// Copyright (C) 2018  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.
#include "opaque_types.h"
#include <dlib/python.h>
#include "dlib/pixel.h"
#include <dlib/image_transforms.h>
#include <dlib/image_processing.h>

using namespace dlib;
using namespace std;

namespace py = pybind11;

// ----------------------------------------------------------------------------------------

string print_rgb_pixel_str(const rgb_pixel& p)
{
    std::ostringstream sout;
    sout << "red: "<< (int)p.red
         << ", green: "<< (int)p.green
         << ", blue: "<< (int)p.blue;
    return sout.str();
}

string print_rgb_pixel_repr(const rgb_pixel& p)
{
    std::ostringstream sout;
    sout << "rgb_pixel(" << (int)p.red << "," << (int)p.green << "," << (int)p.blue << ")";
    return sout.str();
}

// ----------------------------------------------------------------------------------------

template <typename T>
numpy_image<unsigned char> py_threshold_image2(
    const numpy_image<T>& in_img,
    typename pixel_traits<T>::basic_pixel_type thresh
)
{
    numpy_image<unsigned char> out_img;
    threshold_image(in_img, out_img);
    return out_img;
}

template <typename T>
numpy_image<unsigned char> py_threshold_image(
    const numpy_image<T>& in_img
)
{
    numpy_image<unsigned char> out_img;
    threshold_image(in_img, out_img);
    return out_img;
}

// ----------------------------------------------------------------------------------------

template <typename T>
typename pixel_traits<T>::basic_pixel_type py_partition_pixels (
    const numpy_image<T>& img
)
{
    return partition_pixels(img);
}

template <typename T>
py::tuple py_partition_pixels2 (
    const numpy_image<T>& img,
    int num_thresholds
)
{
    DLIB_CASSERT(1 <= num_thresholds && num_thresholds <= 6);

    typename pixel_traits<T>::basic_pixel_type t1,t2,t3,t4,t5,t6;

    switch(num_thresholds)
    {
        case 1: partition_pixels(img,t1); return py::make_tuple(t1);
        case 2: partition_pixels(img,t1,t2); return py::make_tuple(t1,t2);
        case 3: partition_pixels(img,t1,t2,t3); return py::make_tuple(t1,t2,t3);
        case 4: partition_pixels(img,t1,t2,t3,t4); return py::make_tuple(t1,t2,t3,t4);
        case 5: partition_pixels(img,t1,t2,t3,t4,t5); return py::make_tuple(t1,t2,t3,t4,t5);
        case 6: partition_pixels(img,t1,t2,t3,t4,t5,t6); return py::make_tuple(t1,t2,t3,t4,t5,t6);
    }
    DLIB_CASSERT(false, "This should never happen.");
}

// ----------------------------------------------------------------------------------------

template <typename T>
py::tuple py_gaussian_blur (
    const numpy_image<T>& img,
    double sigma = 1,
    int max_size = 1001
)
{
    numpy_image<T> out;
    auto rect = gaussian_blur(img, out, sigma, max_size);
    return py::make_tuple(out, rect);
}

template <typename T>
py::tuple py_label_connected_blobs (
    const numpy_image<T>& img,
    bool zero_pixels_are_background,
    int neighborhood_connectivity,
    bool connected_if_both_not_zero
)
{
    DLIB_CASSERT(neighborhood_connectivity == 4 ||
                 neighborhood_connectivity == 8 ||
                 neighborhood_connectivity == 24);

    unsigned long num_blobs = 0;

    numpy_image<uint32_t> labels;

    if (zero_pixels_are_background && neighborhood_connectivity == 4 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_4(), ::connected_if_both_not_zero(), labels);
    else if (zero_pixels_are_background && neighborhood_connectivity == 4 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_4(), connected_if_equal(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 4 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_4(), ::connected_if_both_not_zero(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 4 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_4(), connected_if_equal(), labels);

    else if (zero_pixels_are_background && neighborhood_connectivity == 8 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_8(), ::connected_if_both_not_zero(), labels);
    else if (zero_pixels_are_background && neighborhood_connectivity == 8 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_8(), connected_if_equal(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 8 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_8(), ::connected_if_both_not_zero(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 8 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_8(), connected_if_equal(), labels);

    else if (zero_pixels_are_background && neighborhood_connectivity == 24 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_24(), ::connected_if_both_not_zero(), labels);
    else if (zero_pixels_are_background && neighborhood_connectivity == 24 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, ::zero_pixels_are_background(), neighbors_24(), connected_if_equal(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 24 && connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_24(), ::connected_if_both_not_zero(), labels);
    else if (!zero_pixels_are_background && neighborhood_connectivity == 24 && !connected_if_both_not_zero )
        num_blobs = label_connected_blobs(img, nothing_is_background(), neighbors_24(), connected_if_equal(), labels);
    else
        DLIB_CASSERT(false, "this should never happen");

    return py::make_tuple(labels, num_blobs);
}

// ----------------------------------------------------------------------------------------

template <typename T>
py::tuple py_label_connected_blobs_watershed (
    const numpy_image<T>& img,
    const T& background_thresh, 
    const double smoothing
)
{
    numpy_image<uint32_t> labels;
    auto num_blobs = label_connected_blobs_watershed(img, labels, background_thresh, smoothing);
    return py::make_tuple(labels, num_blobs);
}

template <typename T>
py::tuple py_label_connected_blobs_watershed2 (
    const numpy_image<T>& img
)
{
    numpy_image<uint32_t> labels;
    auto num_blobs = label_connected_blobs_watershed(img, labels);
    return py::make_tuple(labels, num_blobs);
}

// ----------------------------------------------------------------------------------------

template <typename T>
numpy_image<rgb_pixel> py_randomly_color_image (
    const numpy_image<T>& img
)
{
    numpy_image<rgb_pixel> temp;
    matrix<T> itemp;
    assign_image(itemp, numpy_image<T>(img));
    assign_image(temp, randomly_color_image(itemp));
    return temp;
}

// ----------------------------------------------------------------------------------------

template <typename T>
numpy_image<rgb_pixel> py_jet (
    const numpy_image<T>& img
)
{
    numpy_image<rgb_pixel> temp;
    matrix<T> itemp;
    assign_image(itemp, numpy_image<T>(img));
    assign_image(temp, jet(itemp));
    return temp;
}

// ----------------------------------------------------------------------------------------

template <typename T>
py::array convert_image (
    const numpy_image<T>& img,
    const string& dtype
)
{
    if (dtype == "uint8")    {numpy_image<uint8_t>   out; assign_image(out, img); return out;}
    if (dtype == "uint16")   {numpy_image<uint16_t>  out; assign_image(out, img); return out;}
    if (dtype == "uint32")   {numpy_image<uint32_t>  out; assign_image(out, img); return out;}
    if (dtype == "uint64")   {numpy_image<uint64_t>  out; assign_image(out, img); return out;}
    if (dtype == "int8")     {numpy_image<int8_t>    out; assign_image(out, img); return out;}
    if (dtype == "int16")    {numpy_image<int16_t>   out; assign_image(out, img); return out;}
    if (dtype == "int32")    {numpy_image<int32_t>   out; assign_image(out, img); return out;}
    if (dtype == "int64")    {numpy_image<int64_t>   out; assign_image(out, img); return out;}
    if (dtype == "float32")  {numpy_image<float>     out; assign_image(out, img); return out;}
    if (dtype == "float64")  {numpy_image<double>    out; assign_image(out, img); return out;}
    if (dtype == "float")    {numpy_image<float>     out; assign_image(out, img); return out;}
    if (dtype == "double")   {numpy_image<double>    out; assign_image(out, img); return out;}
    if (dtype == "rgb_pixel"){numpy_image<rgb_pixel> out; assign_image(out, img); return out;}


    throw dlib::error("convert_image() called with invalid dtype, must be one of these strings: \n"
        "uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel");
}

numpy_image<unsigned char> convert_rgb_to_grayscale(
    const numpy_image<rgb_pixel>& img
)
{
    numpy_image<unsigned char> out;
    assign_image(out, img);
    return out;
}

