450 lines
17 KiB
C++
450 lines
17 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::SURF_GPU::SURF_GPU() { throw_nogpu(); }
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cv::gpu::SURF_GPU::SURF_GPU(double, int, int, bool, float, bool) { throw_nogpu(); }
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int cv::gpu::SURF_GPU::descriptorSize() const { throw_nogpu(); return 0;}
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void cv::gpu::SURF_GPU::uploadKeypoints(const vector<KeyPoint>&, GpuMat&) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::downloadKeypoints(const GpuMat&, vector<KeyPoint>&) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::downloadDescriptors(const GpuMat&, vector<float>&) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&, GpuMat&, bool) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&, vector<float>&, bool) { throw_nogpu(); }
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void cv::gpu::SURF_GPU::releaseMemory() { throw_nogpu(); }
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#else /* !defined (HAVE_CUDA) */
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namespace cv { namespace gpu { namespace surf
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{
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void icvCalcLayerDetAndTrace_gpu(const PtrStepf& det, const PtrStepf& trace, int img_rows, int img_cols, int octave, int nOctaveLayers);
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void icvFindMaximaInLayer_gpu(const PtrStepf& det, const PtrStepf& trace, int4* maxPosBuffer, unsigned int* maxCounter,
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int img_rows, int img_cols, int octave, bool use_mask, int nLayers);
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void icvInterpolateKeypoint_gpu(const PtrStepf& det, const int4* maxPosBuffer, unsigned int maxCounter,
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float* featureX, float* featureY, int* featureLaplacian, float* featureSize, float* featureHessian,
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unsigned int* featureCounter);
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void icvCalcOrientation_gpu(const float* featureX, const float* featureY, const float* featureSize, float* featureDir, int nFeatures);
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void compute_descriptors_gpu(const DevMem2Df& descriptors,
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const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures);
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}}}
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using namespace cv::gpu::surf;
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namespace
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{
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int calcSize(int octave, int layer)
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{
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/* Wavelet size at first layer of first octave. */
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const int HAAR_SIZE0 = 9;
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/* Wavelet size increment between layers. This should be an even number,
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such that the wavelet sizes in an octave are either all even or all odd.
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This ensures that when looking for the neighbours of a sample, the layers
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above and below are aligned correctly. */
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const int HAAR_SIZE_INC = 6;
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return (HAAR_SIZE0 + HAAR_SIZE_INC * layer) << octave;
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}
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class SURF_GPU_Invoker : private CvSURFParams
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{
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public:
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SURF_GPU_Invoker(SURF_GPU& surf, const GpuMat& img, const GpuMat& mask) :
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CvSURFParams(surf),
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sum(surf.sum), mask1(surf.mask1), maskSum(surf.maskSum), intBuffer(surf.intBuffer), det(surf.det), trace(surf.trace),
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maxPosBuffer(surf.maxPosBuffer),
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img_cols(img.cols), img_rows(img.rows),
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use_mask(!mask.empty())
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{
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CV_Assert(!img.empty() && img.type() == CV_8UC1);
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CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
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CV_Assert(nOctaves > 0 && nOctaveLayers > 0);
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CV_Assert(TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS));
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const int min_size = calcSize(nOctaves - 1, 0);
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CV_Assert(img_rows - min_size >= 0);
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CV_Assert(img_cols - min_size >= 0);
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const int layer_rows = img_rows >> (nOctaves - 1);
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const int layer_cols = img_cols >> (nOctaves - 1);
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const int min_margin = ((calcSize((nOctaves - 1), 2) >> 1) >> (nOctaves - 1)) + 1;
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CV_Assert(layer_rows - 2 * min_margin > 0);
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CV_Assert(layer_cols - 2 * min_margin > 0);
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maxFeatures = min(static_cast<int>(img.size().area() * surf.keypointsRatio), 65535);
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maxCandidates = min(static_cast<int>(1.5 * maxFeatures), 65535);
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CV_Assert(maxFeatures > 0);
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counters.create(1, nOctaves + 1, CV_32SC1);
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counters.setTo(Scalar::all(0));
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uploadConstant("cv::gpu::surf::c_max_candidates", maxCandidates);
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uploadConstant("cv::gpu::surf::c_max_features", maxFeatures);
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uploadConstant("cv::gpu::surf::c_img_rows", img_rows);
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uploadConstant("cv::gpu::surf::c_img_cols", img_cols);
