matplotlib.github.com/3.1.1/_modules/matplotlib/image.html at main · matplotlib/matplotlib.github.com

History

1758 lines (1495 loc) · 238 KB

Raw

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

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746

747

748

749

750

751

752

753

754

755

756

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

939

940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970

971

972

973

974

975

976

977

978

979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.image — Matplotlib 3.1.2 documentation</title>

type="text/css" />

type="text/css" />

title="Search within Matplotlib 3.1.2 documentation"

href="../../_static/opensearch.xml"/>

</head>

<body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.1.1). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.1.2</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

</div>

<ul>

<li><a href="../../users/installing.html">Installation</a></li>

<li><a href="../../contents.html">Documentation</a></li>

<li><a href="../../gallery/index.html">Examples</a></li>

<li><a href="../../tutorials/index.html">Tutorials</a></li>

<li><a href="../../devel/index.html">Contributing</a></li>

</ul>

</nav>

<h3>Navigation</h3>

<ul>

<a href="../../genindex.html" title="General Index"

accesskey="I">index</a></li>

<a href="../../py-modindex.html" title="Python Module Index"

>modules</a> |</li>

<li><a href="../../contents.html">contents</a> »</li>

<li><a href="../index.html" >Module code</a> »</li>

<li><a href="../matplotlib.html" accesskey="U">matplotlib</a> »</li>

</ul>

</div>

<h3>Quick search</h3>

</form>

</div>

<h3>Related Topics</h3>

<ul>

<li><a href="../../contents.html">Documentation overview</a><ul>

<li><a href="../index.html">Module code</a><ul>

<li><a href="../matplotlib.html">matplotlib</a><ul>

</ul></li>

</ul>

</div>

<h1>Source code for matplotlib.image</h1><div class="highlight"><pre>

"""

The image module supports basic image loading, rescaling and display

operations.

"""

from io import BytesIO

from math import ceil

import os

import logging

from pathlib import Path

import urllib.parse

import numpy as np

from matplotlib import rcParams

import matplotlib.artist as martist

from matplotlib.backend_bases import FigureCanvasBase

import matplotlib.colors as mcolors

import matplotlib.cm as cm

import matplotlib.cbook as cbook

# For clarity, names from _image are given explicitly in this module:

import matplotlib._image as _image

# For user convenience, the names from _image are also imported into

# the image namespace:

from matplotlib._image import *

from matplotlib.transforms import (Affine2D, BboxBase, Bbox, BboxTransform,

IdentityTransform, TransformedBbox)

_log = logging.getLogger(__name__)

# map interpolation strings to module constants

_interpd_ = {

'none': _image.NEAREST, # fall back to nearest when not supported

'nearest': _image.NEAREST,

'bilinear': _image.BILINEAR,

'bicubic': _image.BICUBIC,

'spline16': _image.SPLINE16,

'spline36': _image.SPLINE36,

'hanning': _image.HANNING,

'hamming': _image.HAMMING,

'hermite': _image.HERMITE,

'kaiser': _image.KAISER,

'quadric': _image.QUADRIC,

'catrom': _image.CATROM,

'gaussian': _image.GAUSSIAN,

'bessel': _image.BESSEL,

'mitchell': _image.MITCHELL,

'sinc': _image.SINC,

'lanczos': _image.LANCZOS,

'blackman': _image.BLACKMAN,

}

interpolations_names = set(_interpd_)

<div class="viewcode-block" id="composite_images"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.composite_images">[docs]</a>def composite_images(images, renderer, magnification=1.0):

"""

Composite a number of RGBA images into one. The images are

composited in the order in which they appear in the `images` list.

Parameters

----------

images : list of Images

Each must have a `make_image` method. For each image,

`can_composite` should return `True`, though this is not

enforced by this function. Each image must have a purely

affine transformation with no shear.

renderer : RendererBase instance

magnification : float

The additional magnification to apply for the renderer in use.

Returns

-------

tuple : image, offset_x, offset_y

Returns the tuple:

- image: A numpy array of the same type as the input images.

- offset_x, offset_y: The offset of the image (left, bottom)

in the output figure.

