Blame - docs/dyn/vision_v1.images.html - platform/external/python/google-api-python-client

"safeSearchAnnotation": { # Set of features pertaining to the image, computed by various computer vision # If present, safe-search annotation completed successfully.

230

# methods over safe-search verticals (for example, adult, spoof, medical,

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# violence).

232

"medical": "A String", # Likelihood this is a medical image.

233

"violence": "A String", # Violence likelihood.

234

"spoof": "A String", # Spoof likelihood. The likelihood that an obvious modification

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# was made to the image's canonical version to make it appear

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# funny or offensive.

237

"adult": "A String", # Represents the adult contents likelihood for the image.

238

},

239

"textAnnotations": [ # If present, text (OCR) detection completed successfully.

240

{ # Set of detected entity features.

241

"confidence": 3.14, # The accuracy of the entity detection in an image.

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# For example, for an image containing 'Eiffel Tower,' this field represents

243

# the confidence that there is a tower in the query image. Range [0, 1].

244

"description": "A String", # Entity textual description, expressed in its <code>locale</code> language.

245

"locale": "A String", # The language code for the locale in which the entity textual

246

# <code>description</code> (next field) is expressed.

247

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

248

# image. For example, the relevancy of 'tower' to an image containing

249

# 'Eiffel Tower' is likely higher than an image containing a distant towering

250

# building, though the confidence that there is a tower may be the same.

251

# Range [0, 1].

252

"mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).

253

# For more details on KG please see:

254

# https://developers.google.com/knowledge-graph/

255

"locations": [ # The location information for the detected entity. Multiple

256

# <code>LocationInfo</code> elements can be present since one location may

257

# indicate the location of the scene in the query image, and another the

258

# location of the place where the query image was taken. Location information

259

# is usually present for landmarks.

260

{ # Detected entity location information.

261

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.

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# of doubles representing degrees latitude and degrees longitude. Unless

263

# specified otherwise, this must conform to the

264

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

265

# standard</a>. Values must be within normalized ranges.

266

#

267

# Example of normalization code in Python:

268

#

269

# def NormalizeLongitude(longitude):

270

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

271

# q, r = divmod(longitude, 360.0)

272

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

277

# """Wraps decimal degrees latitude and longitude to

278

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

279

# r = latitude % 360.0

280

# if r <= 90.0:

281

# return r, NormalizeLongitude(longitude)

282

# elif r >= 270.0:

283

# return r - 360, NormalizeLongitude(longitude)

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# else:

285

# return 180 - r, NormalizeLongitude(longitude + 180.0)

286

#

287

# assert 180.0 == NormalizeLongitude(180.0)

288

# assert -180.0 == NormalizeLongitude(-180.0)

289

# assert -179.0 == NormalizeLongitude(181.0)

290

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

291

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

292

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

293

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

294

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

295

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

296

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

297

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

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# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

299

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

300

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

301

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

},

},

],

"score": 3.14, # Overall score of the result. Range [0, 1].

306

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently

307

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

308

# are produced for the entire text detected in an image region, followed by

309

# `boundingPoly`s for each word within the detected text.

310

"vertices": [ # The bounding polygon vertices.

311

{ # A vertex represents a 2D point in the image.

312

# NOTE: the vertex coordinates are in the same scale as the original image.

313

"y": 42, # Y coordinate.

314

"x": 42, # X coordinate.

},

],

},

"properties": [ # Some entities can have additional optional <code>Property</code> fields.

319

# For example a different kind of score or string that qualifies the entity.

320

{ # Arbitrary name/value pair.

321

"name": "A String", # Name of the property.

322

"value": "A String", # Value of the property.

},

],

},

],

"labelAnnotations": [ # If present, label detection completed successfully.

328

{ # Set of detected entity features.

329

"confidence": 3.14, # The accuracy of the entity detection in an image.

330

# For example, for an image containing 'Eiffel Tower,' this field represents

331

# the confidence that there is a tower in the query image. Range [0, 1].

332

"description": "A String", # Entity textual description, expressed in its <code>locale</code> language.

