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/usr/include/boost/gil/image_processing/

/usr/include/boost/gil/image_processing

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filter.hpp	4243	0644	edit dl rm
harris.hpp	3348	0644	edit dl rm
hessian.hpp	2915	0644	edit dl rm
numeric.hpp	10312	0644	edit dl rm
scaling.hpp	4340	0644	edit dl rm
threshold.hpp	16166	0644	edit dl rm

Edit: /usr/include/boost/gil/image_processing/threshold.hpp (16166B)

//
// Copyright 2019 Miral Shah <miralshah2211@gmail.com>
//
// Use, modification and distribution are subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
#ifndef BOOST_GIL_IMAGE_PROCESSING_THRESHOLD_HPP
#define BOOST_GIL_IMAGE_PROCESSING_THRESHOLD_HPP

#include <limits>
#include <array>
#include <type_traits>
#include <cstddef>
#include <algorithm>
#include <vector>
#include <cmath>

#include <boost/assert.hpp>

#include <boost/gil/image.hpp>
#include <boost/gil/extension/numeric/kernel.hpp>
#include <boost/gil/extension/numeric/convolve.hpp>
#include <boost/gil/image_processing/numeric.hpp>

namespace boost { namespace gil {

namespace detail {

template
<
    typename SourceChannelT,
    typename ResultChannelT,
    typename SrcView,
    typename DstView,
    typename Operator
>
void threshold_impl(SrcView const& src_view, DstView const& dst_view, Operator const& threshold_op)
{
    gil_function_requires<ImageViewConcept<SrcView>>();
    gil_function_requires<MutableImageViewConcept<DstView>>();
    static_assert(color_spaces_are_compatible
    <
        typename color_space_type<SrcView>::type,
        typename color_space_type<DstView>::type
    >::value, "Source and destination views must have pixels with the same color space");

//iterate over the image checking each pixel value for the threshold
    for (std::ptrdiff_t y = 0; y < src_view.height(); y++)
    {
        typename SrcView::x_iterator src_it = src_view.row_begin(y);
        typename DstView::x_iterator dst_it = dst_view.row_begin(y);

for (std::ptrdiff_t x = 0; x < src_view.width(); x++)
        {
            static_transform(src_it[x], dst_it[x], threshold_op);
        }
    }
}

} //namespace boost::gil::detail

/// \addtogroup ImageProcessing
/// @{
///
/// \brief Direction of image segmentation.
/// The direction specifies which pixels are considered as corresponding to object
/// and which pixels correspond to background.
enum class threshold_direction
{
    regular, ///< Consider values greater than threshold value
    inverse  ///< Consider values less than or equal to threshold value
};

/// \ingroup ImageProcessing
/// \brief Method of optimal threshold value calculation.
enum class threshold_optimal_value
{
    otsu        ///< \todo TODO
};

/// \ingroup ImageProcessing
/// \brief TODO
enum class threshold_truncate_mode
{
    threshold,  ///< \todo TODO
    zero        ///< \todo TODO
};

enum class threshold_adaptive_method
{
    mean,
    gaussian
};

/// \ingroup ImageProcessing
/// \brief Applies fixed threshold to each pixel of image view.
/// Performs image binarization by thresholding channel value of each
/// pixel of given image view.
/// \param src_view - TODO
/// \param dst_view - TODO
/// \param threshold_value - TODO
/// \param max_value - TODO
/// \param threshold_direction - if regular, values greater than threshold_value are
/// set to max_value else set to 0; if inverse, values greater than threshold_value are
/// set to 0 else set to max_value.
template <typename SrcView, typename DstView>
void threshold_binary(
    SrcView const& src_view,
    DstView const& dst_view,
    typename channel_type<DstView>::type threshold_value,
    typename channel_type<DstView>::type max_value,
    threshold_direction direction = threshold_direction::regular
)
{
    //deciding output channel type and creating functor
    using source_channel_t = typename channel_type<SrcView>::type;
    using result_channel_t = typename channel_type<DstView>::type;

if (direction == threshold_direction::regular)
    {
        detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
            [threshold_value, max_value](source_channel_t px) -> result_channel_t {
                return px > threshold_value ? max_value : 0;
            });
    }
    else
    {
        detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
            [threshold_value, max_value](source_channel_t px) -> result_channel_t {
                return px > threshold_value ? 0 : max_value;
            });
    }
}

/// \ingroup ImageProcessing
/// \brief Applies fixed threshold to each pixel of image view.
/// Performs image binarization by thresholding channel value of each
/// pixel of given image view.
/// This variant of threshold_binary automatically deduces maximum value for each channel
/// of pixel based on channel type.
/// If direction is regular, values greater than threshold_value will be set to maximum
/// numeric limit of channel else 0.
/// If direction is inverse, values greater than threshold_value will be set to 0 else maximum
/// numeric limit of channel.
template <typename SrcView, typename DstView>
void threshold_binary(
    SrcView const& src_view,
    DstView const& dst_view,
    typename channel_type<DstView>::type threshold_value,
    threshold_direction direction = threshold_direction::regular
)
{
    //deciding output channel type and creating functor
    using result_channel_t = typename channel_type<DstView>::type;

result_channel_t max_value = (std::numeric_limits<result_channel_t>::max)();
    threshold_binary(src_view, dst_view, threshold_value, max_value, direction);
}