"""

if len(images) == 0:

return np.empty((0, 0, 4), dtype=np.uint8), 0, 0

parts = []

bboxes = []

for image in images:

data, x, y, trans = image.make_image(renderer, magnification)

if data is not None:

x *= magnification

y *= magnification

parts.append((data, x, y, image.get_alpha() or 1.0))

bboxes.append(

Bbox([[x, y], [x + data.shape[1], y + data.shape[0]]]))

if len(parts) == 0:

bbox = Bbox.union(bboxes)

output = np.zeros(

(int(bbox.height), int(bbox.width), 4), dtype=np.uint8)

for data, x, y, alpha in parts:

trans = Affine2D().translate(x - bbox.x0, y - bbox.y0)

_image.resample(data, output, trans, _image.NEAREST,

resample=False, alpha=alpha)

return output, bbox.x0 / magnification, bbox.y0 / magnification</div>

def _draw_list_compositing_images(

renderer, parent, artists, suppress_composite=None):

"""

Draw a sorted list of artists, compositing images into a single

image where possible.

For internal matplotlib use only: It is here to reduce duplication

between `Figure.draw` and `Axes.draw`, but otherwise should not be

generally useful.

"""

has_images = any(isinstance(x, _ImageBase) for x in artists)

# override the renderer default if suppressComposite is not None

not_composite = (suppress_composite if suppress_composite is not None

else renderer.option_image_nocomposite())

if not_composite or not has_images:

for a in artists:

a.draw(renderer)

else:

# Composite any adjacent images together

image_group = []

mag = renderer.get_image_magnification()

def flush_images():

if len(image_group) == 1:

image_group[0].draw(renderer)

elif len(image_group) > 1:

data, l, b = composite_images(image_group, renderer, mag)

if data.size != 0:

gc = renderer.new_gc()

gc.set_clip_rectangle(parent.bbox)

gc.set_clip_path(parent.get_clip_path())

renderer.draw_image(gc, np.round(l), np.round(b), data)

gc.restore()

del image_group[:]

for a in artists:

if isinstance(a, _ImageBase) and a.can_composite():

image_group.append(a)

else:

flush_images()

a.draw(renderer)

flush_images()

def _rgb_to_rgba(A):

"""

Convert an RGB image to RGBA, as required by the image resample C++

extension.

"""

rgba = np.zeros((A.shape[0], A.shape[1], 4), dtype=A.dtype)

rgba[:, :, :3] = A

if rgba.dtype == np.uint8:

rgba[:, :, 3] = 255

else:

rgba[:, :, 3] = 1.0

return rgba

class _ImageBase(martist.Artist, cm.ScalarMappable):

zorder = 0

def __str__(self):

return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)

def __init__(self, ax,

cmap=None,

norm=None,

interpolation=None,

origin=None,

filternorm=True,

filterrad=4.0,

resample=False,

**kwargs

):

"""

interpolation and cmap default to their rc settings

cmap is a colors.Colormap instance

norm is a colors.Normalize instance to map luminance to 0-1

extent is data axes (left, right, bottom, top) for making image plots

registered with data plots. Default is to label the pixel

centers with the zero-based row and column indices.

Additional kwargs are matplotlib.artist properties

"""

martist.Artist.__init__(self)

cm.ScalarMappable.__init__(self, norm, cmap)

self._mouseover = True

if origin is None:

origin = rcParams['image.origin']

self.origin = origin

self.set_filternorm(filternorm)

self.set_filterrad(filterrad)

self.set_interpolation(interpolation)

self.set_resample(resample)

self.axes = ax

self._imcache = None

self.update(kwargs)

def __getstate__(self):

state = super().__getstate__()

# We can't pickle the C Image cached object.

state['_imcache'] = None

return state

def get_size(self):

"""Get the numrows, numcols of the input image"""

if self._A is None:

raise RuntimeError('You must first set the image array')

return self._A.shape[:2]

def set_alpha(self, alpha):

"""

Set the alpha value used for blending - not supported on all backends.

Parameters

----------

alpha : float

"""

martist.Artist.set_alpha(self, alpha)

self._imcache = None

def changed(self):

"""

Call this whenever the mappable is changed so observers can

update state

"""

self._imcache = None

self._rgbacache = None

cm.ScalarMappable.changed(self)

def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,

unsampled=False, round_to_pixel_border=True):

"""

Normalize, rescale, and colormap the image *A* from the given *in_bbox*

(in data space), to the given *out_bbox* (in pixel space) clipped to

the given *clip_bbox* (also in pixel space), and magnified by the

*magnification* factor.

*A* may be a greyscale image (M, N) with a dtype of float32, float64,

float128, uint16 or uint8, or an (M, N, 4) RGBA image with a dtype of

float32, float64, float128, or uint8.

