Added knearest neighbor of OpenCL version.

It includes the accuracy/performance test and the implementation of KNN.
2026-07-29 23:33:05 +04:00 · 2013-09-22 10:23:54 +08:00
parent 06c33df307
commit 1bfe39f485
4 changed files with 582 additions and 0 deletions
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+///////////////////////////////////////////////////////////////////////////////////////
+//
+//  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
+//    Jin Ma,        jin@multicorewareinc.com
+//    Xiaopeng Fu,   fuxiaopeng2222@163.com
+//    Erping Pang,   pang_er_ping@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 "test_precomp.hpp"
+#ifdef HAVE_OPENCL
+using namespace cv;
+using namespace cv::ocl;
+using namespace cvtest;
+using namespace testing;
+///////K-NEAREST NEIGHBOR//////////////////////////
+static void genTrainData(Mat& trainData, int trainDataRow, int trainDataCol,
+                         Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
+{
+    cv::RNG &rng = TS::ptr()->get_rng();
+    cv::Size size(trainDataCol, trainDataRow);
+    trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
+    if(nClasses != 0)
+    {
+        cv::Size size1(trainDataRow, 1);
+        trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false);
+        trainLabel.convertTo(trainLabel, CV_32FC1);
+    }
+}
+
+PARAM_TEST_CASE(KNN, int, Size, int, bool)
+{
+    int k;
+    int trainDataCol;
+    int testDataRow;
+    int nClass;
+    bool regression;
+    virtual void SetUp()
+    {
+        k = GET_PARAM(0);
+        nClass = GET_PARAM(2);
+        trainDataCol = GET_PARAM(1).width;
+        testDataRow = GET_PARAM(1).height;
+        regression = GET_PARAM(3);
+    }
+};
+
+TEST_P(KNN, Accuracy)
+{
+    Mat trainData, trainLabels;
+    const int trainDataRow = 500;
+    genTrainData(trainData, trainDataRow, trainDataCol, trainLabels, nClass);
+
+    Mat testData, testLabels;
+    genTrainData(testData, testDataRow, trainDataCol);
+
+    KNearestNeighbour knn_ocl;
+    CvKNearest knn_cpu;
+    Mat best_label_cpu;
+    oclMat best_label_ocl;
+
+    /*ocl k-Nearest_Neighbor start*/
+    oclMat trainData_ocl;
+    trainData_ocl.upload(trainData);
+    Mat simpleIdx;
+    knn_ocl.train(trainData, trainLabels, simpleIdx, regression);
+
+    oclMat testdata;
+    testdata.upload(testData);
+    knn_ocl.find_nearest(testdata, k, best_label_ocl);
+    /*ocl k-Nearest_Neighbor end*/
+
+    /*cpu k-Nearest_Neighbor start*/
+    knn_cpu.train(trainData, trainLabels, simpleIdx, regression);
+    knn_cpu.find_nearest(testData, k, &best_label_cpu);
+    /*cpu k-Nearest_Neighbor end*/
+    if(regression)
+    {
+        EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5);
+    }
+    else
+    {
+        EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0);
+    }
+}
+INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
+    Values(4, 3), Values(false, true)));
+#endif // HAVE_OPENCL