2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
961 lines
37 KiB
C++
961 lines
37 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) 2009, 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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
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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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#include <vector>
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#include <iostream>
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using namespace cv;
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using namespace cv::gpu;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() { throw_nogpu(); }
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const std::string&) { throw_nogpu(); }
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cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { throw_nogpu(); }
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bool cv::gpu::CascadeClassifier_GPU::empty() const { throw_nogpu(); return true; }
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bool cv::gpu::CascadeClassifier_GPU::load(const std::string&) { throw_nogpu(); return true; }
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Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu(); return Size();}
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void cv::gpu::CascadeClassifier_GPU::release() { throw_nogpu(); }
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int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat&, GpuMat&, double, int, Size) {throw_nogpu(); return -1;}
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int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat&, GpuMat&, Size, Size, double, int) {throw_nogpu(); return -1;}
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#else
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struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
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{
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public:
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CascadeClassifierImpl(){}
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virtual ~CascadeClassifierImpl(){}
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virtual unsigned int process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
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bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize, cv::Size maxObjectSize) = 0;
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virtual cv::Size getClassifierCvSize() const = 0;
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virtual bool read(const std::string& classifierAsXml) = 0;
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};
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struct cv::gpu::CascadeClassifier_GPU::HaarCascade : cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
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{
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public:
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HaarCascade() : lastAllocatedFrameSize(-1, -1)
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{
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ncvSetDebugOutputHandler(NCVDebugOutputHandler);
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}
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bool read(const std::string& filename)
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{
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ncvSafeCall( load(filename) );
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return true;
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}
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NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
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bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize,
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/*out*/unsigned int& numDetections)
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{
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calculateMemReqsAndAllocate(src.size());
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NCVMemPtr src_beg;
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src_beg.ptr = (void*)src.ptr<Ncv8u>();
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src_beg.memtype = NCVMemoryTypeDevice;
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NCVMemSegment src_seg;
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src_seg.begin = src_beg;
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src_seg.size = src.step * src.rows;
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NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
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ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
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CV_Assert(objects.rows == 1);
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NCVMemPtr objects_beg;
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objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
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objects_beg.memtype = NCVMemoryTypeDevice;
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NCVMemSegment objects_seg;
