1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. 14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved. 15 // Third party copyrights are property of their respective owners. 16 // 17 // Redistribution and use in source and binary forms, with or without modification, 18 // are permitted provided that the following conditions are met: 19 // 20 // * Redistribution's of source code must retain the above copyright notice, 21 // this list of conditions and the following disclaimer. 22 // 23 // * Redistribution's in binary form must reproduce the above copyright notice, 24 // this list of conditions and the following disclaimer in the documentation 25 // and/or other materials provided with the distribution. 26 // 27 // * The name of the copyright holders may not be used to endorse or promote products 28 // derived from this software without specific prior written permission. 29 // 30 // This software is provided by the copyright holders and contributors "as is" and 31 // any express or implied warranties, including, but not limited to, the implied 32 // warranties of merchantability and fitness for a particular purpose are disclaimed. 33 // In no event shall the Intel Corporation or contributors be liable for any direct, 34 // indirect, incidental, special, exemplary, or consequential damages 35 // (including, but not limited to, procurement of substitute goods or services; 36 // loss of use, data, or profits; or business interruption) however caused 37 // and on any theory of liability, whether in contract, strict liability, 38 // or tort (including negligence or otherwise) arising in any way out of 39 // the use of this software, even if advised of the possibility of such damage. 40 // 41 //M*/ 42 43 #include "test_precomp.hpp" 44 45 46 TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_) 47 : 48 NCVTestProvider(testName_), 49 cascadeName(cascadeName_) 50 { 51 } 52 53 54 bool TestHaarCascadeLoader::toString(std::ofstream &strOut) 55 { 56 strOut << "cascadeName=" << cascadeName << std::endl; 57 return true; 58 } 59 60 61 bool TestHaarCascadeLoader::init() 62 { 63 return true; 64 } 65 66 67 bool TestHaarCascadeLoader::process() 68 { 69 NCVStatus ncvStat; 70 bool rcode = false; 71 72 Ncv32u numStages, numNodes, numFeatures; 73 Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0; 74 75 ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures); 76 ncvAssertReturn(ncvStat == NCV_SUCCESS, false); 77 78 NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages); 79 ncvAssertReturn(h_HaarStages.isMemAllocated(), false); 80 NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes); 81 ncvAssertReturn(h_HaarNodes.isMemAllocated(), false); 82 NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures); 83 ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false); 84 85 NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages); 86 ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false); 87 NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes); 88 ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false); 89 NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures); 90 ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false); 91 92 HaarClassifierCascadeDescriptor haar; 93 HaarClassifierCascadeDescriptor haar_2; 94 95 NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting()); 96 NCV_SKIP_COND_BEGIN 97 98 const std::string testNvbinName = cv::tempfile("test.nvbin"); 99 ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures); 100 ncvAssertReturn(ncvStat == NCV_SUCCESS, false); 101 102 ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures); 103 ncvAssertReturn(ncvStat == NCV_SUCCESS, false); 104 105 ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2); 106 ncvAssertReturn(ncvStat == NCV_SUCCESS, false); 107 108 ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2); 109 ncvAssertReturn(ncvStat == NCV_SUCCESS, false); 110 111 NCV_SKIP_COND_END 112 113 //bit-to-bit check 114 bool bLoopVirgin = true; 115 116 NCV_SKIP_COND_BEGIN 117 118 if ( 119 numStages_2 != numStages || 120 numNodes_2 != numNodes || 121 numFeatures_2 != numFeatures || 122 haar.NumStages != haar_2.NumStages || 123 haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes || 124 haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes || 125 haar.NumFeatures != haar_2.NumFeatures || 126 haar.ClassifierSize.width != haar_2.ClassifierSize.width || 127 haar.ClassifierSize.height != haar_2.ClassifierSize.height || 128 haar.bNeedsTiltedII != haar_2.bNeedsTiltedII || 129 haar.bHasStumpsOnly != haar_2.bHasStumpsOnly ) 130 { 131 bLoopVirgin = false; 132 } 133 if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) || 134 memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) || 135 memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) ) 136 { 137 bLoopVirgin = false; 138 } 139 NCV_SKIP_COND_END 140 141 if (bLoopVirgin) 142 { 143 rcode = true; 144 } 145 146 return rcode; 147 } 148 149 150 bool TestHaarCascadeLoader::deinit() 151 { 152 return true; 153 } 154
