matplotlib/src/_image_wrapper.cpp at v3.7.x · matplotlib/matplotlib · GitHub

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#include "mplutils.h"

#include "_image_resample.h"

#include "numpy_cpp.h"

#include "py_converters.h"

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

* Free functions

* */

const char* image_resample__doc__ =

"resample(input_array, output_array, matrix, interpolation=NEAREST, alpha=1.0, norm=False, radius=1)\n"

"--\n\n"

"Resample input_array, blending it in-place into output_array, using an\n"

"affine transformation.\n\n"

"Parameters\n"

"----------\n"

"input_array : 2-d or 3-d Numpy array of float, double or uint8\n"

" If 2-d, the image is grayscale. If 3-d, the image must be of size\n"

" 4 in the last dimension and represents RGBA data.\n\n"

"output_array : 2-d or 3-d Numpy array of float, double or uint8\n"

" The dtype and number of dimensions must match `input_array`.\n\n"

"transform : matplotlib.transforms.Transform instance\n"

" The transformation from the input array to the output\n"

" array.\n\n"

"interpolation : int, optional\n"

" The interpolation method. Must be one of the following constants\n"

" defined in this module:\n\n"

" NEAREST (default), BILINEAR, BICUBIC, SPLINE16, SPLINE36,\n"

" HANNING, HAMMING, HERMITE, KAISER, QUADRIC, CATROM, GAUSSIAN,\n"

" BESSEL, MITCHELL, SINC, LANCZOS, BLACKMAN\n\n"

"resample : bool, optional\n"

" When `True`, use a full resampling method. When `False`, only\n"

" resample when the output image is larger than the input image.\n\n"

"alpha : float, optional\n"

" The level of transparency to apply. 1.0 is completely opaque.\n"

" 0.0 is completely transparent.\n\n"

"norm : bool, optional\n"

" Whether to norm the interpolation function. Default is `False`.\n\n"

"radius: float, optional\n"

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

" Default is 1.\n";

static PyArrayObject *

_get_transform_mesh(PyObject *py_affine, npy_intp *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 in the output image to the input image. This is used

as a lookup table during the actual resampling. */

PyObject *py_inverse = NULL;

npy_intp out_dims[3];

out_dims[0] = dims[0] * dims[1];

out_dims[1] = 2;

py_inverse = PyObject_CallMethod(py_affine, "inverted", NULL);

if (py_inverse == NULL) {

return NULL;

}

numpy::array_view<double, 2> input_mesh(out_dims);

double *p = (double *)input_mesh.data();

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

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

*p++ = (double)x;

*p++ = (double)y;

}

PyObject *output_mesh = PyObject_CallMethod(

py_inverse, "transform", "O", input_mesh.pyobj_steal());

Py_DECREF(py_inverse);

if (output_mesh == NULL) {

return NULL;

}

PyArrayObject *output_mesh_array =

(PyArrayObject *)PyArray_ContiguousFromAny(

output_mesh, NPY_DOUBLE, 2, 2);

Py_DECREF(output_mesh);

if (output_mesh_array == NULL) {

return NULL;

}

return output_mesh_array;

}

template<class T>

static void

resample(PyArrayObject* input, PyArrayObject* output, resample_params_t params)

{

Py_BEGIN_ALLOW_THREADS

resample(

(T*)PyArray_DATA(input), PyArray_DIM(input, 1), PyArray_DIM(input, 0),

(T*)PyArray_DATA(output), PyArray_DIM(output, 1), PyArray_DIM(output, 0),

params);

Py_END_ALLOW_THREADS

}

static PyObject *

image_resample(PyObject *self, PyObject* args, PyObject *kwargs)

{

PyObject *py_input_array = NULL;

PyObject *py_output_array = NULL;

PyObject *py_transform = NULL;

resample_params_t params;

PyArrayObject *input_array = NULL;

PyArrayObject *output_array = NULL;

PyArrayObject *transform_mesh_array = NULL;

params.interpolation = NEAREST;

params.transform_mesh = NULL;

params.resample = false;

params.norm = false;

params.radius = 1.0;

params.alpha = 1.0;

const char *kwlist[] = {

"input_array", "output_array", "transform", "interpolation",

"resample", "alpha", "norm", "radius", NULL };

if (!PyArg_ParseTupleAndKeywords(

args, kwargs, "OOO|iO&dO&d:resample", (char **)kwlist,

&py_input_array, &py_output_array, &py_transform,

&params.interpolation, &convert_bool, &params.resample,

&params.alpha, &convert_bool, &params.norm, &params.radius)) {

return NULL;

}

if (params.interpolation < 0 || params.interpolation >= _n_interpolation) {

PyErr_Format(PyExc_ValueError, "invalid interpolation value %d",

params.interpolation);

goto error;

}

input_array = (PyArrayObject *)PyArray_FromAny(

py_input_array, NULL, 2, 3, NPY_ARRAY_C_CONTIGUOUS, NULL);

if (input_array == NULL) {

goto error;

}

if (!PyArray_Check(py_output_array)) {

PyErr_SetString(PyExc_ValueError, "output array must be a NumPy array");

goto error;

}

output_array = (PyArrayObject *)py_output_array;

if (!PyArray_IS_C_CONTIGUOUS(output_array)) {

PyErr_SetString(PyExc_ValueError, "output array must be C-contiguous");

goto error;

}

if (PyArray_NDIM(output_array) < 2 || PyArray_NDIM(output_array) > 3) {

PyErr_SetString(PyExc_ValueError,

"output array must be 2- or 3-dimensional");

goto error;