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uploadConstant("cv::gpu::surf::c_nOctaveLayers", nOctaveLayers);
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uploadConstant("cv::gpu::surf::c_hessianThreshold", static_cast<float>(hessianThreshold));
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imgTex.bind("cv::gpu::surf::imgTex", (DevMem2D)img);
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integralBuffered(img, sum, intBuffer);
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sumTex.bind("cv::gpu::surf::sumTex", (DevMem2D_<unsigned int>)sum);
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if (use_mask)
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{
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min(mask, 1.0, mask1);
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integralBuffered(mask1, maskSum, intBuffer);
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maskSumTex.bind("cv::gpu::surf::maskSumTex", (DevMem2D_<unsigned int>)maskSum);
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}
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}
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void detectKeypoints(GpuMat& keypoints)
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{
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ensureSizeIsEnough(img_rows * (nOctaveLayers + 2), img_cols, CV_32FC1, det);
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ensureSizeIsEnough(img_rows * (nOctaveLayers + 2), img_cols, CV_32FC1, trace);
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ensureSizeIsEnough(1, maxCandidates, CV_32SC4, maxPosBuffer);
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ensureSizeIsEnough(SURF_GPU::SF_FEATURE_STRIDE, maxFeatures, CV_32FC1, keypoints);
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keypoints.setTo(Scalar::all(0));
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for (int octave = 0; octave < nOctaves; ++octave)
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{
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const int layer_rows = img_rows >> octave;
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const int layer_cols = img_cols >> octave;
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uploadConstant("cv::gpu::surf::c_octave", octave);
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uploadConstant("cv::gpu::surf::c_layer_rows", layer_rows);
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uploadConstant("cv::gpu::surf::c_layer_cols", layer_cols);
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icvCalcLayerDetAndTrace_gpu(det, trace, img_rows, img_cols, octave, nOctaveLayers);
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icvFindMaximaInLayer_gpu(det, trace, maxPosBuffer.ptr<int4>(), counters.ptr<unsigned int>() + 1 + octave,
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img_rows, img_cols, octave, use_mask, nOctaveLayers);
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unsigned int maxCounter;
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cudaSafeCall( cudaMemcpy(&maxCounter, counters.ptr<unsigned int>() + 1 + octave, sizeof(unsigned int), cudaMemcpyDeviceToHost) );
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maxCounter = std::min(maxCounter, static_cast<unsigned int>(maxCandidates));
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if (maxCounter > 0)
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{
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icvInterpolateKeypoint_gpu(det, maxPosBuffer.ptr<int4>(), maxCounter,
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keypoints.ptr<float>(SURF_GPU::SF_X), keypoints.ptr<float>(SURF_GPU::SF_Y),
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keypoints.ptr<int>(SURF_GPU::SF_LAPLACIAN), keypoints.ptr<float>(SURF_GPU::SF_SIZE),
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keypoints.ptr<float>(SURF_GPU::SF_HESSIAN), counters.ptr<unsigned int>());
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}
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}
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unsigned int featureCounter;
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cudaSafeCall( cudaMemcpy(&featureCounter, counters.ptr<unsigned int>(), sizeof(unsigned int), cudaMemcpyDeviceToHost) );
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featureCounter = std::min(featureCounter, static_cast<unsigned int>(maxFeatures));
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keypoints.cols = featureCounter;
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if (!upright)
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findOrientation(keypoints);
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}
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void findOrientation(GpuMat& keypoints)
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{
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const int nFeatures = keypoints.cols;
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if (nFeatures > 0)
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{
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icvCalcOrientation_gpu(keypoints.ptr<float>(SURF_GPU::SF_X), keypoints.ptr<float>(SURF_GPU::SF_Y),
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keypoints.ptr<float>(SURF_GPU::SF_SIZE), keypoints.ptr<float>(SURF_GPU::SF_DIR), nFeatures);
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}
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}
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void computeDescriptors(const GpuMat& keypoints, GpuMat& descriptors, int descriptorSize)
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{
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const int nFeatures = keypoints.cols;
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if (nFeatures > 0)
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{
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ensureSizeIsEnough(nFeatures, descriptorSize, CV_32F, descriptors);
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compute_descriptors_gpu(descriptors, keypoints.ptr<float>(SURF_GPU::SF_X), keypoints.ptr<float>(SURF_GPU::SF_Y),
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keypoints.ptr<float>(SURF_GPU::SF_SIZE), keypoints.ptr<float>(SURF_GPU::SF_DIR), nFeatures);
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}
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}
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private:
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GpuMat& sum;
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GpuMat& mask1;
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GpuMat& maskSum;
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GpuMat& intBuffer;
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GpuMat& det;
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GpuMat& trace;
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GpuMat& maxPosBuffer;