If *unsampled* is True, the image will not be scaled, but an

appropriate affine transformation will be returned instead.

If *round_to_pixel_border* is True, the output image size will be

rounded to the nearest pixel boundary. This makes the images align

correctly with the axes. It should not be used if exact scaling is

needed, such as for `FigureImage`.

Returns

-------

image : (M, N, 4) uint8 array

The RGBA image, resampled unless *unsampled* is True.

x, y : float

The upper left corner where the image should be drawn, in pixel

space.

trans : Affine2D

The affine transformation from image to pixel space.

"""

if A is None:

raise RuntimeError('You must first set the image '

'array or the image attribute')

if A.size == 0:

raise RuntimeError("_make_image must get a non-empty image. "

"Your Artist's draw method must filter before "

"this method is called.")

clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)

if clipped_bbox is None:

return None, 0, 0, None

out_width_base = clipped_bbox.width * magnification

out_height_base = clipped_bbox.height * magnification

if out_width_base == 0 or out_height_base == 0:

if self.origin == 'upper':

# Flip the input image using a transform. This avoids the

# problem with flipping the array, which results in a copy

# when it is converted to contiguous in the C wrapper

t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)

else:

t0 = IdentityTransform()

t0 += (

Affine2D()

.scale(

in_bbox.width / A.shape[1],

in_bbox.height / A.shape[0])

.translate(in_bbox.x0, in_bbox.y0)

+ self.get_transform())

t = (t0

+ Affine2D().translate(

-clipped_bbox.x0,

-clipped_bbox.y0)

.scale(magnification, magnification))

# So that the image is aligned with the edge of the axes, we want

# to round up the output width to the next integer. This also

# means scaling the transform just slightly to account for the

# extra subpixel.

if (t.is_affine and round_to_pixel_border and

(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):

out_width = int(ceil(out_width_base))

out_height = int(ceil(out_height_base))

extra_width = (out_width - out_width_base) / out_width_base

extra_height = (out_height - out_height_base) / out_height_base

t += Affine2D().scale(1.0 + extra_width, 1.0 + extra_height)

else:

out_width = int(out_width_base)

out_height = int(out_height_base)

if not unsampled:

if A.ndim not in (2, 3):

raise ValueError("Invalid shape {} for image data"

.format(A.shape))

if A.ndim == 2:

# if we are a 2D array, then we are running through the

# norm + colormap transformation. However, in general the

# input data is not going to match the size on the screen so we

# have to resample to the correct number of pixels

# need to

# TODO slice input array first

inp_dtype = A.dtype

a_min = A.min()

a_max = A.max()

# figure out the type we should scale to. For floats,

# leave as is. For integers cast to an appropriate-sized

# float. Small integers get smaller floats in an attempt

# to keep the memory footprint reasonable.

if a_min is np.ma.masked:

# all masked, so values don't matter

a_min, a_max = np.int32(0), np.int32(1)

if inp_dtype.kind == 'f':

scaled_dtype = A.dtype

# Cast to float64

if A.dtype not in (np.float32, np.float16):

if A.dtype != np.float64:

cbook._warn_external("Casting input data from "

"'{0}' to 'float64' for "

"imshow".format(A.dtype))

scaled_dtype = np.float64

else:

# probably an integer of some type.

da = a_max.astype(np.float64) - a_min.astype(np.float64)

if da > 1e8:

# give more breathing room if a big dynamic range

scaled_dtype = np.float64

else:

scaled_dtype = np.float32

# scale the input data to [.1, .9]. The Agg

# interpolators clip to [0, 1] internally, use a

# smaller input scale to identify which of the

# interpolated points need to be should be flagged as

# over / under.

# This may introduce numeric instabilities in very broadly

# scaled data

A_scaled = np.empty(A.shape, dtype=scaled_dtype)

A_scaled[:] = A

# clip scaled data around norm if necessary.

# This is necessary for big numbers at the edge of

# float64's ability to represent changes. Applying

# a norm first would be good, but ruins the interpolation

# of over numbers.

self.norm.autoscale_None(A)

dv = (np.float64(self.norm.vmax) -

np.float64(self.norm.vmin))

vmid = self.norm.vmin + dv / 2

fact = 1e7 if scaled_dtype == np.float64 else 1e4

newmin = vmid - dv * fact

if newmin < a_min:

newmin = None

else:

a_min = np.float64(newmin)

newmax = vmid + dv * fact

if newmax > a_max:

newmax = None

else:

a_max = np.float64(newmax)

if newmax is not None or newmin is not None:

A_scaled = np.clip(A_scaled, newmin, newmax)

A_scaled -= a_min

# a_min and a_max might be ndarray subclasses so use

# item to avoid errors

a_min = a_min.astype(scaled_dtype).item()

a_max = a_max.astype(scaled_dtype).item()

if a_min != a_max:

A_scaled /= ((a_max - a_min) / 0.8)

A_scaled += 0.1

A_resampled = np.zeros((out_height, out_width),

dtype=A_scaled.dtype)

# resample the input data to the correct resolution and shape

_image.resample(A_scaled, A_resampled,

t,

_interpd_[self.get_interpolation()],

self.get_resample(), 1.0,

self.get_filternorm(),

self.get_filterrad())

# we are done with A_scaled now, remove from namespace

# to be sure!

del A_scaled

# un-scale the resampled data to approximately the

# original range things that interpolated to above /

# below the original min/max will still be above /

# below, but possibly clipped in the case of higher order

# interpolation + drastically changing data.

A_resampled -= 0.1

if a_min != a_max:

A_resampled *= ((a_max - a_min) / 0.8)

A_resampled += a_min

# if using NoNorm, cast back to the original datatype

if isinstance(self.norm, mcolors.NoNorm):

A_resampled = A_resampled.astype(A.dtype)

mask = np.empty(A.shape, dtype=np.float32)

if A.mask.shape == A.shape:

# this is the case of a nontrivial mask

mask[:] = np.where(A.mask, np.float32(np.nan),

np.float32(1))

else:

mask[:] = 1

# we always have to interpolate the mask to account for

# non-affine transformations

out_mask = np.zeros((out_height, out_width),

dtype=mask.dtype)

_image.resample(mask, out_mask,

t,

_interpd_[self.get_interpolation()],

True, 1,

self.get_filternorm(),

self.get_filterrad())

# we are done with the mask, delete from namespace to be sure!

del mask

# Agg updates the out_mask in place. If the pixel has

# no image data it will not be updated (and still be 0

# as we initialized it), if input data that would go

# into that output pixel than it will be `nan`, if all

# the input data for a pixel is good it will be 1, and

# if there is _some_ good data in that output pixel it

# will be between [0, 1] (such as a rotated image).

out_alpha = np.array(out_mask)

out_mask = np.isnan(out_mask)

out_alpha[out_mask] = 1

# mask and run through the norm

output = self.norm(np.ma.masked_array(A_resampled, out_mask))

else:

# Always convert to RGBA, even if only RGB input

if A.shape[2] == 3:

A = _rgb_to_rgba(A)

elif A.shape[2] != 4:

raise ValueError("Invalid shape {} for image data"

.format(A.shape))

output = np.zeros((out_height, out_width, 4), dtype=A.dtype)

output_a = np.zeros((out_height, out_width), dtype=A.dtype)

alpha = self.get_alpha()

if alpha is None:

alpha = 1

#resample alpha channel

alpha_channel = A[..., 3]

_image.resample(

alpha_channel, output_a, t,

_interpd_[self.get_interpolation()],

self.get_resample(), alpha,

self.get_filternorm(), self.get_filterrad())

#resample rgb channels

A = _rgb_to_rgba(A[..., :3])

_image.resample(

A, output, t,

_interpd_[self.get_interpolation()],

self.get_resample(), alpha,

#recombine rgb and alpha channels

output[..., 3] = output_a

# at this point output is either a 2D array of normed data

# (of int or float)

# or an RGBA array of re-sampled input

output = self.to_rgba(output, bytes=True, norm=False)

# output is now a correctly sized RGBA array of uint8

# Apply alpha *after* if the input was greyscale without a mask

if A.ndim == 2:

alpha = self.get_alpha()

if alpha is None:

alpha = 1

alpha_channel = output[:, :, 3]

alpha_channel[:] = np.asarray(

np.asarray(alpha_channel, np.float32) * out_alpha * alpha,

np.uint8)

else:

if self._imcache is None:

self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))

output = self._imcache

# Subset the input image to only the part that will be

# displayed

subset = TransformedBbox(

clip_bbox, t0.frozen().inverted()).frozen()

output = output[

int(min(subset.ymax + 1, output.shape[0])),

int(min(subset.xmax + 1, output.shape[1]))]

t = Affine2D().translate(

int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t

return output, clipped_bbox.x0, clipped_bbox.y0, t

def make_image(self, renderer, magnification=1.0, unsampled=False):

"""

Normalize, rescale, and colormap this image's data for rendering using

*renderer*, with the given *magnification*.

