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604 lines
21 KiB
C++
604 lines
21 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::softcascade::SCascade::SCascade(const double, const double, const int, const int) { throw_no_cuda(); }
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cv::softcascade::SCascade::~SCascade() { throw_no_cuda(); }
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bool cv::softcascade::SCascade::load(const FileNode&) { throw_no_cuda(); return false;}
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void cv::softcascade::SCascade::detect(InputArray, InputArray, OutputArray, cv::gpu::Stream&) const { throw_no_cuda(); }
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void cv::softcascade::SCascade::read(const FileNode& fn) { Algorithm::read(fn); }
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cv::softcascade::ChannelsProcessor::ChannelsProcessor() { throw_no_cuda(); }
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cv::softcascade::ChannelsProcessor::~ChannelsProcessor() { throw_no_cuda(); }
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cv::Ptr<cv::softcascade::ChannelsProcessor> cv::softcascade::ChannelsProcessor::create(const int, const int, const int)
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{ throw_no_cuda(); return cv::Ptr<cv::softcascade::ChannelsProcessor>(0); }
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#else
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# include "cuda_invoker.hpp"
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cv::softcascade::cudev::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 = (unsigned char)cvRound(w / (float)oct.shrinkage);
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workRect.y = (unsigned char)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 softcascade { namespace cudev {
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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 cv::gpu::PtrStepSzb& objects, cv::gpu::PtrStepSzb overlaps, cv::gpu::PtrStepSzi ndetections,
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cv::gpu::PtrStepSzb suppressed, cudaStream_t stream);
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void bgr2Luv(const cv::gpu::PtrStepSzb& bgr, cv::gpu::PtrStepSzb luv);
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void transform(const cv::gpu::PtrStepSz<uchar3>& bgr, cv::gpu::PtrStepSzb gray);
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void gray2hog(const cv::gpu::PtrStepSzb& gray, cv::gpu::PtrStepSzb mag, const int bins);
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void shrink(const cv::gpu::PtrStepSzb& channels, cv::gpu::PtrStepSzb shrunk);
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void shfl_integral(const cv::gpu::PtrStepSzb& img, cv::gpu::PtrStepSz<unsigned int> integral, cudaStream_t stream);
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}}}
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struct cv::softcascade::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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String stageTypeStr = (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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String featureTypeStr = (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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String fformat = (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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std::vector<cudev::Octave> voctaves;
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std::vector<float> vstages;
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std::vector<cudev::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 = (unsigned short)cvRound(origWidth * scale);
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size.y = (unsigned short)cvRound(origHeight * scale);
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cudev::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(cudev::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(cudev::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(cudev::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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std::vector<cudev::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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cudev::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(cudev::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, cv::gpu::Stream& s) const
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{
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objects.setTo(Scalar::all(0), s);
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cudaSafeCall( cudaGetLastError());
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cudev::CascadeInvoker<cudev::GK107PolicyX4> invoker
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= cudev::CascadeInvoker<cudev::GK107PolicyX4>(levels, stages, nodes, leaves);
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cudaStream_t stream = cv::gpu::StreamAccessor::getStream(s);
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invoker(mask, hogluv, objects, downscales, stream);
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}
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void suppress(cv::gpu::GpuMat& objects, cv::gpu::Stream& s)
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{
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cv::gpu::GpuMat ndetections = cv::gpu::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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overlaps.setTo(0, s);
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suppressed.setTo(0, s);
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cudaStream_t stream = cv::gpu::StreamAccessor::getStream(s);
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cudev::suppress(objects, overlaps, ndetections, suppressed, stream);
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}
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private:
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typedef std::vector<cudev::Octave>::const_iterator octIt_t;
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static int fitOctave(const std::vector<cudev::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 cudev::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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cv::gpu::GpuMat shrunk;
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// temporal mat for integral
