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429 lines
14 KiB
C++
429 lines
14 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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 materials provided with the distribution.
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//
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// * The name of Intel Corporation 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 implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "opencl_kernels.hpp"
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#include <cstdio>
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#include <vector>
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#include <iostream>
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#include <functional>
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namespace cv
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{
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struct greaterThanPtr :
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public std::binary_function<const float *, const float *, bool>
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{
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bool operator () (const float * a, const float * b) const
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{ return *a > *b; }
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};
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#ifdef HAVE_OPENCL
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struct Corner
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{
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float val;
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short y;
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short x;
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bool operator < (const Corner & c) const
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{ return val > c.val; }
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};
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static bool ocl_goodFeaturesToTrack( InputArray _image, OutputArray _corners,
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int maxCorners, double qualityLevel, double minDistance,
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InputArray _mask, int blockSize,
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bool useHarrisDetector, double harrisK )
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{
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UMat eig, maxEigenValue;
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if( useHarrisDetector )
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cornerHarris( _image, eig, blockSize, 3, harrisK );
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else
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cornerMinEigenVal( _image, eig, blockSize, 3 );
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Size imgsize = _image.size();
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std::vector<Corner> tmpCorners;
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size_t total, i, j, ncorners = 0, possibleCornersCount =
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std::max(1024, static_cast<int>(imgsize.area() * 0.1));
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bool haveMask = !_mask.empty();
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// find threshold
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{
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CV_Assert(eig.type() == CV_32FC1);
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int dbsize = ocl::Device::getDefault().maxComputeUnits();
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size_t wgs = ocl::Device::getDefault().maxWorkGroupSize();
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int wgs2_aligned = 1;
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while (wgs2_aligned < (int)wgs)
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wgs2_aligned <<= 1;
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wgs2_aligned >>= 1;
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ocl::Kernel k("maxEigenVal", ocl::imgproc::gftt_oclsrc,
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format("-D OP_MAX_EIGEN_VAL -D WGS=%d -D groupnum=%d -D WGS2_ALIGNED=%d%s",
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(int)wgs, dbsize, wgs2_aligned, haveMask ? " -D HAVE_MASK" : ""));
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if (k.empty())
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return false;
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UMat mask = _mask.getUMat();
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maxEigenValue.create(1, dbsize, CV_32FC1);
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ocl::KernelArg eigarg = ocl::KernelArg::ReadOnlyNoSize(eig),
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dbarg = ocl::KernelArg::PtrWriteOnly(maxEigenValue),
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maskarg = ocl::KernelArg::ReadOnlyNoSize(mask);
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if (haveMask)
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k.args(eigarg, eig.cols, (int)eig.total(), dbarg, maskarg);
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else
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k.args(eigarg, eig.cols, (int)eig.total(), dbarg);
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size_t globalsize = dbsize * wgs;
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if (!k.run(1, &globalsize, &wgs, false))
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return false;
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ocl::Kernel k2("maxEigenValTask", ocl::imgproc::gftt_oclsrc,
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format("-D OP_MAX_EIGEN_VAL -D WGS=%d -D WGS2_ALIGNED=%d -D groupnum=%d",
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wgs, wgs2_aligned, dbsize));
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if (k2.empty())
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return false;
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k2.args(dbarg, (float)qualityLevel);
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if (!k2.runTask(false))
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return false;
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}
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// collect list of pointers to features - put them into temporary image
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{
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ocl::Kernel k("findCorners", ocl::imgproc::gftt_oclsrc,
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format("-D OP_FIND_CORNERS%s", haveMask ? " -D HAVE_MASK" : ""));
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if (k.empty())
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return false;
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UMat counter(1, 1, CV_32SC1, Scalar::all(0)),
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corners(1, (int)possibleCornersCount, CV_32FC2, Scalar::all(-1));
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CV_Assert(sizeof(Corner) == corners.elemSize());
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ocl::KernelArg eigarg = ocl::KernelArg::ReadOnlyNoSize(eig),
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cornersarg = ocl::KernelArg::PtrWriteOnly(corners),
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counterarg = ocl::KernelArg::PtrReadWrite(counter),
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thresholdarg = ocl::KernelArg::PtrReadOnly(maxEigenValue);
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if (!haveMask)
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k.args(eigarg, cornersarg, counterarg,
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eig.rows - 2, eig.cols - 2, thresholdarg,
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(int)possibleCornersCount);
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else
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{
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UMat mask = _mask.getUMat();
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k.args(eigarg, ocl::KernelArg::ReadOnlyNoSize(mask),
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cornersarg, counterarg, eig.rows - 2, eig.cols - 2,
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thresholdarg, (int)possibleCornersCount);
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}