333

"locale": "A String", # The language code for the locale in which the entity textual

334

# <code>description</code> (next field) is expressed.

335

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

336

# image. For example, the relevancy of 'tower' to an image containing

337

# 'Eiffel Tower' is likely higher than an image containing a distant towering

338

# building, though the confidence that there is a tower may be the same.

339

# Range [0, 1].

340

"mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).

341

# For more details on KG please see:

342

# https://developers.google.com/knowledge-graph/

343

"locations": [ # The location information for the detected entity. Multiple

344

# <code>LocationInfo</code> elements can be present since one location may

345

# indicate the location of the scene in the query image, and another the

346

# location of the place where the query image was taken. Location information

347

# is usually present for landmarks.

348

{ # Detected entity location information.

349

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.

350

# of doubles representing degrees latitude and degrees longitude. Unless

351

# specified otherwise, this must conform to the

352

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

353

# standard</a>. Values must be within normalized ranges.

354

#

355

# Example of normalization code in Python:

356

#

357

# def NormalizeLongitude(longitude):

358

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

359

# q, r = divmod(longitude, 360.0)

360

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

365

# """Wraps decimal degrees latitude and longitude to

366

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

367

# r = latitude % 360.0

368

# if r <= 90.0:

369

# return r, NormalizeLongitude(longitude)

370

# elif r >= 270.0:

371

# return r - 360, NormalizeLongitude(longitude)

372

# else:

373

# return 180 - r, NormalizeLongitude(longitude + 180.0)

374

#

375

# assert 180.0 == NormalizeLongitude(180.0)

376

# assert -180.0 == NormalizeLongitude(-180.0)

377

# assert -179.0 == NormalizeLongitude(181.0)

378

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

379

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

380

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

381

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

382

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

383

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

384

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

385

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

386

# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

387

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

388

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

389

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

},

},

],

"score": 3.14, # Overall score of the result. Range [0, 1].

394

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently

395

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

396

# are produced for the entire text detected in an image region, followed by

397

# `boundingPoly`s for each word within the detected text.

398

"vertices": [ # The bounding polygon vertices.

399

{ # A vertex represents a 2D point in the image.

400

# NOTE: the vertex coordinates are in the same scale as the original image.

401

"y": 42, # Y coordinate.

402

"x": 42, # X coordinate.

},

],

},

"properties": [ # Some entities can have additional optional <code>Property</code> fields.

407

# For example a different kind of score or string that qualifies the entity.

408

{ # Arbitrary name/value pair.

409

"name": "A String", # Name of the property.

410

"value": "A String", # Value of the property.

},

],

},

],

"imagePropertiesAnnotation": { # Stores image properties (e.g. dominant colors). # If present, image properties were extracted successfully.

416

"dominantColors": { # Set of dominant colors and their corresponding scores. # If present, dominant colors completed successfully.

417

"colors": [ # RGB color values, with their score and pixel fraction.

418

{ # Color information consists of RGB channels, score and fraction of

419

# image the color occupies in the image.

420

"color": { # Represents a color in the RGBA color space. This representation is designed # RGB components of the color.