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objects_seg.begin = objects_beg;
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objects_seg.size = objects.step * objects.rows;
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NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
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ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
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NcvSize32u roi;
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roi.width = d_src.width();
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roi.height = d_src.height();
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NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height);
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Ncv32u flags = 0;
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flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0;
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flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace : 0;
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ncvStat = ncvDetectObjectsMultiScale_device(
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d_src, roi, d_rects, numDetections, haar, *h_haarStages,
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*d_haarStages, *d_haarNodes, *d_haarFeatures,
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winMinSize,
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minNeighbors,
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scaleStep, 1,
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flags,
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*gpuAllocator, *cpuAllocator, devProp, 0);
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ncvAssertReturnNcvStat(ncvStat);
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
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return NCV_SUCCESS;
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}
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unsigned int process(const GpuMat& image, GpuMat& objectsBuf, float scaleFactor, int minNeighbors,
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bool findLargestObject, bool visualizeInPlace, cv::Size minSize, cv::Size /*maxObjectSize*/)
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{
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CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
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const int defaultObjSearchNum = 100;
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if (objectsBuf.empty())
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{
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objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
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}
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cv::Size ncvMinSize = this->getClassifierCvSize();
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if (ncvMinSize.width < minSize.width && ncvMinSize.height < minSize.height)
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{
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ncvMinSize.width = minSize.width;
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ncvMinSize.height = minSize.height;
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}
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unsigned int numDetections;
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ncvSafeCall(this->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections));
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return numDetections;
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}
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cv::Size getClassifierCvSize() const { return cv::Size(haar.ClassifierSize.width, haar.ClassifierSize.height); }
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private:
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static void NCVDebugOutputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }
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NCVStatus load(const std::string& classifierFile)
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{
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int devId = cv::gpu::getDevice();
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ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
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// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
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gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
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cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<int>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);
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Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
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ncvStat = ncvHaarGetClassifierSize(classifierFile, haarNumStages, haarNumNodes, haarNumFeatures);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", NCV_FILE_ERROR);
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h_haarStages = new NCVVectorAlloc<HaarStage64>(*cpuCascadeAllocator, haarNumStages);
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h_haarNodes = new NCVVectorAlloc<HaarClassifierNode128>(*cpuCascadeAllocator, haarNumNodes);