/// \ingroup ImageProcessing
/// \brief Applies truncating threshold to each pixel of image view.
/// Takes an image view and performs truncating threshold operation on each chennel.
/// If mode is threshold and direction is regular:
/// values greater than threshold_value will be set to threshold_value else no change
/// If mode is threshold and direction is inverse:
/// values less than or equal to threshold_value will be set to threshold_value else no change
/// If mode is zero and direction is regular:
/// values less than or equal to threshold_value will be set to 0 else no change
/// If mode is zero and direction is inverse:
/// values more than threshold_value will be set to 0 else no change
template <typename SrcView, typename DstView>
void threshold_truncate(
    SrcView const& src_view,
    DstView const& dst_view,
    typename channel_type<DstView>::type threshold_value,
    threshold_truncate_mode mode = threshold_truncate_mode::threshold,
    threshold_direction direction = threshold_direction::regular
)
{
    //deciding output channel type and creating functor
    using source_channel_t = typename channel_type<SrcView>::type;
    using result_channel_t = typename channel_type<DstView>::type;

std::function<result_channel_t(source_channel_t)> threshold_logic;

if (mode == threshold_truncate_mode::threshold)
    {
        if (direction == threshold_direction::regular)
        {
            detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
                [threshold_value](source_channel_t px) -> result_channel_t {
                    return px > threshold_value ? threshold_value : px;
                });
        }
        else
        {
            detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
                [threshold_value](source_channel_t px) -> result_channel_t {
                    return px > threshold_value ? px : threshold_value;
                });
        }
    }
    else
    {
        if (direction == threshold_direction::regular)
        {
            detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
                [threshold_value](source_channel_t px) -> result_channel_t {
                    return px > threshold_value ? px : 0;
                });
        }
        else
        {
            detail::threshold_impl<source_channel_t, result_channel_t>(src_view, dst_view,
                [threshold_value](source_channel_t px) -> result_channel_t {
                    return px > threshold_value ? 0 : px;
                });
        }
    }
}

namespace detail{

template <typename SrcView, typename DstView>
void otsu_impl(SrcView const& src_view, DstView const& dst_view, threshold_direction direction)
{
    //deciding output channel type and creating functor
    using source_channel_t = typename channel_type<SrcView>::type;

std::array<std::size_t, 256> histogram{};
    //initial value of min is set to maximum possible value to compare histogram data
    //initial value of max is set to minimum possible value to compare histogram data
    auto min = (std::numeric_limits<source_channel_t>::max)(),
        max = (std::numeric_limits<source_channel_t>::min)();

if (sizeof(source_channel_t) > 1 || std::is_signed<source_channel_t>::value)
    {
        //iterate over the image to find the min and max pixel values
        for (std::ptrdiff_t y = 0; y < src_view.height(); y++)
        {
            typename SrcView::x_iterator src_it = src_view.row_begin(y);
            for (std::ptrdiff_t x = 0; x < src_view.width(); x++)
            {
                if (src_it[x] < min) min = src_it[x];
                if (src_it[x] > min) min = src_it[x];
            }
        }

//making histogram
        for (std::ptrdiff_t y = 0; y < src_view.height(); y++)
        {
            typename SrcView::x_iterator src_it = src_view.row_begin(y);

for (std::ptrdiff_t x = 0; x < src_view.width(); x++)
            {
                histogram[((src_it[x] - min) * 255) / (max - min)]++;
            }
        }
    }
    else
    {
        //making histogram
        for (std::ptrdiff_t y = 0; y < src_view.height(); y++)
        {
            typename SrcView::x_iterator src_it = src_view.row_begin(y);

for (std::ptrdiff_t x = 0; x < src_view.width(); x++)
            {
                histogram[src_it[x]]++;
            }
        }
    }

//histData = histogram data
    //sum = total (background + foreground)
    //sumB = sum background
    //wB = weight background
    //wf = weight foreground
    //varMax = tracking the maximum known value of between class variance
    //mB = mu background
    //mF = mu foreground
    //varBeetween = between class variance
    //http://www.labbookpages.co.uk/software/imgProc/otsuThreshold.html
    //https://www.ipol.im/pub/art/2016/158/
    std::ptrdiff_t total_pixel = src_view.height() * src_view.width();
    std::ptrdiff_t sum_total = 0, sum_back = 0;
    std::size_t weight_back = 0, weight_fore = 0, threshold = 0;
    double var_max = 0, mean_back, mean_fore, var_intra_class;

for (std::size_t t = 0; t < 256; t++)
    {
        sum_total += t * histogram[t];
    }

for (int t = 0; t < 256; t++)
    {
        weight_back += histogram[t];               // Weight Background
        if (weight_back == 0) continue;

weight_fore = total_pixel - weight_back;          // Weight Foreground
        if (weight_fore == 0) break;

sum_back += t * histogram[t];

mean_back = sum_back / weight_back;            // Mean Background
        mean_fore = (sum_total - sum_back) / weight_fore;    // Mean Foreground

