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267 lines
8.5 KiB
267 lines
8.5 KiB
/*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) 2010-2012, Multicoreware, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Yao Wang, bitwangyaoyao@gmail.com |
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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 "opencv2/core/core.hpp" |
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#include "opencv2/objdetect/objdetect.hpp" |
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using namespace cv; |
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using namespace testing; |
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#ifdef HAVE_OPENCL |
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extern string workdir; |
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///////////////////// HOG ///////////////////////////// |
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PARAM_TEST_CASE(HOG, Size, int) |
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{ |
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Size winSize; |
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int type; |
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Mat img_rgb; |
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virtual void SetUp() |
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{ |
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winSize = GET_PARAM(0); |
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type = GET_PARAM(1); |
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img_rgb = readImage("gpu/hog/road.png"); |
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ASSERT_FALSE(img_rgb.empty()); |
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} |
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}; |
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TEST_P(HOG, GetDescriptors) |
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{ |
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// Convert image |
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Mat img; |
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switch (type) |
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{ |
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case CV_8UC1: |
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cvtColor(img_rgb, img, CV_BGR2GRAY); |
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break; |
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case CV_8UC4: |
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default: |
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cvtColor(img_rgb, img, CV_BGR2BGRA); |
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break; |
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} |
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ocl::oclMat d_img(img); |
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// HOGs |
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ocl::HOGDescriptor ocl_hog; |
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ocl_hog.gamma_correction = true; |
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HOGDescriptor hog; |
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hog.gammaCorrection = true; |
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// Compute descriptor |
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ocl::oclMat d_descriptors; |
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ocl_hog.getDescriptors(d_img, ocl_hog.win_size, d_descriptors, ocl_hog.DESCR_FORMAT_COL_BY_COL); |
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Mat down_descriptors; |
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d_descriptors.download(down_descriptors); |
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down_descriptors = down_descriptors.reshape(0, down_descriptors.cols * down_descriptors.rows); |
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hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
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std::vector<float> descriptors; |
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switch (type) |
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{ |
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case CV_8UC1: |
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hog.compute(img, descriptors, ocl_hog.win_size); |
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break; |
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case CV_8UC4: |
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default: |
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hog.compute(img_rgb, descriptors, ocl_hog.win_size); |
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break; |
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} |
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Mat cpu_descriptors(descriptors); |
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EXPECT_MAT_SIMILAR(down_descriptors, cpu_descriptors, 1e-2); |
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} |
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TEST_P(HOG, Detect) |
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{ |
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// Convert image |
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Mat img; |
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switch (type) |
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{ |
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case CV_8UC1: |
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cvtColor(img_rgb, img, CV_BGR2GRAY); |
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break; |
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case CV_8UC4: |
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default: |
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cvtColor(img_rgb, img, CV_BGR2BGRA); |
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break; |
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} |
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ocl::oclMat d_img(img); |
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// HOGs |
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if ((winSize != Size(48, 96)) && (winSize != Size(64, 128))) |
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winSize = Size(64, 128); |
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ocl::HOGDescriptor ocl_hog(winSize); |
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ocl_hog.gamma_correction = true; |
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HOGDescriptor hog; |
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hog.winSize = winSize; |
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hog.gammaCorrection = true; |
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if (winSize.width == 48 && winSize.height == 96) |
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{ |
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// daimler's base |
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ocl_hog.setSVMDetector(hog.getDaimlerPeopleDetector()); |
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hog.setSVMDetector(hog.getDaimlerPeopleDetector()); |
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} |
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else if (winSize.width == 64 && winSize.height == 128) |
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{ |
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ocl_hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
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hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
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} |
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else |
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{ |
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ocl_hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