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h_haarFeatures = new NCVVectorAlloc<HaarFeature64>(*cpuCascadeAllocator, haarNumFeatures);
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ncvAssertPrintReturn(h_haarStages->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(h_haarNodes->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(h_haarFeatures->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
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ncvStat = ncvHaarLoadFromFile_host(classifierFile, haar, *h_haarStages, *h_haarNodes, *h_haarFeatures);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", NCV_FILE_ERROR);
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d_haarStages = new NCVVectorAlloc<HaarStage64>(*gpuCascadeAllocator, haarNumStages);
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d_haarNodes = new NCVVectorAlloc<HaarClassifierNode128>(*gpuCascadeAllocator, haarNumNodes);
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d_haarFeatures = new NCVVectorAlloc<HaarFeature64>(*gpuCascadeAllocator, haarNumFeatures);
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ncvAssertPrintReturn(d_haarStages->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(d_haarNodes->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(d_haarFeatures->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
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ncvStat = h_haarStages->copySolid(*d_haarStages, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
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ncvStat = h_haarNodes->copySolid(*d_haarNodes, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
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ncvStat = h_haarFeatures->copySolid(*d_haarFeatures, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
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return NCV_SUCCESS;
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}
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NCVStatus calculateMemReqsAndAllocate(const Size& frameSize)
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{
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if (lastAllocatedFrameSize == frameSize)
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{
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return NCV_SUCCESS;
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}
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// Calculate memory requirements and create real allocators
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NCVMemStackAllocator gpuCounter(static_cast<int>(devProp.textureAlignment));
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NCVMemStackAllocator cpuCounter(static_cast<int>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", NCV_CUDA_ERROR);
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NCVMatrixAlloc<Ncv8u> d_src(gpuCounter, frameSize.width, frameSize.height);
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NCVMatrixAlloc<Ncv8u> h_src(cpuCounter, frameSize.width, frameSize.height);
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ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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NCVVectorAlloc<NcvRect32u> d_rects(gpuCounter, 100);
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ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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NcvSize32u roi;
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roi.width = d_src.width();
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roi.height = d_src.height();
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Ncv32u numDetections;
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ncvStat = ncvDetectObjectsMultiScale_device(d_src, roi, d_rects, numDetections, haar, *h_haarStages,
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*d_haarStages, *d_haarNodes, *d_haarFeatures, haar.ClassifierSize, 4, 1.2f, 1, 0, gpuCounter, cpuCounter, devProp, 0);
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ncvAssertReturnNcvStat(ncvStat);
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
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gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
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cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuAllocator->isInitialized(), "Error creating GPU memory allocator", NCV_CUDA_ERROR);
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ncvAssertPrintReturn(cpuAllocator->isInitialized(), "Error creating CPU memory allocator", NCV_CUDA_ERROR);
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lastAllocatedFrameSize = frameSize;
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return NCV_SUCCESS;
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}
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cudaDeviceProp devProp;
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NCVStatus ncvStat;
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Ptr<NCVMemNativeAllocator> gpuCascadeAllocator;
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Ptr<NCVMemNativeAllocator> cpuCascadeAllocator;
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Ptr<NCVVectorAlloc<HaarStage64> > h_haarStages;
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Ptr<NCVVectorAlloc<HaarClassifierNode128> > h_haarNodes;
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Ptr<NCVVectorAlloc<HaarFeature64> > h_haarFeatures;
