nogpu(); }
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cv::Ptr<cv::gpu::ChannelsProcessor> cv::gpu::ChannelsProcessor::create(const int, const int, const int)
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{ throw_nogpu(); return cv::Ptr<cv::gpu::ChannelsProcessor>(0); }
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#else
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# include "icf.hpp"
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cv::gpu::device::icf::Level::Level(int idx, const Octave& oct, const float scale, const int w, const int h)
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: octave(idx), step(oct.stages), relScale(scale / oct.scale)
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{
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workRect.x = cvRound(w / (float)oct.shrinkage);
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workRect.y = cvRound(h / (float)oct.shrinkage);
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objSize.x = cv::saturate_cast<uchar>(oct.size.x * relScale);
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objSize.y = cv::saturate_cast<uchar>(oct.size.y * relScale);
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// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool's and Dallal's papers
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if (fabs(relScale - 1.f) < FLT_EPSILON)
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scaling[0] = scaling[1] = 1.f;
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else
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{
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scaling[0] = (relScale < 1.f) ? 0.89f * ::pow(relScale, 1.099f / ::log(2.0f)) : 1.f;
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scaling[1] = relScale * relScale;
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}
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}
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namespace cv { namespace gpu { namespace device {
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namespace icf {
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void fillBins(cv::gpu::PtrStepSzb hogluv, const cv::gpu::PtrStepSzf& nangle,
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const int fw, const int fh, const int bins, cudaStream_t stream);
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void suppress(const PtrStepSzb& objects, PtrStepSzb overlaps, PtrStepSzi ndetections,
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PtrStepSzb suppressed, cudaStream_t stream);
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void bgr2Luv(const PtrStepSzb& bgr, PtrStepSzb luv);
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void gray2hog(const PtrStepSzb& gray, PtrStepSzb mag, const int bins);
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void shrink(const cv::gpu::PtrStepSzb& channels, cv::gpu::PtrStepSzb shrunk);
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}
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}}}
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struct cv::gpu::SCascade::Fields
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{
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static Fields* parseCascade(const FileNode &root, const float mins, const float maxs, const int totals, const int method)
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{
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static const char *const SC_STAGE_TYPE = "stageType";
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static const char *const SC_BOOST = "BOOST";
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static const char *const SC_FEATURE_TYPE = "featureType";
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static const char *const SC_ICF = "ICF";
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static const char *const SC_ORIG_W = "width";
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static const char *const SC_ORIG_H = "height";
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static const char *const SC_FEATURE_FORMAT = "featureFormat";
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static const char *const SC_SHRINKAGE = "shrinkage";
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static const char *const SC_OCTAVES = "octaves";
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static const char *const SC_OCT_SCALE = "scale";
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static const char *const SC_OCT_WEAKS = "weaks";
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static const char *const SC_TREES = "trees";
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static const char *const SC_WEAK_THRESHOLD = "treeThreshold";
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static const char *const SC_FEATURES = "features";
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static const char *const SC_INTERNAL = "internalNodes";
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static const char *const SC_LEAF = "leafValues";
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static const char *const SC_F_CHANNEL = "channel";
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static const char *const SC_F_RECT = "rect";
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// only Ada Boost supported
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std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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// only HOG-like integral channel features supported
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std::string featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
