396 lines
15 KiB
C++
396 lines
15 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other GpuMaterials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or bpied warranties, including, but not limited to, the bpied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "precomp.hpp"
|
|
|
|
using namespace cv;
|
|
using namespace cv::gpu;
|
|
using namespace std;
|
|
|
|
#if !defined (HAVE_CUDA)
|
|
|
|
int cv::gpu::SURF_GPU::descriptorSize() const { throw_nogpu(); return 0;}
|
|
void cv::gpu::SURF_GPU::uploadKeypoints(const vector<KeyPoint>&, GpuMat&) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::downloadKeypoints(const GpuMat&, vector<KeyPoint>&) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::downloadDescriptors(const GpuMat&, vector<float>&) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, bool) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&, GpuMat&, bool, bool) { throw_nogpu(); }
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat&, const GpuMat&, vector<KeyPoint>&, vector<float>&, bool, bool) { throw_nogpu(); }
|
|
|
|
#else /* !defined (HAVE_CUDA) */
|
|
|
|
namespace cv { namespace gpu { namespace surf
|
|
{
|
|
dim3 calcBlockSize(int nIntervals);
|
|
|
|
void fasthessian_gpu(PtrStepf hessianBuffer, int x_size, int y_size, const dim3& threads);
|
|
|
|
void nonmaxonly_gpu(PtrStepf hessianBuffer, int4* maxPosBuffer, unsigned int& maxCounter,
|
|
int x_size, int y_size, bool use_mask, const dim3& threads);
|
|
|
|
void fh_interp_extremum_gpu(PtrStepf hessianBuffer, const int4* maxPosBuffer, unsigned int maxCounter,
|
|
KeyPoint_GPU* featuresBuffer, unsigned int& featureCounter);
|
|
|
|
void find_orientation_gpu(KeyPoint_GPU* features, int nFeatures);
|
|
|
|
void compute_descriptors_gpu(const DevMem2Df& descriptors, const KeyPoint_GPU* features, int nFeatures);
|
|
}}}
|
|
|
|
using namespace cv::gpu::surf;
|
|
|
|
namespace
|
|
{
|
|
class SURF_GPU_Invoker : private SURFParams_GPU
|
|
{
|
|
public:
|
|
SURF_GPU_Invoker(SURF_GPU& surf, const GpuMat& img, const GpuMat& mask) :
|
|
SURFParams_GPU(surf),
|
|
|
|
sum(surf.sum), sumf(surf.sumf),
|
|
|
|
mask1(surf.mask1), maskSum(surf.maskSum),
|
|
|
|
hessianBuffer(surf.hessianBuffer),
|
|
maxPosBuffer(surf.maxPosBuffer),
|
|
featuresBuffer(surf.featuresBuffer),
|
|
|
|
img_cols(img.cols), img_rows(img.rows),
|
|
|
|
use_mask(!mask.empty()),
|
|
|
|
mask_width(0), mask_height(0),
|
|
|
|
featureCounter(0), maxCounter(0)
|
|
{
|
|
CV_Assert(!img.empty() && img.type() == CV_8UC1);
|
|
CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
|
|
CV_Assert(nOctaves > 0 && nIntervals > 2 && nIntervals < 22);
|
|
CV_Assert(DeviceInfo().supports(ATOMICS));
|
|
|
|
max_features = static_cast<int>(img.size().area() * featuresRatio);
|
|
max_candidates = static_cast<int>(1.5 * max_features);
|
|
|
|
CV_Assert(max_features > 0);
|
|
|
|
featuresBuffer.create(1, max_features, CV_32FC(6));
|
|
maxPosBuffer.create(1, max_candidates, CV_32SC4);
|
|
|
|
mask_width = l2 * 0.5f;
|
|
mask_height = 1.0f + l1;
|
|
|
|
// Dxy gap half-width
|
|
float dxy_center_offset = 0.5f * (l4 + l3);
|
|
// Dxy squares half-width
|
|
float dxy_half_width = 0.5f * l3;
|
|
|
|
// rescale edge_scale to fit with the filter dimensions
|
|
float dxy_scale = edgeScale * std::pow((2.f + 2.f * l1) * l2 / (4.f * l3 * l3), 2.f);
|
|
|
|
// Compute border required such that the filters don't overstep the image boundaries
|
|
float smax0 = 2.0f * initialScale + 0.5f;
|
|
int border0 = static_cast<int>(std::ceil(smax0 * std::max(std::max(mask_width, mask_height), l3 + l4 * 0.5f)));
|
|
|
|
int width0 = (img_cols - 2 * border0) / initialStep;
|
|
int height0 = (img_rows - 2 * border0) / initialStep;