// ----------------------------------------------------------------------------------------

template <typename T>
py::array convert_image_scaled (
    const numpy_image<T>& img,
    const string& dtype,
    const double thresh = 4
)
{
    if (dtype == "uint8")    {numpy_image<uint8_t>   out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "uint16")   {numpy_image<uint16_t>  out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "uint32")   {numpy_image<uint32_t>  out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "uint64")   {numpy_image<uint64_t>  out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "int8")     {numpy_image<int8_t>    out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "int16")    {numpy_image<int16_t>   out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "int32")    {numpy_image<int32_t>   out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "int64")    {numpy_image<int64_t>   out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "float32")  {numpy_image<float>     out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "float64")  {numpy_image<double>    out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "float")    {numpy_image<float>     out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "double")   {numpy_image<double>    out; assign_image_scaled(out, img, thresh); return out;}
    if (dtype == "rgb_pixel"){numpy_image<rgb_pixel> out; assign_image_scaled(out, img, thresh); return out;}


    throw dlib::error("convert_image() called with invalid dtype, must be one of these strings: \n"
        "uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel");
}

// ----------------------------------------------------------------------------------------

struct py_pyramid_down
{

    void dostuff(point) {}

    py_pyramid_down(
    ) = default;

    py_pyramid_down (
        unsigned int N_
    ) : N(N_) 
    {
        DLIB_CASSERT( 1 <= N && N <= 20, "pyramid downsampling rate must be between 1 and 20.");
    }

    unsigned int pyramid_downsampling_rate (
    ) const { return N; }

    template <typename T>
    dlib::vector<double,2> point_down (
        const dlib::vector<T,2>& pp
    ) const
    {
        dpoint p = pp;
        switch(N)
        {
            case 1: return pyr1.point_down(p);
            case 2: return pyr2.point_down(p);
            case 3: return pyr3.point_down(p);
            case 4: return pyr4.point_down(p);
            case 5: return pyr5.point_down(p);
            case 6: return pyr6.point_down(p);
            case 7: return pyr7.point_down(p);
            case 8: return pyr8.point_down(p);
            case 9: return pyr9.point_down(p);
            case 10: return pyr10.point_down(p);
            case 11: return pyr11.point_down(p);
            case 12: return pyr12.point_down(p);
            case 13: return pyr13.point_down(p);
            case 14: return pyr14.point_down(p);
            case 15: return pyr15.point_down(p);
            case 16: return pyr16.point_down(p);
            case 17: return pyr17.point_down(p);
            case 18: return pyr18.point_down(p);
            case 19: return pyr19.point_down(p);
            case 20: return pyr20.point_down(p);
        }

        DLIB_CASSERT(false, "This should never happen");
    }

    template <typename T>
    dlib::vector<double,2> point_up (
        const dlib::vector<T,2>& pp
    ) const
    {
        dpoint p = pp;
        switch(N)
        {
            case 1: return pyr1.point_up(p);
            case 2: return pyr2.point_up(p);
            case 3: return pyr3.point_up(p);
            case 4: return pyr4.point_up(p);
            case 5: return pyr5.point_up(p);
            case 6: return pyr6.point_up(p);
            case 7: return pyr7.point_up(p);
            case 8: return pyr8.point_up(p);
            case 9: return pyr9.point_up(p);
            case 10: return pyr10.point_up(p);
            case 11: return pyr11.point_up(p);
            case 12: return pyr12.point_up(p);
            case 13: return pyr13.point_up(p);
            case 14: return pyr14.point_up(p);
            case 15: return pyr15.point_up(p);
            case 16: return pyr16.point_up(p);
            case 17: return pyr17.point_up(p);
            case 18: return pyr18.point_up(p);
            case 19: return pyr19.point_up(p);
            case 20: return pyr20.point_up(p);
        }
        DLIB_CASSERT(false, "This should never happen");
    }

// -----------------------------

    template <typename T>
    dlib::vector<double,2> point_down2 (
        const dlib::vector<T,2>& p,
        unsigned int levels
    ) const
    {
        dlib::vector<double,2> temp = p;
        for (unsigned int i = 0; i < levels; ++i)
            temp = point_down(temp);
        return temp;
    }

    template <typename T>
    dlib::vector<double,2> point_up2 (
        const dlib::vector<T,2>& p,
        unsigned int levels
    ) const
    {
        dlib::vector<double,2> temp = p;
        for (unsigned int i = 0; i < levels; ++i)
            temp = point_up(temp);
        return temp;
    }

// -----------------------------

    template <typename rect_type>
    rect_type rect_up (
        const rect_type& rect
    ) const
    {
        return rect_type(point_up(rect.tl_corner()), point_up(rect.br_corner()));
    }

    template <typename rect_type>
    rect_type rect_up2 (
        const rect_type& rect,
        unsigned int levels
    ) const
    {
        return rect_type(point_up2(rect.tl_corner(),levels), point_up2(rect.br_corner(),levels));
    }

// -----------------------------

    template <typename rect_type>
    rect_type rect_down (
        const rect_type& rect
    ) const
    {
        return rect_type(point_down(rect.tl_corner()), point_down(rect.br_corner()));
    }

    template <typename rect_type>
    rect_type rect_down2 (
        const rect_type& rect,
        unsigned int levels
    ) const
    {
        return rect_type(point_down2(rect.tl_corner(),levels), point_down2(rect.br_corner(),levels));
    }

    template <
        typename T
        >
    numpy_image<T> down (
        const numpy_image<T>& img
    ) const
    {

        numpy_image<T> down;
        switch(N)
        {
            case 1: pyr1(img,down); break;
            case 2: pyr2(img,down); break;
            case 3: pyr3(img,down); break;
            case 4: pyr4(img,down); break;
            case 5: pyr5(img,down); break;
            case 6: pyr6(img,down); break;
            case 7: pyr7(img,down); break;
            case 8: pyr8(img,down); break;
            case 9: pyr9(img,down); break;
            case 10: pyr10(img,down); break;
            case 11: pyr11(img,down); break;
            case 12: pyr12(img,down); break;
            case 13: pyr13(img,down); break;
            case 14: pyr14(img,down); break;
            case 15: pyr15(img,down); break;
            case 16: pyr16(img,down); break;
            case 17: pyr17(img,down); break;
            case 18: pyr18(img,down); break;
            case 19: pyr19(img,down); break;
            case 20: pyr20(img,down); break;
        }

        return down;
    }

private:
    unsigned int N = 2;

    pyramid_down<1> pyr1;
    pyramid_down<2> pyr2;
    pyramid_down<3> pyr3;
    pyramid_down<4> pyr4;
    pyramid_down<5> pyr5;
    pyramid_down<6> pyr6;
    pyramid_down<7> pyr7;
    pyramid_down<8> pyr8;
    pyramid_down<9> pyr9;
    pyramid_down<10> pyr10;
    pyramid_down<11> pyr11;
    pyramid_down<12> pyr12;
    pyramid_down<13> pyr13;
    pyramid_down<14> pyr14;
    pyramid_down<15> pyr15;
    pyramid_down<16> pyr16;
    pyramid_down<17> pyr17;
    pyramid_down<18> pyr18;
    pyramid_down<19> pyr19;
    pyramid_down<20> pyr20;

};

// ----------------------------------------------------------------------------------------

py::tuple py_find_bright_lines (
    const numpy_image<float>& xx,
    const numpy_image<float>& xy,
    const numpy_image<float>& yy
)
{
    numpy_image<float> horz, vert;
    find_bright_lines(xx,xy,yy,horz,vert);
    return py::make_tuple(horz,vert);
}

py::tuple py_find_dark_lines (
    const numpy_image<float>& xx,
    const numpy_image<float>& xy,
    const numpy_image<float>& yy
)
{
    numpy_image<float> horz, vert;
    find_dark_lines(xx,xy,yy,horz,vert);
    return py::make_tuple(horz,vert);
}

numpy_image<float> py_find_bright_keypoints (
    const numpy_image<float>& xx,
    const numpy_image<float>& xy,
    const numpy_image<float>& yy
)
{
    numpy_image<float> sal;
    find_bright_keypoints(xx,xy,yy,sal);
    return sal;
}

numpy_image<float> py_find_dark_keypoints (
    const numpy_image<float>& xx,
    const numpy_image<float>& xy,
    const numpy_image<float>& yy
)
{
    numpy_image<float> sal;
    find_dark_keypoints(xx,xy,yy,sal);
    return sal;
}

template <typename T>
py::tuple py_sobel_edge_detector (
    const numpy_image<T>& img
)
{
    numpy_image<float> horz, vert;
    sobel_edge_detector(img, horz, vert);
    return py::make_tuple(horz,vert);
}

numpy_image<float> py_suppress_non_maximum_edges (
    const numpy_image<float>& horz,
    const numpy_image<float>& vert
)
{
    numpy_image<float> out;
    suppress_non_maximum_edges(horz,vert,out);
    return out;
}

numpy_image<float> py_suppress_non_maximum_edges2 (
    const py::tuple& horz_and_vert_gradients 
)
{
    numpy_image<float> out, horz, vert;
    horz = horz_and_vert_gradients[0];
    vert = horz_and_vert_gradients[1];
    suppress_non_maximum_edges(horz,vert,out);
    return out;
}

template <typename T> 
std::vector<point> py_find_peaks (
    const numpy_image<T>& img,
    const double non_max_suppression_radius,
    const T& thresh
)
{
    return find_peaks(img, non_max_suppression_radius, thresh);
}

template <typename T> 
std::vector<point> py_find_peaks2 (
    const numpy_image<T>& img,
    const double non_max_suppression_radius
)
{
    return find_peaks(img, non_max_suppression_radius, partition_pixels(img));
}