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int img_cols, img_rows;
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bool use_mask;
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int maxCandidates;
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int maxFeatures;
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GpuMat counters;
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TextureBinder imgTex, sumTex, maskSumTex;
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};
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}
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cv::gpu::SURF_GPU::SURF_GPU()
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{
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hessianThreshold = 100;
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extended = 1;
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nOctaves = 4;
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nOctaveLayers = 2;
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keypointsRatio = 0.01f;
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upright = false;
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}
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cv::gpu::SURF_GPU::SURF_GPU(double _threshold, int _nOctaves, int _nOctaveLayers, bool _extended, float _keypointsRatio, bool _upright)
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{
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hessianThreshold = _threshold;
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extended = _extended;
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nOctaves = _nOctaves;
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nOctaveLayers = _nOctaveLayers;
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keypointsRatio = _keypointsRatio;
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upright = _upright;
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}
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int cv::gpu::SURF_GPU::descriptorSize() const
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{
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return extended ? 128 : 64;
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}
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void cv::gpu::SURF_GPU::uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU)
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{
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if (keypoints.empty())
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keypointsGPU.release();
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else
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{
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Mat keypointsCPU(SURF_GPU::SF_FEATURE_STRIDE, static_cast<int>(keypoints.size()), CV_32FC1);
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float* kp_x = keypointsCPU.ptr<float>(SURF_GPU::SF_X);
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float* kp_y = keypointsCPU.ptr<float>(SURF_GPU::SF_Y);
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int* kp_laplacian = keypointsCPU.ptr<int>(SURF_GPU::SF_LAPLACIAN);
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float* kp_size = keypointsCPU.ptr<float>(SURF_GPU::SF_SIZE);
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float* kp_dir = keypointsCPU.ptr<float>(SURF_GPU::SF_DIR);
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float* kp_hessian = keypointsCPU.ptr<float>(SURF_GPU::SF_HESSIAN);
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for (size_t i = 0, size = keypoints.size(); i < size; ++i)
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{
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const KeyPoint& kp = keypoints[i];
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kp_x[i] = kp.pt.x;
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kp_y[i] = kp.pt.y;
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kp_size[i] = kp.size;
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kp_dir[i] = kp.angle;
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kp_hessian[i] = kp.response;
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kp_laplacian[i] = 1;
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}
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keypointsGPU.upload(keypointsCPU);
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}
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}
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namespace
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{
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int getPointOctave(float size, const CvSURFParams& params)
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{
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int best_octave = 0;
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float min_diff = numeric_limits<float>::max();
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for (int octave = 1; octave < params.nOctaves; ++octave)
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{
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for (int layer = 0; layer < params.nOctaveLayers; ++layer)
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{
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float diff = std::abs(size - (float)calcSize(octave, layer));
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if (min_diff > diff)
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{
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min_diff = diff;
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best_octave = octave;
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if (min_diff == 0)
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return best_octave;
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}
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}
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}
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return best_octave;
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}
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}
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void cv::gpu::SURF_GPU::downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints)
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{
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const int nFeatures = keypointsGPU.cols;
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if (nFeatures == 0)
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keypoints.clear();
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else
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{
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CV_Assert(keypointsGPU.type() == CV_32FC1 && keypointsGPU.rows == SF_FEATURE_STRIDE);
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Mat keypointsCPU = keypointsGPU;
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keypoints.resize(nFeatures);
