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Open Source Computer Vision Library
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419 lines
16 KiB
419 lines
16 KiB
/*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 device |
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{ |
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namespace surf |
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{ |
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void loadGlobalConstants(int maxCandidates, int maxFeatures, int img_rows, int img_cols, int nOctaveLayers, float hessianThreshold); |
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void loadOctaveConstants(int octave, int layer_rows, int layer_cols); |
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void bindImgTex(DevMem2Db img); |
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void bindSumTex(DevMem2D_<unsigned int> sum); |
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void bindMaskSumTex(DevMem2D_<unsigned int> maskSum); |
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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, int* featureOctave, 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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}}} |
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using namespace ::cv::gpu::device::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 |
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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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surf_(surf), |
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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(surf_.nOctaves > 0 && surf_.nOctaveLayers > 0); |
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if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS)) |
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CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics"); |
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const int min_size = calcSize(surf_.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 >> (surf_.nOctaves - 1); |
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const int layer_cols = img_cols >> (surf_.nOctaves - 1); |
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const int min_margin = ((calcSize((surf_.nOctaves - 1), 2) >> 1) >> (surf_.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, surf_.nOctaves + 1, CV_32SC1); |
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counters.setTo(Scalar::all(0)); |
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loadGlobalConstants(maxCandidates, maxFeatures, img_rows, img_cols, surf_.nOctaveLayers, static_cast<float>(surf_.hessianThreshold)); |
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bindImgTex(img); |
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integralBuffered(img, surf_.sum, surf_.intBuffer); |
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bindSumTex(surf_.sum); |
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if (use_mask) |
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{ |
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min(mask, 1.0, surf_.mask1); |
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integralBuffered(surf_.mask1, surf_.maskSum, surf_.intBuffer); |
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bindMaskSumTex(surf_.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 * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.det); |
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ensureSizeIsEnough(img_rows * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.trace); |
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ensureSizeIsEnough(1, maxCandidates, CV_32SC4, surf_.maxPosBuffer); |
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ensureSizeIsEnough(SURF_GPU::ROWS_COUNT, maxFeatures, CV_32FC1, keypoints); |
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keypoints.setTo(Scalar::all(0)); |
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for (int octave = 0; octave < surf_.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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loadOctaveConstants(octave, layer_rows, layer_cols); |
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icvCalcLayerDetAndTrace_gpu(surf_.det, surf_.trace, img_rows, img_cols, octave, surf_.nOctaveLayers); |
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icvFindMaximaInLayer_gpu(surf_.det, surf_.trace, surf_.maxPosBuffer.ptr<int4>(), counters.ptr<unsigned int>() + 1 + octave, |
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img_rows, img_cols, octave, use_mask, surf_.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(surf_.det, surf_.maxPosBuffer.ptr<int4>(), maxCounter, |
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keypoints.ptr<float>(SURF_GPU::X_ROW), keypoints.ptr<float>(SURF_GPU::Y_ROW), |
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keypoints.ptr<int>(SURF_GPU::LAPLACIAN_ROW), keypoints.ptr<int>(SURF_GPU::OCTAVE_ROW), |
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keypoints.ptr<float>(SURF_GPU::SIZE_ROW), keypoints.ptr<float>(SURF_GPU::HESSIAN_ROW), |
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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 (surf_.upright) |
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keypoints.row(SURF_GPU::ANGLE_ROW).setTo(Scalar::all(90.0)); |
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else |
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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::X_ROW), keypoints.ptr<float>(SURF_GPU::Y_ROW), |
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keypoints.ptr<float>(SURF_GPU::SIZE_ROW), keypoints.ptr<float>(SURF_GPU::ANGLE_ROW), 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::X_ROW), keypoints.ptr<float>(SURF_GPU::Y_ROW), |
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keypoints.ptr<float>(SURF_GPU::SIZE_ROW), keypoints.ptr<float>(SURF_GPU::ANGLE_ROW), nFeatures); |
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} |
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} |
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private: |
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SURF_GPU& surf_; |
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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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}; |
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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 = true; |
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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::ROWS_COUNT, static_cast<int>(keypoints.size()), CV_32FC1); |
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float* kp_x = keypointsCPU.ptr<float>(SURF_GPU::X_ROW); |
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float* kp_y = keypointsCPU.ptr<float>(SURF_GPU::Y_ROW); |
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int* kp_laplacian = keypointsCPU.ptr<int>(SURF_GPU::LAPLACIAN_ROW); |
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int* kp_octave = keypointsCPU.ptr<int>(SURF_GPU::OCTAVE_ROW); |
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float* kp_size = keypointsCPU.ptr<float>(SURF_GPU::SIZE_ROW); |
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float* kp_dir = keypointsCPU.ptr<float>(SURF_GPU::ANGLE_ROW); |
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float* kp_hessian = keypointsCPU.ptr<float>(SURF_GPU::HESSIAN_ROW); |
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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_octave[i] = kp.octave; |
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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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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 == ROWS_COUNT); |
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Mat keypointsCPU(keypointsGPU); |
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keypoints.resize(nFeatures); |
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float* kp_x = keypointsCPU.ptr<float>(SURF_GPU::X_ROW); |
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float* kp_y = keypointsCPU.ptr<float>(SURF_GPU::Y_ROW); |
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int* kp_laplacian = keypointsCPU.ptr<int>(SURF_GPU::LAPLACIAN_ROW); |
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int* kp_octave = keypointsCPU.ptr<int>(SURF_GPU::OCTAVE_ROW); |
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float* kp_size = keypointsCPU.ptr<float>(SURF_GPU::SIZE_ROW); |
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float* kp_dir = keypointsCPU.ptr<float>(SURF_GPU::ANGLE_ROW); |
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float* kp_hessian = keypointsCPU.ptr<float>(SURF_GPU::HESSIAN_ROW); |
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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.octave = kp_octave[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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} |
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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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