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HaarClassifierCascadeDescriptor haar;
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Ptr<NCVVectorAlloc<HaarStage64> > d_haarStages;
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Ptr<NCVVectorAlloc<HaarClassifierNode128> > d_haarNodes;
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Ptr<NCVVectorAlloc<HaarFeature64> > d_haarFeatures;
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Size lastAllocatedFrameSize;
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Ptr<NCVMemStackAllocator> gpuAllocator;
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Ptr<NCVMemStackAllocator> cpuAllocator;
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virtual ~HaarCascade(){}
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};
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cv::Size operator -(const cv::Size& a, const cv::Size& b)
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{
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return cv::Size(a.width - b.width, a.height - b.height);
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}
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cv::Size operator +(const cv::Size& a, const int& i)
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{
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return cv::Size(a.width + i, a.height + i);
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}
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cv::Size operator *(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
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}
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cv::Size operator /(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
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}
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bool operator <=(const cv::Size& a, const cv::Size& b)
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{
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return a.width <= b.width && a.height <= b.width;
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}
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struct PyrLavel
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{
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PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window, cv::Size minObjectSize)
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{
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do
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{
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order = _order;
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scale = pow(_scale, order);
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sFrame = frame / scale;
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workArea = sFrame - window + 1;
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sWindow = window * scale;
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_order++;
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} while (sWindow <= minObjectSize);
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}
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bool isFeasible(cv::Size maxObj)
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{
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return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
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}
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PyrLavel next(float factor, cv::Size frame, cv::Size window, cv::Size minObjectSize)
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{
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return PyrLavel(order + 1, factor, frame, window, minObjectSize);
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}
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int order;
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float scale;
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cv::Size sFrame;
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cv::Size workArea;
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cv::Size sWindow;
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};
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namespace cv { namespace gpu { namespace device
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{
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namespace lbp
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{
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void classifyPyramid(int frameW,
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int frameH,
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int windowW,
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int windowH,
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float initalScale,
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float factor,
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int total,
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const PtrStepSzb& mstages,
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const int nstages,
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const PtrStepSzi& mnodes,
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const PtrStepSzf& mleaves,
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const PtrStepSzi& msubsets,