// ----------------------------------------------------------------------------------------

template <typename T>
numpy_image<T> py_hysteresis_threshold (
    const numpy_image<T>& img,
    T lower_thresh,
    T upper_thresh
)
{
    numpy_image<T> out;
    hysteresis_threshold(img, out, lower_thresh, upper_thresh);
    return out;
}

template <typename T>
numpy_image<T> py_hysteresis_threshold2 (
    const numpy_image<T>& img
)
{
    numpy_image<T> out;
    hysteresis_threshold(img, out);
    return out;
}

// ----------------------------------------------------------------------------------------

void bind_image_classes(py::module& m)
{



    py::class_<rgb_pixel>(m, "rgb_pixel")
        .def(py::init<unsigned char,unsigned char,unsigned char>(), py::arg("red"), py::arg("green"), py::arg("blue"))
        .def("__str__", &print_rgb_pixel_str)
        .def("__repr__", &print_rgb_pixel_repr)
        .def_readwrite("red", &rgb_pixel::red)
        .def_readwrite("green", &rgb_pixel::green)
        .def_readwrite("blue", &rgb_pixel::blue);

    const char* docs = "Thresholds img and returns the result.  Pixels in img with grayscale values >= partition_pixels(img) \n" 
              "have an output value of 255 and all others have a value of 0.";
    m.def("threshold_image", &py_threshold_image<unsigned char>, py::arg("img") );
    m.def("threshold_image", &py_threshold_image<uint16_t>, py::arg("img") );
    m.def("threshold_image", &py_threshold_image<uint32_t>, py::arg("img") );
    m.def("threshold_image", &py_threshold_image<float>, py::arg("img") );
    m.def("threshold_image", &py_threshold_image<double>, py::arg("img") );
    m.def("threshold_image", &py_threshold_image<rgb_pixel>,docs, py::arg("img") );

    docs = "Thresholds img and returns the result.  Pixels in img with grayscale values >= thresh \n"
              "have an output value of 255 and all others have a value of 0.";
    m.def("threshold_image", &py_threshold_image2<unsigned char>, py::arg("img"), py::arg("thresh") );
    m.def("threshold_image", &py_threshold_image2<uint16_t>, py::arg("img"), py::arg("thresh") );
    m.def("threshold_image", &py_threshold_image2<uint32_t>, py::arg("img"), py::arg("thresh") );
    m.def("threshold_image", &py_threshold_image2<float>, py::arg("img"), py::arg("thresh") );
    m.def("threshold_image", &py_threshold_image2<double>, py::arg("img"), py::arg("thresh") );
    m.def("threshold_image", &py_threshold_image2<rgb_pixel>,docs, py::arg("img"), py::arg("thresh") );


    docs = 
"Finds a threshold value that would be reasonable to use with \n\
threshold_image(img, threshold).  It does this by finding the threshold that \n\
partitions the pixels in img into two groups such that the sum of absolute \n\
deviations between each pixel and the mean of its group is minimized.";
    m.def("partition_pixels", &py_partition_pixels<rgb_pixel>, py::arg("img") );
    m.def("partition_pixels", &py_partition_pixels<unsigned char>, py::arg("img") );
    m.def("partition_pixels", &py_partition_pixels<uint16_t>, py::arg("img") );
    m.def("partition_pixels", &py_partition_pixels<uint32_t>, py::arg("img") );
    m.def("partition_pixels", &py_partition_pixels<float>, py::arg("img") );
    m.def("partition_pixels", &py_partition_pixels<double>,docs, py::arg("img") );

    docs = 
"This version of partition_pixels() finds multiple partitions rather than just \n\
one partition.  It does this by first partitioning the pixels just as the \n\
above partition_pixels(img) does.  Then it forms a new image with only pixels \n\
>= that first partition value and recursively partitions this new image. \n\
However, the recursion is implemented in an efficient way which is faster than \n\
explicitly forming these images and calling partition_pixels(), but the \n\
output is the same as if you did.  For example, suppose you called \n\
[t1,t2,t2] = partition_pixels(img,3).  Then we would have: \n\
   - t1 == partition_pixels(img) \n\
   - t2 == partition_pixels(an image with only pixels with values >= t1 in it) \n\
   - t3 == partition_pixels(an image with only pixels with values >= t2 in it)" ;
    m.def("partition_pixels", &py_partition_pixels2<rgb_pixel>, py::arg("img"), py::arg("num_thresholds") );
    m.def("partition_pixels", &py_partition_pixels2<unsigned char>, py::arg("img"), py::arg("num_thresholds") );
    m.def("partition_pixels", &py_partition_pixels2<uint16_t>, py::arg("img"), py::arg("num_thresholds") );
    m.def("partition_pixels", &py_partition_pixels2<uint32_t>, py::arg("img"), py::arg("num_thresholds") );
    m.def("partition_pixels", &py_partition_pixels2<float>, py::arg("img"), py::arg("num_thresholds") );
    m.def("partition_pixels", &py_partition_pixels2<double>,docs, py::arg("img"), py::arg("num_thresholds") );

    docs = 
"requires \n\
    - sigma > 0 \n\
    - max_size > 0 \n\
    - max_size is an odd number \n\
ensures \n\
    - Filters img with a Gaussian filter of sigma width.  The actual spatial filter will \n\
      be applied to pixel blocks that are at most max_size wide and max_size tall (note that \n\
      this function will automatically select a smaller block size as appropriate).  The  \n\
      results are returned.  We also return a rectangle which indicates what pixels \n\
      in the returned image are considered non-border pixels and therefore contain \n\
      output from the filter.  E.g. \n\
        - filtered_img,rect = gaussian_blur(img) \n\
      would give you the filtered image and the rectangle in question. \n\
    - The filter is applied to each color channel independently. \n\
    - Pixels close enough to the edge of img to not have the filter still fit  \n\
      inside the image are set to zero. \n\
    - The returned image has the same dimensions as the input image.";
    /*!
        requires
            - sigma > 0
            - max_size > 0
            - max_size is an odd number
        ensures
            - Filters img with a Gaussian filter of sigma width.  The actual spatial filter will
              be applied to pixel blocks that are at most max_size wide and max_size tall (note that
              this function will automatically select a smaller block size as appropriate).  The 
              results are returned.  We also return a rectangle which indicates what pixels
              in the returned image are considered non-border pixels and therefore contain
              output from the filter.  E.g.
                - filtered_img,rect = gaussian_blur(img)
              would give you the filtered image and the rectangle in question.
            - The filter is applied to each color channel independently.
            - Pixels close enough to the edge of img to not have the filter still fit 
              inside the image are set to zero.
            - The returned image has the same dimensions as the input image.
    !*/
    m.def("gaussian_blur", &py_gaussian_blur<rgb_pixel>,py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );
    m.def("gaussian_blur", &py_gaussian_blur<unsigned char>,py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );
    m.def("gaussian_blur", &py_gaussian_blur<uint16>,py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );
    m.def("gaussian_blur", &py_gaussian_blur<uint32>,py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );
    m.def("gaussian_blur", &py_gaussian_blur<float>, py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );
    m.def("gaussian_blur", &py_gaussian_blur<double>,docs, py::arg("img"), py::arg("sigma"), py::arg("max_size")=1000 );