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size_t globalsize[2] = { eig.cols - 2, eig.rows - 2 };
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if (!k.run(2, globalsize, NULL, false))
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return false;
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total = std::min<size_t>(counter.getMat(ACCESS_READ).at<int>(0, 0), possibleCornersCount);
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if (total == 0)
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{
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_corners.release();
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return true;
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}
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tmpCorners.resize(total);
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Mat mcorners(1, (int)total, CV_32FC2, &tmpCorners[0]);
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corners.colRange(0, (int)total).copyTo(mcorners);
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}
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std::sort(tmpCorners.begin(), tmpCorners.end());
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std::vector<Point2f> corners;
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corners.reserve(total);
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if (minDistance >= 1)
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{
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// Partition the image into larger grids
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int w = imgsize.width, h = imgsize.height;
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const int cell_size = cvRound(minDistance);
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const int grid_width = (w + cell_size - 1) / cell_size;
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const int grid_height = (h + cell_size - 1) / cell_size;
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std::vector<std::vector<Point2f> > grid(grid_width*grid_height);
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minDistance *= minDistance;
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for( i = 0; i < total; i++ )
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{
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const Corner & c = tmpCorners[i];
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bool good = true;
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int x_cell = c.x / cell_size;
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int y_cell = c.y / cell_size;
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int x1 = x_cell - 1;
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int y1 = y_cell - 1;
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int x2 = x_cell + 1;
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int y2 = y_cell + 1;
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// boundary check
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x1 = std::max(0, x1);
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y1 = std::max(0, y1);
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x2 = std::min(grid_width - 1, x2);
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y2 = std::min(grid_height - 1, y2);
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for( int yy = y1; yy <= y2; yy++ )
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for( int xx = x1; xx <= x2; xx++ )
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{
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std::vector<Point2f> &m = grid[yy * grid_width + xx];
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if( m.size() )
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{
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for(j = 0; j < m.size(); j++)
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{
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float dx = c.x - m[j].x;
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float dy = c.y - m[j].y;
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if( dx*dx + dy*dy < minDistance )
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{
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good = false;
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goto break_out;
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}
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}
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}
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}
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break_out:
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if (good)
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{
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grid[y_cell*grid_width + x_cell].push_back(Point2f((float)c.x, (float)c.y));
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corners.push_back(Point2f((float)c.x, (float)c.y));
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++ncorners;
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if( maxCorners > 0 && (int)ncorners == maxCorners )
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break;
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}
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}
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}
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else
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{
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for( i = 0; i < total; i++ )
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{
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const Corner & c = tmpCorners[i];
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corners.push_back(Point2f((float)c.x, (float)c.y));
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++ncorners;
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if( maxCorners > 0 && (int)ncorners == maxCorners )
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break;
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}
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}
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Mat(corners).convertTo(_corners, _corners.fixedType() ? _corners.type() : CV_32F);
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return true;
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}
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#endif
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}
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void cv::goodFeaturesToTrack( InputArray _image, OutputArray _corners,
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int maxCorners, double qualityLevel, double minDistance,
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InputArray _mask, int blockSize,
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bool useHarrisDetector, double harrisK )
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{
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CV_Assert( qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0 );
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CV_Assert( _mask.empty() || (_mask.type() == CV_8UC1 && _mask.sameSize(_image)) );
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CV_OCL_RUN(_image.dims() <= 2 && _image.isUMat(),
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ocl_goodFeaturesToTrack(_image, _corners, maxCorners, qualityLevel, minDistance,
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_mask, blockSize, useHarrisDetector, harrisK))
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Mat image = _image.getMat(), eig, tmp;
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if( useHarrisDetector )
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cornerHarris( image, eig, blockSize, 3, harrisK );
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else
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cornerMinEigenVal( image, eig, blockSize, 3 );
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double maxVal = 0;
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minMaxLoc( eig, 0, &maxVal, 0, 0, _mask );
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threshold( eig, eig, maxVal*qualityLevel, 0, THRESH_TOZERO );
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dilate( eig, tmp, Mat());
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Size imgsize = image.size();
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std::vector<const float*> tmpCorners;
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// collect list of pointers to features - put them into temporary image