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const PtrStepSzb& mfeatures,
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const int subsetSize,
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PtrStepSz<int4> objects,
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unsigned int* classified,
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PtrStepSzi integral);
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void connectedConmonents(PtrStepSz<int4> candidates, int ncandidates, PtrStepSz<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
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}
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}}}
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struct cv::gpu::CascadeClassifier_GPU::LbpCascade : cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
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{
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public:
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struct Stage
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{
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int first;
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int ntrees;
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float threshold;
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};
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LbpCascade(){}
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virtual ~LbpCascade(){}
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virtual unsigned int process(const GpuMat& image, GpuMat& objects, float scaleFactor, int groupThreshold, bool /*findLargestObject*/,
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bool /*visualizeInPlace*/, cv::Size minObjectSize, cv::Size maxObjectSize)
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{
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CV_Assert(scaleFactor > 1 && image.depth() == CV_8U);
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// const int defaultObjSearchNum = 100;
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const float grouping_eps = 0.2f;
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if( !objects.empty() && objects.depth() == CV_32S)
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objects.reshape(4, 1);
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else
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objects.create(1 , image.cols >> 4, CV_32SC4);
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// used for debug
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// candidates.setTo(cv::Scalar::all(0));
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// objects.setTo(cv::Scalar::all(0));
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if (maxObjectSize == cv::Size())
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maxObjectSize = image.size();
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allocateBuffers(image.size());
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unsigned int classified = 0;
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GpuMat dclassified(1, 1, CV_32S);
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cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
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PyrLavel level(0, 1.0f, image.size(), NxM, minObjectSize);
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while (level.isFeasible(maxObjectSize))
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{
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int acc = level.sFrame.width + 1;
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float iniScale = level.scale;
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cv::Size area = level.workArea;
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int step = 1 + (level.scale <= 2.f);
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int total = 0, prev = 0;
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while (acc <= integralFactor * (image.cols + 1) && level.isFeasible(maxObjectSize))
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{
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// create sutable matrix headers
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GpuMat src = resuzeBuffer(cv::Rect(0, 0, level.sFrame.width, level.sFrame.height));
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GpuMat sint = integral(cv::Rect(prev, 0, level.sFrame.width + 1, level.sFrame.height + 1));
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GpuMat buff = integralBuffer;
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// generate integral for scale
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gpu::resize(image, src, level.sFrame, 0, 0, CV_INTER_LINEAR);
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gpu::integralBuffered(src, sint, buff);
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// calculate job
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int totalWidth = level.workArea.width / step;
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total += totalWidth * (level.workArea.height / step);
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// go to next pyramide level
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level = level.next(scaleFactor, image.size(), NxM, minObjectSize);
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area = level.workArea;