    docs = 
"requires \n\
    - all pixels in img are set to either 255 or 0. \n\
ensures \n\
    - This function computes the skeletonization of img and stores the result in \n\
      #img.  That is, given a binary image, we progressively thin the binary blobs \n\
      (composed of on_pixel values) until only a single pixel wide skeleton of the \n\
      original blobs remains. \n\
    - Doesn't change the shape or size of img.";
    /*!
        requires
            - all pixels in img are set to either 255 or 0.
        ensures
            - This function computes the skeletonization of img and stores the result in
              #img.  That is, given a binary image, we progressively thin the binary blobs
              (composed of on_pixel values) until only a single pixel wide skeleton of the
              original blobs remains.
            - Doesn't change the shape or size of img.
    !*/
    m.def("skeleton", skeleton<numpy_image<unsigned char>>, docs, py::arg("img"));




    docs = 
"requires \n\
    - neighborhood_connectivity == 4, 8, or 24 \n\
ensures \n\
    - This function labels each of the connected blobs in img with a unique integer  \n\
      label.   \n\
    - An image can be thought of as a graph where pixels A and B are connected if \n\
      they are close to each other and satisfy some criterion like having the same \n\
      value or both being non-zero.  Then this function can be understood as \n\
      labeling all the connected components of this pixel graph such that all \n\
      pixels in a component get the same label while pixels in different components \n\
      get different labels.   \n\
    - If zero_pixels_are_background==true then there is a special background component \n\
      and all pixels with value 0 are assigned to it. Moreover, all such background pixels \n\
      will always get a blob id of 0 regardless of any other considerations. \n\
    - This function returns a label image and a count of the number of blobs found. \n\
      I.e., if you ran this function like: \n\
        label_img, num_blobs = label_connected_blobs(img) \n\
      You would obtain the noted label image and number of blobs. \n\
    - The output label_img has the same dimensions as the input image. \n\
    - for all valid r and c: \n\
        - label_img[r][c] == the blob label number for pixel img[r][c].   \n\
        - label_img[r][c] >= 0 \n\
        - if (img[r][c]==0) then \n\
            - label_img[r][c] == 0 \n\
        - else \n\
            - label_img[r][c] != 0 \n\
    - if (len(img) != 0) then  \n\
        - The returned num_blobs will be == label_img.max()+1 \n\
          (i.e. returns a number one greater than the maximum blob id number,  \n\
          this is the number of blobs found.) \n\
    - else \n\
        - num_blobs will be 0. \n\
    - blob labels are contiguous, therefore, the number returned by this function is \n\
      the number of blobs in the image (including the background blob).";
    /*!
        requires
            - neighborhood_connectivity == 4, 8, or 24
        ensures
            - This function labels each of the connected blobs in img with a unique integer 
              label.  
            - An image can be thought of as a graph where pixels A and B are connected if
              they are close to each other and satisfy some criterion like having the same
              value or both being non-zero.  Then this function can be understood as
              labeling all the connected components of this pixel graph such that all
              pixels in a component get the same label while pixels in different components
              get different labels.  
            - If zero_pixels_are_background==true then there is a special background component
              and all pixels with value 0 are assigned to it. Moreover, all such background pixels
              will always get a blob id of 0 regardless of any other considerations.
            - This function returns a label image and a count of the number of blobs found.
              I.e., if you ran this function like:
                label_img, num_blobs = label_connected_blobs(img)
              You would obtain the noted label image and number of blobs.
            - The output label_img has the same dimensions as the input image.
            - for all valid r and c:
                - label_img[r][c] == the blob label number for pixel img[r][c].  
                - label_img[r][c] >= 0
                - if (img[r][c]==0) then
                    - label_img[r][c] == 0
                - else
                    - label_img[r][c] != 0
            - if (len(img) != 0) then 
                - The returned num_blobs will be == label_img.max()+1
                  (i.e. returns a number one greater than the maximum blob id number, 
                  this is the number of blobs found.)
            - else
                - num_blobs will be 0.
            - blob labels are contiguous, therefore, the number returned by this function is
              the number of blobs in the image (including the background blob).
    !*/
    m.def("label_connected_blobs", py_label_connected_blobs<unsigned char>, py::arg("img"),py::arg("zero_pixels_are_background")=true,py::arg("neighborhood_connectivity")=8,py::arg("connected_if_both_not_zero")=false);
    m.def("label_connected_blobs", py_label_connected_blobs<uint16_t>, py::arg("img"),py::arg("zero_pixels_are_background")=true,py::arg("neighborhood_connectivity")=8,py::arg("connected_if_both_not_zero")=false);
    m.def("label_connected_blobs", py_label_connected_blobs<uint32_t>, docs, py::arg("img"),py::arg("zero_pixels_are_background")=true,py::arg("neighborhood_connectivity")=8,py::arg("connected_if_both_not_zero")=false);


    docs =
"requires \n\
    - smoothing >= 0 \n\
ensures \n\
    - This routine performs a watershed segmentation of the given input image and \n\
      labels each resulting flooding region with a unique integer label. It does \n\
      this by marking the brightest pixels as sources of flooding and then flood \n\
      fills the image outward from those sources.  Each flooded area is labeled \n\
      with the identity of the source pixel and flooding stops when another flooded \n\
      area is reached or pixels with values < background_thresh are encountered.   \n\
    - The flooding will also overrun a source pixel if that source pixel has yet to \n\
      label any neighboring pixels.  This behavior helps to mitigate spurious \n\
      splits of objects due to noise.  You can further control this behavior by \n\
      setting the smoothing parameter.  The flooding will take place on an image \n\
      that has been Gaussian blurred with a sigma==smoothing.  So setting smoothing \n\
      to a larger number will in general cause more regions to be merged together. \n\
      Note that the smoothing parameter has no effect on the interpretation of \n\
      background_thresh since the decision of \"background or not background\" is \n\
      always made relative to the unsmoothed input image. \n\
    - This function returns a tuple of the labeled image and number of blobs found.  \n\
      i.e. you can call it like this: \n\
        label_img, num_blobs = label_connected_blobs_watershed(img,background_thresh,smoothing) \n\
    - The returned label_img will have the same dimensions as img.  \n\
    - for all valid r and c: \n\
        - if (img[r][c] < background_thresh) then \n\
            - label_img[r][c] == 0, (i.e. the pixel is labeled as background) \n\
        - else \n\
            - label_img[r][c] == an integer value indicating the identity of the segment \n\
              containing the pixel img[r][c].   \n\
    - The returned num_blobs is the number of labeled segments, including the \n\
      background segment.  Therefore, the returned number is 1+(the max value in \n\
      label_img).";
    /*!
        requires
            - smoothing >= 0
        ensures
            - This routine performs a watershed segmentation of the given input image and
              labels each resulting flooding region with a unique integer label. It does
              this by marking the brightest pixels as sources of flooding and then flood
              fills the image outward from those sources.  Each flooded area is labeled
              with the identity of the source pixel and flooding stops when another flooded
              area is reached or pixels with values < background_thresh are encountered.  
            - The flooding will also overrun a source pixel if that source pixel has yet to
              label any neighboring pixels.  This behavior helps to mitigate spurious
              splits of objects due to noise.  You can further control this behavior by
              setting the smoothing parameter.  The flooding will take place on an image
              that has been Gaussian blurred with a sigma==smoothing.  So setting smoothing
              to a larger number will in general cause more regions to be merged together.
              Note that the smoothing parameter has no effect on the interpretation of
              background_thresh since the decision of "background or not background" is
              always made relative to the unsmoothed input image.
            - This function returns a tuple of the labeled image and number of blobs found. 
              i.e. you can call it like this:
                label_img, num_blobs = label_connected_blobs_watershed(img,background_thresh,smoothing)
            - The returned label_img will have the same dimensions as img. 
            - for all valid r and c:
                - if (img[r][c] < background_thresh) then
                    - label_img[r][c] == 0, (i.e. the pixel is labeled as background)
                - else
                    - label_img[r][c] == an integer value indicating the identity of the segment
                      containing the pixel img[r][c].  
            - The returned num_blobs is the number of labeled segments, including the
              background segment.  Therefore, the returned number is 1+(the max value in
              label_img).
    !*/
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed<unsigned char>, py::arg("img"),py::arg("background_thresh"),py::arg("smoothing")=0);
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed<uint16_t>, py::arg("img"),py::arg("background_thresh"),py::arg("smoothing")=0);
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed<uint32_t>, py::arg("img"),py::arg("background_thresh"),py::arg("smoothing")=0);
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed<float>, py::arg("img"),py::arg("background_thresh"),py::arg("smoothing")=0);
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed<double>, docs, py::arg("img"),py::arg("background_thresh"),py::arg("smoothing")=0);

