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/*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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage 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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// 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 the copyright holders 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 <test_precomp.hpp>
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#ifdef HAVE_CUDA
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using cv::gpu::GpuMat;
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TEST(SoftCascade, readCascade)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/icf-template.xml";
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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}
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TEST(SoftCascade, detect)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
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+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
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ASSERT_FALSE(coloredCpu.empty());
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GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
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rois.setTo(0);
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GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
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sub.setTo(cv::Scalar::all(1));
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cascade.detectMultiScale(colored, rois, objectBoxes);
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}
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class SCSpecific : public ::testing::TestWithParam<std::tr1::tuple<std::string, int> > {
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};
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namespace {
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std::string itoa(long i)
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{
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static char s[65];
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sprintf(s, "%ld", i);
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return std::string(s);
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}
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}
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TEST_P(SCSpecific, detect)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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std::string path = GET_PARAM(0);
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + path);
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ASSERT_FALSE(coloredCpu.empty());
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GpuMat colored(coloredCpu), objectBoxes(1, 1000, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
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rois.setTo(0);
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GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
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sub.setTo(cv::Scalar::all(1));
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int level = GET_PARAM(1);
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cascade.detectMultiScale(colored, rois, objectBoxes, 1, level);
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cv::Mat dt(objectBoxes);
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typedef cv::gpu::SoftCascade::Detection detection_t;
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detection_t* dts = (detection_t*)dt.data;
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cv::Mat result(coloredCpu);
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std::cout << "Total detections " << (dt.cols / sizeof(detection_t)) << std::endl;
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for(int i = 0; i < (int)(dt.cols / sizeof(detection_t)); ++i)
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{
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detection_t d = dts[i];
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std::cout << "detection: [" << std::setw(4) << d.x << " " << std::setw(4) << d.y
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<< "] [" << std::setw(4) << d.w << " " << std::setw(4) << d.h << "] "
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<< std::setw(12) << d.confidence << std::endl;
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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std::cout << "Result stored in " << "/home/kellan/gpu_res_1_oct_" + itoa(level) << "_"
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+ itoa((dt.cols / sizeof(detection_t))) + ".png" << std::endl;
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cv::imwrite("/home/kellan/gpu_res_1_oct_" + itoa(level) + "_" + itoa((dt.cols / sizeof(detection_t))) + ".png",
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result);
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cv::imshow("res", result);
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cv::waitKey(0);
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}
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INSTANTIATE_TEST_CASE_P(inLevel, SCSpecific,
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testing::Combine(
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 47)
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));
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#endif
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