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// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
// its contributors may 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 COPYRIGHT HOLDERS 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.
//
// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#define TEST_NAME "optim/ransac"
#include "util/testing.h"
#include <Eigen/Core>
#include <Eigen/Geometry>
#include "base/pose.h"
#include "base/similarity_transform.h"
#include "estimators/similarity_transform.h"
#include "optim/ransac.h"
#include "util/random.h"
using namespace colmap;
BOOST_AUTO_TEST_CASE(TestOptions) {
RANSACOptions options;
BOOST_CHECK_EQUAL(options.max_error, 0);
BOOST_CHECK_EQUAL(options.min_inlier_ratio, 0.1);
BOOST_CHECK_EQUAL(options.confidence, 0.99);
BOOST_CHECK_EQUAL(options.min_num_trials, 0);
BOOST_CHECK_EQUAL(options.max_num_trials, std::numeric_limits<size_t>::max());
}
BOOST_AUTO_TEST_CASE(TestReport) {
RANSAC<SimilarityTransformEstimator<3>>::Report report;
BOOST_CHECK_EQUAL(report.success, false);
BOOST_CHECK_EQUAL(report.num_trials, 0);
BOOST_CHECK_EQUAL(report.support.num_inliers, 0);
BOOST_CHECK_EQUAL(report.support.residual_sum,
std::numeric_limits<double>::max());
BOOST_CHECK_EQUAL(report.inlier_mask.size(), 0);
}
BOOST_AUTO_TEST_CASE(TestNumTrials) {
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
1, 100, 0.99, 1.0),
4605168);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
10, 100, 0.99, 1.0),
4603);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
10, 100, 0.999, 1.0),
6905);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
10, 100, 0.999, 2.0),
13809);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
100, 100, 0.99, 1.0),
1);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
100, 100, 0.999, 1.0),
1);
BOOST_CHECK_EQUAL(RANSAC<SimilarityTransformEstimator<3>>::ComputeNumTrials(
100, 100, 0, 1.0),
1);
}
BOOST_AUTO_TEST_CASE(TestSimilarityTransform) {
SetPRNGSeed(0);
const size_t num_samples = 1000;
const size_t num_outliers = 400;
// Create some arbitrary transformation.
const SimilarityTransform3 orig_tform(2, ComposeIdentityQuaternion(),
Eigen::Vector3d(100, 10, 10));
// Generate exact data.
std::vector<Eigen::Vector3d> src;
std::vector<Eigen::Vector3d> dst;
for (size_t i = 0; i < num_samples; ++i) {
src.emplace_back(i, std::sqrt(i) + 2, std::sqrt(2 * i + 2));
dst.push_back(src.back());
orig_tform.TransformPoint(&dst.back());
}
// Add some faulty data.
for (size_t i = 0; i < num_outliers; ++i) {
dst[i] = Eigen::Vector3d(RandomReal(-3000.0, -2000.0),
RandomReal(-4000.0, -3000.0),
RandomReal(-5000.0, -4000.0));
}
// Robustly estimate transformation using RANSAC.
RANSACOptions options;
options.max_error = 10;
RANSAC<SimilarityTransformEstimator<3>> ransac(options);
const auto report = ransac.Estimate(src, dst);
BOOST_CHECK_EQUAL(report.success, true);
BOOST_CHECK_GT(report.num_trials, 0);
// Make sure outliers were detected correctly.
BOOST_CHECK_EQUAL(report.support.num_inliers, num_samples - num_outliers);
for (size_t i = 0; i < num_samples; ++i) {
if (i < num_outliers) {
BOOST_CHECK(!report.inlier_mask[i]);
} else {
BOOST_CHECK(report.inlier_mask[i]);
}
}
// Make sure original transformation is estimated correctly.
const double matrix_diff =
(orig_tform.Matrix().topLeftCorner<3, 4>() - report.model).norm();
BOOST_CHECK(std::abs(matrix_diff) < 1e-6);
}
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