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#include <pybind11/pybind11.h>

#include <pybind11/numpy.h>

#include <algorithm>

#include "_image_resample.h"

#include "py_converters.h"

namespace py = pybind11;

using namespace pybind11::literals;

/**********************************************************************

* Free functions

* */

const char* image_resample__doc__ =

R"""(Resample input_array, blending it in-place into output_array, using an affine transform.

Parameters

----------

input_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`

If 2-d, the image is grayscale. If 3-d, the image must be of size 4 in the last

dimension and represents RGBA data.

output_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`

The dtype and number of dimensions must match `input_array`.

transform : matplotlib.transforms.Transform instance

The transformation from the input array to the output array.

interpolation : int, default: NEAREST

The interpolation method. Must be one of the following constants defined in this

module:

NEAREST, BILINEAR, BICUBIC, SPLINE16, SPLINE36, HANNING, HAMMING, HERMITE, KAISER,

QUADRIC, CATROM, GAUSSIAN, BESSEL, MITCHELL, SINC, LANCZOS, BLACKMAN

resample : bool, optional

When `True`, use a full resampling method. When `False`, only resample when the

output image is larger than the input image.

alpha : float, default: 1

The transparency level, from 0 (transparent) to 1 (opaque).

norm : bool, default: False

Whether to norm the interpolation function.

radius: float, default: 1

The radius of the kernel, if method is SINC, LANCZOS or BLACKMAN.

)""";

static py::array_t<double>

_get_transform_mesh(const py::object& transform, const py::ssize_t *dims)

{

/* TODO: Could we get away with float, rather than double, arrays here? */

/* Given a non-affine transform object, create a mesh that maps

every pixel center in the output image to the input image. This is used

as a lookup table during the actual resampling. */

// If attribute doesn't exist, raises Python AttributeError

auto inverse = transform.attr("inverted")();

py::ssize_t mesh_dims[2] = {dims[0]*dims[1], 2};

py::array_t<double> input_mesh(mesh_dims);

auto p = input_mesh.mutable_data();

for (auto y = 0; y < dims[0]; ++y) {

for (auto x = 0; x < dims[1]; ++x) {

// The convention for the supplied transform is that pixel centers

// are at 0.5, 1.5, 2.5, etc.

*p++ = (double)x + 0.5;

*p++ = (double)y + 0.5;

}

auto output_mesh = inverse.attr("transform")(input_mesh);

auto output_mesh_array =

py::array_t<double, py::array::c_style | py::array::forcecast>(output_mesh);

if (output_mesh_array.ndim() != 2) {

throw std::runtime_error(

"Inverse transformed mesh array should be 2D not {}D"_s.format(

output_mesh_array.ndim()));

}

return output_mesh_array;

}

// Using generic py::array for input and output arrays rather than the more usual

// py::array_t<type> as this function supports multiple array dtypes.

static void

image_resample(py::array input_array,

py::array& output_array,

const py::object& transform,

interpolation_e interpolation,

bool resample_, // Avoid name clash with resample() function

float alpha,

bool norm,

float radius)

{

// Validate input_array

auto dtype = input_array.dtype(); // Validated when determine resampler below

auto ndim = input_array.ndim();

if (ndim != 2 && ndim != 3) {

throw std::invalid_argument("Input array must be a 2D or 3D array");

}

if (ndim == 3 && input_array.shape(2) != 4) {

throw std::invalid_argument(

"3D input array must be RGBA with shape (M, N, 4), has trailing dimension of {}"_s.format(

input_array.shape(2)));

}

// Ensure input array is contiguous, regardless of dtype

input_array = py::array::ensure(input_array, py::array::c_style);

// Validate output array

auto out_ndim = output_array.ndim();

if (out_ndim != ndim) {

throw std::invalid_argument(

"Input ({}D) and output ({}D) arrays have different dimensionalities"_s.format(

ndim, out_ndim));

}

if (out_ndim == 3 && output_array.shape(2) != 4) {

throw std::invalid_argument(

"3D output array must be RGBA with shape (M, N, 4), has trailing dimension of {}"_s.format(

output_array.shape(2)));

}

if (!output_array.dtype().is(dtype)) {

throw std::invalid_argument("Input and output arrays have mismatched types");

}

if ((output_array.flags() & py::array::c_style) == 0) {

throw std::invalid_argument("Output array must be C-contiguous");

}

if (!output_array.writeable()) {

throw std::invalid_argument("Output array must be writeable");

}

resample_params_t params;

params.interpolation = interpolation;

params.transform_mesh = nullptr;

params.resample = resample_;

params.norm = norm;

params.radius = radius;

params.alpha = alpha;

// Only used if transform is not affine.