    docs = "This version of label_connected_blobs_watershed simple invokes: \n"
           "   return label_connected_blobs_watershed(img, partition_pixels(img))";
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed2<unsigned char>, py::arg("img"));
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed2<uint16_t>, py::arg("img"));
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed2<uint32_t>, py::arg("img"));
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed2<float>, py::arg("img"));
    m.def("label_connected_blobs_watershed", py_label_connected_blobs_watershed2<double>, docs, py::arg("img"));


    docs = 
"Converts a grayscale image into a jet colored image.  This is an image where dark \n\
pixels are dark blue and larger values become light blue, then yellow, and then \n\
finally red as they approach the maximum pixel values." ;
    m.def("jet", py_jet<unsigned char>, py::arg("img"));
    m.def("jet", py_jet<uint16_t>, py::arg("img"));
    m.def("jet", py_jet<uint32_t>, py::arg("img"));
    m.def("jet", py_jet<float>, py::arg("img"));
    m.def("jet", py_jet<double>, docs, py::arg("img"));

    docs = 
"- randomly generates a mapping from gray level pixel values \n\
  to the RGB pixel space and then uses this mapping to create \n\
  a colored version of img.  Returns an image which represents \n\
  this colored version of img. \n\
- black pixels in img will remain black in the output image.  ";
    /*!
        - randomly generates a mapping from gray level pixel values
          to the RGB pixel space and then uses this mapping to create
          a colored version of img.  Returns an image which represents
          this colored version of img.
        - black pixels in img will remain black in the output image.  
    !*/
    m.def("randomly_color_image", py_randomly_color_image<unsigned char>, py::arg("img"));
    m.def("randomly_color_image", py_randomly_color_image<uint16_t>, py::arg("img"));
    m.def("randomly_color_image", py_randomly_color_image<uint32_t>, docs, py::arg("img"));



    docs =
"requires \n\
    - all pixels in img are set to either 255 or 0. \n\
      (i.e. it must be a binary image) \n\
ensures \n\
    - This routine finds endpoints of lines in a thinned binary image.  For \n\
      example, if the image was produced by skeleton() or something like a Canny \n\
      edge detector then you can use find_line_endpoints() to find the pixels \n\
      sitting on the ends of lines.";
    /*!
        requires
            - all pixels in img are set to either 255 or 0.
              (i.e. it must be a binary image)
        ensures
            - This routine finds endpoints of lines in a thinned binary image.  For
              example, if the image was produced by skeleton() or something like a Canny
              edge detector then you can use find_line_endpoints() to find the pixels
              sitting on the ends of lines.
    !*/
    m.def("find_line_endpoints", find_line_endpoints<numpy_image<unsigned char>>, docs, py::arg("img"));


    m.def("get_rect", [](const py::array& img){ return rectangle(0,0,(long)img.shape(1)-1,(long)img.shape(0)-1); },
        "returns a rectangle(0,0,img.shape(1)-1,img.shape(0)-1).  Therefore, it is the rectangle that bounds the image.", 
        py::arg("img")  );


    const char* grad_docs =
"- Let VALID_AREA = shrink_rect(get_rect(img),get_scale()). \n\
- This routine computes the requested gradient of img at each location in VALID_AREA. \n\
  The gradients are returned in a new image of the same dimensions as img.  All pixels \n\
  outside VALID_AREA are set to 0.  VALID_AREA is also returned.  I.e. we return a tuple \n\
  where the first element is the gradient image and the second is VALID_AREA.";

    const char* filt_docs =
"- Returns the filter used by the indicated derivative to compute the image gradient. \n\
  That is, the output gradients are found by cross correlating the returned filter with \n\
  the input image. \n\
- The returned filter has get_scale()*2+1 rows and columns." ;

    const char* class_docs = 
"This class is a tool for computing first and second derivatives of an \n\
image.  It does this by fitting a quadratic surface around each pixel and \n\
then computing the gradients of that quadratic surface.  For the details \n\
see the paper: \n\
    Quadratic models for curved line detection in SAR CCD by Davis E. King \n\
    and Rhonda D. Phillips \n\
 \n\
This technique gives very accurate gradient estimates and is also very fast \n\
since the entire gradient estimation procedure, for each type of gradient, \n\
is accomplished by cross-correlating the image with a single separable \n\
filter.  This means you can compute gradients at very large scales (e.g. by \n\
fitting the quadratic to a large window, like a 99x99 window) and it still \n\
runs very quickly.";

    py::class_<image_gradients>(m, "image_gradients", class_docs)
        .def(py::init<long>(), "Creates this class with the provided scale. i.e. get_scale()==scale. \nscale must be >= 1.", py::arg("scale"))
        .def(py::init<>(), "Creates this class with a scale of 1. i.e. get_scale()==1")
        .def("gradient_x", [](image_gradients& g, const numpy_image<unsigned char>& img){
            numpy_image<float> out;
            auto rect=g.gradient_x(img,out); 
            return py::make_tuple(out,rect);
            },  py::arg("img"))
        .def("gradient_x", [](image_gradients& g, const numpy_image<float>& img){
            numpy_image<float> out;
            auto rect=g.gradient_x(img,out); 
            return py::make_tuple(out,rect);
            }, grad_docs, py::arg("img"))
        .def("gradient_y", [](image_gradients& g, const numpy_image<unsigned char>& img){
            numpy_image<float> out;
            auto rect=g.gradient_y(img,out); 
            return py::make_tuple(out,rect);
            },  py::arg("img"))
        .def("gradient_y", [](image_gradients& g, const numpy_image<float>& img){
            numpy_image<float> out;
            auto rect=g.gradient_y(img,out); 
            return py::make_tuple(out,rect);
            }, grad_docs, py::arg("img"))
        .def("gradient_xx", [](image_gradients& g, const numpy_image<unsigned char>& img){
            numpy_image<float> out;
            auto rect=g.gradient_xx(img,out); 
            return py::make_tuple(out,rect);
            }, py::arg("img"))
        .def("gradient_xx", [](image_gradients& g, const numpy_image<float>& img){
            numpy_image<float> out;
            auto rect=g.gradient_xx(img,out); 
            return py::make_tuple(out,rect);
            }, grad_docs, py::arg("img"))
        .def("gradient_xy", [](image_gradients& g, const numpy_image<unsigned char>& img){
            numpy_image<float> out;
            auto rect=g.gradient_xy(img,out); 
            return py::make_tuple(out,rect);
            }, py::arg("img"))
        .def("gradient_xy", [](image_gradients& g, const numpy_image<float>& img){
            numpy_image<float> out;
            auto rect=g.gradient_xy(img,out); 
            return py::make_tuple(out,rect);
            }, grad_docs, py::arg("img"))
        .def("gradient_yy", [](image_gradients& g, const numpy_image<unsigned char>& img){
            numpy_image<float> out;
            auto rect=g.gradient_yy(img,out); 
            return py::make_tuple(out,rect);
            }, py::arg("img"))
        .def("gradient_yy", [](image_gradients& g, const numpy_image<float>& img){
            numpy_image<float> out;
            auto rect=g.gradient_yy(img,out); 
            return py::make_tuple(out,rect);
            }, grad_docs, py::arg("img"))
        .def("get_x_filter", [](image_gradients& g){ return numpy_image<float>(g.get_x_filter()); }, filt_docs)
        .def("get_y_filter", [](image_gradients& g){ return numpy_image<float>(g.get_y_filter()); }, filt_docs)
        .def("get_xx_filter", [](image_gradients& g){ return numpy_image<float>(g.get_xx_filter()); }, filt_docs)
        .def("get_xy_filter", [](image_gradients& g){ return numpy_image<float>(g.get_xy_filter()); }, filt_docs)
        .def("get_yy_filter", [](image_gradients& g){ return numpy_image<float>(g.get_yy_filter()); }, filt_docs)
        .def("get_scale", &image_gradients::get_scale, 
"When we estimate a gradient we do so by fitting a quadratic filter so a window of size \n\
get_scale()*2+1 centered on each pixel.  Therefore, the scale parameter controls the size \n\
of gradients we will find.  For example, a very large scale will cause the gradient_xx() \n\
to be insensitive to high frequency noise in the image while smaller scales would be more \n\
sensitive to such fluctuations in the image." 
        );


    docs = 
"Converts an image to a target pixel type.  dtype must be a string containing one of the following: \n\
    uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel \n\
 \n\
When converting from a color space with more than 255 values the pixel intensity is \n\
saturated at the minimum and maximum pixel values of the target pixel type.  For \n\
example, if you convert a float valued image to uint8 then float values will be \n\
truncated to integers and values larger than 255 are converted to 255 while values less \n\
than 0 are converted to 0.";
    /*!
    Converts an image to a target pixel type.  dtype must be a string containing one of the following:
        uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel

    When converting from a color space with more than 255 values the pixel intensity is
    saturated at the minimum and maximum pixel values of the target pixel type.  For
    example, if you convert a float valued image to uint8 then float values will be
    truncated to integers and values larger than 255 are converted to 255 while values less
    than 0 are converted to 0.
    !*/
    m.def("convert_image", convert_image<uint8_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<uint16_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<uint32_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<uint64_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<int8_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<int16_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<int32_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<int64_t>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<float>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<double>, py::arg("img"), py::arg("dtype"));
    m.def("convert_image", convert_image<rgb_pixel>, docs, py::arg("img"), py::arg("dtype"));

    m.def("convert_rgb_to_grayscale", &convert_rgb_to_grayscale, 
        "Convert a RGB image to a uint8 grayscale image.", py::arg("img"));

    docs = 
"requires \n\
    - thresh > 0 \n\
ensures \n\
    - Converts an image to a target pixel type.  dtype must be a string containing one of the following: \n\
      uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel \n\
 \n\
      The contents of img will be scaled to fit the dynamic range of the target \n\
      pixel type.  The thresh parameter is used to filter source pixel values which \n\
      are outliers.  These outliers will saturate at the edge of the destination \n\
      image's dynamic range. \n\
    - Specifically, for all valid r and c: \n\
        - We scale img[r][c] into the dynamic range of the target pixel type.  This \n\
          is done using the mean and standard deviation of img. Call the mean M and \n\
          the standard deviation D.  Then the scaling from source to destination is \n\
          performed using the following mapping: \n\
            let SRC_UPPER  = min(M + thresh*D, max(img)) \n\
            let SRC_LOWER  = max(M - thresh*D, min(img)) \n\
            let DEST_UPPER = max value possible for the selected dtype.  \n\
            let DEST_LOWER = min value possible for the selected dtype. \n\
 \n\
            MAPPING: [SRC_LOWER, SRC_UPPER] -> [DEST_LOWER, DEST_UPPER] \n\
 \n\
          Where this mapping is a linear mapping of values from the left range \n\
          into the right range of values.  Source pixel values outside the left \n\
          range are modified to be at the appropriate end of the range.";
    /*!
        requires
            - thresh > 0
        ensures
            - Converts an image to a target pixel type.  dtype must be a string containing one of the following:
              uint8, int8, uint16, int16, uint32, int32, uint64, int64, float32, float, float64, double, or rgb_pixel

              The contents of img will be scaled to fit the dynamic range of the target
              pixel type.  The thresh parameter is used to filter source pixel values which
              are outliers.  These outliers will saturate at the edge of the destination
              image's dynamic range.
            - Specifically, for all valid r and c:
                - We scale img[r][c] into the dynamic range of the target pixel type.  This
                  is done using the mean and standard deviation of img. Call the mean M and
                  the standard deviation D.  Then the scaling from source to destination is
                  performed using the following mapping:
                    let SRC_UPPER  = min(M + thresh*D, max(img))
                    let SRC_LOWER  = max(M - thresh*D, min(img))
                    let DEST_UPPER = max value possible for the selected dtype. 
                    let DEST_LOWER = min value possible for the selected dtype.

                    MAPPING: [SRC_LOWER, SRC_UPPER] -> [DEST_LOWER, DEST_UPPER]

                  Where this mapping is a linear mapping of values from the left range
                  into the right range of values.  Source pixel values outside the left
                  range are modified to be at the appropriate end of the range.
    !*/
    m.def("convert_image_scaled", convert_image_scaled<uint8_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<uint16_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<uint32_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<uint64_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<int8_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<int16_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<int32_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<int64_t>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<float>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<double>, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);
    m.def("convert_image_scaled", convert_image_scaled<rgb_pixel>, docs, py::arg("img"), py::arg("dtype"), py::arg("thresh")=4);





    class_docs =
"This is a simple object to help create image pyramids.  In particular, it \n\
downsamples images at a ratio of N to N-1. \n\
 \n\
Note that setting N to 1 means that this object functions like \n\
pyramid_disable (defined at the bottom of this file).   \n\
 \n\
WARNING, when mapping rectangles from one layer of a pyramid \n\
to another you might end up with rectangles which extend slightly  \n\
outside your images.  This is because points on the border of an  \n\
image at a higher pyramid layer might correspond to points outside  \n\
images at lower layers.  So just keep this in mind.  Note also \n\
that it's easy to deal with.  Just say something like this: \n\
    rect = rect.intersect(get_rect(my_image)); # keep rect inside my_image ";
        /*!
                This is a simple object to help create image pyramids.  In particular, it
                downsamples images at a ratio of N to N-1.

                Note that setting N to 1 means that this object functions like
                pyramid_disable (defined at the bottom of this file).  

                WARNING, when mapping rectangles from one layer of a pyramid
                to another you might end up with rectangles which extend slightly 
                outside your images.  This is because points on the border of an 
                image at a higher pyramid layer might correspond to points outside 
                images at lower layers.  So just keep this in mind.  Note also
                that it's easy to deal with.  Just say something like this:
                    rect = rect.intersect(get_rect(my_image)); # keep rect inside my_image 
        !*/

    docs =
"- Downsamples img to make a new image that is roughly (pyramid_downsampling_rate()-1)/pyramid_downsampling_rate()  \n\
  times the size of the original image.   \n\
- The location of a point P in original image will show up at point point_down(P) \n\
  in the downsampled image.   \n\
- Note that some points on the border of the original image might correspond to  \n\
  points outside the downsampled image.";
        /*!
          - Downsamples img to make a new image that is roughly (pyramid_downsampling_rate()-1)/pyramid_downsampling_rate() 
            times the size of the original image.  
          - The location of a point P in original image will show up at point point_down(P)
            in the downsampled image.  
          - Note that some points on the border of the original image might correspond to 
            points outside the downsampled image.  
        !*/
    py::class_<py_pyramid_down>(m, "pyramid_down", class_docs)
        .def(py::init<unsigned int>(), "Creates this class with the provided downsampling rate. i.e. pyramid_downsampling_rate()==N. \nN must be in the range 1 to 20.", py::arg("N"))
        .def(py::init<>(), "Creates this class with pyramid_downsampling_rate()==2")
        .def("pyramid_downsampling_rate", &py_pyramid_down::pyramid_downsampling_rate,
            "Returns a number N that defines the downsampling rate.  In particular, images are downsampled by a factor of N to N-1.")
        .def("point_up", &py_pyramid_down::point_up<long>,   py::arg("p"))
        .def("point_up", &py_pyramid_down::point_up<double>, 
            "Maps from pixels in a downsampled image to pixels in the original image.",  py::arg("p"))
        .def("point_up", &py_pyramid_down::point_up2<long>,   py::arg("p"), py::arg("levels"))
        .def("point_up", &py_pyramid_down::point_up2<double>, 
            "Applies point_up() to p levels times and returns the result.",  py::arg("p"), py::arg("levels"))
        .def("point_down", &py_pyramid_down::point_down<long>,   py::arg("p"))
        .def("point_down", &py_pyramid_down::point_down<double>, 
            "Maps from pixels in a source image to the corresponding pixels in the downsampled image.", py::arg("p"))
        .def("point_down", &py_pyramid_down::point_down2<long>,   py::arg("p"), py::arg("levels"))
        .def("point_down", &py_pyramid_down::point_down2<double>, "Applies point_down() to p levels times and returns the result.",   
            py::arg("p"), py::arg("levels"))
        .def("rect_down", &py_pyramid_down::rect_down<rectangle>,   py::arg("rect"))
        .def("rect_down", &py_pyramid_down::rect_down<drectangle>,
          "returns drectangle(point_down(rect.tl_corner()), point_down(rect.br_corner()));\n (i.e. maps rect into a downsampled)",
          py::arg("rect"))
        .def("rect_down", &py_pyramid_down::rect_down2<rectangle>,   py::arg("rect"), py::arg("levels"))
        .def("rect_down", &py_pyramid_down::rect_down2<drectangle>, "Applies rect_down() to rect levels times and returns the result.",
            py::arg("rect"), py::arg("levels"))
        .def("rect_up", &py_pyramid_down::rect_up<rectangle>,   py::arg("rect"))
        .def("rect_up", &py_pyramid_down::rect_up<drectangle>,   
          "returns drectangle(point_up(rect.tl_corner()), point_up(rect.br_corner()));\n (i.e. maps rect into a parent image)",
            py::arg("rect"))
        .def("rect_up", &py_pyramid_down::rect_up2<rectangle>,   py::arg("rect"), py::arg("levels"))
        .def("rect_up", &py_pyramid_down::rect_up2<drectangle>,  "Applies rect_up() to rect levels times and returns the result.",
            py::arg("p"), py::arg("levels"))
        .def("__call__", &py_pyramid_down::down<uint8_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<uint16_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<uint32_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<uint64_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<int8_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<int16_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<int32_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<int64_t>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<float>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<double>,   py::arg("img"))
        .def("__call__", &py_pyramid_down::down<rgb_pixel>, docs,  py::arg("img"));