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CV_Assert(featureTypeStr == SC_ICF);
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int origWidth = (int)root[SC_ORIG_W];
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int origHeight = (int)root[SC_ORIG_H];
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std::string fformat = (std::string)root[SC_FEATURE_FORMAT];
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bool useBoxes = (fformat == "BOX");
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ushort shrinkage = cv::saturate_cast<ushort>((int)root[SC_SHRINKAGE]);
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FileNode fn = root[SC_OCTAVES];
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if (fn.empty()) return 0;
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using namespace device::icf;
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std::vector<Octave> voctaves;
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std::vector<float> vstages;
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std::vector<Node> vnodes;
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std::vector<float> vleaves;
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FileNodeIterator it = fn.begin(), it_end = fn.end();
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for (ushort octIndex = 0; it != it_end; ++it, ++octIndex)
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{
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FileNode fns = *it;
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float scale = powf(2.f,saturate_cast<float>((int)fns[SC_OCT_SCALE]));
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bool isUPOctave = scale >= 1;
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ushort nweaks = saturate_cast<ushort>((int)fns[SC_OCT_WEAKS]);
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ushort2 size;
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size.x = cvRound(origWidth * scale);
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size.y = cvRound(origHeight * scale);
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Octave octave(octIndex, nweaks, shrinkage, size, scale);
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CV_Assert(octave.stages > 0);
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voctaves.push_back(octave);
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FileNode ffs = fns[SC_FEATURES];
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if (ffs.empty()) return 0;
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std::vector<cv::Rect> feature_rects;
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std::vector<int> feature_channels;
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FileNodeIterator ftrs = ffs.begin(), ftrs_end = ffs.end();
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int feature_offset = 0;
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for (; ftrs != ftrs_end; ++ftrs, ++feature_offset )
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{
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cv::FileNode ftn = (*ftrs)[SC_F_RECT];
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cv::FileNodeIterator r_it = ftn.begin();
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int x = (int)*(r_it++);
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int y = (int)*(r_it++);
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int w = (int)*(r_it++);
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int h = (int)*(r_it++);
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if (useBoxes)
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{
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if (isUPOctave)
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{
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w -= x;
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h -= y;
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}
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}
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else
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{
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if (!isUPOctave)
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{
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w += x;
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h += y;
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}
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}
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feature_rects.push_back(cv::Rect(x, y, w, h));
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feature_channels.push_back((int)(*ftrs)[SC_F_CHANNEL]);
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}
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fns = fns[SC_TREES];
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if (fn.empty()) return false;
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// for each stage (~ decision tree with H = 2)
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FileNodeIterator st = fns.begin(), st_end = fns.end();
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for (; st != st_end; ++st )
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{
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FileNode octfn = *st;
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float threshold = (float)octfn[SC_WEAK_THRESHOLD];
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vstages.push_back(threshold);
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FileNode intfns = octfn[SC_INTERNAL];
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FileNodeIterator inIt = intfns.begin(), inIt_end = intfns.end();
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for (; inIt != inIt_end;)