If *unsampled* is True, the image will not be scaled, but an

appropriate affine transformation will be returned instead.

Returns

-------

image : (M, N, 4) uint8 array

The RGBA image, resampled unless *unsampled* is True.

x, y : float

The upper left corner where the image should be drawn, in pixel

space.

trans : Affine2D

The affine transformation from image to pixel space.

"""

raise NotImplementedError('The make_image method must be overridden')

def _draw_unsampled_image(self, renderer, gc):

"""

draw unsampled image. The renderer should support a draw_image method

with scale parameter.

"""

im, l, b, trans = self.make_image(renderer, unsampled=True)

if im is None:

return

trans = Affine2D().scale(im.shape[1], im.shape[0]) + trans

renderer.draw_image(gc, l, b, im, trans)

def _check_unsampled_image(self, renderer):

"""

return True if the image is better to be drawn unsampled.

The derived class needs to override it.

"""

return False

@martist.allow_rasterization

def draw(self, renderer, *args, **kwargs):

# if not visible, declare victory and return

if not self.get_visible():

self.stale = False

return

# for empty images, there is nothing to draw!

if self.get_array().size == 0:

self.stale = False

return

# actually render the image.

gc = renderer.new_gc()

self._set_gc_clip(gc)

gc.set_alpha(self.get_alpha())

gc.set_url(self.get_url())

gc.set_gid(self.get_gid())

if (self._check_unsampled_image(renderer) and

self.get_transform().is_affine):

self._draw_unsampled_image(renderer, gc)

else:

renderer, renderer.get_image_magnification())

if im is not None:

gc.restore()

self.stale = False

def contains(self, mouseevent):

"""

Test whether the mouse event occurred within the image.

"""

if self._contains is not None:

return self._contains(self, mouseevent)

# 1) This doesn't work for figimage; but figimage also needs a fix

# below (as the check cannot use x/ydata and extents).

# 2) As long as the check below uses x/ydata, we need to test axes

# identity instead of `self.axes.contains(event)` because even if

# axes overlap, x/ydata is only valid for event.inaxes anyways.

if self.axes is not mouseevent.inaxes:

return False, {}

# TODO: make sure this is consistent with patch and patch

# collection on nonlinear transformed coordinates.

# TODO: consider returning image coordinates (shouldn't

# be too difficult given that the image is rectilinear

x, y = mouseevent.xdata, mouseevent.ydata

xmin, xmax, ymin, ymax = self.get_extent()

if xmin > xmax:

xmin, xmax = xmax, xmin

if ymin > ymax:

ymin, ymax = ymax, ymin

if x is not None and y is not None:

inside = (xmin <= x <= xmax) and (ymin <= y <= ymax)

else:

inside = False

return inside, {}

def write_png(self, fname):

"""Write the image to png file with fname"""

from matplotlib import _png

im = self.to_rgba(self._A[::-1] if self.origin == 'lower' else self._A,

bytes=True, norm=True)

_png.write_png(im, fname)

def set_data(self, A):

"""

Set the image array.

Note that this function does *not* update the normalization used.

Parameters

----------

A : array-like or `PIL.Image.Image`

"""

try:

from PIL import Image

except ImportError:

pass

else:

if isinstance(A, Image.Image):

A = pil_to_array(A) # Needed e.g. to apply png palette.

self._A = cbook.safe_masked_invalid(A, copy=True)

if (self._A.dtype != np.uint8 and

not np.can_cast(self._A.dtype, float, "same_kind")):

raise TypeError("Image data of dtype {} cannot be converted to "

"float".format(self._A.dtype))

if not (self._A.ndim == 2

or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):

raise TypeError("Invalid shape {} for image data"

.format(self._A.shape))

if self._A.ndim == 3:

# If the input data has values outside the valid range (after

# normalisation), we issue a warning and then clip X to the bounds

# - otherwise casting wraps extreme values, hiding outliers and

# making reliable interpretation impossible.

high = 255 if np.issubdtype(self._A.dtype, np.integer) else 1

if self._A.min() < 0 or high < self._A.max():

_log.warning(

'Clipping input data to the valid range for imshow with '

'RGB data ([0..1] for floats or [0..255] for integers).'

