mat_cerealisation.hpp

/*
 * superviseddescent: A C++11 implementation of the supervised descent
 *                    optimisation method
 * File: superviseddescent/matcerealisation.hpp
 *
 * Copyright 2015 Patrik Huber
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
#pragma once

#ifndef MATCEREALISATION_HPP_
#define MATCEREALISATION_HPP_

#include "opencv2/core/core.hpp"

namespace cv {

template<class Archive>
void save(Archive& ar, const cv::Mat& mat)
{
    int rows, cols, type;
    bool continuous;

    rows = mat.rows;
    cols = mat.cols;
    type = mat.type();
    continuous = mat.isContinuous();

    ar & rows & cols & type & continuous;

    if (continuous) {
        const int data_size = rows * cols * static_cast<int>(mat.elemSize());
        auto mat_data = cereal::binary_data(mat.ptr(), data_size);
        ar & mat_data;
    }
    else {
        const int row_size = cols * static_cast<int>(mat.elemSize());
        for (int i = 0; i < rows; i++) {
            auto row_data = cereal::binary_data(mat.ptr(i), row_size);
            ar & row_data;
        }
    }
};

template<class Archive>
void load(Archive& ar, cv::Mat& mat)
{
    int rows, cols, type;
    bool continuous;

    ar & rows & cols & type & continuous;

    if (continuous) {
        mat.create(rows, cols, type);
        const int data_size = rows * cols * static_cast<int>(mat.elemSize());
        auto mat_data = cereal::binary_data(mat.ptr(), data_size);
        ar & mat_data;
    }
    else {
        mat.create(rows, cols, type);
        const int row_size = cols * static_cast<int>(mat.elemSize());
        for (int i = 0; i < rows; i++) {
            auto row_data = cereal::binary_data(mat.ptr(i), row_size);
            ar & row_data;
        }
    }
};

}

#endif /* MATCEREALISATION_HPP_ */