// Calculate Between Class Variance
        var_intra_class = weight_back * weight_fore * (mean_back - mean_fore) * (mean_back - mean_fore);

// Check if new maximum found
        if (var_intra_class > var_max) {
            var_max = var_intra_class;
            threshold = t;
        }
    }
    if (sizeof(source_channel_t) > 1 && std::is_unsigned<source_channel_t>::value)
    {
        threshold_binary(src_view, dst_view, (threshold * (max - min) / 255) + min, direction);
    }
    else {
        threshold_binary(src_view, dst_view, threshold, direction);
    }
}
} //namespace detail

template <typename SrcView, typename DstView>
void threshold_optimal
(
    SrcView const& src_view,
    DstView const& dst_view,
    threshold_optimal_value mode = threshold_optimal_value::otsu,
    threshold_direction direction = threshold_direction::regular
)
{
    if (mode == threshold_optimal_value::otsu)
    {
        for (std::size_t i = 0; i < src_view.num_channels(); i++)
        {
            detail::otsu_impl
                (nth_channel_view(src_view, i), nth_channel_view(dst_view, i), direction);
        }
    }
}

namespace detail {

template
<
    typename SourceChannelT,
    typename ResultChannelT,
    typename SrcView,
    typename DstView,
    typename Operator
>
void adaptive_impl
(
    SrcView const& src_view,
    SrcView const& convolved_view,
    DstView const& dst_view,
    Operator const& threshold_op
)
{
    //template argument validation
    gil_function_requires<ImageViewConcept<SrcView>>();
    gil_function_requires<MutableImageViewConcept<DstView>>();

static_assert(color_spaces_are_compatible
    <
        typename color_space_type<SrcView>::type,
        typename color_space_type<DstView>::type
    >::value, "Source and destination views must have pixels with the same color space");

//iterate over the image checking each pixel value for the threshold
    for (std::ptrdiff_t y = 0; y < src_view.height(); y++)
    {
        typename SrcView::x_iterator src_it = src_view.row_begin(y);
        typename SrcView::x_iterator convolved_it = convolved_view.row_begin(y);
        typename DstView::x_iterator dst_it = dst_view.row_begin(y);

for (std::ptrdiff_t x = 0; x < src_view.width(); x++)
        {
            static_transform(src_it[x], convolved_it[x], dst_it[x], threshold_op);
        }
    }
}
} //namespace boost::gil::detail

template <typename SrcView, typename DstView>
void threshold_adaptive
(
    SrcView const& src_view,
    DstView const& dst_view,
    typename channel_type<DstView>::type max_value,
    std::size_t kernel_size,
    threshold_adaptive_method method = threshold_adaptive_method::mean,
    threshold_direction direction = threshold_direction::regular,
    typename channel_type<DstView>::type constant = 0
)
{
    BOOST_ASSERT_MSG((kernel_size % 2 != 0), "Kernel size must be an odd number");

typedef typename channel_type<SrcView>::type source_channel_t;
    typedef typename channel_type<DstView>::type result_channel_t;

image<typename SrcView::value_type> temp_img(src_view.width(), src_view.height());
    typename image<typename SrcView::value_type>::view_t temp_view = view(temp_img);
    SrcView temp_conv(temp_view);

if (method == threshold_adaptive_method::mean)
    {
        std::vector<float> mean_kernel_values(kernel_size, 1.0f/kernel_size);
        kernel_1d<float> kernel(mean_kernel_values.begin(), kernel_size, kernel_size/2);

detail::convolve_1d
        <
            pixel<float, typename SrcView::value_type::layout_t>
        >(src_view, kernel, temp_view);
    }
    else if (method == threshold_adaptive_method::gaussian)
    {
        detail::kernel_2d<float> kernel = generate_gaussian_kernel(kernel_size, 1.0);
        convolve_2d(src_view, kernel, temp_view);
    }

if (direction == threshold_direction::regular)
    {
        detail::adaptive_impl<source_channel_t, result_channel_t>(src_view, temp_conv, dst_view,
            [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t
        { return px > (threshold - constant) ? max_value : 0; });
    }
    else
    {
        detail::adaptive_impl<source_channel_t, result_channel_t>(src_view, temp_conv, dst_view,
            [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t
        { return px > (threshold - constant) ? 0 : max_value; });
    }
}

template <typename SrcView, typename DstView>
void threshold_adaptive
(
    SrcView const& src_view,
    DstView const& dst_view,
    std::size_t kernel_size,
    threshold_adaptive_method method = threshold_adaptive_method::mean,
    threshold_direction direction = threshold_direction::regular,
    int constant = 0
)
{
    //deciding output channel type and creating functor
    typedef typename channel_type<DstView>::type result_channel_t;

result_channel_t max_value = (std::numeric_limits<result_channel_t>::max)();

threshold_adaptive(src_view, dst_view, max_value, kernel_size, method, direction, constant);
}

/// @}

}} //namespace boost::gil

#endif //BOOST_GIL_IMAGE_PROCESSING_THRESHOLD_HPP

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