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add kmeans

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yao 2013-07-03 13:13:04 +08:00
parent 6bf8f474fa
commit 8d8dc29ced
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12
modules/ocl/include/opencv2/ocl/ocl.hpp
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@ -834,6 +834,18 @@ namespace cv
CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
/////////////////////////////////// ML ///////////////////////////////////////////
//! Compute closest centers for each lines in source and lable it after center's index
// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
void DistanceComputer(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers);
//!Does k-means procedure on GPU
// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
TermCriteria criteria, int attemps, int flags, oclMat &centers);
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

471
modules/ocl/src/kmeans.cpp Normal file
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@ -0,0 +1,471 @@
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Xiaopeng Fu, fuxiaopeng2222@163.com
//
// 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 oclMaterials 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 implied warranties, including, but not limited to, the implied
// 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 <iomanip>
#include "precomp.hpp"
using namespace cv;
using namespace ocl;
namespace cv
{
namespace ocl
{
////////////////////////////////////OpenCL kernel strings//////////////////////////
extern const char *kmeans_kernel;
}
}
//////////////////////////////////////////////////////////////////////////
//////////////////common/////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////
void swap( Mat& a, Mat& b )
{
std::swap(a.flags, b.flags);
std::swap(a.dims, b.dims);
std::swap(a.rows, b.rows);
std::swap(a.cols, b.cols);
std::swap(a.data, b.data);
std::swap(a.refcount, b.refcount);
std::swap(a.datastart, b.datastart);
std::swap(a.dataend, b.dataend);
std::swap(a.datalimit, b.datalimit);
std::swap(a.allocator, b.allocator);
std::swap(a.size.p, b.size.p);
std::swap(a.step.p, b.step.p);
std::swap(a.step.buf[0], b.step.buf[0]);
std::swap(a.step.buf[1], b.step.buf[1]);
if( a.step.p == b.step.buf )
{
a.step.p = a.step.buf;
a.size.p = &a.rows;
}
if( b.step.p == a.step.buf )
{
b.step.p = b.step.buf;
b.size.p = &b.rows;
}
}
static void generateRandomCenter(const vector<Vec2f>& box, float* center, RNG& rng)
{
size_t j, dims = box.size();
float margin = 1.f/dims;
for( j = 0; j < dims; j++ )
center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
}
// This class is copied from matrix.cpp in core module.
class KMeansPPDistanceComputer : public ParallelLoopBody
{
public:
KMeansPPDistanceComputer( float *_tdist2,
const float *_data,
const float *_dist,
int _dims,
size_t _step,
size_t _stepci )
: tdist2(_tdist2),
data(_data),
dist(_dist),
dims(_dims),
step(_step),
stepci(_stepci) { }
void operator()( const cv::Range& range ) const
{
const int begin = range.start;
const int end = range.end;
for ( int i = begin; i<end; i++ )
{
tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
}
}
private:
KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
float *tdist2;
const float *data;
const float *dist;
const int dims;
const size_t step;
const size_t stepci;
};
/*
k-means center initialization using the following algorithm:
Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
*/
static void generateCentersPP(const Mat& _data, Mat& _out_centers,
int K, RNG& rng, int trials)
{
int i, j, k, dims = _data.cols, N = _data.rows;
const float* data = (float*)_data.data;