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cv::gpu::GpuMat integralBuffer;
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// 161x121x10
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cv::gpu::GpuMat hogluv;
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// used for suppression
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cv::gpu::GpuMat suppressed;
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// used for area overlap computing during
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cv::gpu::GpuMat overlaps;
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// Cascade from xml
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cv::gpu::GpuMat octaves;
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cv::gpu::GpuMat stages;
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cv::gpu::GpuMat nodes;
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cv::gpu::GpuMat leaves;
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cv::gpu::GpuMat levels;
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// For ROI
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cv::gpu::GpuMat mask;
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cv::gpu::GpuMat genRoiTmp;
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// cv::gpu::GpuMat collected;
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std::vector<cudev::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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private:
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cv::softcascade::SCascade::Fields& operator=( const cv::softcascade::SCascade::Fields & );
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};
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cv::softcascade::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::softcascade::SCascade::~SCascade() { delete fields; }
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bool cv::softcascade::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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namespace {
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void integral(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& sum, cv::gpu::GpuMat& buffer, cv::gpu::Stream& s)
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{
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CV_Assert(src.type() == CV_8UC1);
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cudaStream_t stream = cv::gpu::StreamAccessor::getStream(s);
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cv::Size whole;
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cv::Point offset;
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src.locateROI(whole, offset);
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|
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if (cv::gpu::deviceSupports(cv::gpu::WARP_SHUFFLE_FUNCTIONS) && src.cols <= 2048
|
|
&& offset.x % 16 == 0 && ((src.cols + 63) / 64) * 64 <= (static_cast<int>(src.step) - offset.x))
|
|
{
|
|
ensureSizeIsEnough(((src.rows + 7) / 8) * 8, ((src.cols + 63) / 64) * 64, CV_32SC1, buffer);
|
|
|
|
cv::softcascade::cudev::shfl_integral(src, buffer, stream);
|
|
|
|
sum.create(src.rows + 1, src.cols + 1, CV_32SC1);
|
|
sum.setTo(cv::Scalar::all(0), s);
|
|
|
|
cv::gpu::GpuMat inner = sum(cv::Rect(1, 1, src.cols, src.rows));
|
|
cv::gpu::GpuMat res = buffer(cv::Rect(0, 0, src.cols, src.rows));
|
|
|
|
res.copyTo(inner, s);
|
|
}
|
|
else {CV_Error(cv::Error::GpuNotSupported, ": CC 3.x required.");}
|
|
}
|
|
|
|
}
|
|
|
|
void cv::softcascade::SCascade::detect(InputArray _image, InputArray _rois, OutputArray _objects, cv::gpu::Stream& s) const
|
|
{
|
|
CV_Assert(fields);
|
|
|
|
// only color images and precomputed integrals are supported
|
|
int type = _image.type();
|
|
CV_Assert(type == CV_8UC3 || type == CV_32SC1 || (!_rois.empty()));
|
|
|
|
const cv::gpu::GpuMat image = _image.getGpuMat();
|
|
|
|
if (_objects.empty()) _objects.create(1, 4096 * sizeof(Detection), CV_8UC1);
|
|
|
|
cv::gpu::GpuMat rois = _rois.getGpuMat(), objects = _objects.getGpuMat();
|
|
|
|
/// roi
|
|
Fields& flds = *fields;
|
|
int shr = flds.shrinkage;
|
|
|
|
flds.mask.create( rois.cols / shr, rois.rows / shr, rois.type());
|
|
|
|
cudev::shrink(rois, flds.mask);
|
|
//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);
|
|
::integral(flds.shrunk, flds.hogluv, flds.integralBuffer, s);
|
|
}
|
|
else
|
|
{
|
|
image.copyTo(flds.hogluv, s);
|
|
}
|
|
|
|
flds.detect(objects, s);
|
|
|
|
if ( (flags && NMS_MASK) != NO_REJECT)
|
|
{
|
|
cv::gpu::GpuMat spr(objects, cv::Rect(0, 0, flds.suppressed.cols, flds.suppressed.rows));
|
|
flds.suppress(objects, s);
|
|
flds.suppressed.copyTo(spr);
|
|
}
|
|
}
|
|
|
|
void cv::softcascade::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, cv::gpu::Stream& s)
|
|
{
|
|
m.setTo(0, s);
|
|
}
|
|
|
|
struct SeparablePreprocessor : public cv::softcascade::ChannelsProcessor
|
|
{
|
|
SeparablePreprocessor(const int s, const int b) : cv::softcascade::ChannelsProcessor(), shrinkage(s), bins(b) {}
|
|
virtual ~SeparablePreprocessor() {}
|
|
|
|
virtual void apply(InputArray _frame, OutputArray _shrunk, cv::gpu::Stream& s = cv::gpu::Stream::Null())
|
|
{
|
|
bgr = _frame.getGpuMat();
|
|
//cv::gpu::GaussianBlur(frame, bgr, cv::Size(3, 3), -1.0);
|
|
|
|
_shrunk.create(bgr.rows * (4 + bins) / shrinkage, bgr.cols / shrinkage, CV_8UC1);
|
|
cv::gpu::GpuMat shrunk = _shrunk.getGpuMat();
|
|
|
|
channels.create(bgr.rows * (4 + bins), bgr.cols, CV_8UC1);
|
|
setZero(channels, s);
|
|
|
|
gray.create(bgr.size(), CV_8UC1);
|
|
cv::softcascade::cudev::transform(bgr, gray); //cv::gpu::cvtColor(bgr, gray, CV_BGR2GRAY);
|
|
cv::softcascade::cudev::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::softcascade::cudev::bgr2Luv(bgr, luv);
|
|
cv::softcascade::cudev::shrink(channels, shrunk);
|
|
}
|
|
|
|
private:
|
|
const int shrinkage;
|
|
const int bins;
|
|
|
|
cv::gpu::GpuMat bgr;
|
|
cv::gpu::GpuMat gray;
|
|
cv::gpu::GpuMat channels;
|
|
SeparablePreprocessor& operator=( const SeparablePreprocessor& );
|
|
};
|
|
|
|
}
|
|
|
|
cv::Ptr<cv::softcascade::ChannelsProcessor> cv::softcascade::ChannelsProcessor::create(const int s, const int b, const int m)
|
|
{
|
|
CV_Assert((m && SEPARABLE));
|
|
return cv::Ptr<cv::softcascade::ChannelsProcessor>(new SeparablePreprocessor(s, b));
|
|
}
|
|
|
|
cv::softcascade::ChannelsProcessor::ChannelsProcessor() { }
|
|
cv::softcascade::ChannelsProcessor::~ChannelsProcessor() { }
|
|
|
|
#endif
|