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.

229

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

230

# violence).

231

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

232

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

233

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

234

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

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# funny or offensive.

236

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

237

},

238

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

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{ # Set of detected entity features.

240

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

241

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

242

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

243

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

244

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

245

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

246

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

247

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

248

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

249

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

250

# Range [0, 1].

251

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

252

# For more details on KG please see:

253

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

254

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

255

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

256

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

257

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

258

# is usually present for landmarks.

259

{ # Detected entity location information.

260

"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

262

# specified otherwise, this must conform to the

263

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

264

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

265

#

266

# Example of normalization code in Python:

267

#

268

# def NormalizeLongitude(longitude):

269

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

270

# q, r = divmod(longitude, 360.0)

271

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

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

276

# """Wraps decimal degrees latitude and longitude to

277

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

278

# r = latitude % 360.0

279

# if r <= 90.0:

280

# return r, NormalizeLongitude(longitude)

281

# elif r >= 270.0:

282

# return r - 360, NormalizeLongitude(longitude)

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# else:

284

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

285

#

286

# assert 180.0 == NormalizeLongitude(180.0)

287

# assert -180.0 == NormalizeLongitude(-180.0)

288

# assert -179.0 == NormalizeLongitude(181.0)

289

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

290

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

291

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

292

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

293

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

294

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

295

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

296

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

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

298

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

299

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

300

"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].

305

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

306

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

307

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

308

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

309

"vertices": [ # The bounding polygon vertices.

310

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

311

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

312

"y": 42, # Y coordinate.

313

"x": 42, # X coordinate.

},

],

},

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

318

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

319

{ # Arbitrary name/value pair.

320

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

321

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

},

],

},

],

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

327

{ # Set of detected entity features.

328

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

329

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

330

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

331

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

332

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

333

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

334

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

335

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

336

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

337

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

338

# Range [0, 1].

339

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

340

# For more details on KG please see:

341

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

342

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

343

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

344

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

345

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

346

# is usually present for landmarks.

347

{ # Detected entity location information.

348

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

349

# of doubles representing degrees latitude and degrees longitude. Unless

350

# specified otherwise, this must conform to the

351

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

352

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

353

#

354

# Example of normalization code in Python:

355

#

356

# def NormalizeLongitude(longitude):

357

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

358

# q, r = divmod(longitude, 360.0)

359

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

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

364

# """Wraps decimal degrees latitude and longitude to

365

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

366

# r = latitude % 360.0

367

# if r <= 90.0:

368

# return r, NormalizeLongitude(longitude)

369

# elif r >= 270.0:

370

# return r - 360, NormalizeLongitude(longitude)

371

# else:

372

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

373

#

374

# assert 180.0 == NormalizeLongitude(180.0)

375

# assert -180.0 == NormalizeLongitude(-180.0)

376

# assert -179.0 == NormalizeLongitude(181.0)

377

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

378

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

379

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

380

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

381

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

382

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

383

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

384

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

385

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

386

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

387

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

388

"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].

393

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

394

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

395

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

396

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

397

"vertices": [ # The bounding polygon vertices.

398

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

399

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

400

"y": 42, # Y coordinate.

401

"x": 42, # X coordinate.

},

],

},

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

406

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

407

{ # Arbitrary name/value pair.

