matplotlib/src/_image_wrapper.cpp at v3.3.x · matplotlib/matplotlib · GitHub

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

#include "mplutils.h"

#include "_image_resample.h"

#include "_image.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"

"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;

}

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_BYTE:

case NPY_UINT8:

Py_BEGIN_ALLOW_THREADS

resample(

(agg::rgba8 *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(agg::rgba8 *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_UINT16:

case NPY_INT16:

Py_BEGIN_ALLOW_THREADS

resample(

(agg::rgba16 *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(agg::rgba16 *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_FLOAT32:

Py_BEGIN_ALLOW_THREADS

resample(

(agg::rgba32 *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(agg::rgba32 *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_FLOAT64:

Py_BEGIN_ALLOW_THREADS

resample(

(agg::rgba64 *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(agg::rgba64 *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

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:

Py_BEGIN_ALLOW_THREADS

resample(

(double *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(double *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_FLOAT:

Py_BEGIN_ALLOW_THREADS

resample(

(float *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(float *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_UINT8:

case NPY_BYTE:

Py_BEGIN_ALLOW_THREADS

resample(

(unsigned char *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(unsigned char *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

break;

case NPY_UINT16:

case NPY_INT16:

Py_BEGIN_ALLOW_THREADS

resample(

(unsigned short *)PyArray_DATA(input_array),

PyArray_DIM(input_array, 1),

PyArray_DIM(input_array, 0),

(unsigned short *)PyArray_DATA(output_array),

PyArray_DIM(output_array, 1),

PyArray_DIM(output_array, 0),

params);

Py_END_ALLOW_THREADS

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;

}

const char *image_pcolor__doc__ =

"pcolor(x, y, data, rows, cols, bounds)\n"

"\n"

"Generate a pseudo-color image from data on a non-uniform grid using\n"

"nearest neighbour or linear interpolation.\n"

"bounds = (x_min, x_max, y_min, y_max)\n"

"interpolation = NEAREST or BILINEAR \n";

static PyObject *image_pcolor(PyObject *self, PyObject *args, PyObject *kwds)

{

numpy::array_view<const float, 1> x;

numpy::array_view<const float, 1> y;

numpy::array_view<const agg::int8u, 3> d;

npy_intp rows, cols;

float bounds[4];

int interpolation;

if (!PyArg_ParseTuple(args,

"O&O&O&nn(ffff)i:pcolor",

&x.converter,

&x,

&y.converter,

&y,

&d.converter_contiguous,

&d,

&rows,

&cols,

&bounds[0],

&bounds[1],

&bounds[2],

&bounds[3],

&interpolation)) {

return NULL;

}

npy_intp dim[3] = {rows, cols, 4};

numpy::array_view<const agg::int8u, 3> output(dim);

CALL_CPP("pcolor", (pcolor(x, y, d, rows, cols, bounds, interpolation, output)));

return output.pyobj();

}

const char *image_pcolor2__doc__ =

"pcolor2(x, y, data, rows, cols, bounds, bg)\n"

"\n"

"Generate a pseudo-color image from data on a non-uniform grid\n"

"specified by its cell boundaries.\n"

"bounds = (x_left, x_right, y_bot, y_top)\n"

"bg = ndarray of 4 uint8 representing background rgba\n";

static PyObject *image_pcolor2(PyObject *self, PyObject *args, PyObject *kwds)

{

numpy::array_view<const double, 1> x;

numpy::array_view<const double, 1> y;

numpy::array_view<const agg::int8u, 3> d;

npy_intp rows, cols;

float bounds[4];

numpy::array_view<const agg::int8u, 1> bg;

if (!PyArg_ParseTuple(args,

"O&O&O&nn(ffff)O&:pcolor2",

&x.converter_contiguous,

&x,

&y.converter_contiguous,

&y,

&d.converter_contiguous,

&d,

&rows,

&cols,

&bounds[0],

&bounds[1],

&bounds[2],

&bounds[3],

&bg.converter,

&bg)) {

return NULL;

}

npy_intp dim[3] = {rows, cols, 4};

numpy::array_view<const agg::int8u, 3> output(dim);

CALL_CPP("pcolor2", (pcolor2(x, y, d, rows, cols, bounds, bg, output)));

return output.pyobj();

}

static PyMethodDef module_functions[] = {

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

{"pcolor", (PyCFunction)image_pcolor, METH_VARARGS, image_pcolor__doc__},

{"pcolor2", (PyCFunction)image_pcolor2, METH_VARARGS, image_pcolor2__doc__},

{NULL}

};

static struct PyModuleDef moduledef = {

PyModuleDef_HEAD_INIT,

"_image",

NULL,

module_functions,

NULL,

NULL

};

#pragma GCC visibility push(default)

PyMODINIT_FUNC PyInit__image(void)

{

PyObject *m;

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)) {

return NULL;

}

import_array();

return m;

}

#pragma GCC visibility pop

matplotlib/src/_image_wrapper.cpp at v3.3.x · matplotlib/matplotlib · GitHub

Repository navigation

Expand file tree

Breadcrumbs

_image_wrapper.cpp

Latest commit

History

Breadcrumbs

_image_wrapper.cpp

File metadata and controls