size_t step = _data.step/sizeof(data[0]);
vector<int> _centers(K);
int* centers = &_centers[0];
vector<float> _dist(N*3);
float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
double sum0 = 0;
centers[0] = (unsigned)rng % N;
for( i = 0; i < N; i++ )
{
dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
sum0 += dist[i];
}
for( k = 1; k < K; k++ )
{
double bestSum = DBL_MAX;
int bestCenter = -1;
for( j = 0; j < trials; j++ )
{
double p = (double)rng*sum0, s = 0;
for( i = 0; i < N-1; i++ )
if( (p -= dist[i]) <= 0 )
break;
int ci = i;
parallel_for_(Range(0, N),
KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
for( i = 0; i < N; i++ )
{
s += tdist2[i];
}
if( s < bestSum )
{
bestSum = s;
bestCenter = ci;
std::swap(tdist, tdist2);
}
}
centers[k] = bestCenter;
sum0 = bestSum;
std::swap(dist, tdist);
}
for( k = 0; k < K; k++ )
{
const float* src = data + step*centers[k];
float* dst = _out_centers.ptr<float>(k);
for( j = 0; j < dims; j++ )
dst[j] = src[j];
}
}
void cv::ocl::DistanceComputer(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers)
{
//if(src.clCxt -> impl -> double_support == 0 && src.type() == CV_64F)
//{
// CV_Error(CV_GpuNotSupported, "Selected device don't support double\r\n");
// return;
//}
Context *clCxt = src.clCxt;
int labels_step = (int)(labels.step/labels.elemSize());
string kernelname = "kmeansComputeDistance";
int threadNum = src.rows > 256 ? 256 : src.rows;
size_t localThreads[3] = {1, threadNum, 1};
size_t globalThreads[3] = {1, src.rows, 1};
vector<pair<size_t, const void *> > args;
args.push_back(make_pair(sizeof(cl_int), (void *)&labels_step));
args.push_back(make_pair(sizeof(cl_int), (void *)&centers.rows));
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
args.push_back(make_pair(sizeof(cl_int), (void *)&centers.cols));
args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
args.push_back(make_pair(sizeof(cl_mem), (void *)&centers.data));
args.push_back(make_pair(sizeof(cl_mem), (void*)&dists.data));
openCLExecuteKernel(clCxt, &kmeans_kernel, kernelname, globalThreads, localThreads, args, -1, -1, NULL);
}
///////////////////////////////////k - means /////////////////////////////////////////////////////////
double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
TermCriteria criteria, int attempts, int flags, oclMat &_centers)
{
const int SPP_TRIALS = 3;
bool isrow = _src.rows == 1 && _src.oclchannels() > 1;
int N = !isrow ? _src.rows : _src.cols;
int dims = (!isrow ? _src.cols : 1) * _src.oclchannels();
int type = _src.depth();
attempts = std::max(attempts, 1);
CV_Assert(type == CV_32F && K > 0 );
CV_Assert( N >= K );
Mat _labels;
if( flags & CV_KMEANS_USE_INITIAL_LABELS )
{
CV_Assert( (_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
_bestLabels.cols * _bestLabels.rows == N &&
_bestLabels.type() == CV_32S );
_bestLabels.download(_labels);
}
else
{
if( !((_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
_bestLabels.cols * _bestLabels.rows == N &&
_bestLabels.type() == CV_32S &&
_bestLabels.isContinuous()))
_bestLabels.create(N, 1, CV_32S);
_labels.create(_bestLabels.size(), _bestLabels.type());
}
int* labels = _labels.ptr<int>();
Mat data;
_src.download(data);
Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
vector<int> counters(K);
vector<Vec2f> _box(dims);
Vec2f* box = &_box[0];
double best_compactness = DBL_MAX, compactness = 0;
RNG& rng = theRNG();
int a, iter, i, j, k;
if( criteria.type & TermCriteria::EPS )
criteria.epsilon = std::max(criteria.epsilon, 0.);
else
criteria.epsilon = FLT_EPSILON;
criteria.epsilon *= criteria.epsilon;
if( criteria.type & TermCriteria::COUNT )
criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
else