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{
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inIt +=2;
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int featureIdx = (int)(*(inIt++));
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float orig_threshold = (float)(*(inIt++));
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unsigned int th = saturate_cast<unsigned int>((int)orig_threshold);
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cv::Rect& r = feature_rects[featureIdx];
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uchar4 rect;
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rect.x = saturate_cast<uchar>(r.x);
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rect.y = saturate_cast<uchar>(r.y);
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rect.z = saturate_cast<uchar>(r.width);
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rect.w = saturate_cast<uchar>(r.height);
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unsigned int channel = saturate_cast<unsigned int>(feature_channels[featureIdx]);
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vnodes.push_back(Node(rect, channel, th));
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}
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intfns = octfn[SC_LEAF];
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inIt = intfns.begin(), inIt_end = intfns.end();
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for (; inIt != inIt_end; ++inIt)
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{
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vleaves.push_back((float)(*inIt));
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}
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}
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}
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cv::Mat hoctaves(1, (int) (voctaves.size() * sizeof(Octave)), CV_8UC1, (uchar*)&(voctaves[0]));
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CV_Assert(!hoctaves.empty());
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cv::Mat hstages(cv::Mat(vstages).reshape(1,1));
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CV_Assert(!hstages.empty());
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cv::Mat hnodes(1, (int) (vnodes.size() * sizeof(Node)), CV_8UC1, (uchar*)&(vnodes[0]) );
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CV_Assert(!hnodes.empty());
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cv::Mat hleaves(cv::Mat(vleaves).reshape(1,1));
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CV_Assert(!hleaves.empty());
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Fields* fields = new Fields(mins, maxs, totals, origWidth, origHeight, shrinkage, 0,
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hoctaves, hstages, hnodes, hleaves, method);
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fields->voctaves = voctaves;
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fields->createLevels(DEFAULT_FRAME_HEIGHT, DEFAULT_FRAME_WIDTH);
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return fields;
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}
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bool check(float mins,float maxs, int scales)
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{
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bool updated = (minScale == mins) || (maxScale == maxs) || (totals = scales);
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minScale = mins;
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maxScale = maxScale;
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totals = scales;
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return updated;
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}
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int createLevels(const int fh, const int fw)
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{
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using namespace device::icf;
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std::vector<Level> vlevels;
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float logFactor = (::log(maxScale) - ::log(minScale)) / (totals -1);
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float scale = minScale;
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int dcs = 0;
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for (int sc = 0; sc < totals; ++sc)
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{
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int width = (int)::std::max(0.0f, fw - (origObjWidth * scale));
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int height = (int)::std::max(0.0f, fh - (origObjHeight * scale));
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float logScale = ::log(scale);
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int fit = fitOctave(voctaves, logScale);
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Level level(fit, voctaves[fit], scale, width, height);
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if (!width || !height)
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break;
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else
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{
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vlevels.push_back(level);
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if (voctaves[fit].scale < 1) ++dcs;
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}
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if (::fabs(scale - maxScale) < FLT_EPSILON) break;
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scale = ::std::min(maxScale, ::expf(::log(scale) + logFactor));
