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804 lines
34 KiB
C++
804 lines
34 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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using namespace cv;
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using namespace cv::gpu;
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using namespace std;
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#if !defined (HAVE_CUDA)
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() { throw_nogpu(); }
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const 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 string&) { throw_nogpu(); return true; }
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Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu(); return Size(); }
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int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
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#else
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struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
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{
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CascadeClassifierImpl(const string& filename) : lastAllocatedFrameSize(-1, -1)
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{
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ncvSetDebugOutputHandler(NCVDebugOutputHandler);
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if (ncvStat != load(filename))
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{
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CV_Error(CV_GpuApiCallError, "Error in GPU cacade load");
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}
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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, NcvSize32u 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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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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ncvMinSize,
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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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NcvSize32u getClassifierSize() const { return haar.ClassifierSize; }
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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 char* msg) { CV_Error(CV_GpuApiCallError, msg); }
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NCVStatus load(const 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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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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};
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() : findLargestObject(false), visualizeInPlace(false), impl(0) {}
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cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename) : findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
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cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { release(); }
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bool cv::gpu::CascadeClassifier_GPU::empty() const { return impl == 0; }
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void cv::gpu::CascadeClassifier_GPU::release() { if (impl) { delete impl; impl = 0; } }
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bool cv::gpu::CascadeClassifier_GPU::load(const string& filename)
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{
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release();
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impl = new CascadeClassifierImpl(filename);
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return !this->empty();
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}
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Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const
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{
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return this->empty() ? Size() : impl->getClassifierCvSize();
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}
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int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
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{
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CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
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CV_Assert( !this->empty());
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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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NcvSize32u ncvMinSize = impl->getClassifierSize();
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if (ncvMinSize.width < (unsigned)minSize.width && ncvMinSize.height < (unsigned)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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NCVStatus ncvStat = impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections);
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if (ncvStat != NCV_SUCCESS)
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{
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CV_Error(CV_GpuApiCallError, "Error in face detectioln");
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}
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return numDetections;
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}
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struct RectConvert
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{
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Rect operator()(const NcvRect32u& nr) const { return Rect(nr.x, nr.y, nr.width, nr.height); }
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NcvRect32u operator()(const Rect& nr) const
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{