criteria.maxCount = 100;
if( K == 1 )
{
attempts = 1;
criteria.maxCount = 2;
}
const float* sample = data.ptr<float>();
for( j = 0; j < dims; j++ )
box[j] = Vec2f(sample[j], sample[j]);
for( i = 1; i < N; i++ )
{
sample = data.ptr<float>(i);
for( j = 0; j < dims; j++ )
{
float v = sample[j];
box[j][0] = std::min(box[j][0], v);
box[j][1] = std::max(box[j][1], v);
}
}
for( a = 0; a < attempts; a++ )
{
double max_center_shift = DBL_MAX;
for( iter = 0;; )
{
swap(centers, old_centers);
if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
{
if( flags & KMEANS_PP_CENTERS )
generateCentersPP(data, centers, K, rng, SPP_TRIALS);
else
{
for( k = 0; k < K; k++ )
generateRandomCenter(_box, centers.ptr<float>(k), rng);
}
}
else
{
if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
{
for( i = 0; i < N; i++ )
CV_Assert( (unsigned)labels[i] < (unsigned)K );
}
// compute centers
centers = Scalar(0);
for( k = 0; k < K; k++ )
counters[k] = 0;
for( i = 0; i < N; i++ )
{
sample = data.ptr<float>(i);
k = labels[i];
float* center = centers.ptr<float>(k);
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= dims - 4; j += 4 )
{
float t0 = center[j] + sample[j];
float t1 = center[j+1] + sample[j+1];
center[j] = t0;
center[j+1] = t1;
t0 = center[j+2] + sample[j+2];
t1 = center[j+3] + sample[j+3];
center[j+2] = t0;
center[j+3] = t1;
}
#endif
for( ; j < dims; j++ )
center[j] += sample[j];
counters[k]++;
}
if( iter > 0 )
max_center_shift = 0;
for( k = 0; k < K; k++ )
{
if( counters[k] != 0 )
continue;
// if some cluster appeared to be empty then:
// 1. find the biggest cluster
// 2. find the farthest from the center point in the biggest cluster
// 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
int max_k = 0;
for( int k1 = 1; k1 < K; k1++ )
{
if( counters[max_k] < counters[k1] )
max_k = k1;
}
double max_dist = 0;
int farthest_i = -1;
float* new_center = centers.ptr<float>(k);
float* old_center = centers.ptr<float>(max_k);
float* _old_center = temp.ptr<float>(); // normalized
float scale = 1.f/counters[max_k];
for( j = 0; j < dims; j++ )
_old_center[j] = old_center[j]*scale;
for( i = 0; i < N; i++ )
{
if( labels[i] != max_k )
continue;
sample = data.ptr<float>(i);
double dist = normL2Sqr_(sample, _old_center, dims);
if( max_dist <= dist )
{
max_dist = dist;
farthest_i = i;
}
}
counters[max_k]--;
counters[k]++;
labels[farthest_i] = k;
sample = data.ptr<float>(farthest_i);
for( j = 0; j < dims; j++ )
{
old_center[j] -= sample[j];
new_center[j] += sample[j];
}
}
for( k = 0; k < K; k++ )
{
float* center = centers.ptr<float>(k);
CV_Assert( counters[k] != 0 );
float scale = 1.f/counters[k];
for( j = 0; j < dims; j++ )
center[j] *= scale;
if( iter > 0 )
{
double dist = 0;
const float* old_center = old_centers.ptr<float>(k);
for( j = 0; j < dims; j++ )
{
double t = center[j] - old_center[j];
dist += t*t;
}
max_center_shift = std::max(max_center_shift, dist);
}
}
}
if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
break;
// assign labels
oclMat _dists(1, N, CV_64F);
_bestLabels.upload(_labels);
_centers.upload(centers);
DistanceComputer(_dists, _bestLabels, _src, _centers);
Mat dists;
_dists.download(dists);
_bestLabels.download(_labels);
double* dist = dists.ptr<double>(0);
compactness = 0;
for( i = 0; i < N; i++ )
{
compactness += dist[i];
}
}
if( compactness < best_compactness )
{
best_compactness = compactness;
}
}
return best_compactness;
}

83
modules/ocl/src/opencl/kmeans_kernel.cl Normal file
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@ -0,0 +1,83 @@
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Xiaopeng Fu, fuxiaopeng2222@163.com
//
// 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 implied warranties, including, but not limited to, the implied