421

# for simplicity of conversion to/from color representations in various

422

# languages over compactness; for example, the fields of this representation

423

# can be trivially provided to the constructor of "java.awt.Color" in Java; it

424

# can also be trivially provided to UIColor's "+colorWithRed:green:blue:alpha"

425

# method in iOS; and, with just a little work, it can be easily formatted into

426

# a CSS "rgba()" string in JavaScript, as well. Here are some examples:

#

# Example (Java):

#

# import com.google.type.Color;

431

#

432

# // ...

433

# public static java.awt.Color fromProto(Color protocolor) {

434

# float alpha = protocolor.hasAlpha()

435

# ? protocolor.getAlpha().getValue()

436

# : 1.0;

437

#

438

# return new java.awt.Color(

439

# protocolor.getRed(),

440

# protocolor.getGreen(),

441

# protocolor.getBlue(),

# alpha);

# }

#

# public static Color toProto(java.awt.Color color) {

446

# float red = (float) color.getRed();

447

# float green = (float) color.getGreen();

448

# float blue = (float) color.getBlue();

449

# float denominator = 255.0;

450

# Color.Builder resultBuilder =

451

# Color

452

# .newBuilder()

453

# .setRed(red / denominator)

454

# .setGreen(green / denominator)

455

# .setBlue(blue / denominator);

456

# int alpha = color.getAlpha();

457

# if (alpha != 255) {

# result.setAlpha(

# FloatValue

# .newBuilder()

# .setValue(((float) alpha) / denominator)

462

# .build());

463

# }

464

# return resultBuilder.build();

# }

# // ...

#

# Example (iOS / Obj-C):

469

#

470

# // ...

471

# static UIColor* fromProto(Color* protocolor) {

472

# float red = [protocolor red];

473

# float green = [protocolor green];

474

# float blue = [protocolor blue];

475

# FloatValue* alpha_wrapper = [protocolor alpha];

476

# float alpha = 1.0;

477

# if (alpha_wrapper != nil) {

478

# alpha = [alpha_wrapper value];

479

# }

480

# return [UIColor colorWithRed:red green:green blue:blue alpha:alpha];

481

# }

482

#

483

# static Color* toProto(UIColor* color) {

484

# CGFloat red, green, blue, alpha;

485

# if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) {

486

# return nil;

487

# }

488

# Color* result = [Color alloc] init];

489

# [result setRed:red];

490

# [result setGreen:green];

491

# [result setBlue:blue];

492

# if (alpha <= 0.9999) {

493

# [result setAlpha:floatWrapperWithValue(alpha)];

494

# }

495

# [result autorelease];

# return result;

# }

# // ...

#

# Example (JavaScript):

#

# // ...

#

# var protoToCssColor = function(rgb_color) {

505

# var redFrac = rgb_color.red || 0.0;

506

# var greenFrac = rgb_color.green || 0.0;

507

# var blueFrac = rgb_color.blue || 0.0;

508

# var red = Math.floor(redFrac * 255);

509

# var green = Math.floor(greenFrac * 255);

510

# var blue = Math.floor(blueFrac * 255);

511

#

512

# if (!('alpha' in rgb_color)) {

513

# return rgbToCssColor_(red, green, blue);

514

# }

515

#

516

# var alphaFrac = rgb_color.alpha.value || 0.0;

517

# var rgbParams = [red, green, blue].join(',');

518

# return ['rgba(', rgbParams, ',', alphaFrac, ')'].join('');

519

# };

520

#

521

# var rgbToCssColor_ = function(red, green, blue) {

522

# var rgbNumber = new Number((red << 16) | (green << 8) | blue);

523

# var hexString = rgbNumber.toString(16);

524

# var missingZeros = 6 - hexString.length;

525

# var resultBuilder = ['#'];

526

# for (var i = 0; i < missingZeros; i++) {

527

# resultBuilder.push('0');

528

# }

529

# resultBuilder.push(hexString);

530

# return resultBuilder.join('');

# };

#

# // ...

"blue": 3.14, # The amount of blue in the color as a value in the interval [0, 1].

535

"alpha": 3.14, # The fraction of this color that should be applied to the pixel. That is,

536

# the final pixel color is defined by the equation:

537

#

538

# pixel color = alpha * (this color) + (1.0 - alpha) * (background color)

539

#

540

# This means that a value of 1.0 corresponds to a solid color, whereas

541

# a value of 0.0 corresponds to a completely transparent color. This

542

# uses a wrapper message rather than a simple float scalar so that it is

543

# possible to distinguish between a default value and the value being unset.

544

# If omitted, this color object is to be rendered as a solid color

545

# (as if the alpha value had been explicitly given with a value of 1.0).

546

"green": 3.14, # The amount of green in the color as a value in the interval [0, 1].

547

"red": 3.14, # The amount of red in the color as a value in the interval [0, 1].

548

},

549

"pixelFraction": 3.14, # Stores the fraction of pixels the color occupies in the image.

550

# Value in range [0, 1].

551

"score": 3.14, # Image-specific score for this color. Value in range [0, 1].

},

],

},

},

"faceAnnotations": [ # If present, face detection completed successfully.

557

{ # A face annotation object contains the results of face detection.