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}
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cv::Mat hlevels = cv::Mat(1, (int) (vlevels.size() * sizeof(Level)), CV_8UC1, (uchar*)&(vlevels[0]) );
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CV_Assert(!hlevels.empty());
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levels.upload(hlevels);
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downscales = dcs;
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return dcs;
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}
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bool update(int fh, int fw, int shr)
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{
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shrunk.create(fh / shr * HOG_LUV_BINS, fw / shr, CV_8UC1);
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integralBuffer.create(shrunk.rows, shrunk.cols, CV_32SC1);
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hogluv.create((fh / shr) * HOG_LUV_BINS + 1, fw / shr + 1, CV_32SC1);
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hogluv.setTo(cv::Scalar::all(0));
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overlaps.create(1, 5000, CV_8UC1);
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suppressed.create(1, sizeof(Detection) * 51, CV_8UC1);
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return true;
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}
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Fields( const float mins, const float maxs, const int tts, const int ow, const int oh, const int shr, const int ds,
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cv::Mat hoctaves, cv::Mat hstages, cv::Mat hnodes, cv::Mat hleaves, int method)
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: minScale(mins), maxScale(maxs), totals(tts), origObjWidth(ow), origObjHeight(oh), shrinkage(shr), downscales(ds)
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{
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update(DEFAULT_FRAME_HEIGHT, DEFAULT_FRAME_WIDTH, shr);
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octaves.upload(hoctaves);
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stages.upload(hstages);
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nodes.upload(hnodes);
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leaves.upload(hleaves);
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preprocessor = ChannelsProcessor::create(shrinkage, 6, method);
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}
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void detect(cv::gpu::GpuMat& objects, Stream& s) const
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{
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if (s)
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s.enqueueMemSet(objects, 0);
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else
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cudaMemset(objects.data, 0, sizeof(Detection));
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cudaSafeCall( cudaGetLastError());
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device::icf::CascadeInvoker<device::icf::GK107PolicyX4> invoker
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= device::icf::CascadeInvoker<device::icf::GK107PolicyX4>(levels, stages, nodes, leaves);
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cudaStream_t stream = StreamAccessor::getStream(s);
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invoker(mask, hogluv, objects, downscales, stream);
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}
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void suppress(GpuMat& objects, Stream& s)
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{
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GpuMat ndetections = GpuMat(objects, cv::Rect(0, 0, sizeof(Detection), 1));
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ensureSizeIsEnough(objects.rows, objects.cols, CV_8UC1, overlaps);
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if (s)
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{
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s.enqueueMemSet(overlaps, 0);
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s.enqueueMemSet(suppressed, 0);
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}
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else
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{
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overlaps.setTo(0);
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suppressed.setTo(0);
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}
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cudaStream_t stream = StreamAccessor::getStream(s);
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device::icf::suppress(objects, overlaps, ndetections, suppressed, stream);
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}
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private:
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typedef std::vector<device::icf::Octave>::const_iterator octIt_t;
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static int fitOctave(const std::vector<device::icf::Octave>& octs, const float& logFactor)
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{
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float minAbsLog = FLT_MAX;
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int res = 0;
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for (int oct = 0; oct < (int)octs.size(); ++oct)
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{
