GPU interface for soft cascade
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@ -1532,6 +1532,49 @@ public:
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
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};
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// ======================== GPU version for soft cascade ===================== //
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class CV_EXPORTS SoftCascade
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{
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public:
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//! An empty cascade will be created.
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SoftCascade();
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//! Cascade will be created from file for scales from minScale to maxScale.
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//! Param filename is a path to xml-serialized cascade.
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//! Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
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//! Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
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SoftCascade( const string& filename, const float minScale = 0.4f, const float maxScale = 5.f);
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//! cascade will be loaded from file "filename". The previous cascade will be destroyed.
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//! Param filename is a path to xml-serialized cascade.
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//! Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
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//! Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
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bool load( const string& filename, const float minScale = 0.4f, const float maxScale = 5.f);
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virtual ~SoftCascade();
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//! return vector of bounding boxes. Each box contains one detected object
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virtual void detectMultiScale(const GpuMat& image, const GpuMat& rois, GpuMat& objects,
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int rejectfactor = 1, Stream stream = Stream::Null()); // ToDo store objects in GPU mem
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protected:
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enum { BOOST = 0 };
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enum
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{
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FRAME_WIDTH = 640,
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FRAME_HEIGHT = 480,
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TOTAL_SCALES = 55,
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CLASSIFIERS = 5,
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ORIG_OBJECT_WIDTH = 64,
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ORIG_OBJECT_HEIGHT = 128
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};
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private:
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struct Filds;
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Filds* filds;
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};
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////////////////////////////////// SURF //////////////////////////////////////////
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class CV_EXPORTS SURF_GPU
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100
modules/gpu/src/softcascade.cpp
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100
modules/gpu/src/softcascade.cpp
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@ -0,0 +1,100 @@
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/*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::SoftCascade::SoftCascade() : filds(0) { throw_nogpu(); }
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cv::gpu::SoftCascade::SoftCascade( const string&, const float, const float) : filds(0) { throw_nogpu(); }
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cv::gpu::SoftCascade::~SoftCascade() { throw_nogpu(); }
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bool cv::gpu::SoftCascade::load( const string&, const float, const float) { throw_nogpu(); }
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void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, const int, Stream) { throw_nogpu(); }
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#else
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struct cv::gpu::SoftCascade::Filds
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{
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bool fill(const FileNode &root, const float mins, const float maxs){return true;}
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void calcLevels(int frameW, int frameH, int scales) {}
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};
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cv::gpu::SoftCascade::SoftCascade() : filds(0) {}
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cv::gpu::SoftCascade::SoftCascade( const string& filename, const float minScale, const float maxScale) : filds(0)
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{
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load(filename, minScale, maxScale);
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}
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cv::gpu::SoftCascade::~SoftCascade()
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{
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delete filds;
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}
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bool cv::gpu::SoftCascade::load( const string& filename, const float minScale, const float maxScale)
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{
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if (filds)
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delete filds;
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filds = 0;
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cv::FileStorage fs(filename, FileStorage::READ);
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if (!fs.isOpened()) return false;
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filds = new Filds;
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Filds& flds = *filds;
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if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
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flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
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return true;
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}
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void cv::gpu::SoftCascade::detectMultiScale(const GpuMat& /*image*/, const GpuMat& /*rois*/,
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GpuMat& /*objects*/, const int /*rejectfactor*/, Stream /*stream*/)
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{
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// empty
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}
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#endif
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