/******************************************************************************
 * Copyright (c) 2017, Bradley J Chambers (brad.chambers@gmail.com)
 *
 * 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 Hobu, Inc. or Flaxen Geo Consulting 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 OWNER 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.
 ****************************************************************************/

// PDAL implementation of the Extended Local Minimum (ELM) method as published
// in Z. Chen, B. Devereux, B. Gao, and G. Amable, “Upward-fusion urban DTM
// generating method using airborne Lidar data,” ISPRS J. Photogramm. Remote
// Sens., vol. 72, pp. 121–130, 2012.

#include "ELMFilter.hpp"

#include <map>
#include <string>

namespace pdal
{

static StaticPluginInfo const s_info
{
    "filters.elm",
    "Marks low points as noise.",
    "http://pdal.io/stages/filters.elm.html"
};

CREATE_STATIC_STAGE(ELMFilter, s_info)

std::string ELMFilter::getName() const
{
    return s_info.name;
}

void ELMFilter::addArgs(ProgramArgs& args)
{
    args.add("cell", "Cell size", m_cell, 10.0);
    args.add("class", "Class to use for noise points", m_class, ClassLabel::LowPoint);
    args.add("threshold", "Threshold value", m_threshold, 1.0);
}

void ELMFilter::addDimensions(PointLayoutPtr layout)
{
    layout->registerDim(Dimension::Id::Classification);
}

void ELMFilter::filter(PointView& view)
{
    BOX2D bounds;
    view.calculateBounds(bounds);

    size_t cols =
        static_cast<size_t>(((bounds.maxx - bounds.minx) / m_cell) + 1);
    size_t rows =
        static_cast<size_t>(((bounds.maxy - bounds.miny) / m_cell) + 1);

    // Make an initial pass through the input PointView to index elevation
    // values and PointIds by row and column.
    std::map<uint32_t, std::multimap<double, PointId>> hash;
    for (PointId id = 0; id < view.size(); ++id)
    {
        double x = view.getFieldAs<double>(Dimension::Id::X, id);
        double y = view.getFieldAs<double>(Dimension::Id::Y, id);
        double z = view.getFieldAs<double>(Dimension::Id::Z, id);

        size_t c = static_cast<size_t>(std::floor(x - bounds.minx) / m_cell);
        size_t r = static_cast<size_t>(std::floor(y - bounds.miny) / m_cell);

        hash[c * rows + r].emplace(z, id);
    }

    // Count the number of points we classify as noise.
    point_count_t num(0);

    // Make a second pass through the now rasterized PointView to compute the
    // extended local minimum.
    for (size_t c = 0; c < cols; ++c)
    {
        for (size_t r = 0; r < rows; ++r)
        {
            std::multimap<double, PointId> ids(hash[c * rows + r]);

            if (ids.size() <= 1)
                continue;

            for (auto it = ids.begin(); it != std::prev(ids.end()); ++it)
            {
                // Where the current value is sufficiently close to the next, we
                // consider that this is not a low outlier and break the current
                // loop.
                if (std::fabs(it->first - std::next(it)->first) < m_threshold)
                    break;

                // Otherwise this point is classified as noise, and we proceed
                // to the next lowest value.
                view.setField(Dimension::Id::Classification, it->second,
                              m_class);
                ++num;
            }
        }
    }

    log()->get(LogLevel::Info)
        << "Classified " << num
        << " points as noise by Extended Local Minimum (ELM).\n";
}

} // namespace pdal