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float* kp_x = keypointsCPU.ptr<float>(SF_X);
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float* kp_y = keypointsCPU.ptr<float>(SF_Y);
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int* kp_laplacian = keypointsCPU.ptr<int>(SF_LAPLACIAN);
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float* kp_size = keypointsCPU.ptr<float>(SF_SIZE);
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float* kp_dir = keypointsCPU.ptr<float>(SF_DIR);
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float* kp_hessian = keypointsCPU.ptr<float>(SF_HESSIAN);
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for (int i = 0; i < nFeatures; ++i)
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{
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KeyPoint& kp = keypoints[i];
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kp.pt.x = kp_x[i];
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kp.pt.y = kp_y[i];
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kp.class_id = kp_laplacian[i];
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kp.size = kp_size[i];
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kp.angle = kp_dir[i];
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kp.response = kp_hessian[i];
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kp.octave = getPointOctave(kp.size, *this);
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}
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}
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}
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void cv::gpu::SURF_GPU::downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors)
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{
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if (descriptorsGPU.empty())
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descriptors.clear();
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else
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{
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CV_Assert(descriptorsGPU.type() == CV_32F);
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descriptors.resize(descriptorsGPU.rows * descriptorsGPU.cols);
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Mat descriptorsCPU(descriptorsGPU.size(), CV_32F, &descriptors[0]);
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descriptorsGPU.download(descriptorsCPU);
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}
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}
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void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints)
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{
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if (!img.empty())
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{
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SURF_GPU_Invoker surf(*this, img, mask);
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surf.detectKeypoints(keypoints);
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}
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}
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void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
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bool useProvidedKeypoints)
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{
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if (!img.empty())
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{
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SURF_GPU_Invoker surf(*this, img, mask);
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if (!useProvidedKeypoints)
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surf.detectKeypoints(keypoints);
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else if (!upright)
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{
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surf.findOrientation(keypoints);
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}
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surf.computeDescriptors(keypoints, descriptors, descriptorSize());
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}
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}
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void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints)
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{
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GpuMat keypointsGPU;
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(*this)(img, mask, keypointsGPU);
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downloadKeypoints(keypointsGPU, keypoints);
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}
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void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints,
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GpuMat& descriptors, bool useProvidedKeypoints)
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{
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GpuMat keypointsGPU;
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if (useProvidedKeypoints)
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uploadKeypoints(keypoints, keypointsGPU);
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(*this)(img, mask, keypointsGPU, descriptors, useProvidedKeypoints);
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downloadKeypoints(keypointsGPU, keypoints);
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}
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void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints,
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vector<float>& descriptors, bool useProvidedKeypoints)
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{
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GpuMat descriptorsGPU;
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(*this)(img, mask, keypoints, descriptorsGPU, useProvidedKeypoints);
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downloadDescriptors(descriptorsGPU, descriptors);
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}
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void cv::gpu::SURF_GPU::releaseMemory()
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{
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sum.release();
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mask1.release();
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maskSum.release();
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intBuffer.release();
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det.release();
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trace.release();
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maxPosBuffer.release();
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}
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#endif /* !defined (HAVE_CUDA) */
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