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Mat mask = _mask.getMat();
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for( int y = 1; y < imgsize.height - 1; y++ )
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{
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const float* eig_data = (const float*)eig.ptr(y);
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const float* tmp_data = (const float*)tmp.ptr(y);
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const uchar* mask_data = mask.data ? mask.ptr(y) : 0;
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for( int x = 1; x < imgsize.width - 1; x++ )
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{
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float val = eig_data[x];
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if( val != 0 && val == tmp_data[x] && (!mask_data || mask_data[x]) )
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tmpCorners.push_back(eig_data + x);
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}
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}
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std::sort( tmpCorners.begin(), tmpCorners.end(), greaterThanPtr() );
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std::vector<Point2f> corners;
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size_t i, j, total = tmpCorners.size(), ncorners = 0;
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if (minDistance >= 1)
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{
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// Partition the image into larger grids
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int w = image.cols;
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int h = image.rows;
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const int cell_size = cvRound(minDistance);
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const int grid_width = (w + cell_size - 1) / cell_size;
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const int grid_height = (h + cell_size - 1) / cell_size;
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std::vector<std::vector<Point2f> > grid(grid_width*grid_height);
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minDistance *= minDistance;
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for( i = 0; i < total; i++ )
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{
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int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
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int y = (int)(ofs / eig.step);
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int x = (int)((ofs - y*eig.step)/sizeof(float));
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bool good = true;
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int x_cell = x / cell_size;
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int y_cell = y / cell_size;
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int x1 = x_cell - 1;
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int y1 = y_cell - 1;
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int x2 = x_cell + 1;
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int y2 = y_cell + 1;
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// boundary check
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x1 = std::max(0, x1);
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y1 = std::max(0, y1);
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x2 = std::min(grid_width-1, x2);
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y2 = std::min(grid_height-1, y2);
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for( int yy = y1; yy <= y2; yy++ )
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for( int xx = x1; xx <= x2; xx++ )
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{
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std::vector <Point2f> &m = grid[yy*grid_width + xx];
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if( m.size() )
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{
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for(j = 0; j < m.size(); j++)
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{
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float dx = x - m[j].x;
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float dy = y - m[j].y;
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if( dx*dx + dy*dy < minDistance )
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{
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good = false;
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goto break_out;
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}
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}
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}
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}
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break_out:
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if (good)
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{
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grid[y_cell*grid_width + x_cell].push_back(Point2f((float)x, (float)y));
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corners.push_back(Point2f((float)x, (float)y));
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++ncorners;
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if( maxCorners > 0 && (int)ncorners == maxCorners )
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break;
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}
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}
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}
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else
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{
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for( i = 0; i < total; i++ )
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{
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int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
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int y = (int)(ofs / eig.step);
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int x = (int)((ofs - y*eig.step)/sizeof(float));
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corners.push_back(Point2f((float)x, (float)y));
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++ncorners;
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if( maxCorners > 0 && (int)ncorners == maxCorners )
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break;
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}
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}
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Mat(corners).convertTo(_corners, _corners.fixedType() ? _corners.type() : CV_32F);
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}
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CV_IMPL void
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cvGoodFeaturesToTrack( const void* _image, void*, void*,
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CvPoint2D32f* _corners, int *_corner_count,
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double quality_level, double min_distance,
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const void* _maskImage, int block_size,
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int use_harris, double harris_k )
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{
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cv::Mat image = cv::cvarrToMat(_image), mask;
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std::vector<cv::Point2f> corners;
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if( _maskImage )
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mask = cv::cvarrToMat(_maskImage);
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CV_Assert( _corners && _corner_count );
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cv::goodFeaturesToTrack( image, corners, *_corner_count, quality_level,
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min_distance, mask, block_size, use_harris != 0, harris_k );
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size_t i, ncorners = corners.size();
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for( i = 0; i < ncorners; i++ )
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_corners[i] = corners[i];
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*_corner_count = (int)ncorners;
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}
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/* End of file. */
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