    docs =
"requires \n\
    - xx, xy, and yy all have the same dimensions. \n\
ensures \n\
    - This routine is similar to sobel_edge_detector(), except instead of finding \n\
      an edge it finds a bright/white line.  For example, the border between a \n\
      black piece of paper and a white table is an edge, but a curve drawn with a \n\
      pencil on a piece of paper makes a line.  Therefore, the output of this \n\
      routine is a vector field encoded in the horz and vert images, which are \n\
      returned in a tuple where the first element is horz and the second is vert. \n\
 \n\
      The vector obtains a large magnitude when centered on a bright line in an image and the \n\
      direction of the vector is perpendicular to the line.  To be very precise, \n\
      each vector points in the direction of greatest change in second derivative \n\
      and the magnitude of the vector encodes the derivative magnitude in that \n\
      direction.  Moreover, if the second derivative is positive then the output \n\
      vector is zero.  This zeroing if positive gradients causes the output to be \n\
      sensitive only to bright lines surrounded by darker pixels. \n\
 \n\
    - We assume that xx, xy, and yy are the 3 second order gradients of the image \n\
      in question.  You can obtain these gradients using the image_gradients class. \n\
    - The output images will have the same dimensions as the input images. ";
    /*!
        requires
            - xx, xy, and yy all have the same dimensions.
        ensures
            - This routine is similar to sobel_edge_detector(), except instead of finding
              an edge it finds a bright/white line.  For example, the border between a
              black piece of paper and a white table is an edge, but a curve drawn with a
              pencil on a piece of paper makes a line.  Therefore, the output of this
              routine is a vector field encoded in the horz and vert images, which are
              returned in a tuple where the first element is horz and the second is vert.

              The vector obtains a large magnitude when centered on a bright line in an image and the
              direction of the vector is perpendicular to the line.  To be very precise,
              each vector points in the direction of greatest change in second derivative
              and the magnitude of the vector encodes the derivative magnitude in that
              direction.  Moreover, if the second derivative is positive then the output
              vector is zero.  This zeroing if positive gradients causes the output to be
              sensitive only to bright lines surrounded by darker pixels.

            - We assume that xx, xy, and yy are the 3 second order gradients of the image
              in question.  You can obtain these gradients using the image_gradients class.
            - The output images will have the same dimensions as the input images. 
    !*/
    m.def("find_bright_lines",     &py_find_bright_lines,     docs, py::arg("xx"), py::arg("xy"), py::arg("yy"));



    docs =
"requires \n\
    - xx, xy, and yy all have the same dimensions. \n\
ensures \n\
    - This routine is similar to sobel_edge_detector(), except instead of finding \n\
      an edge it finds a dark line.  For example, the border between a black piece \n\
      of paper and a white table is an edge, but a curve drawn with a pencil on a \n\
      piece of paper makes a line.  Therefore, the output of this routine is a \n\
      vector field encoded in the horz and vert images, which are returned in a \n\
      tuple where the first element is horz and the second is vert. \n\
 \n\
      The vector obtains a large magnitude when centered on a dark line in an image \n\
      and the direction of the vector is perpendicular to the line.  To be very \n\
      precise, each vector points in the direction of greatest change in second \n\
      derivative and the magnitude of the vector encodes the derivative magnitude \n\
      in that direction.  Moreover, if the second derivative is negative then the \n\
      output vector is zero.  This zeroing if negative gradients causes the output \n\
      to be sensitive only to dark lines surrounded by darker pixels. \n\
 \n\
    - We assume that xx, xy, and yy are the 3 second order gradients of the image \n\
      in question.  You can obtain these gradients using the image_gradients class. \n\
    - The output images will have the same dimensions as the input images. ";
    /*!
        requires
            - xx, xy, and yy all have the same dimensions.
        ensures
            - This routine is similar to sobel_edge_detector(), except instead of finding
              an edge it finds a dark line.  For example, the border between a black piece
              of paper and a white table is an edge, but a curve drawn with a pencil on a
              piece of paper makes a line.  Therefore, the output of this routine is a
              vector field encoded in the horz and vert images, which are returned in a
              tuple where the first element is horz and the second is vert.

              The vector obtains a large magnitude when centered on a dark line in an image
              and the direction of the vector is perpendicular to the line.  To be very
              precise, each vector points in the direction of greatest change in second
              derivative and the magnitude of the vector encodes the derivative magnitude
              in that direction.  Moreover, if the second derivative is negative then the
              output vector is zero.  This zeroing if negative gradients causes the output
              to be sensitive only to dark lines surrounded by darker pixels.

            - We assume that xx, xy, and yy are the 3 second order gradients of the image
              in question.  You can obtain these gradients using the image_gradients class.
            - The output images will have the same dimensions as the input images. 
    !*/
    m.def("find_dark_lines",       &py_find_dark_lines,       docs, py::arg("xx"), py::arg("xy"), py::arg("yy"));



    docs =
"requires \n\
    - xx, xy, and yy all have the same dimensions. \n\
ensures \n\
    - This routine finds bright \"keypoints\" in an image.  In general, these are \n\
      bright/white localized blobs.  It does this by computing the determinant of \n\
      the image Hessian at each location and storing this value into the returned \n\
      image if both eigenvalues of the Hessian are negative.  If either eigenvalue \n\
      is positive then the output value for that pixel is 0.  I.e. \n\
        - Let OUT denote the returned image. \n\
        - for all valid r,c: \n\
            - OUT[r][c] == a number >= 0 and larger values indicate the \n\
              presence of a keypoint at this pixel location. \n\
    - We assume that xx, xy, and yy are the 3 second order gradients of the image \n\
      in question.  You can obtain these gradients using the image_gradients class. \n\
    - The output image will have the same dimensions as the input images.";
    /*!
        requires
            - xx, xy, and yy all have the same dimensions.
        ensures
            - This routine finds bright "keypoints" in an image.  In general, these are
              bright/white localized blobs.  It does this by computing the determinant of
              the image Hessian at each location and storing this value into the returned
              image if both eigenvalues of the Hessian are negative.  If either eigenvalue
              is positive then the output value for that pixel is 0.  I.e.
                - Let OUT denote the returned image.
                - for all valid r,c:
                    - OUT[r][c] == a number >= 0 and larger values indicate the
                      presence of a keypoint at this pixel location.
            - We assume that xx, xy, and yy are the 3 second order gradients of the image
              in question.  You can obtain these gradients using the image_gradients class.
            - The output image will have the same dimensions as the input images.
    !*/
    m.def("find_bright_keypoints", &py_find_bright_keypoints, docs, py::arg("xx"), py::arg("xy"), py::arg("yy"));