|
|
|
|
uploadConstant("cv::gpu::surf::c_max_candidates", max_candidates);
|
|
uploadConstant("cv::gpu::surf::c_max_features", max_features);
|
|
uploadConstant("cv::gpu::surf::c_nIntervals", nIntervals);
|
|
uploadConstant("cv::gpu::surf::c_mask_width", mask_width);
|
|
uploadConstant("cv::gpu::surf::c_mask_height", mask_height);
|
|
uploadConstant("cv::gpu::surf::c_dxy_center_offset", dxy_center_offset);
|
|
uploadConstant("cv::gpu::surf::c_dxy_half_width", dxy_half_width);
|
|
uploadConstant("cv::gpu::surf::c_dxy_scale", dxy_scale);
|
|
uploadConstant("cv::gpu::surf::c_initialScale", initialScale);
|
|
uploadConstant("cv::gpu::surf::c_threshold", threshold);
|
|
|
|
hessianBuffer.create(height0 * nIntervals, width0, CV_32F);
|
|
|
|
integral(img, sum);
|
|
sum.convertTo(sumf, CV_32F, 1.0 / 255.0);
|
|
|
|
bindTexture("cv::gpu::surf::sumTex", (DevMem2Df)sumf);
|
|
|
|
if (!mask.empty())
|
|
{
|
|
min(mask, 1.0, mask1);
|
|
integral(mask1, maskSum);
|
|
|
|
bindTexture("cv::gpu::surf::maskSumTex", (DevMem2Di)maskSum);
|
|
}
|
|
}
|
|
|
|
~SURF_GPU_Invoker()
|
|
{
|
|
unbindTexture("cv::gpu::surf::sumTex");
|
|
if (use_mask)
|
|
unbindTexture("cv::gpu::surf::maskSumTex");
|
|
}
|
|
|
|
void detectKeypoints(GpuMat& keypoints)
|
|
{
|
|
dim3 threads = calcBlockSize(nIntervals);
|
|
for(int octave = 0; octave < nOctaves; ++octave)
|
|
{
|
|
int step = initialStep * (1 << octave);
|
|
|
|
// Compute border required such that the filters don't overstep the image boundaries
|
|
float d = (initialScale * (1 << octave)) / (nIntervals - 2);
|
|
float smax = initialScale * (1 << octave) + d * (nIntervals - 2.0f) + 0.5f;
|
|
int border = static_cast<int>(std::ceil(smax * std::max(std::max(mask_width, mask_height), l3 + l4 * 0.5f)));
|
|
|
|
int x_size = (img_cols - 2 * border) / step;
|
|
int y_size = (img_rows - 2 * border) / step;
|
|
|
|
if (x_size <= 0 || y_size <= 0)
|
|
break;
|
|
|
|
uploadConstant("cv::gpu::surf::c_octave", octave);
|
|
uploadConstant("cv::gpu::surf::c_x_size", x_size);
|
|
uploadConstant("cv::gpu::surf::c_y_size", y_size);
|
|
uploadConstant("cv::gpu::surf::c_border", border);
|
|
uploadConstant("cv::gpu::surf::c_step", step);
|
|
|
|
fasthessian_gpu(hessianBuffer, x_size, y_size, threads);
|
|
|
|
// Reset the candidate count.
|
|
maxCounter = 0;
|
|
|
|
nonmaxonly_gpu(hessianBuffer, maxPosBuffer.ptr<int4>(), maxCounter, x_size, y_size, use_mask, threads);
|
|
|
|
maxCounter = std::min(maxCounter, static_cast<unsigned int>(max_candidates));
|
|
|
|
fh_interp_extremum_gpu(hessianBuffer, maxPosBuffer.ptr<int4>(), maxCounter,
|
|
featuresBuffer.ptr<KeyPoint_GPU>(), featureCounter);
|
|
|
|
featureCounter = std::min(featureCounter, static_cast<unsigned int>(max_features));
|
|
}
|
|
|
|
if (featureCounter > 0)
|
|
featuresBuffer.colRange(0, featureCounter).copyTo(keypoints);
|
|
else
|
|
keypoints.release();
|
|
}
|
|
|
|
void findOrientation(GpuMat& keypoints)
|
|
{
|
|
if (keypoints.cols > 0)
|
|
find_orientation_gpu(keypoints.ptr<KeyPoint_GPU>(), keypoints.cols);
|
|
}
|
|
|
|
void computeDescriptors(const GpuMat& keypoints, GpuMat& descriptors, int descriptorSize)
|
|
{
|
|
if (keypoints.cols > 0)
|
|
{
|
|
descriptors.create(keypoints.cols, descriptorSize, CV_32F);
|
|
compute_descriptors_gpu(descriptors, keypoints.ptr<KeyPoint_GPU>(), keypoints.cols);
|
|
}
|
|
}
|
|
|
|
private:
|
|
GpuMat& sum;
|
|
GpuMat& sumf;
|
|
|
|
GpuMat& mask1;
|
|
GpuMat& maskSum;
|
|
|
|
GpuMat& hessianBuffer;
|
|
GpuMat& maxPosBuffer;
|
|
GpuMat& featuresBuffer;
|
|
|
|
int img_cols, img_rows;
|
|
|
|
bool use_mask;
|
|
|
|
float mask_width, mask_height;
|
|
|
|
unsigned int featureCounter;
|
|