|
|
|
|
step = (1 + (level.scale <= 2.f));
|
|
prev = acc;
|
|
acc += level.sFrame.width + 1;
|
|
}
|
|
|
|
device::lbp::classifyPyramid(image.cols, image.rows, NxM.width - 1, NxM.height - 1, iniScale, scaleFactor, total, stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat,
|
|
leaves_mat, subsets_mat, features_mat, subsetSize, candidates, dclassified.ptr<unsigned int>(), integral);
|
|
}
|
|
|
|
if (groupThreshold <= 0 || objects.empty())
|
|
return 0;
|
|
|
|
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
|
|
device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());
|
|
|
|
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
return classified;
|
|
}
|
|
|
|
virtual cv::Size getClassifierCvSize() const { return NxM; }
|
|
|
|
bool read(const std::string& classifierAsXml)
|
|
{
|
|
FileStorage fs(classifierAsXml, FileStorage::READ);
|
|
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
|
|
}
|
|
|
|
private:
|
|
|
|
void allocateBuffers(cv::Size frame)
|
|
{
|
|
if (frame == cv::Size())
|
|
return;
|
|
|
|
if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
|
|
{
|
|
resuzeBuffer.create(frame, CV_8UC1);
|
|
|
|
integral.create(frame.height + 1, integralFactor * (frame.width + 1), CV_32SC1);
|
|
NcvSize32u roiSize;
|
|
roiSize.width = frame.width;
|
|
roiSize.height = frame.height;
|
|
|
|
cudaDeviceProp prop;
|
|
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
|
|
|
|
Ncv32u bufSize;
|
|
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
|
|
integralBuffer.create(1, bufSize, CV_8UC1);
|
|
|
|
candidates.create(1 , frame.width >> 1, CV_32SC4);
|
|
}
|
|
}
|
|
|
|
bool read(const FileNode &root)
|
|
{
|
|
const char *GPU_CC_STAGE_TYPE = "stageType";
|
|
const char *GPU_CC_FEATURE_TYPE = "featureType";
|
|
const char *GPU_CC_BOOST = "BOOST";
|
|
const char *GPU_CC_LBP = "LBP";
|
|
const char *GPU_CC_MAX_CAT_COUNT = "maxCatCount";
|
|
const char *GPU_CC_HEIGHT = "height";
|
|
const char *GPU_CC_WIDTH = "width";
|
|
const char *GPU_CC_STAGE_PARAMS = "stageParams";
|
|
const char *GPU_CC_MAX_DEPTH = "maxDepth";
|
|
const char *GPU_CC_FEATURE_PARAMS = "featureParams";
|
|
const char *GPU_CC_STAGES = "stages";
|
|
const char *GPU_CC_STAGE_THRESHOLD = "stageThreshold";
|
|
const float GPU_THRESHOLD_EPS = 1e-5f;
|
|
const char *GPU_CC_WEAK_CLASSIFIERS = "weakClassifiers";
|
|
const char *GPU_CC_INTERNAL_NODES = "internalNodes";
|
|
const char *GPU_CC_LEAF_VALUES = "leafValues";
|
|
const char *GPU_CC_FEATURES = "features";
|
|
const char *GPU_CC_RECT = "rect";
|
|
|
|
std::string stageTypeStr = (std::string)root[GPU_CC_STAGE_TYPE];
|
|
CV_Assert(stageTypeStr == GPU_CC_BOOST);
|
|
|
|
std::string featureTypeStr = (std::string)root[GPU_CC_FEATURE_TYPE];
|
|
CV_Assert(featureTypeStr == GPU_CC_LBP);
|
|
|
|
NxM.width = (int)root[GPU_CC_WIDTH];
|
|
NxM.height = (int)root[GPU_CC_HEIGHT];
|
|
CV_Assert( NxM.height > 0 && NxM.width > 0 );
|
|
|
|
isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false;
|
|
CV_Assert(isStumps);
|
|
|
|
FileNode fn = root[GPU_CC_FEATURE_PARAMS];
|
|
if (fn.empty())
|
|
return false;
|
|
|
|
ncategories = fn[GPU_CC_MAX_CAT_COUNT];
|
|
|
|
subsetSize = (ncategories + 31) / 32;
|
|
nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );
|
|
|
|
fn = root[GPU_CC_STAGES];
|
|
if (fn.empty())
|
|
return false;
|
|
|
|
std::vector<Stage> stages;
|
|
stages.reserve(fn.size());
|
|
|
|
std::vector<int> cl_trees;
|
|
std::vector<int> cl_nodes;
|
|
std::vector<float> cl_leaves;
|
|
std::vector<int> subsets;
|
|
|
|
FileNodeIterator it = fn.begin(), it_end = fn.end();
|
|
for (size_t si = 0; it != it_end; si++, ++it )
|
|
{
|
|
FileNode fns = *it;
|
|
Stage st;
|
|
st.threshold = (float)fns[GPU_CC_STAGE_THRESHOLD] - GPU_THRESHOLD_EPS;
|
|
|
|
fns = fns[GPU_CC_WEAK_CLASSIFIERS];
|
|
if (fns.empty())
|
|
return false;
|
|
|
|
st.ntrees = (int)fns.size();
|
|
st.first = (int)cl_trees.size();
|
|
|
|
stages.push_back(st);// (int, int, float)
|
|
|
|
cl_trees.reserve(stages[si].first + stages[si].ntrees);
|
|
|
|
// weak trees
|
|
FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
|
|
for ( ; it1 != it1_end; ++it1 )
|
|
{
|
|
FileNode fnw = *it1;
|
|
|
|
FileNode internalNodes = fnw[GPU_CC_INTERNAL_NODES];
|
|
FileNode leafValues = fnw[GPU_CC_LEAF_VALUES];
|
|
if ( internalNodes.empty() || leafValues.empty() )
|
|
return false;
|
|
|
|
int nodeCount = (int)internalNodes.size()/nodeStep;
|
|
cl_trees.push_back(nodeCount);
|
|
|
|
cl_nodes.reserve((cl_nodes.size() + nodeCount) * 3);
|
|
cl_leaves.reserve(cl_leaves.size() + leafValues.size());
|
|
|
|
if( subsetSize > 0 )
|
|
subsets.reserve(subsets.size() + nodeCount * subsetSize);
|
|
|
|
// nodes
|
|
FileNodeIterator iIt = internalNodes.begin(), iEnd = internalNodes.end();
|
|
|
|
for( ; iIt != iEnd; )
|
|
{
|
|
cl_nodes.push_back((int)*(iIt++));
|
|
cl_nodes.push_back((int)*(iIt++));
|
|
cl_nodes.push_back((int)*(iIt++));
|
|
|
|
if( subsetSize > 0 )
|
|
for( int j = 0; j < subsetSize; j++, ++iIt )
|
|
subsets.push_back((int)*iIt);
|
|
}
|
|
|
|
// leaves
|
|
iIt = leafValues.begin(), iEnd = leafValues.end();
|
|
for( ; iIt != iEnd; ++iIt )
|
|
cl_leaves.push_back((float)*iIt);
|
|
}
|
|
}
|
|
|
|
fn = root[GPU_CC_FEATURES];
|
|
if( fn.empty() )
|
|
return false;
|
|
std::vector<uchar> features;
|
|
features.reserve(fn.size() * 4);
|
|
FileNodeIterator f_it = fn.begin(), f_end = fn.end();
|
|
for (; f_it != f_end; ++f_it)