    docs =
"requires \n\
    - xx, xy, and yy all have the same dimensions. \n\
ensures \n\
    - This routine finds dark \"keypoints\" in an image.  In general, these are \n\
      dark localized blobs.  It does this by computing the determinant of \n\
      the image Hessian at each location and storing this value into the returned \n\
      image if both eigenvalues of the Hessian are negative.  If either eigenvalue \n\
      is negative then the output value for that pixel is 0.  I.e. \n\
        - Let OUT denote the returned image. \n\
        - for all valid r,c: \n\
            - OUT[r][c] == a number >= 0 and larger values indicate the \n\
              presence of a keypoint at this pixel location. \n\
    - We assume that xx, xy, and yy are the 3 second order gradients of the image \n\
      in question.  You can obtain these gradients using the image_gradients class. \n\
    - The output image will have the same dimensions as the input images.";
    /*!
        requires
            - xx, xy, and yy all have the same dimensions.
        ensures
            - This routine finds dark "keypoints" in an image.  In general, these are
              dark localized blobs.  It does this by computing the determinant of
              the image Hessian at each location and storing this value into the returned
              image if both eigenvalues of the Hessian are negative.  If either eigenvalue
              is negative then the output value for that pixel is 0.  I.e.
                - Let OUT denote the returned image.
                - for all valid r,c:
                    - OUT[r][c] == a number >= 0 and larger values indicate the
                      presence of a keypoint at this pixel location.
            - We assume that xx, xy, and yy are the 3 second order gradients of the image
              in question.  You can obtain these gradients using the image_gradients class.
            - The output image will have the same dimensions as the input images.
    !*/
    m.def("find_dark_keypoints",   &py_find_dark_keypoints,   docs, py::arg("xx"), py::arg("xy"), py::arg("yy"));



    docs = 
"requires \n\
    - The two input images have the same dimensions. \n\
ensures \n\
    - Returns an image, of the same dimensions as the input.  Each element in this \n\
      image holds the edge strength at that location.  Moreover, edge pixels that are not  \n\
      local maximizers have been set to 0. \n\
    - let edge_strength(r,c) == sqrt(pow(horz[r][c],2) + pow(vert[r][c],2)) \n\
      (i.e. The Euclidean norm of the gradient) \n\
    - let OUT denote the returned image. \n\
    - for all valid r and c: \n\
        - if (edge_strength(r,c) is at a maximum with respect to its 2 neighboring \n\
          pixels along the line indicated by the image gradient vector (horz[r][c],vert[r][c])) then \n\
            - OUT[r][c] == edge_strength(r,c) \n\
        - else \n\
            - OUT[r][c] == 0";
    /*!
        requires
            - The two input images have the same dimensions.
        ensures
            - Returns an image, of the same dimensions as the input.  Each element in this
              image holds the edge strength at that location.  Moreover, edge pixels that are not 
              local maximizers have been set to 0.
            - let edge_strength(r,c) == sqrt(pow(horz[r][c],2) + pow(vert[r][c],2))
              (i.e. The Euclidean norm of the gradient)
            - let OUT denote the returned image.
            - for all valid r and c:
                - if (edge_strength(r,c) is at a maximum with respect to its 2 neighboring
                  pixels along the line indicated by the image gradient vector (horz[r][c],vert[r][c])) then
                    - OUT[r][c] == edge_strength(r,c)
                - else
                    - OUT[r][c] == 0
    !*/
    m.def("suppress_non_maximum_edges", &py_suppress_non_maximum_edges, docs, py::arg("horz"), py::arg("vert"));
    m.def("suppress_non_maximum_edges", &py_suppress_non_maximum_edges2,
        "Performs: return suppress_non_maximum_edges(horz_and_vert_gradients[0], horz_and_vert_gradients[1])",
        py::arg("horz_and_vert_gradients"));



    docs =
"requires \n\
    - non_max_suppression_radius >= 0 \n\
ensures \n\
    - Scans the given image and finds all pixels with values >= thresh that are \n\
      also local maximums within their 8-connected neighborhood of the image.  Such \n\
      pixels are collected, sorted in decreasing order of their pixel values, and \n\
      then non-maximum suppression is applied to this list of points using the \n\
      given non_max_suppression_radius.  The final list of peaks is then returned. \n\
 \n\
      Therefore, the returned list, V, will have these properties: \n\
        - len(V) == the number of peaks found in the image. \n\
        - When measured in image coordinates, no elements of V are within \n\
          non_max_suppression_radius distance of each other.  That is, for all valid i!=j \n\
          it is true that length(V[i]-V[j]) > non_max_suppression_radius. \n\
        - For each element of V, that element has the maximum pixel value of all \n\
          pixels in the ball centered on that pixel with radius \n\
          non_max_suppression_radius.";
    /*!
        requires
            - non_max_suppression_radius >= 0
        ensures
            - Scans the given image and finds all pixels with values >= thresh that are
              also local maximums within their 8-connected neighborhood of the image.  Such
              pixels are collected, sorted in decreasing order of their pixel values, and
              then non-maximum suppression is applied to this list of points using the
              given non_max_suppression_radius.  The final list of peaks is then returned.

              Therefore, the returned list, V, will have these properties:
                - len(V) == the number of peaks found in the image.
                - When measured in image coordinates, no elements of V are within
                  non_max_suppression_radius distance of each other.  That is, for all valid i!=j
                  it is true that length(V[i]-V[j]) > non_max_suppression_radius.
                - For each element of V, that element has the maximum pixel value of all
                  pixels in the ball centered on that pixel with radius
                  non_max_suppression_radius.
    !*/
    m.def("find_peaks", &py_find_peaks<float>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<double>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<uint8_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<uint16_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<uint32_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<uint64_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<int8_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<int16_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<int32_t>, py::arg("img"), py::arg("non_max_suppression_radius"), py::arg("thresh"));
    m.def("find_peaks", &py_find_peaks<int64_t>, py::arg("img"), docs, py::arg("non_max_suppression_radius"), py::arg("thresh"));

    m.def("find_peaks", &py_find_peaks2<float>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<double>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<uint8_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<uint16_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<uint32_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<uint64_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<int8_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<int16_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<int32_t>, py::arg("img"), py::arg("non_max_suppression_radius")=0);
    m.def("find_peaks", &py_find_peaks2<int64_t>, py::arg("img"),
        "performs: return find_peaks(img, non_max_suppression_radius, partition_pixels(img))",
        py::arg("non_max_suppression_radius")=0);



    docs =
"Applies the sobel edge detector to the given input image and returns two gradient \n\
images in a tuple.  The first contains the x gradients and the second contains the \n\
y gradients of the image.";
    /*!
         Applies the sobel edge detector to the given input image and returns two gradient
         images in a tuple.  The first contains the x gradients and the second contains the
         y gradients of the image.
    !*/
    m.def("sobel_edge_detector", &py_sobel_edge_detector<uint8_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<uint16_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<uint32_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<uint64_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<int8_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<int16_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<int32_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<int64_t>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<float>, py::arg("img"));
    m.def("sobel_edge_detector", &py_sobel_edge_detector<double>, docs, py::arg("img"));


    docs =
"Applies hysteresis thresholding to img and returns the results.  In particular, \n\
pixels in img with values >= upper_thresh have an output value of 255 and all \n\
others have a value of 0 unless they are >= lower_thresh and are connected to a \n\
pixel with a value >= upper_thresh, in which case they have a value of 255.  Here \n\
pixels are connected if there is a path between them composed of pixels that would \n\
receive an output of 255.";
    /*!
        Applies hysteresis thresholding to img and returns the results.  In particular,
        pixels in img with values >= upper_thresh have an output value of 255 and all
        others have a value of 0 unless they are >= lower_thresh and are connected to a
        pixel with a value >= upper_thresh, in which case they have a value of 255.  Here
        pixels are connected if there is a path between them composed of pixels that would
        receive an output of 255.
    !*/
    m.def("hysteresis_threshold", &py_hysteresis_threshold<uint8_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<uint16_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<uint32_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<uint64_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<int8_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<int16_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<int32_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<int64_t>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<float>, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold<double>, docs, py::arg("img"), py::arg("lower_thresh"), py::arg("upper_thresh"));

    docs =
"performs: return hysteresis_threshold(img, t1, t2) where the thresholds \n\
are first obtained by calling [t1, t2]=partition_pixels(img).";
    /*!
        performs: return hysteresis_threshold(img, t1, t2) where the thresholds
        are first obtained by calling [t1, t2]=partition_pixels(img).
    !*/
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<uint8_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<uint16_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<uint32_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<uint64_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<int8_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<int16_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<int32_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<int64_t>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<float>, py::arg("img"));
    m.def("hysteresis_threshold", &py_hysteresis_threshold2<double>, docs, py::arg("img"));
}