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const device::icf::Octave& octave =octs[oct];
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float logOctave = ::log(octave.scale);
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float logAbsScale = ::fabs(logFactor - logOctave);
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if(logAbsScale < minAbsLog)
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{
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res = oct;
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minAbsLog = logAbsScale;
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}
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}
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return res;
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}
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public:
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cv::Ptr<ChannelsProcessor> preprocessor;
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// scales range
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float minScale;
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float maxScale;
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int totals;
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int origObjWidth;
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int origObjHeight;
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const int shrinkage;
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int downscales;
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// 160x120x10
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GpuMat shrunk;
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// temporal mat for integral
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GpuMat integralBuffer;
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// 161x121x10
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GpuMat hogluv;
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// used for suppression
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GpuMat suppressed;
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// used for area overlap computing during
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GpuMat overlaps;
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// Cascade from xml
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GpuMat octaves;
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GpuMat stages;
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GpuMat nodes;
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GpuMat leaves;
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GpuMat levels;
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// For ROI
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GpuMat mask;
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GpuMat genRoiTmp;
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// GpuMat collected;
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std::vector<device::icf::Octave> voctaves;
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// DeviceInfo info;
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enum { BOOST = 0 };
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enum
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{
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DEFAULT_FRAME_WIDTH = 640,
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DEFAULT_FRAME_HEIGHT = 480,
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HOG_LUV_BINS = 10
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};
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};
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cv::gpu::SCascade::SCascade(const double mins, const double maxs, const int sc, const int fl)
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: fields(0), minScale(mins), maxScale(maxs), scales(sc), flags(fl) {}
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cv::gpu::SCascade::~SCascade() { delete fields; }
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bool cv::gpu::SCascade::load(const FileNode& fn)
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{
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if (fields) delete fields;
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fields = Fields::parseCascade(fn, (float)minScale, (float)maxScale, scales, flags);
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return fields != 0;
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}
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void cv::gpu::SCascade::detect(InputArray _image, InputArray _rois, OutputArray _objects, Stream& s) const
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{
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CV_Assert(fields);
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// only color images and precomputed integrals are supported
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int type = _image.type();
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CV_Assert(type == CV_8UC3 || type == CV_32SC1 || (!_rois.empty()));
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const GpuMat image = _image.getGpuMat();
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if (_objects.empty()) _objects.create(1, 4096 * sizeof(Detection), CV_8UC1);
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GpuMat rois = _rois.getGpuMat(), objects = _objects.getGpuMat();
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/// roi
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Fields& flds = *fields;
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int shr = flds.shrinkage;
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flds.mask.create( rois.cols / shr, rois.rows / shr, rois.type());