|
|
{
|
|
FileNode rect = (*f_it)[GPU_CC_RECT];
|
|
FileNodeIterator r_it = rect.begin();
|
|
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
|
|
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
|
|
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
|
|
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
|
|
}
|
|
|
|
// copy data structures on gpu
|
|
stage_mat.upload(cv::Mat(1, (int) (stages.size() * sizeof(Stage)), CV_8UC1, (uchar*)&(stages[0]) ));
|
|
trees_mat.upload(cv::Mat(cl_trees).reshape(1,1));
|
|
nodes_mat.upload(cv::Mat(cl_nodes).reshape(1,1));
|
|
leaves_mat.upload(cv::Mat(cl_leaves).reshape(1,1));
|
|
subsets_mat.upload(cv::Mat(subsets).reshape(1,1));
|
|
features_mat.upload(cv::Mat(features).reshape(4,1));
|
|
|
|
return true;
|
|
}
|
|
|
|
enum stage { BOOST = 0 };
|
|
enum feature { LBP = 1, HAAR = 2 };
|
|
static const stage stageType = BOOST;
|
|
static const feature featureType = LBP;
|
|
|
|
cv::Size NxM;
|
|
bool isStumps;
|
|
int ncategories;
|
|
int subsetSize;
|
|
int nodeStep;
|
|
|
|
// gpu representation of classifier
|
|
GpuMat stage_mat;
|
|
GpuMat trees_mat;
|
|
GpuMat nodes_mat;
|
|
GpuMat leaves_mat;
|
|
GpuMat subsets_mat;
|
|
GpuMat features_mat;
|
|
|
|
GpuMat integral;
|
|
GpuMat integralBuffer;
|
|
GpuMat resuzeBuffer;
|
|
|
|
GpuMat candidates;
|
|
static const int integralFactor = 4;
|
|
};
|
|
|
|
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU()
|
|
: findLargestObject(false), visualizeInPlace(false), impl(0) {}
|
|
|
|
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const std::string& filename)
|
|
: findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
|
|
|
|
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { release(); }
|
|
|
|
void cv::gpu::CascadeClassifier_GPU::release() { if (impl) { delete impl; impl = 0; } }
|
|
|
|
bool cv::gpu::CascadeClassifier_GPU::empty() const { return impl == 0; }
|
|
|
|
Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const
|
|
{
|
|
return this->empty() ? Size() : impl->getClassifierCvSize();
|
|
}
|
|
|
|
int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
|
|
{
|
|
CV_Assert( !this->empty());
|
|
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, cv::Size());
|
|
}
|
|
|
|
int cv::gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize, double scaleFactor, int minNeighbors)
|
|
{
|
|
CV_Assert( !this->empty());
|
|
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, maxObjectSize);
|
|
}
|
|
|
|
bool cv::gpu::CascadeClassifier_GPU::load(const std::string& filename)
|
|
{
|
|
release();
|
|
|
|
std::string fext = filename.substr(filename.find_last_of(".") + 1);
|
|
std::transform(fext.begin(), fext.end(), fext.begin(), ::tolower);
|
|
|
|
if (fext == "nvbin")
|
|
{
|
|
impl = new HaarCascade();
|
|
return impl->read(filename);
|
|
}
|
|
|
|
FileStorage fs(filename, FileStorage::READ);
|
|
|
|
if (!fs.isOpened())
|
|
{
|
|
impl = new HaarCascade();
|
|
return impl->read(filename);
|
|
}
|
|
|
|
const char *GPU_CC_LBP = "LBP";
|
|
std::string featureTypeStr = (std::string)fs.getFirstTopLevelNode()["featureType"];
|
|
if (featureTypeStr == GPU_CC_LBP)
|
|
impl = new LbpCascade();
|
|
else
|
|
impl = new HaarCascade();
|
|
|
|
impl->read(filename);
|
|
return !this->empty();
|
|
}
|
|
|
|
#endif
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
#if defined (HAVE_CUDA)
|
|
|
|
struct RectConvert
|
|
{
|
|
Rect operator()(const NcvRect32u& nr) const { return Rect(nr.x, nr.y, nr.width, nr.height); }
|
|
NcvRect32u operator()(const Rect& nr) const
|
|
{
|
|
NcvRect32u rect;
|
|
rect.x = nr.x;
|
|
rect.y = nr.y;
|
|
rect.width = nr.width;
|
|
rect.height = nr.height;
|
|
return rect;
|
|
}
|
|
};
|
|
|
|
void groupRectangles(std::vector<NcvRect32u> &hypotheses, int groupThreshold, double eps, std::vector<Ncv32u> *weights)
|
|
{
|
|
std::vector<Rect> rects(hypotheses.size());
|
|
std::transform(hypotheses.begin(), hypotheses.end(), rects.begin(), RectConvert());
|
|
|
|
if (weights)
|
|
{
|
|
std::vector<int> weights_int;
|
|
weights_int.assign(weights->begin(), weights->end());
|
|
cv::groupRectangles(rects, weights_int, groupThreshold, eps);
|
|
}
|
|
else
|
|
{
|
|
cv::groupRectangles(rects, groupThreshold, eps);
|
|
}
|
|
std::transform(rects.begin(), rects.end(), hypotheses.begin(), RectConvert());
|
|
hypotheses.resize(rects.size());
|
|
}
|
|
|
|
NCVStatus loadFromXML(const std::string &filename,
|
|
HaarClassifierCascadeDescriptor &haar,
|
|
std::vector<HaarStage64> &haarStages,
|
|
std::vector<HaarClassifierNode128> &haarClassifierNodes,
|
|
std::vector<HaarFeature64> &haarFeatures)
|
|
{
|
|
NCVStatus ncvStat;
|
|
|
|
haar.NumStages = 0;
|
|
haar.NumClassifierRootNodes = 0;
|
|
haar.NumClassifierTotalNodes = 0;
|
|
haar.NumFeatures = 0;
|
|
haar.ClassifierSize.width = 0;
|
|
haar.ClassifierSize.height = 0;
|
|
haar.bHasStumpsOnly = true;
|
|
haar.bNeedsTiltedII = false;
|
|
Ncv32u curMaxTreeDepth;
|
|
|
|
std::vector<char> xmlFileCont;
|
|
|
|
std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
|
|
haarStages.resize(0);
|
|
haarClassifierNodes.resize(0);
|
|
haarFeatures.resize(0);
|
|
|
|
Ptr<CvHaarClassifierCascade> oldCascade = (CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0);
|
|
if (oldCascade.empty())
|
|
{
|
|
return NCV_HAAR_XML_LOADING_EXCEPTION;
|
|
}
|
|
|
|
haar.ClassifierSize.width = oldCascade->orig_window_size.width;
|
|
haar.ClassifierSize.height = oldCascade->orig_window_size.height;