558

"panAngle": 3.14, # Yaw angle. Indicates the leftward/rightward angle that the face is

559

# pointing, relative to the vertical plane perpendicular to the image. Range

560

# [-180,180].

561

"sorrowLikelihood": "A String", # Sorrow likelihood.

562

"underExposedLikelihood": "A String", # Under-exposed likelihood.

563

"detectionConfidence": 3.14, # Detection confidence. Range [0, 1].

564

"joyLikelihood": "A String", # Joy likelihood.

565

"landmarks": [ # Detected face landmarks.

566

{ # A face-specific landmark (for example, a face feature).

567

# Landmark positions may fall outside the bounds of the image

568

# when the face is near one or more edges of the image.

569

# Therefore it is NOT guaranteed that 0 <= x < width or 0 <= y < height.

570

"position": { # A 3D position in the image, used primarily for Face detection landmarks. # Face landmark position.

571

# A valid Position must have both x and y coordinates.

572

# The position coordinates are in the same scale as the original image.

573

"y": 3.14, # Y coordinate.

574

"x": 3.14, # X coordinate.

575

"z": 3.14, # Z coordinate (or depth).

576

},

577

"type": "A String", # Face landmark type.

578

},

579

],

580

"surpriseLikelihood": "A String", # Surprise likelihood.

581

"blurredLikelihood": "A String", # Blurred likelihood.

582

"tiltAngle": 3.14, # Pitch angle. Indicates the upwards/downwards angle that the face is

583

# pointing

584

# relative to the image's horizontal plane. Range [-180,180].

585

"angerLikelihood": "A String", # Anger likelihood.

586

"boundingPoly": { # A bounding polygon for the detected image annotation. # The bounding polygon around the face. The coordinates of the bounding box

587

# are in the original image's scale, as returned in ImageParams.

588

# The bounding box is computed to "frame" the face in accordance with human

589

# expectations. It is based on the landmarker results.

590

# Note that one or more x and/or y coordinates may not be generated in the

591

# BoundingPoly (the polygon will be unbounded) if only a partial face appears in

592

# the image to be annotated.

593

"vertices": [ # The bounding polygon vertices.

594

{ # A vertex represents a 2D point in the image.

595

# NOTE: the vertex coordinates are in the same scale as the original image.

596

"y": 42, # Y coordinate.

597

"x": 42, # X coordinate.

},

],

},

"rollAngle": 3.14, # Roll angle. Indicates the amount of clockwise/anti-clockwise rotation of

602

# the

603

# face relative to the image vertical, about the axis perpendicular to the

604

# face. Range [-180,180].

605

"headwearLikelihood": "A String", # Headwear likelihood.

606

"fdBoundingPoly": { # A bounding polygon for the detected image annotation. # This bounding polygon is tighter than the previous

607

# <code>boundingPoly</code>, and

608

# encloses only the skin part of the face. Typically, it is used to

609

# eliminate the face from any image analysis that detects the

610

# "amount of skin" visible in an image. It is not based on the

611

# landmarker results, only on the initial face detection, hence

612

# the <code>fd</code> (face detection) prefix.

613

"vertices": [ # The bounding polygon vertices.

614

{ # A vertex represents a 2D point in the image.

615

# NOTE: the vertex coordinates are in the same scale as the original image.

616

"y": 42, # Y coordinate.

617

"x": 42, # X coordinate.

},

],

},

"landmarkingConfidence": 3.14, # Face landmarking confidence. Range [0, 1].

622

},

623

],

624

"logoAnnotations": [ # If present, logo detection completed successfully.

625

{ # Set of detected entity features.

626

"confidence": 3.14, # The accuracy of the entity detection in an image.

627

# For example, for an image containing 'Eiffel Tower,' this field represents

628

# the confidence that there is a tower in the query image. Range [0, 1].

629

"description": "A String", # Entity textual description, expressed in its <code>locale</code> language.

630

"locale": "A String", # The language code for the locale in which the entity textual

631

# <code>description</code> (next field) is expressed.

632

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

633

# image. For example, the relevancy of 'tower' to an image containing

634

# 'Eiffel Tower' is likely higher than an image containing a distant towering

635

# building, though the confidence that there is a tower may be the same.

636

# Range [0, 1].

637

"mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).