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NcvRect32u rect;
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rect.x = nr.x;
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rect.y = nr.y;
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rect.width = nr.width;
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rect.height = nr.height;
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return rect;
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}
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};
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void groupRectangles(std::vector<NcvRect32u> &hypotheses, int groupThreshold, double eps, std::vector<Ncv32u> *weights)
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{
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vector<Rect> rects(hypotheses.size());
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std::transform(hypotheses.begin(), hypotheses.end(), rects.begin(), RectConvert());
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if (weights)
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{
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vector<int> weights_int;
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weights_int.assign(weights->begin(), weights->end());
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cv::groupRectangles(rects, weights_int, groupThreshold, eps);
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}
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else
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{
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cv::groupRectangles(rects, groupThreshold, eps);
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}
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std::transform(rects.begin(), rects.end(), hypotheses.begin(), RectConvert());
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hypotheses.resize(rects.size());
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}
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#if 1 /* loadFromXML implementation switch */
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NCVStatus loadFromXML(const std::string &filename,
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HaarClassifierCascadeDescriptor &haar,
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std::vector<HaarStage64> &haarStages,
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std::vector<HaarClassifierNode128> &haarClassifierNodes,
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std::vector<HaarFeature64> &haarFeatures)
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{
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NCVStatus ncvStat;
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haar.NumStages = 0;
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haar.NumClassifierRootNodes = 0;
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haar.NumClassifierTotalNodes = 0;
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haar.NumFeatures = 0;
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haar.ClassifierSize.width = 0;
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haar.ClassifierSize.height = 0;
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haar.bHasStumpsOnly = true;
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haar.bNeedsTiltedII = false;
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Ncv32u curMaxTreeDepth;
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std::vector<char> xmlFileCont;
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std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
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haarStages.resize(0);
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haarClassifierNodes.resize(0);
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haarFeatures.resize(0);
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Ptr<CvHaarClassifierCascade> oldCascade = (CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0);
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if (oldCascade.empty())
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{
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return NCV_HAAR_XML_LOADING_EXCEPTION;
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}
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haar.ClassifierSize.width = oldCascade->orig_window_size.width;
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haar.ClassifierSize.height = oldCascade->orig_window_size.height;
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int stagesCound = oldCascade->count;
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for(int s = 0; s < stagesCound; ++s) // by stages
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{
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HaarStage64 curStage;
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curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));
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curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);
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int treesCount = oldCascade->stage_classifier[s].count;
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for(int t = 0; t < treesCount; ++t) // by trees
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{
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Ncv32u nodeId = 0;
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CvHaarClassifier* tree = &oldCascade->stage_classifier[s].classifier[t];
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int nodesCount = tree->count;
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for(int n = 0; n < nodesCount; ++n) //by features
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{
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CvHaarFeature* feature = &tree->haar_feature[n];