|
|
|
|
int stagesCound = oldCascade->count;
|
|
for(int s = 0; s < stagesCound; ++s) // by stages
|
|
{
|
|
HaarStage64 curStage;
|
|
curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));
|
|
|
|
curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);
|
|
|
|
int treesCount = oldCascade->stage_classifier[s].count;
|
|
for(int t = 0; t < treesCount; ++t) // by trees
|
|
{
|
|
Ncv32u nodeId = 0;
|
|
CvHaarClassifier* tree = &oldCascade->stage_classifier[s].classifier[t];
|
|
|
|
int nodesCount = tree->count;
|
|
for(int n = 0; n < nodesCount; ++n) //by features
|
|
{
|
|
CvHaarFeature* feature = &tree->haar_feature[n];
|
|
|
|
HaarClassifierNode128 curNode;
|
|
curNode.setThreshold(tree->threshold[n]);
|
|
|
|
NcvBool bIsLeftNodeLeaf = false;
|
|
NcvBool bIsRightNodeLeaf = false;
|
|
|
|
HaarClassifierNodeDescriptor32 nodeLeft;
|
|
if ( tree->left[n] <= 0 )
|
|
{
|
|
Ncv32f leftVal = tree->alpha[-tree->left[n]];
|
|
ncvStat = nodeLeft.create(leftVal);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
|
|
bIsLeftNodeLeaf = true;
|
|
}
|
|
else
|
|
{
|
|
Ncv32u leftNodeOffset = tree->left[n];
|
|
nodeLeft.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + leftNodeOffset - 1));
|
|
haar.bHasStumpsOnly = false;
|
|
}
|
|
curNode.setLeftNodeDesc(nodeLeft);
|
|
|
|
HaarClassifierNodeDescriptor32 nodeRight;
|
|
if ( tree->right[n] <= 0 )
|
|
{
|
|
Ncv32f rightVal = tree->alpha[-tree->right[n]];
|
|
ncvStat = nodeRight.create(rightVal);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
|
|
bIsRightNodeLeaf = true;
|
|
}
|
|
else
|
|
{
|
|
Ncv32u rightNodeOffset = tree->right[n];
|
|
nodeRight.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + rightNodeOffset - 1));
|
|
haar.bHasStumpsOnly = false;
|
|
}
|
|
curNode.setRightNodeDesc(nodeRight);
|
|
|
|
Ncv32u tiltedVal = feature->tilted;
|
|
haar.bNeedsTiltedII = (tiltedVal != 0);
|
|
|
|
Ncv32u featureId = 0;
|
|
for(int l = 0; l < CV_HAAR_FEATURE_MAX; ++l) //by rects
|
|
{
|
|
Ncv32u rectX = feature->rect[l].r.x;
|
|
Ncv32u rectY = feature->rect[l].r.y;
|
|
Ncv32u rectWidth = feature->rect[l].r.width;
|
|
Ncv32u rectHeight = feature->rect[l].r.height;
|
|
|
|
Ncv32f rectWeight = feature->rect[l].weight;
|
|
|
|
if (rectWeight == 0/* && rectX == 0 &&rectY == 0 && rectWidth == 0 && rectHeight == 0*/)
|
|
break;
|
|
|
|
HaarFeature64 curFeature;
|
|
ncvStat = curFeature.setRect(rectX, rectY, rectWidth, rectHeight, haar.ClassifierSize.width, haar.ClassifierSize.height);
|
|
curFeature.setWeight(rectWeight);
|
|
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
|
|
haarFeatures.push_back(curFeature);
|
|
|
|
featureId++;
|
|
}
|
|
|
|
HaarFeatureDescriptor32 tmpFeatureDesc;
|
|
ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, bIsLeftNodeLeaf, bIsRightNodeLeaf,
|
|
featureId, static_cast<Ncv32u>(haarFeatures.size()) - featureId);
|
|
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
|
|
curNode.setFeatureDesc(tmpFeatureDesc);
|
|
|
|
if (!nodeId)
|
|
{
|
|
//root node
|
|
haarClassifierNodes.push_back(curNode);
|
|
curMaxTreeDepth = 1;
|
|
}
|
|
else
|
|
{
|
|
//other node
|
|
h_TmpClassifierNotRootNodes.push_back(curNode);
|
|
curMaxTreeDepth++;
|
|
}
|
|
|
|
nodeId++;
|
|
}
|
|
}
|
|
|
|
curStage.setNumClassifierRootNodes(treesCount);
|
|
haarStages.push_back(curStage);
|
|
}
|
|
|
|
//fill in cascade stats
|
|
haar.NumStages = static_cast<Ncv32u>(haarStages.size());
|
|
haar.NumClassifierRootNodes = static_cast<Ncv32u>(haarClassifierNodes.size());
|
|
haar.NumClassifierTotalNodes = static_cast<Ncv32u>(haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size());
|
|
haar.NumFeatures = static_cast<Ncv32u>(haarFeatures.size());
|
|
|
|
//merge root and leaf nodes in one classifiers array
|
|
Ncv32u offsetRoot = static_cast<Ncv32u>(haarClassifierNodes.size());
|
|
for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
|
|
{
|
|
HaarFeatureDescriptor32 featureDesc = haarClassifierNodes[i].getFeatureDesc();
|
|
|
|
HaarClassifierNodeDescriptor32 nodeLeft = haarClassifierNodes[i].getLeftNodeDesc();
|
|
if (!featureDesc.isLeftNodeLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
|
|
nodeLeft.create(newOffset);
|
|
}
|
|
haarClassifierNodes[i].setLeftNodeDesc(nodeLeft);
|
|
|
|
HaarClassifierNodeDescriptor32 nodeRight = haarClassifierNodes[i].getRightNodeDesc();
|
|
if (!featureDesc.isRightNodeLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
|
|
nodeRight.create(newOffset);
|
|
}
|
|
haarClassifierNodes[i].setRightNodeDesc(nodeRight);
|
|
}
|
|
|
|
for (Ncv32u i=0; i<h_TmpClassifierNotRootNodes.size(); i++)
|
|
{
|
|
HaarFeatureDescriptor32 featureDesc = h_TmpClassifierNotRootNodes[i].getFeatureDesc();
|
|
|
|
HaarClassifierNodeDescriptor32 nodeLeft = h_TmpClassifierNotRootNodes[i].getLeftNodeDesc();
|
|
if (!featureDesc.isLeftNodeLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
|
|
nodeLeft.create(newOffset);
|
|
}
|
|
h_TmpClassifierNotRootNodes[i].setLeftNodeDesc(nodeLeft);
|
|
|
|
HaarClassifierNodeDescriptor32 nodeRight = h_TmpClassifierNotRootNodes[i].getRightNodeDesc();
|
|
if (!featureDesc.isRightNodeLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
|
|
nodeRight.create(newOffset);
|
|
}
|
|
h_TmpClassifierNotRootNodes[i].setRightNodeDesc(nodeRight);
|
|
|
|
haarClassifierNodes.push_back(h_TmpClassifierNotRootNodes[i]);
|
|
}
|
|
|
|
return NCV_SUCCESS;
|
|
}
|
|
|
|
#endif /* HAVE_CUDA */
|