638

# For more details on KG please see:

639

# https://developers.google.com/knowledge-graph/

640

"locations": [ # The location information for the detected entity. Multiple

641

# <code>LocationInfo</code> elements can be present since one location may

642

# indicate the location of the scene in the query image, and another the

643

# location of the place where the query image was taken. Location information

644

# is usually present for landmarks.

645

{ # Detected entity location information.

646

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.

647

# of doubles representing degrees latitude and degrees longitude. Unless

648

# specified otherwise, this must conform to the

649

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

650

# standard</a>. Values must be within normalized ranges.

651

#

652

# Example of normalization code in Python:

653

#

654

# def NormalizeLongitude(longitude):

655

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

656

# q, r = divmod(longitude, 360.0)

657

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

662

# """Wraps decimal degrees latitude and longitude to

663

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

664

# r = latitude % 360.0

665

# if r <= 90.0:

666

# return r, NormalizeLongitude(longitude)

667

# elif r >= 270.0:

668

# return r - 360, NormalizeLongitude(longitude)

669

# else:

670

# return 180 - r, NormalizeLongitude(longitude + 180.0)

671

#

672

# assert 180.0 == NormalizeLongitude(180.0)

673

# assert -180.0 == NormalizeLongitude(-180.0)

674

# assert -179.0 == NormalizeLongitude(181.0)

675

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

676

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

677

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

678

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

679

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

680

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

681

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

682

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

683

# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

684

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

685

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

686

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

},

},

],

"score": 3.14, # Overall score of the result. Range [0, 1].

691

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently

692

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

693

# are produced for the entire text detected in an image region, followed by

694

# `boundingPoly`s for each word within the detected text.

695

"vertices": [ # The bounding polygon vertices.

696

{ # A vertex represents a 2D point in the image.

697

# NOTE: the vertex coordinates are in the same scale as the original image.

698

"y": 42, # Y coordinate.

699

"x": 42, # X coordinate.

},

],

},

"properties": [ # Some entities can have additional optional <code>Property</code> fields.

704

# For example a different kind of score or string that qualifies the entity.

705

{ # Arbitrary name/value pair.

706

"name": "A String", # Name of the property.

707

"value": "A String", # Value of the property.

},

],

},

],

"landmarkAnnotations": [ # If present, landmark detection completed successfully.

713

{ # Set of detected entity features.

714

"confidence": 3.14, # The accuracy of the entity detection in an image.

715

# For example, for an image containing 'Eiffel Tower,' this field represents

716

# the confidence that there is a tower in the query image. Range [0, 1].

717

"description": "A String", # Entity textual description, expressed in its <code>locale</code> language.

718

"locale": "A String", # The language code for the locale in which the entity textual

719

# <code>description</code> (next field) is expressed.

720

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

721

# image. For example, the relevancy of 'tower' to an image containing

722

# 'Eiffel Tower' is likely higher than an image containing a distant towering

723

# building, though the confidence that there is a tower may be the same.

724

# Range [0, 1].

725

"mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).

726

# For more details on KG please see:

727

# https://developers.google.com/knowledge-graph/

728

"locations": [ # The location information for the detected entity. Multiple

729

# <code>LocationInfo</code> elements can be present since one location may

730

# indicate the location of the scene in the query image, and another the

731

# location of the place where the query image was taken. Location information

732

# is usually present for landmarks.

733

{ # Detected entity location information.

734

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.

735

# of doubles representing degrees latitude and degrees longitude. Unless

736

# specified otherwise, this must conform to the

737

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

738

# standard</a>. Values must be within normalized ranges.

739

#

740

# Example of normalization code in Python:

741

#

742

# def NormalizeLongitude(longitude):

743

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

744

# q, r = divmod(longitude, 360.0)

745

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

750

# """Wraps decimal degrees latitude and longitude to

751

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

752

# r = latitude % 360.0

753

# if r <= 90.0:

754

# return r, NormalizeLongitude(longitude)

755

# elif r >= 270.0:

756

# return r - 360, NormalizeLongitude(longitude)

757

# else:

758

# return 180 - r, NormalizeLongitude(longitude + 180.0)

759

#

760

# assert 180.0 == NormalizeLongitude(180.0)

761

# assert -180.0 == NormalizeLongitude(-180.0)

762

# assert -179.0 == NormalizeLongitude(181.0)

763

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

764

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

765

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

766

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

767

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

768

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

769

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

770

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

771

# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

772

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

773

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

774

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

},

},

],

"score": 3.14, # Overall score of the result. Range [0, 1].

