homography_matrix.cc

// Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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// Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)

#include <iostream>
#include <fstream>

#include "colmap/base/camera.h"
#include "colmap/estimators/homography_matrix.h"
#include "colmap/optim/loransac.h"

using namespace colmap;

#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/eigen.h>

namespace py = pybind11;

py::dict homography_matrix_estimation(
        const std::vector<Eigen::Vector2d> points2D1,
        const std::vector<Eigen::Vector2d> points2D2,
        const double max_error_px,
        const double min_inlier_ratio,
        const size_t min_num_trials,
        const size_t max_num_trials,
        const double confidence
) {
    SetPRNGSeed(0);

    // Check that both vectors have the same size.
    assert(points2D1.size() == points2D2.size());

    // Failure output dictionary.
    py::dict failure_dict;
    failure_dict["success"] = false;

    // Homography matrix estimation parameters.
    RANSACOptions ransac_options;

    // Maximum error for a sample to be considered as an inlier. Note that
    // the residual of an estimator corresponds to a squared error.
    ransac_options.max_error = max_error_px;

    // A priori assumed minimum inlier ratio, which determines the maximum number
    // of iterations. Only applies if smaller than `max_num_trials`.
    ransac_options.min_inlier_ratio = min_inlier_ratio;

    // Number of random trials to estimate model from random subset.
    ransac_options.min_num_trials = min_num_trials;
    ransac_options.max_num_trials = max_num_trials;

    // Abort the iteration if minimum probability that one sample is free from
    // outliers is reached.
    ransac_options.confidence = confidence;

    LORANSAC<
        HomographyMatrixEstimator,
        HomographyMatrixEstimator
    > ransac(ransac_options);

    // Homography matrix estimation.
    const auto report = ransac.Estimate(points2D1, points2D2);

    if (!report.success) {
        return failure_dict;
    }

    // Recover data from report.
    const Eigen::Matrix3d H = report.model;
    const size_t num_inliers = report.support.num_inliers;
    const auto inlier_mask = report.inlier_mask;

    // Convert vector<char> to vector<int>.
    std::vector<bool> inliers;
    for (auto it : inlier_mask) {
        if (it) {
            inliers.push_back(true);
        } else {
            inliers.push_back(false);
        }
    }

    // Success output dictionary.
    py::dict success_dict;
    success_dict["success"] = true;
    success_dict["H"] = H;
    success_dict["num_inliers"] = num_inliers;
    success_dict["inliers"] = inliers;

    return success_dict;
}