|
|
|
|
cv::gpu::resize(rois, flds.genRoiTmp, cv::Size(), 1.f / shr, 1.f / shr, CV_INTER_AREA, s);
|
|
cv::gpu::transpose(flds.genRoiTmp, flds.mask, s);
|
|
|
|
if (type == CV_8UC3)
|
|
{
|
|
flds.update(image.rows, image.cols, flds.shrinkage);
|
|
|
|
if (flds.check((float)minScale, (float)maxScale, scales))
|
|
flds.createLevels(image.rows, image.cols);
|
|
|
|
flds.preprocessor->apply(image, flds.shrunk);
|
|
cv::gpu::integralBuffered(flds.shrunk, flds.hogluv, flds.integralBuffer, s);
|
|
}
|
|
else
|
|
{
|
|
if (s)
|
|
s.enqueueCopy(image, flds.hogluv);
|
|
else
|
|
image.copyTo(flds.hogluv);
|
|
}
|
|
|
|
flds.detect(objects, s);
|
|
|
|
if ( (flags && NMS_MASK) != NO_REJECT)
|
|
{
|
|
GpuMat spr(objects, cv::Rect(0, 0, flds.suppressed.cols, flds.suppressed.rows));
|
|
flds.suppress(objects, s);
|
|
flds.suppressed.copyTo(spr);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SCascade::read(const FileNode& fn)
|
|
{
|
|
Algorithm::read(fn);
|
|
}
|
|
|
|
namespace {
|
|
|
|
using cv::InputArray;
|
|
using cv::OutputArray;
|
|
using cv::gpu::Stream;
|
|
using cv::gpu::GpuMat;
|
|
|
|
inline void setZero(cv::gpu::GpuMat& m, Stream& s)
|
|
{
|
|
if (s)
|
|
s.enqueueMemSet(m, 0);
|
|
else
|
|
m.setTo(0);
|
|
}
|
|
|
|
struct GenricPreprocessor : public cv::gpu::ChannelsProcessor
|
|
{
|
|
GenricPreprocessor(const int s, const int b) : cv::gpu::ChannelsProcessor(), shrinkage(s), bins(b) {}
|
|
virtual ~GenricPreprocessor() {}
|
|
|
|
virtual void apply(InputArray _frame, OutputArray _shrunk, Stream& s = Stream::Null())
|
|
{
|
|
const GpuMat frame = _frame.getGpuMat();
|
|
|
|
_shrunk.create(frame.rows * (4 + bins) / shrinkage, frame.cols / shrinkage, CV_8UC1);
|
|
GpuMat shrunk = _shrunk.getGpuMat();
|
|
|
|
channels.create(frame.rows * (4 + bins), frame.cols, CV_8UC1);
|
|
setZero(channels, s);
|
|
|
|
cv::gpu::cvtColor(frame, gray, CV_BGR2GRAY, s);
|
|
createHogBins(s);
|
|
|
|
createLuvBins(frame, s);
|
|
|
|
cv::gpu::resize(channels, shrunk, cv::Size(), 1.f / shrinkage, 1.f / shrinkage, CV_INTER_AREA, s);
|
|
}
|
|
|
|
private:
|
|
|
|
void createHogBins(Stream& s)
|
|
{
|
|
static const int fw = gray.cols;
|
|
static const int fh = gray.rows;
|
|
|
|
fplane.create(fh * HOG_BINS, fw, CV_32FC1);
|
|
|
|
GpuMat dfdx(fplane, cv::Rect(0, 0, fw, fh));
|
|
GpuMat dfdy(fplane, cv::Rect(0, fh, fw, fh));
|
|
|
|
cv::gpu::Sobel(gray, dfdx, CV_32F, 1, 0, sobelBuf, 3, 1, cv::BORDER_DEFAULT, -1, s);
|
|
cv::gpu::Sobel(gray, dfdy, CV_32F, 0, 1, sobelBuf, 3, 1, cv::BORDER_DEFAULT, -1, s);
|
|
|
|
GpuMat mag(fplane, cv::Rect(0, 2 * fh, fw, fh));
|
|
GpuMat ang(fplane, cv::Rect(0, 3 * fh, fw, fh));
|
|
|
|
cv::gpu::cartToPolar(dfdx, dfdy, mag, ang, true, s);
|
|
|
|
// normalize magnitude to uchar interval and angles to 6 bins
|
|
GpuMat nmag(fplane, cv::Rect(0, 4 * fh, fw, fh));
|
|
GpuMat nang(fplane, cv::Rect(0, 5 * fh, fw, fh));
|
|
|
|
cv::gpu::multiply(mag, cv::Scalar::all(1.f / (8 *::log(2.0f))), nmag, 1, -1, s);
|
|
cv::gpu::multiply(ang, cv::Scalar::all(1.f / 60.f), nang, 1, -1, s);
|
|
|
|
//create uchar magnitude
|
|
GpuMat cmag(channels, cv::Rect(0, fh * HOG_BINS, fw, fh));
|
|
if (s)
|
|
s.enqueueConvert(nmag, cmag, CV_8UC1);
|
|
else
|
|
nmag.convertTo(cmag, CV_8UC1);
|
|
|
|
cudaStream_t stream = cv::gpu::StreamAccessor::getStream(s);
|
|
cv::gpu::device::icf::fillBins(channels, nang, fw, fh, HOG_BINS, stream);
|
|
}
|
|
|
|
void createLuvBins(const cv::gpu::GpuMat& colored, Stream& s)
|
|
{
|
|
static const int fw = colored.cols;
|
|
static const int fh = colored.rows;
|
|
|
|
cv::gpu::cvtColor(colored, luv, CV_BGR2Luv, s);
|
|
|
|
std::vector<GpuMat> splited;
|
|
for(int i = 0; i < LUV_BINS; ++i)
|
|
{
|
|
splited.push_back(GpuMat(channels, cv::Rect(0, fh * (7 + i), fw, fh)));
|
|
}
|
|
|
|
cv::gpu::split(luv, splited, s);
|
|
}
|
|
|
|
enum {HOG_BINS = 6, LUV_BINS = 3};
|
|
|
|
const int shrinkage;
|
|
const int bins;
|
|
|
|
GpuMat gray;
|
|
GpuMat luv;
|
|
GpuMat channels;
|
|
|
|
// preallocated buffer for floating point operations
|
|
GpuMat fplane;
|
|
GpuMat sobelBuf;
|
|
};
|
|
|
|
|
|
struct SeparablePreprocessor : public cv::gpu::ChannelsProcessor
|
|
{
|
|
SeparablePreprocessor(const int s, const int b) : cv::gpu::ChannelsProcessor(), shrinkage(s), bins(b) {}
|
|
virtual ~SeparablePreprocessor() {}
|
|
|
|
virtual void apply(InputArray _frame, OutputArray _shrunk, Stream& s = Stream::Null())
|
|
{
|
|
const GpuMat frame = _frame.getGpuMat();
|
|
cv::gpu::GaussianBlur(frame, bgr, cv::Size(3, 3), -1.0);
|
|
|
|
_shrunk.create(frame.rows * (4 + bins) / shrinkage, frame.cols / shrinkage, CV_8UC1);
|
|
GpuMat shrunk = _shrunk.getGpuMat();
|
|
|
|
channels.create(frame.rows * (4 + bins), frame.cols, CV_8UC1);
|
|
setZero(channels, s);
|
|
|
|
cv::gpu::cvtColor(bgr, gray, CV_BGR2GRAY);
|
|
cv::gpu::device::icf::gray2hog(gray, channels(cv::Rect(0, 0, bgr.cols, bgr.rows * (bins + 1))), bins);
|
|
|
|
cv::gpu::GpuMat luv(channels, cv::Rect(0, bgr.rows * (bins + 1), bgr.cols, bgr.rows * 3));
|
|
cv::gpu::device::icf::bgr2Luv(bgr, luv);
|
|
cv::gpu::device::icf::shrink(channels, shrunk);
|
|
}
|
|
|
|
private:
|
|
const int shrinkage;
|
|
const int bins;
|
|
|
|
GpuMat bgr;
|
|
GpuMat gray;
|
|
GpuMat channels;
|
|
};
|
|
|
|
}
|
|
|
|
cv::Ptr<cv::gpu::ChannelsProcessor> cv::gpu::ChannelsProcessor::create(const int s, const int b, const int m)
|
|
{
|
|
CV_Assert((m && SEPARABLE) || (m && GENERIC));
|
|
|
|
if (m && GENERIC)
|
|
return cv::Ptr<cv::gpu::ChannelsProcessor>(new GenricPreprocessor(s, b));
|
|
|
|
return cv::Ptr<cv::gpu::ChannelsProcessor>(new SeparablePreprocessor(s, b));
|
|
}
|
|
|
|
cv::gpu::ChannelsProcessor::ChannelsProcessor() { }
|
|
cv::gpu::ChannelsProcessor::~ChannelsProcessor() { }
|
|
|
|
#endif
|