// Need to keep it in scope for the duration of this function.

py::array_t<double> transform_mesh;

// Validate transform

if (transform.is_none()) {

params.is_affine = true;

} else {

// Raises Python AttributeError if no such attribute or TypeError if cast fails

bool is_affine = py::cast<bool>(transform.attr("is_affine"));

if (is_affine) {

convert_trans_affine(transform, params.affine);

params.is_affine = is_affine;

} else {

transform_mesh = _get_transform_mesh(transform, output_array.shape());

params.transform_mesh = transform_mesh.data();

params.is_affine = false;

}

if (auto resampler =

(ndim == 2) ? (

(dtype.equal(py::dtype::of<std::uint8_t>())) ? resample<agg::gray8> :

(dtype.equal(py::dtype::of<std::int8_t>())) ? resample<agg::gray8> :

(dtype.equal(py::dtype::of<std::uint16_t>())) ? resample<agg::gray16> :

(dtype.equal(py::dtype::of<std::int16_t>())) ? resample<agg::gray16> :

(dtype.equal(py::dtype::of<float>())) ? resample<agg::gray32> :

(dtype.equal(py::dtype::of<double>())) ? resample<agg::gray64> :

nullptr) : (

// ndim == 3

(dtype.equal(py::dtype::of<std::uint8_t>())) ? resample<agg::rgba8> :

(dtype.equal(py::dtype::of<std::int8_t>())) ? resample<agg::rgba8> :

(dtype.equal(py::dtype::of<std::uint16_t>())) ? resample<agg::rgba16> :

(dtype.equal(py::dtype::of<std::int16_t>())) ? resample<agg::rgba16> :

(dtype.equal(py::dtype::of<float>())) ? resample<agg::rgba32> :

(dtype.equal(py::dtype::of<double>())) ? resample<agg::rgba64> :

nullptr)) {

Py_BEGIN_ALLOW_THREADS

resampler(

input_array.data(), input_array.shape(1), input_array.shape(0),

output_array.mutable_data(), output_array.shape(1), output_array.shape(0),

params);

Py_END_ALLOW_THREADS

} else {

throw std::invalid_argument("arrays must be of dtype byte, short, float32 or float64");

}

// This is used by matplotlib.testing.compare to calculate RMS and a difference image.

static py::tuple

calculate_rms_and_diff(py::array_t<unsigned char> expected_image,

py::array_t<unsigned char> actual_image)

{

for (const auto & [image, name] : {std::pair{expected_image, "Expected"},

std::pair{actual_image, "Actual"}})

{

if (image.ndim() != 3) {

auto exceptions = py::module_::import("matplotlib.testing.exceptions");

auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");

py::set_error(

ImageComparisonFailure,

"{name} image must be 3-dimensional, but is {ndim}-dimensional"_s.format(

"name"_a=name, "ndim"_a=image.ndim()));

throw py::error_already_set();

}

auto height = expected_image.shape(0);

auto width = expected_image.shape(1);

auto depth = expected_image.shape(2);

if (depth != 3 && depth != 4) {

auto exceptions = py::module_::import("matplotlib.testing.exceptions");

auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");

py::set_error(

ImageComparisonFailure,

"Image must be RGB or RGBA but has depth {depth}"_s.format(

"depth"_a=depth));

throw py::error_already_set();

}

if (height != actual_image.shape(0) || width != actual_image.shape(1) ||

depth != actual_image.shape(2)) {

auto exceptions = py::module_::import("matplotlib.testing.exceptions");

auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");

py::set_error(

ImageComparisonFailure,

"Image sizes do not match expected size: {expected_image.shape} "_s

"actual size {actual_image.shape}"_s.format(

"expected_image"_a=expected_image, "actual_image"_a=actual_image));

throw py::error_already_set();

}

auto expected = expected_image.unchecked<3>();

auto actual = actual_image.unchecked<3>();

py::ssize_t diff_dims[3] = {height, width, 3};

py::array_t<unsigned char> diff_image(diff_dims);

auto diff = diff_image.mutable_unchecked<3>();

double total = 0.0;

for (auto i = 0; i < height; i++) {

for (auto j = 0; j < width; j++) {

for (auto k = 0; k < depth; k++) {

auto pixel_diff = static_cast<double>(expected(i, j, k)) -

static_cast<double>(actual(i, j, k));

total += pixel_diff*pixel_diff;

if (k != 3) { // Hard-code a fully solid alpha channel by omitting it.

diff(i, j, k) = static_cast<unsigned char>(std::clamp(

abs(pixel_diff) * 10, // Expand differences in luminance domain.

0.0, 255.0));

}

total = total / (width * height * depth);

return py::make_tuple(sqrt(total), diff_image);

}

PYBIND11_MODULE(_image, m, py::mod_gil_not_used())

{

py::enum_<interpolation_e>(m, "_InterpolationType")

.value("NEAREST", NEAREST)

.value("BILINEAR", BILINEAR)

.value("BICUBIC", BICUBIC)

.value("SPLINE16", SPLINE16)

.value("SPLINE36", SPLINE36)

.value("HANNING", HANNING)

.value("HAMMING", HAMMING)

.value("HERMITE", HERMITE)

.value("KAISER", KAISER)

.value("QUADRIC", QUADRIC)

.value("CATROM", CATROM)

.value("GAUSSIAN", GAUSSIAN)

.value("BESSEL", BESSEL)

.value("MITCHELL", MITCHELL)

.value("SINC", SINC)

.value("LANCZOS", LANCZOS)

.value("BLACKMAN", BLACKMAN)

.export_values();

m.def("resample", &image_resample,

"input_array"_a,

"output_array"_a,

"transform"_a,

"interpolation"_a = interpolation_e::NEAREST,

"resample"_a = false,

"alpha"_a = 1,

"norm"_a = false,

"radius"_a = 1,

image_resample__doc__);

m.def("calculate_rms_and_diff", &calculate_rms_and_diff,

"expected_image"_a, "actual_image"_a);

}