// 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*/
__kernel void kmeansComputeDistance(
int label_step, int K,
__global float *src,
__global int *labels, int dims, int rows,
__global float *centers,
__global float *dists)
{
int gid = get_global_id(1);
float dist, euDist, min;
int minCentroid;
if(gid >= rows)
return;
for(int i = 0 ;i < K; i++)
{
euDist = 0;
for(int j = 0; j < dims; j++)
{
dist = (src[j + gid * dims]
- centers[j + i * dims]);
euDist += dist * dist;
}
if(i == 0)
{
min = euDist;
minCentroid = 0;
} else if(euDist < min)
{
min = euDist;
minCentroid = i;
}
}
dists[gid] = min;
labels[label_step * gid] = minCentroid;
}

162
modules/ocl/test/test_kmeans.cpp Normal file
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@ -0,0 +1,162 @@
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Erping Pang, pang_er_ping@163.com
// Xiaopeng Fu, fuxiaopeng2222@163.com
//
// 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 oclMaterials 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 implied warranties, including, but not limited to, the implied
// 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"
#ifdef HAVE_OPENCL
using namespace cvtest;
using namespace testing;
using namespace std;
using namespace cv;
#define OCL_KMEANS_USE_INITIAL_LABELS 1
#define OCL_KMEANS_PP_CENTERS 2
PARAM_TEST_CASE(Kmeans, int, int, int)
{
int type;
int K;
int flags;
cv::Mat src ;
ocl::oclMat d_src, d_dists;
Mat labels, centers;
ocl::oclMat d_labels, d_centers;
cv::RNG rng ;
virtual void SetUp(){
K = GET_PARAM(0);
type = GET_PARAM(1);
flags = GET_PARAM(2);
rng = TS::ptr()->get_rng();
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src.create(size, type);
int row_idx = 0;
const int max_neighbour = MHEIGHT / K - 1;
CV_Assert(K <= MWIDTH);
for(int i = 0; i < K; i++ )
{
Mat center_row_header = src.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
center_row_header.at<float>(0, i*nchannel+j) = 50000.0;
for(int j = 0; (j < max_neighbour) ||
(i == K-1 && j < max_neighbour + MHEIGHT%K); j ++)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), -200, 200, false);
cur_row_header += tmpmat;
}
row_idx += 1 + max_neighbour;
}
}
};
TEST_P(Kmeans, Mat){
if(flags & KMEANS_USE_INITIAL_LABELS)
{
// inital a given labels
labels.create(src.rows, 1, CV_32S);
int *label = labels.ptr<int>();
for(int i = 0; i < src.rows; i++)
label[i] = rng.uniform(0, K);
d_labels.upload(labels);
}
d_src.upload(src);
for(int j = 0; j < LOOP_TIMES; j++)
{
kmeans(src, K, labels,
TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
1, flags, centers);
ocl::kmeans(d_src, K, d_labels,
TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
1, flags, d_centers);
Mat dd_labels(d_labels);
Mat dd_centers(d_centers);
if(flags & KMEANS_USE_INITIAL_LABELS)
{
EXPECT_MAT_NEAR(labels, dd_labels, 0);
EXPECT_MAT_NEAR(centers, dd_centers, 1e-3);
}
else
{
int row_idx = 0;
for(int i = 0; i < K; i++)
{
// verify lables with ground truth resutls
int label = labels.at<int>(row_idx);
int header_label = dd_labels.at<int>(row_idx);
for(int j = 0; (j < MHEIGHT/K)||(i == K-1 && j < MHEIGHT/K+MHEIGHT%K); j++)
{
ASSERT_NEAR(labels.at<int>(row_idx+j), label, 0);
ASSERT_NEAR(dd_labels.at<int>(row_idx+j), header_label, 0);
}
// verify centers
float *center = centers.ptr<float>(label);
float *header_center = dd_centers.ptr<float>(header_label);
for(int t = 0; t < centers.cols; t++)
ASSERT_NEAR(center[t], header_center[t], 1e-3);
row_idx += MHEIGHT/K;
}
}
}
}
INSTANTIATE_TEST_CASE_P(OCL_ML, Kmeans, Combine(
Values(3, 5, 8),
Values(CV_32FC1, CV_32FC2, CV_32FC4),
Values(OCL_KMEANS_USE_INITIAL_LABELS/*, OCL_KMEANS_PP_CENTERS*/)));
#endif