)

self._A = np.clip(self._A, 0, high)

# Cast unsupported integer types to uint8

if self._A.dtype != np.uint8 and np.issubdtype(self._A.dtype,

np.integer):

self._A = self._A.astype(np.uint8)

self._imcache = None

self._rgbacache = None

self.stale = True

def set_array(self, A):

"""

Retained for backwards compatibility - use set_data instead.

Parameters

----------

A : array-like

"""

# This also needs to be here to override the inherited

# cm.ScalarMappable.set_array method so it is not invoked by mistake.

self.set_data(A)

def get_interpolation(self):

"""

Return the interpolation method the image uses when resizing.

One of 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36',

'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom',

'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', or 'none'.

"""

return self._interpolation

def set_interpolation(self, s):

"""

Set the interpolation method the image uses when resizing.

if None, use a value from rc setting. If 'none', the image is

shown as is without interpolating. 'none' is only supported in

agg, ps and pdf backends and will fall back to 'nearest' mode

for other backends.

Parameters

----------

s : {'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', \

'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', \

'bessel', 'mitchell', 'sinc', 'lanczos', 'none'}

"""

if s is None:

s = rcParams['image.interpolation']

s = s.lower()

if s not in _interpd_:

raise ValueError('Illegal interpolation string')

self._interpolation = s

self.stale = True

def can_composite(self):

"""Return whether the image can be composited with its neighbors."""

trans = self.get_transform()

return (

self._interpolation != 'none' and

trans.is_affine and

trans.is_separable)

def set_resample(self, v):

"""

Set whether image resampling is used.

Parameters

----------

v : bool or None

If None, use :rc:`image.resample` = True.

"""

if v is None:

v = rcParams['image.resample']

self._resample = v

self.stale = True

def get_resample(self):

"""Return whether image resampling is used."""

return self._resample

def set_filternorm(self, filternorm):

"""

Set whether the resize filter normalizes the weights.

See help for `~.Axes.imshow`.

Parameters

----------

filternorm : bool

"""

self._filternorm = bool(filternorm)

self.stale = True

def get_filternorm(self):

"""Return whether the resize filter normalizes the weights."""

return self._filternorm

def set_filterrad(self, filterrad):

"""

Set the resize filter radius only applicable to some

interpolation schemes -- see help for imshow

Parameters

----------

filterrad : positive float

"""

r = float(filterrad)

if r <= 0:

raise ValueError("The filter radius must be a positive number")

self._filterrad = r

self.stale = True

def get_filterrad(self):

"""Return the filterrad setting."""

return self._filterrad

<div class="viewcode-block" id="AxesImage"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage">[docs]</a>class AxesImage(_ImageBase):

def __str__(self):

def __init__(self, ax,

cmap=None,

norm=None,

interpolation=None,

origin=None,

extent=None,

filternorm=1,

filterrad=4.0,

resample=False,

**kwargs

):

"""

interpolation and cmap default to their rc settings

cmap is a colors.Colormap instance

norm is a colors.Normalize instance to map luminance to 0-1

extent is data axes (left, right, bottom, top) for making image plots

registered with data plots. Default is to label the pixel

centers with the zero-based row and column indices.

Additional kwargs are matplotlib.artist properties

"""

self._extent = extent

super().__init__(

ax,

cmap=cmap,

norm=norm,

interpolation=interpolation,

origin=origin,

filternorm=filternorm,

filterrad=filterrad,

resample=resample,

**kwargs

)

<div class="viewcode-block" id="AxesImage.get_window_extent"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.get_window_extent">[docs]</a> def get_window_extent(self, renderer=None):

x0, x1, y0, y1 = self._extent

bbox = Bbox.from_extents([x0, y0, x1, y1])

return bbox.transformed(self.axes.transData)</div>

<div class="viewcode-block" id="AxesImage.make_image"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.make_image">[docs]</a> def make_image(self, renderer, magnification=1.0, unsampled=False):

# docstring inherited

trans = self.get_transform()

# image is created in the canvas coordinate.

x1, x2, y1, y2 = self.get_extent()

bbox = Bbox(np.array([[x1, y1], [x2, y2]]))

View remainder of file in raw view

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

FilesExpand file tree

image.html

Latest commit

History

image.html

File metadata and controls