unsigned int maxCounter;
|
|
|
|
int max_candidates;
|
|
int max_features;
|
|
};
|
|
}
|
|
|
|
int cv::gpu::SURF_GPU::descriptorSize() const
|
|
{
|
|
return extended ? 128 : 64;
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU)
|
|
{
|
|
if (keypoints.empty())
|
|
keypointsGPU.release();
|
|
else
|
|
{
|
|
Mat keypointsCPU(1, keypoints.size(), CV_32FC(6));
|
|
|
|
const KeyPoint* keypoints_ptr = &keypoints[0];
|
|
KeyPoint_GPU* keypointsCPU_ptr = keypointsCPU.ptr<KeyPoint_GPU>();
|
|
for (size_t i = 0; i < keypoints.size(); ++i, ++keypoints_ptr, ++keypointsCPU_ptr)
|
|
{
|
|
const KeyPoint& kp = *keypoints_ptr;
|
|
KeyPoint_GPU& gkp = *keypointsCPU_ptr;
|
|
|
|
gkp.x = kp.pt.x;
|
|
gkp.y = kp.pt.y;
|
|
|
|
gkp.size = kp.size;
|
|
|
|
gkp.octave = static_cast<float>(kp.octave);
|
|
gkp.angle = kp.angle;
|
|
gkp.response = kp.response;
|
|
}
|
|
|
|
keypointsGPU.upload(keypointsCPU);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints)
|
|
{
|
|
if (keypointsGPU.empty())
|
|
keypoints.clear();
|
|
else
|
|
{
|
|
CV_Assert(keypointsGPU.type() == CV_32FC(6) && keypointsGPU.isContinuous());
|
|
|
|
Mat keypointsCPU = keypointsGPU;
|
|
keypoints.resize(keypointsGPU.cols);
|
|
|
|
KeyPoint* keypoints_ptr = &keypoints[0];
|
|
const KeyPoint_GPU* keypointsCPU_ptr = keypointsCPU.ptr<KeyPoint_GPU>();
|
|
for (int i = 0; i < keypointsGPU.cols; ++i, ++keypoints_ptr, ++keypointsCPU_ptr)
|
|
{
|
|
KeyPoint& kp = *keypoints_ptr;
|
|
const KeyPoint_GPU& gkp = *keypointsCPU_ptr;
|
|
|
|
kp.pt.x = gkp.x;
|
|
kp.pt.y = gkp.y;
|
|
|
|
kp.size = gkp.size;
|
|
|
|
kp.octave = static_cast<int>(gkp.octave);
|
|
kp.angle = gkp.angle;
|
|
kp.response = gkp.response;
|
|
}
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors)
|
|
{
|
|
if (descriptorsGPU.empty())
|
|
descriptors.clear();
|
|
else
|
|
{
|
|
CV_Assert(descriptorsGPU.type() == CV_32F);
|
|
|
|
descriptors.resize(descriptorsGPU.rows * descriptorsGPU.cols);
|
|
Mat descriptorsCPU(descriptorsGPU.size(), CV_32F, &descriptors[0]);
|
|
descriptorsGPU.download(descriptorsCPU);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints)
|
|
{
|
|
if (!img.empty())
|
|
{
|
|
SURF_GPU_Invoker surf(*this, img, mask);
|
|
|
|
surf.detectKeypoints(keypoints);
|
|
|
|
surf.findOrientation(keypoints);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
|
|
bool useProvidedKeypoints, bool calcOrientation)
|
|
{
|
|
if (!img.empty())
|
|
{
|
|
SURF_GPU_Invoker surf(*this, img, mask);
|
|
|
|
if (!useProvidedKeypoints)
|
|
surf.detectKeypoints(keypoints);
|
|
|
|
if (calcOrientation)
|
|
surf.findOrientation(keypoints);
|
|
|
|
surf.computeDescriptors(keypoints, descriptors, descriptorSize());
|
|
}
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints)
|
|
{
|
|
GpuMat keypointsGPU;
|
|
|
|
(*this)(img, mask, keypointsGPU);
|
|
|
|
downloadKeypoints(keypointsGPU, keypoints);
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints,
|
|
GpuMat& descriptors, bool useProvidedKeypoints, bool calcOrientation)
|
|
{
|
|
GpuMat keypointsGPU;
|
|
|
|
if (useProvidedKeypoints)
|
|
uploadKeypoints(keypoints, keypointsGPU);
|
|
|
|
(*this)(img, mask, keypointsGPU, descriptors, useProvidedKeypoints, calcOrientation);
|
|
|
|
downloadKeypoints(keypointsGPU, keypoints);
|
|
}
|
|
|
|
void cv::gpu::SURF_GPU::operator()(const GpuMat& img, const GpuMat& mask, vector<KeyPoint>& keypoints,
|
|
vector<float>& descriptors, bool useProvidedKeypoints, bool calcOrientation)
|
|
{
|
|
GpuMat descriptorsGPU;
|
|
|
|
(*this)(img, mask, keypoints, descriptorsGPU, useProvidedKeypoints, calcOrientation);
|
|
|
|
downloadDescriptors(descriptorsGPU, descriptors);
|
|
}
|
|
|
|
#endif /* !defined (HAVE_CUDA) */
|