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d6c699c014
stereo module in opencv_contrib is renamed to xstereo
166 lines
5.3 KiB
C++
166 lines
5.3 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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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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// (3-clause BSD License)
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//
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// Copyright (C) 2015-2016, OpenCV Foundation, 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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// * Redistributions 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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// * Redistributions 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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// * Neither the names of the copyright holders nor the names of the contributors
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// may be used to endorse or promote products derived from this software
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// 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 copyright holders 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 "perf_precomp.hpp"
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#include <algorithm>
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#include <functional>
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namespace opencv_test
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{
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using namespace perf;
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CV_ENUM(Method, RANSAC, LMEDS)
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typedef tuple<int, double, Method, size_t> AffineParams;
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typedef TestBaseWithParam<AffineParams> EstimateAffine;
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#define ESTIMATE_PARAMS Combine(Values(100000, 5000, 100), Values(0.99, 0.95, 0.9), Method::all(), Values(10, 0))
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static float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); }
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static Mat rngPartialAffMat() {
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double theta = rngIn(0, (float)CV_PI*2.f);
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double scale = rngIn(0, 3);
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double tx = rngIn(-2, 2);
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double ty = rngIn(-2, 2);
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double aff[2*3] = { std::cos(theta) * scale, -std::sin(theta) * scale, tx,
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std::sin(theta) * scale, std::cos(theta) * scale, ty };
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return Mat(2, 3, CV_64F, aff).clone();
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}
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PERF_TEST_P( EstimateAffine, EstimateAffine2D, ESTIMATE_PARAMS )
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{
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AffineParams params = GetParam();
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const int n = get<0>(params);
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const double confidence = get<1>(params);
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const int method = get<2>(params);
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const size_t refining = get<3>(params);
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Mat aff(2, 3, CV_64F);
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cv::randu(aff, -2., 2.);
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// LMEDS can't handle more than 50% outliers (by design)
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int m;
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if (method == LMEDS)
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m = 3*n/5;
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else
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m = 2*n/5;
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const float shift_outl = 15.f;
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const float noise_level = 20.f;
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Mat fpts(1, n, CV_32FC2);
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Mat tpts(1, n, CV_32FC2);
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randu(fpts, 0., 100.);
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transform(fpts, tpts, aff);
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/* adding noise to some points */
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Mat outliers = tpts.colRange(m, n);
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outliers.reshape(1) += shift_outl;
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Mat noise (outliers.size(), outliers.type());
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randu(noise, 0., noise_level);
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outliers += noise;
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Mat aff_est;
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vector<uchar> inliers (n);
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warmup(inliers, WARMUP_WRITE);
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warmup(fpts, WARMUP_READ);
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warmup(tpts, WARMUP_READ);
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TEST_CYCLE()
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{
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aff_est = estimateAffine2D(fpts, tpts, inliers, method, 3, 2000, confidence, refining);
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}
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// we already have accuracy tests
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P( EstimateAffine, EstimateAffinePartial2D, ESTIMATE_PARAMS )
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{
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AffineParams params = GetParam();
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const int n = get<0>(params);
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const double confidence = get<1>(params);
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const int method = get<2>(params);
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const size_t refining = get<3>(params);
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Mat aff = rngPartialAffMat();
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int m;
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// LMEDS can't handle more than 50% outliers (by design)
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if (method == LMEDS)
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m = 3*n/5;
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else
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m = 2*n/5;
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const float shift_outl = 15.f; const float noise_level = 20.f;
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Mat fpts(1, n, CV_32FC2);
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Mat tpts(1, n, CV_32FC2);
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randu(fpts, 0., 100.);
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transform(fpts, tpts, aff);
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/* adding noise*/
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Mat outliers = tpts.colRange(m, n);
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outliers.reshape(1) += shift_outl;
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Mat noise (outliers.size(), outliers.type());
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randu(noise, 0., noise_level);
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outliers += noise;
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Mat aff_est;
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vector<uchar> inliers (n);
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warmup(inliers, WARMUP_WRITE);
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warmup(fpts, WARMUP_READ);
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warmup(tpts, WARMUP_READ);
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TEST_CYCLE()
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{
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aff_est = estimateAffinePartial2D(fpts, tpts, inliers, method, 3, 2000, confidence, refining);
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}
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// we already have accuracy tests
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SANITY_CHECK_NOTHING();
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}
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} // namespace opencv_test
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