779

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently

780

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

781

# are produced for the entire text detected in an image region, followed by

782

# `boundingPoly`s for each word within the detected text.

783

"vertices": [ # The bounding polygon vertices.

784

{ # A vertex represents a 2D point in the image.

785

# NOTE: the vertex coordinates are in the same scale as the original image.

786

"y": 42, # Y coordinate.

787

"x": 42, # X coordinate.

},

],

},

"properties": [ # Some entities can have additional optional <code>Property</code> fields.

792

# For example a different kind of score or string that qualifies the entity.

793

{ # Arbitrary name/value pair.

794

"name": "A String", # Name of the property.

795

"value": "A String", # Value of the property.

},

],

},

],

"error": { # The `Status` type defines a logical error model that is suitable for different # If set, represents the error message for the operation.

801

# Note that filled-in mage annotations are guaranteed to be

802

# correct, even when <code>error</code> is non-empty.

803

# programming environments, including REST APIs and RPC APIs. It is used by

804

# [gRPC](https://github.com/grpc). The error model is designed to be:

805

#

806

# - Simple to use and understand for most users

807

# - Flexible enough to meet unexpected needs

#

# # Overview

#

# The `Status` message contains three pieces of data: error code, error message,

812

# and error details. The error code should be an enum value of

813

# google.rpc.Code, but it may accept additional error codes if needed. The

814

# error message should be a developer-facing English message that helps

815

# developers *understand* and *resolve* the error. If a localized user-facing

816

# error message is needed, put the localized message in the error details or

817

# localize it in the client. The optional error details may contain arbitrary

818

# information about the error. There is a predefined set of error detail types

819

# in the package `google.rpc` which can be used for common error conditions.

#

# # Language mapping

#

# The `Status` message is the logical representation of the error model, but it

824

# is not necessarily the actual wire format. When the `Status` message is

825

# exposed in different client libraries and different wire protocols, it can be

826

# mapped differently. For example, it will likely be mapped to some exceptions

827

# in Java, but more likely mapped to some error codes in C.

#

# # Other uses

#

# The error model and the `Status` message can be used in a variety of

832

# environments, either with or without APIs, to provide a

833

# consistent developer experience across different environments.

834

#

835

# Example uses of this error model include:

836

#

837

# - Partial errors. If a service needs to return partial errors to the client,

838

# it may embed the `Status` in the normal response to indicate the partial

839

# errors.

840

#

841

# - Workflow errors. A typical workflow has multiple steps. Each step may

842

# have a `Status` message for error reporting purpose.

843

#

844

# - Batch operations. If a client uses batch request and batch response, the

845

# `Status` message should be used directly inside batch response, one for

846

# each error sub-response.

847

#

848

# - Asynchronous operations. If an API call embeds asynchronous operation

849

# results in its response, the status of those operations should be

850

# represented directly using the `Status` message.

851

#

852

# - Logging. If some API errors are stored in logs, the message `Status` could

853

# be used directly after any stripping needed for security/privacy reasons.

854

"message": "A String", # A developer-facing error message, which should be in English. Any

855

# user-facing error message should be localized and sent in the

856

# google.rpc.Status.details field, or localized by the client.

857

"code": 42, # The status code, which should be an enum value of google.rpc.Code.

858

"details": [ # A list of messages that carry the error details. There will be a

859

# common set of message types for APIs to use.

860

{

Jon Wayne Parrott

7d5badb

2016-08-16 12:44:29 -0700

[diff] [blame^]

861

"a_key": "", # Properties of the object. Contains field @type with type URL.

Jon Wayne Parrott

0a471d3

2016-05-19 10:54:38 -0700

[diff] [blame]

},

],

},

},

],

}</pre>

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