}

if (py_transform == NULL || py_transform == Py_None) {

params.is_affine = true;

} else {

PyObject *py_is_affine;

int py_is_affine2;

py_is_affine = PyObject_GetAttrString(py_transform, "is_affine");

if (py_is_affine == NULL) {

goto error;

}

py_is_affine2 = PyObject_IsTrue(py_is_affine);

Py_DECREF(py_is_affine);

if (py_is_affine2 == -1) {

goto error;

} else if (py_is_affine2) {

if (!convert_trans_affine(py_transform, &params.affine)) {

goto error;

}

params.is_affine = true;

} else {

transform_mesh_array = _get_transform_mesh(

py_transform, PyArray_DIMS(output_array));

if (transform_mesh_array == NULL) {

goto error;

}

params.transform_mesh = (double *)PyArray_DATA(transform_mesh_array);

params.is_affine = false;

}

if (PyArray_NDIM(input_array) != PyArray_NDIM(output_array)) {

PyErr_Format(

PyExc_ValueError,

"Mismatched number of dimensions. Got %d and %d.",

PyArray_NDIM(input_array), PyArray_NDIM(output_array));

goto error;

}

if (PyArray_TYPE(input_array) != PyArray_TYPE(output_array)) {

PyErr_SetString(PyExc_ValueError, "Mismatched types");

goto error;

}

if (PyArray_NDIM(input_array) == 3) {

if (PyArray_DIM(output_array, 2) != 4) {

PyErr_SetString(

PyExc_ValueError,

"Output array must be RGBA");

goto error;

}

if (PyArray_DIM(input_array, 2) == 4) {

switch (PyArray_TYPE(input_array)) {

case NPY_UINT8:

case NPY_INT8:

resample<agg::rgba8>(input_array, output_array, params);

break;

case NPY_UINT16:

case NPY_INT16:

resample<agg::rgba16>(input_array, output_array, params);

break;

case NPY_FLOAT32:

resample<agg::rgba32>(input_array, output_array, params);

break;

case NPY_FLOAT64:

resample<agg::rgba64>(input_array, output_array, params);

break;

default:

PyErr_SetString(

PyExc_ValueError,

"3-dimensional arrays must be of dtype unsigned byte, "

"unsigned short, float32 or float64");

goto error;

}

} else {

PyErr_Format(

PyExc_ValueError,

"If 3-dimensional, array must be RGBA. Got %" NPY_INTP_FMT " planes.",

PyArray_DIM(input_array, 2));

goto error;

}

} else { // NDIM == 2

switch (PyArray_TYPE(input_array)) {

case NPY_DOUBLE:

resample<double>(input_array, output_array, params);

break;

case NPY_FLOAT:

resample<float>(input_array, output_array, params);

break;

case NPY_UINT8:

case NPY_INT8:

resample<unsigned char>(input_array, output_array, params);

break;

case NPY_UINT16:

case NPY_INT16:

resample<unsigned short>(input_array, output_array, params);

break;

default:

PyErr_SetString(PyExc_ValueError, "Unsupported dtype");

goto error;

}

Py_DECREF(input_array);

Py_XDECREF(transform_mesh_array);

Py_RETURN_NONE;

error:

Py_XDECREF(input_array);

Py_XDECREF(transform_mesh_array);

return NULL;

}

static PyMethodDef module_functions[] = {

{"resample", (PyCFunction)image_resample, METH_VARARGS|METH_KEYWORDS, image_resample__doc__},

{NULL}

};

static struct PyModuleDef moduledef = {

PyModuleDef_HEAD_INIT, "_image", NULL, 0, module_functions,

};

#pragma GCC visibility push(default)

PyMODINIT_FUNC PyInit__image(void)

{

PyObject *m;

import_array();

m = PyModule_Create(&moduledef);

if (m == NULL) {

return NULL;

}

if (PyModule_AddIntConstant(m, "NEAREST", NEAREST) ||

PyModule_AddIntConstant(m, "BILINEAR", BILINEAR) ||

PyModule_AddIntConstant(m, "BICUBIC", BICUBIC) ||

PyModule_AddIntConstant(m, "SPLINE16", SPLINE16) ||

PyModule_AddIntConstant(m, "SPLINE36", SPLINE36) ||

PyModule_AddIntConstant(m, "HANNING", HANNING) ||

PyModule_AddIntConstant(m, "HAMMING", HAMMING) ||

PyModule_AddIntConstant(m, "HERMITE", HERMITE) ||

PyModule_AddIntConstant(m, "KAISER", KAISER) ||

PyModule_AddIntConstant(m, "QUADRIC", QUADRIC) ||

PyModule_AddIntConstant(m, "CATROM", CATROM) ||

PyModule_AddIntConstant(m, "GAUSSIAN", GAUSSIAN) ||

PyModule_AddIntConstant(m, "BESSEL", BESSEL) ||

PyModule_AddIntConstant(m, "MITCHELL", MITCHELL) ||

PyModule_AddIntConstant(m, "SINC", SINC) ||

PyModule_AddIntConstant(m, "LANCZOS", LANCZOS) ||

PyModule_AddIntConstant(m, "BLACKMAN", BLACKMAN) ||

PyModule_AddIntConstant(m, "_n_interpolation", _n_interpolation)) {

Py_DECREF(m);

return NULL;

}

return m;

}

#pragma GCC visibility pop

matplotlib/src/_image_wrapper.cpp at v3.7.x · matplotlib/matplotlib · GitHub

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