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#include "open3d/pipelines/registration/Feature.h"

#include <Eigen/Dense>

#include "open3d/geometry/KDTreeFlann.h"
#include "open3d/geometry/PointCloud.h"
#include "open3d/utility/Logging.h"
#include "open3d/utility/Parallel.h"

namespace open3d {
namespace pipelines {
namespace registration {

static Eigen::Vector4d ComputePairFeatures(const Eigen::Vector3d &p1,
                                           const Eigen::Vector3d &n1,
                                           const Eigen::Vector3d &p2,
                                           const Eigen::Vector3d &n2) {
    Eigen::Vector4d result;
    Eigen::Vector3d dp2p1 = p2 - p1;
    result(3) = dp2p1.norm();
    if (result(3) == 0.0) {
        return Eigen::Vector4d::Zero();
    }
    auto n1_copy = n1;
    auto n2_copy = n2;
    double angle1 = n1_copy.dot(dp2p1) / result(3);
    double angle2 = n2_copy.dot(dp2p1) / result(3);
    if (acos(fabs(angle1)) > acos(fabs(angle2))) {
        n1_copy = n2;
        n2_copy = n1;
        dp2p1 *= -1.0;
        result(2) = -angle2;
    } else {
        result(2) = angle1;
    }
    auto v = dp2p1.cross(n1_copy);
    double v_norm = v.norm();
    if (v_norm == 0.0) {
        return Eigen::Vector4d::Zero();
    }
    v /= v_norm;
    auto w = n1_copy.cross(v);
    result(1) = v.dot(n2_copy);
    result(0) = atan2(w.dot(n2_copy), n1_copy.dot(n2_copy));
    return result;
}

static std::shared_ptr<Feature> ComputeSPFHFeature(
        const geometry::PointCloud &input,
        const geometry::KDTreeFlann &kdtree,
        const geometry::KDTreeSearchParam &search_param) {
    auto feature = std::make_shared<Feature>();
    feature->Resize(33, (int)input.points_.size());
#pragma omp parallel for schedule(static) \
        num_threads(utility::EstimateMaxThreads())
    for (int i = 0; i < (int)input.points_.size(); i++) {
        const auto &point = input.points_[i];
        const auto &normal = input.normals_[i];
        std::vector<int> indices;
        std::vector<double> distance2;
        if (kdtree.Search(point, search_param, indices, distance2) > 1) {
            // only compute SPFH feature when a point has neighbors
            double hist_incr = 100.0 / (double)(indices.size() - 1);
            for (size_t k = 1; k < indices.size(); k++) {
                // skip the point itself, compute histogram
                auto pf = ComputePairFeatures(point, normal,
                                              input.points_[indices[k]],
                                              input.normals_[indices[k]]);
                int h_index = (int)(floor(11 * (pf(0) + M_PI) / (2.0 * M_PI)));
                if (h_index < 0) h_index = 0;
                if (h_index >= 11) h_index = 10;
                feature->data_(h_index, i) += hist_incr;
                h_index = (int)(floor(11 * (pf(1) + 1.0) * 0.5));
                if (h_index < 0) h_index = 0;
                if (h_index >= 11) h_index = 10;
                feature->data_(h_index + 11, i) += hist_incr;
                h_index = (int)(floor(11 * (pf(2) + 1.0) * 0.5));
                if (h_index < 0) h_index = 0;
                if (h_index >= 11) h_index = 10;
                feature->data_(h_index + 22, i) += hist_incr;
            }
        }
    }
    return feature;
}

std::shared_ptr<Feature> ComputeFPFHFeature(
        const geometry::PointCloud &input,
        const geometry::KDTreeSearchParam
                &search_param /* = geometry::KDTreeSearchParamKNN()*/) {
    auto feature = std::make_shared<Feature>();
    feature->Resize(33, (int)input.points_.size());
    if (!input.HasNormals()) {
        utility::LogError(
                "[ComputeFPFHFeature] Failed because input point cloud has no "
                "normal.");
    }
    geometry::KDTreeFlann kdtree(input);
    auto spfh = ComputeSPFHFeature(input, kdtree, search_param);
    if (spfh == nullptr) {
        utility::LogError("Internal error: SPFH feature is nullptr.");
    }
#pragma omp parallel for schedule(static) \
        num_threads(utility::EstimateMaxThreads())
    for (int i = 0; i < (int)input.points_.size(); i++) {
        const auto &point = input.points_[i];
        std::vector<int> indices;
        std::vector<double> distance2;
        if (kdtree.Search(point, search_param, indices, distance2) > 1) {
            double sum[3] = {0.0, 0.0, 0.0};
            for (size_t k = 1; k < indices.size(); k++) {
                // skip the point itself
                double dist = distance2[k];
                if (dist == 0.0) continue;
                for (int j = 0; j < 33; j++) {
                    double val = spfh->data_(j, indices[k]) / dist;
                    sum[j / 11] += val;
                    feature->data_(j, i) += val;
                }
            }
            for (int j = 0; j < 3; j++)
                if (sum[j] != 0.0) sum[j] = 100.0 / sum[j];
            for (int j = 0; j < 33; j++) {
                feature->data_(j, i) *= sum[j / 11];
                // The commented line is the fpfh function in the paper.
                // But according to PCL implementation, it is skipped.
                // Our initial test shows that the full fpfh function in the
                // paper seems to be better than PCL implementation. Further
                // test required.
                feature->data_(j, i) += spfh->data_(j, i);
            }
        }
    }
    return feature;
}

}  // namespace registration
}  // namespace pipelines
}  // namespace open3d
