/*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, Advanced Micro Devices, Inc., all rights reserved. // Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // @Authors // Jin Ma, jin@multicorewareinc.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 materials 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" #include "opencl_kernels.hpp" using namespace cv; using namespace cv::ocl; KNearestNeighbour::KNearestNeighbour() { clear(); } KNearestNeighbour::~KNearestNeighbour() { clear(); samples_ocl.release(); } void KNearestNeighbour::clear() { CvKNearest::clear(); } bool KNearestNeighbour::train(const Mat& trainData, Mat& labels, Mat& sampleIdx, bool isRegression, int _max_k, bool updateBase) { max_k = _max_k; bool cv_knn_train = CvKNearest::train(trainData, labels, sampleIdx, isRegression, max_k, updateBase); CvVectors* s = CvKNearest::samples; cv::Mat samples_mat(s->count, CvKNearest::var_count + 1, s->type); float* s1 = (float*)(s + 1); for(int i = 0; i < s->count; i++) { float* t1 = s->data.fl[i]; for(int j = 0; j < CvKNearest::var_count; j++) { Point pos(j, i); samples_mat.at<float>(pos) = t1[j]; } Point pos_label(CvKNearest::var_count, i); samples_mat.at<float>(pos_label) = s1[i]; } samples_ocl = samples_mat; return cv_knn_train; } void KNearestNeighbour::find_nearest(const oclMat& lsamples, int k, oclMat& lables) { CV_Assert(!samples_ocl.empty()); lables.create(lsamples.rows, 1, CV_32FC1); CV_Assert(lsamples.cols == CvKNearest::var_count); CV_Assert(lsamples.type() == CV_32FC1); CV_Assert(k >= 1 && k <= max_k); int k1 = KNearest::get_sample_count(); k1 = MIN( k1, k ); String kernel_name = "knn_find_nearest"; cl_ulong local_memory_size = (cl_ulong)Context::getContext()->getDeviceInfo().localMemorySize; int nThreads = local_memory_size / (2 * k * 4); if(nThreads >= 256) nThreads = 256; int smem_size = nThreads * k * 4 * 2; size_t local_thread[] = {1, (size_t)nThreads, 1}; size_t global_thread[] = {1, (size_t)lsamples.rows, 1}; char build_option[50]; if(!Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE)) { sprintf(build_option, " "); }else sprintf(build_option, "-D DOUBLE_SUPPORT"); std::vector< std::pair<size_t, const void*> > args; int samples_ocl_step = samples_ocl.step/samples_ocl.elemSize(); int samples_step = lsamples.step/lsamples.elemSize(); int lables_step = lables.step/lables.elemSize(); int _regression = 0; if(CvKNearest::regression) _regression = 1; args.push_back(make_pair(sizeof(cl_mem), (void*)&lsamples.data)); args.push_back(make_pair(sizeof(cl_int), (void*)&lsamples.rows)); args.push_back(make_pair(sizeof(cl_int), (void*)&lsamples.cols)); args.push_back(make_pair(sizeof(cl_int), (void*)&samples_step)); args.push_back(make_pair(sizeof(cl_int), (void*)&k)); args.push_back(make_pair(sizeof(cl_mem), (void*)&samples_ocl.data)); args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.rows)); args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl_step)); args.push_back(make_pair(sizeof(cl_mem), (void*)&lables.data)); args.push_back(make_pair(sizeof(cl_int), (void*)&lables_step)); args.push_back(make_pair(sizeof(cl_int), (void*)&_regression)); args.push_back(make_pair(sizeof(cl_int), (void*)&k1)); args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.cols)); args.push_back(make_pair(sizeof(cl_int), (void*)&nThreads)); args.push_back(make_pair(smem_size, (void*)NULL)); openCLExecuteKernel(Context::getContext(), &knearest, kernel_name, global_thread, local_thread, args, -1, -1, build_option); }