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_image_wrapper.cpp
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316 lines (262 loc) · 11.5 KB
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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);
}
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