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HaarClassifierNode128 curNode;
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curNode.setThreshold(tree->threshold[n]);
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NcvBool bIsLeftNodeLeaf = false;
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NcvBool bIsRightNodeLeaf = false;
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HaarClassifierNodeDescriptor32 nodeLeft;
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if ( tree->left[n] <= 0 )
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{
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Ncv32f leftVal = tree->alpha[-tree->left[n]];
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ncvStat = nodeLeft.create(leftVal);
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ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
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bIsLeftNodeLeaf = true;
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}
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else
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{
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Ncv32u leftNodeOffset = tree->left[n];
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nodeLeft.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + leftNodeOffset - 1));
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haar.bHasStumpsOnly = false;
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}
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curNode.setLeftNodeDesc(nodeLeft);
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HaarClassifierNodeDescriptor32 nodeRight;
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if ( tree->right[n] <= 0 )
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{
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Ncv32f rightVal = tree->alpha[-tree->right[n]];
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ncvStat = nodeRight.create(rightVal);
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ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
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bIsRightNodeLeaf = true;
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}
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else
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{
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Ncv32u rightNodeOffset = tree->right[n];
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nodeRight.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + rightNodeOffset - 1));
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haar.bHasStumpsOnly = false;
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}
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curNode.setRightNodeDesc(nodeRight);
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Ncv32u tiltedVal = feature->tilted;
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haar.bNeedsTiltedII = (tiltedVal != 0);
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Ncv32u featureId = 0;
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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;
|
|
}
|
|
|
|
#else /* loadFromXML implementation switch */
|
|
|
|
#include "e:/devNPP-OpenCV/src/external/_rapidxml-1.13/rapidxml.hpp"
|
|
|
|
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.bNeedsTiltedII = false;
|
|
haar.bHasStumpsOnly = false;
|
|
|
|
FILE *fp;
|
|
fopen_s(&fp, filename.c_str(), "r");
|
|
ncvAssertReturn(fp != NULL, NCV_FILE_ERROR);
|
|
|
|
//get file size
|
|
fseek(fp, 0, SEEK_END);
|
|
Ncv32u xmlSize = ftell(fp);
|
|
fseek(fp, 0, SEEK_SET);
|
|
|
|
//load file to vector
|
|
std::vector<char> xmlFileCont;
|
|
xmlFileCont.resize(xmlSize+1);
|
|
memset(&xmlFileCont[0], 0, xmlSize+1);
|
|
fread_s(&xmlFileCont[0], xmlSize, 1, xmlSize, fp);
|
|
fclose(fp);
|
|
|
|
haar.bHasStumpsOnly = true;
|
|
haar.bNeedsTiltedII = false;
|
|
Ncv32u curMaxTreeDepth;
|
|
|
|
std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
|
|
haarStages.resize(0);
|
|
haarClassifierNodes.resize(0);
|
|
haarFeatures.resize(0);
|
|
|
|
//XML loading and OpenCV XML classifier syntax verification
|
|
try
|
|
{
|
|
rapidxml::xml_document<> doc;
|
|
doc.parse<0>(&xmlFileCont[0]);
|
|
|
|
//opencv_storage
|
|
rapidxml::xml_node<> *parserGlobal = doc.first_node();
|
|
ncvAssertReturn(!strcmp(parserGlobal->name(), "opencv_storage"), NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
|
|
//classifier type
|
|
parserGlobal = parserGlobal->first_node();
|
|
ncvAssertReturn(parserGlobal, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
rapidxml::xml_attribute<> *attr = parserGlobal->first_attribute("type_id");
|
|
ncvAssertReturn(!strcmp(attr->value(), "opencv-haar-classifier"), NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
|
|
//classifier size
|
|
parserGlobal = parserGlobal->first_node("size");
|
|
ncvAssertReturn(parserGlobal, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
sscanf_s(parserGlobal->value(), "%d %d", &(haar.ClassifierSize.width), &(haar.ClassifierSize.height));
|
|
|
|
//parse stages
|
|
parserGlobal = parserGlobal->next_sibling("stages");
|
|
ncvAssertReturn(parserGlobal, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
parserGlobal = parserGlobal->first_node("_");
|
|
ncvAssertReturn(parserGlobal, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
|
|
while (parserGlobal)
|
|
{
|
|
HaarStage64 curStage;
|
|
curStage.setStartClassifierRootNodeOffset(haarClassifierNodes.size());
|
|
Ncv32u tmpNumClassifierRootNodes = 0;
|
|
|
|
rapidxml::xml_node<> *parserStageThreshold = parserGlobal->first_node("stage_threshold");
|
|
ncvAssertReturn(parserStageThreshold, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32f tmpStageThreshold;
|
|
sscanf_s(parserStageThreshold->value(), "%f", &tmpStageThreshold);
|
|
curStage.setStageThreshold(tmpStageThreshold);
|
|
|
|
//parse trees
|
|
rapidxml::xml_node<> *parserTree;
|
|
parserTree = parserGlobal->first_node("trees");