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hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
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} |
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// OpenCL detection |
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std::vector<Rect> d_found; |
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ocl_hog.detectMultiScale(d_img, d_found, 0, Size(8, 8), Size(0, 0), 1.05, 6); |
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// CPU detection |
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std::vector<Rect> found; |
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switch (type) |
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{ |
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case CV_8UC1: |
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hog.detectMultiScale(img, found, 0, Size(8, 8), Size(0, 0), 1.05, 6); |
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break; |
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case CV_8UC4: |
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default: |
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hog.detectMultiScale(img_rgb, found, 0, Size(8, 8), Size(0, 0), 1.05, 6); |
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break; |
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} |
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EXPECT_LT(checkRectSimilarity(img.size(), found, d_found), 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine( |
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testing::Values(Size(64, 128), Size(48, 96)), |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)))); |
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///////////////////////////// Haar ////////////////////////////// |
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IMPLEMENT_PARAM_CLASS(CascadeName, std::string); |
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CascadeName cascade_frontalface_alt(std::string("haarcascade_frontalface_alt.xml")); |
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CascadeName cascade_frontalface_alt2(std::string("haarcascade_frontalface_alt2.xml")); |
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struct getRect |
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{ |
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Rect operator ()(const CvAvgComp &e) const |
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{ |
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return e.rect; |
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} |
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}; |
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PARAM_TEST_CASE(Haar, int, CascadeName) |
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{ |
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ocl::OclCascadeClassifier cascade, nestedCascade; |
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CascadeClassifier cpucascade, cpunestedCascade; |
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int flags; |
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std::string cascadeName; |
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vector<Rect> faces, oclfaces; |
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Mat img; |
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ocl::oclMat d_img; |
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virtual void SetUp() |
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{ |
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flags = GET_PARAM(0); |
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cascadeName = (string(cvtest::TS::ptr()->get_data_path()) + "cv/cascadeandhog/cascades/").append(GET_PARAM(1)); |
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ASSERT_TRUE(cascade.load( cascadeName )); |
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ASSERT_TRUE(cpucascade.load(cascadeName)); |
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img = readImage("cv/shared/lena.png", IMREAD_GRAYSCALE); |
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ASSERT_FALSE(img.empty()); |
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equalizeHist(img, img); |
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d_img.upload(img); |
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} |
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}; |
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TEST_P(Haar, FaceDetect) |
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{ |
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MemStorage storage(cvCreateMemStorage(0)); |
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CvSeq *_objects; |
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_objects = cascade.oclHaarDetectObjects(d_img, storage, 1.1, 3, |
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flags, Size(30, 30), Size(0, 0)); |
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vector<CvAvgComp> vecAvgComp; |
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Seq<CvAvgComp>(_objects).copyTo(vecAvgComp); |
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oclfaces.resize(vecAvgComp.size()); |
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std::transform(vecAvgComp.begin(), vecAvgComp.end(), oclfaces.begin(), getRect()); |
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cpucascade.detectMultiScale(img, faces, 1.1, 3, |
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flags, |
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Size(30, 30), Size(0, 0)); |
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EXPECT_LT(checkRectSimilarity(img.size(), faces, oclfaces), 1.0); |
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} |
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TEST_P(Haar, FaceDetectUseBuf) |
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{ |
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ocl::OclCascadeClassifierBuf cascadebuf; |
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if(!cascadebuf.load(cascadeName)) |
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{ |
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std::cout << "ERROR: Could not load classifier cascade for FaceDetectUseBuf!" << std::endl; |
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return; |
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} |
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cascadebuf.detectMultiScale(d_img, oclfaces, 1.1, 3, |
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flags, |
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Size(30, 30), Size(0, 0)); |
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cpucascade.detectMultiScale(img, faces, 1.1, 3, |
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flags, |
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Size(30, 30), Size(0, 0)); |
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// intentionally run ocl facedetect again and check if it still works after the first run |
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cascadebuf.detectMultiScale(d_img, oclfaces, 1.1, 3, |
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flags, |
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Size(30, 30)); |
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cascadebuf.release(); |
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EXPECT_LT(checkRectSimilarity(img.size(), faces, oclfaces), 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, Haar, |
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Combine(Values(CV_HAAR_SCALE_IMAGE, 0), |
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Values(cascade_frontalface_alt/*, cascade_frontalface_alt2*/))); |
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#endif //HAVE_OPENCL
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