|
|
ncvAssertReturn(parserTree, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
parserTree = parserTree->first_node("_");
|
|
ncvAssertReturn(parserTree, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
|
|
while (parserTree)
|
|
{
|
|
rapidxml::xml_node<> *parserNode;
|
|
parserNode = parserTree->first_node("_");
|
|
ncvAssertReturn(parserNode, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32u nodeId = 0;
|
|
|
|
while (parserNode)
|
|
{
|
|
HaarClassifierNode128 curNode;
|
|
|
|
rapidxml::xml_node<> *parserNodeThreshold = parserNode->first_node("threshold");
|
|
ncvAssertReturn(parserNodeThreshold, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32f tmpThreshold;
|
|
sscanf_s(parserNodeThreshold->value(), "%f", &tmpThreshold);
|
|
curNode.setThreshold(tmpThreshold);
|
|
|
|
rapidxml::xml_node<> *parserNodeLeft = parserNode->first_node("left_val");
|
|
HaarClassifierNodeDescriptor32 nodeLeft;
|
|
if (parserNodeLeft)
|
|
{
|
|
Ncv32f leftVal;
|
|
sscanf_s(parserNodeLeft->value(), "%f", &leftVal);
|
|
ncvStat = nodeLeft.create(leftVal);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
|
|
}
|
|
else
|
|
{
|
|
parserNodeLeft = parserNode->first_node("left_node");
|
|
ncvAssertReturn(parserNodeLeft, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32u leftNodeOffset;
|
|
sscanf_s(parserNodeLeft->value(), "%d", &leftNodeOffset);
|
|
nodeLeft.create(h_TmpClassifierNotRootNodes.size() + leftNodeOffset - 1);
|
|
haar.bHasStumpsOnly = false;
|
|
}
|
|
curNode.setLeftNodeDesc(nodeLeft);
|
|
|
|
rapidxml::xml_node<> *parserNodeRight = parserNode->first_node("right_val");
|
|
HaarClassifierNodeDescriptor32 nodeRight;
|
|
if (parserNodeRight)
|
|
{
|
|
Ncv32f rightVal;
|
|
sscanf_s(parserNodeRight->value(), "%f", &rightVal);
|
|
ncvStat = nodeRight.create(rightVal);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
|
|
}
|
|
else
|
|
{
|
|
parserNodeRight = parserNode->first_node("right_node");
|
|
ncvAssertReturn(parserNodeRight, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32u rightNodeOffset;
|
|
sscanf_s(parserNodeRight->value(), "%d", &rightNodeOffset);
|
|
nodeRight.create(h_TmpClassifierNotRootNodes.size() + rightNodeOffset - 1);
|
|
haar.bHasStumpsOnly = false;
|
|
}
|
|
curNode.setRightNodeDesc(nodeRight);
|
|
|
|
rapidxml::xml_node<> *parserNodeFeatures = parserNode->first_node("feature");
|
|
ncvAssertReturn(parserNodeFeatures, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
|
|
rapidxml::xml_node<> *parserNodeFeaturesTilted = parserNodeFeatures->first_node("tilted");
|
|
ncvAssertReturn(parserNodeFeaturesTilted, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32u tiltedVal;
|
|
sscanf_s(parserNodeFeaturesTilted->value(), "%d", &tiltedVal);
|
|
haar.bNeedsTiltedII = (tiltedVal != 0);
|
|
|
|
rapidxml::xml_node<> *parserNodeFeaturesRects = parserNodeFeatures->first_node("rects");
|
|
ncvAssertReturn(parserNodeFeaturesRects, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
parserNodeFeaturesRects = parserNodeFeaturesRects->first_node("_");
|
|
ncvAssertReturn(parserNodeFeaturesRects, NCV_HAAR_XML_LOADING_EXCEPTION);
|
|
Ncv32u featureId = 0;
|
|
|
|
while (parserNodeFeaturesRects)
|
|
{
|
|
Ncv32u rectX, rectY, rectWidth, rectHeight;
|
|
Ncv32f rectWeight;
|
|
sscanf_s(parserNodeFeaturesRects->value(), "%d %d %d %d %f", &rectX, &rectY, &rectWidth, &rectHeight, &rectWeight);
|
|
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);
|
|
|
|
parserNodeFeaturesRects = parserNodeFeaturesRects->next_sibling("_");
|
|
featureId++;
|
|
}
|
|
|
|
HaarFeatureDescriptor32 tmpFeatureDesc;
|
|
ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, featureId, 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++;
|
|
}
|
|
|
|
parserNode = parserNode->next_sibling("_");
|
|
nodeId++;
|
|
}
|
|
|
|
parserTree = parserTree->next_sibling("_");
|
|
tmpNumClassifierRootNodes++;
|
|
}
|
|
|
|
curStage.setNumClassifierRootNodes(tmpNumClassifierRootNodes);
|
|
haarStages.push_back(curStage);
|
|
|
|
parserGlobal = parserGlobal->next_sibling("_");
|
|
}
|
|
}
|
|
catch (...)
|
|
{
|
|
return NCV_HAAR_XML_LOADING_EXCEPTION;
|
|
}
|
|
|
|
//fill in cascade stats
|
|
haar.NumStages = haarStages.size();
|
|
haar.NumClassifierRootNodes = haarClassifierNodes.size();
|
|
haar.NumClassifierTotalNodes = haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size();
|
|
haar.NumFeatures = haarFeatures.size();
|
|
|
|
//merge root and leaf nodes in one classifiers array
|
|
Ncv32u offsetRoot = haarClassifierNodes.size();
|
|
for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
|
|
{
|
|
HaarClassifierNodeDescriptor32 nodeLeft = haarClassifierNodes[i].getLeftNodeDesc();
|
|
if (!nodeLeft.isLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
|
|
nodeLeft.create(newOffset);
|
|
}
|
|
haarClassifierNodes[i].setLeftNodeDesc(nodeLeft);
|
|
|
|
HaarClassifierNodeDescriptor32 nodeRight = haarClassifierNodes[i].getRightNodeDesc();
|
|
if (!nodeRight.isLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
|
|
nodeRight.create(newOffset);
|
|
}
|
|
haarClassifierNodes[i].setRightNodeDesc(nodeRight);
|
|
}
|
|
for (Ncv32u i=0; i<h_TmpClassifierNotRootNodes.size(); i++)
|
|
{
|
|
HaarClassifierNodeDescriptor32 nodeLeft = h_TmpClassifierNotRootNodes[i].getLeftNodeDesc();
|
|
if (!nodeLeft.isLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
|
|
nodeLeft.create(newOffset);
|
|
}
|
|
h_TmpClassifierNotRootNodes[i].setLeftNodeDesc(nodeLeft);
|
|
|
|
HaarClassifierNodeDescriptor32 nodeRight = h_TmpClassifierNotRootNodes[i].getRightNodeDesc();
|
|
if (!nodeRight.isLeaf())
|
|
{
|
|
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
|
|
nodeRight.create(newOffset);
|
|
}
|
|
h_TmpClassifierNotRootNodes[i].setRightNodeDesc(nodeRight);
|
|
|
|
haarClassifierNodes.push_back(h_TmpClassifierNotRootNodes[i]);
|
|
}
|
|
|
|
return NCV_SUCCESS;
|
|
}
|
|
|
|
#endif /* loadFromXML implementation switch */
|
|
|
|
#endif /* HAVE_CUDA */
|