408

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

409

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

},

],

},

],

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

415

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

416

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

417

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

418

# image the color occupies in the image.

419

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

420

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

421

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

422

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

423

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

424

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

425

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

#

# Example (Java):

#

# import com.google.type.Color;

430

#

431

# // ...

432

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

433

# float alpha = protocolor.hasAlpha()

434

# ? protocolor.getAlpha().getValue()

435

# : 1.0;

436

#

437

# return new java.awt.Color(

438

# protocolor.getRed(),

439

# protocolor.getGreen(),

440

# protocolor.getBlue(),

# alpha);

# }

#

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

445

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

446

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

447

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

448

# float denominator = 255.0;

449

# Color.Builder resultBuilder =

450

# Color

451

# .newBuilder()

452

# .setRed(red / denominator)

453

# .setGreen(green / denominator)

454

# .setBlue(blue / denominator);

455

# int alpha = color.getAlpha();

456

# if (alpha != 255) {

# result.setAlpha(

# FloatValue

# .newBuilder()

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

461

# .build());

462

# }

463

# return resultBuilder.build();

# }

# // ...

#

# Example (iOS / Obj-C):

468

#

469

# // ...

470

# static UIColor* fromProto(Color* protocolor) {

471

# float red = [protocolor red];

472

# float green = [protocolor green];

473

# float blue = [protocolor blue];

474

# FloatValue* alpha_wrapper = [protocolor alpha];

475

# float alpha = 1.0;

476

# if (alpha_wrapper != nil) {

477

# alpha = [alpha_wrapper value];

478

# }

479

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

480

# }

481

#

482

# static Color* toProto(UIColor* color) {

483

# CGFloat red, green, blue, alpha;

484

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

485

# return nil;

486

# }

487

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

488

# [result setRed:red];

489

# [result setGreen:green];

490

# [result setBlue:blue];

491

# if (alpha <= 0.9999) {

492

# [result setAlpha:floatWrapperWithValue(alpha)];

493

# }

494

# [result autorelease];

# return result;

# }

# // ...

#

# Example (JavaScript):

#

# // ...

#

# var protoToCssColor = function(rgb_color) {

504

# var redFrac = rgb_color.red || 0.0;

505

# var greenFrac = rgb_color.green || 0.0;

506

# var blueFrac = rgb_color.blue || 0.0;

507

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

508

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

509

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

510

#

511

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

512

# return rgbToCssColor_(red, green, blue);

513

# }

514

#

515

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

516

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

517

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

518

# };

519

#

520

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

521

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

522

# var hexString = rgbNumber.toString(16);

523

# var missingZeros = 6 - hexString.length;

524

# var resultBuilder = ['#'];

525

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

526

# resultBuilder.push('0');

527

# }

528

# resultBuilder.push(hexString);

529

# return resultBuilder.join('');

# };

#

# // ...

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

534

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

535

# the final pixel color is defined by the equation:

536

#

537

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

538

#

539

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

540

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

541

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

542

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

543

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

544

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

545

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

546

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

547

},

548

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

549

# Value in range [0, 1].

550

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

},

],

},

},

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

556

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

557

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

558

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

559

# [-180,180].

560

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

561

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

562

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

563

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

564

"landmarks": [ # Detected face landmarks.

565

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

566

# Landmark positions may fall outside the bounds of the image

567

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

568

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

569

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

570

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

571

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

572

"y": 3.14, # Y coordinate.

573

"x": 3.14, # X coordinate.

574

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

575

},

576

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

577

},

578

],

579

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

580

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

581

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

582

# pointing

583

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

584

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

585

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

586

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

587

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

588

# expectations. It is based on the landmarker results.

589

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

590

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

591

# the image to be annotated.

592

"vertices": [ # The bounding polygon vertices.

593

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

594

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

595

"y": 42, # Y coordinate.

596

"x": 42, # X coordinate.

},

],

},

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

601

# the

602

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

603

# face. Range [-180,180].

604

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

605

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

606

# <code>boundingPoly</code>, and

607

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

608

# eliminate the face from any image analysis that detects the

609

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

610

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

611

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

612

"vertices": [ # The bounding polygon vertices.

613

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

614

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

615

"y": 42, # Y coordinate.

616

"x": 42, # X coordinate.

},

],

},

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

621

},

622

],

623

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

624

{ # Set of detected entity features.

625

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

626

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

627

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

628

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

629

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

630

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

631

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

632

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

633

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

634

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

635

# Range [0, 1].

636

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

637

# For more details on KG please see:

638

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

639

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

640

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

641

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

642

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

643

# is usually present for landmarks.

644

{ # Detected entity location information.

645

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

646

# of doubles representing degrees latitude and degrees longitude. Unless

647

# specified otherwise, this must conform to the

648

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

649

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

650

#

651

# Example of normalization code in Python:

652

#

653

# def NormalizeLongitude(longitude):

654

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

655

# q, r = divmod(longitude, 360.0)

656

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

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

661

# """Wraps decimal degrees latitude and longitude to

662

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

663

# r = latitude % 360.0

664

# if r <= 90.0:

665

# return r, NormalizeLongitude(longitude)

666

# elif r >= 270.0:

667

# return r - 360, NormalizeLongitude(longitude)

668

# else:

669

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

670

#

671

# assert 180.0 == NormalizeLongitude(180.0)

672

# assert -180.0 == NormalizeLongitude(-180.0)

673

# assert -179.0 == NormalizeLongitude(181.0)

674

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

675

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

676

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

677

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

678

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

679

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

680

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

681

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

682

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

683

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

684

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

685

"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].

690

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

691

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

692

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

693

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

694

"vertices": [ # The bounding polygon vertices.

695

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

696

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

697

"y": 42, # Y coordinate.

698

"x": 42, # X coordinate.

},

],

},

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

703

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

704

{ # Arbitrary name/value pair.

705

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

706

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

},

],

},

],

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

712

{ # Set of detected entity features.

713

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

714

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

715

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

716

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

717

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

718

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

719

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

720

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

721

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

722

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

723

# Range [0, 1].

724

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

725

# For more details on KG please see:

726

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

727

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

728

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

729

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

730

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

731

# is usually present for landmarks.

732

{ # Detected entity location information.

733

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

734

# of doubles representing degrees latitude and degrees longitude. Unless

735

# specified otherwise, this must conform to the

736

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

737

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

738

#

739

# Example of normalization code in Python:

740

#

741

# def NormalizeLongitude(longitude):

742

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

743

# q, r = divmod(longitude, 360.0)

744

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

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

749

# """Wraps decimal degrees latitude and longitude to

750

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

751

# r = latitude % 360.0

752

# if r <= 90.0:

753

# return r, NormalizeLongitude(longitude)

754

# elif r >= 270.0:

755

# return r - 360, NormalizeLongitude(longitude)

756

# else:

757

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

758

#

759

# assert 180.0 == NormalizeLongitude(180.0)

760

# assert -180.0 == NormalizeLongitude(-180.0)

761

# assert -179.0 == NormalizeLongitude(181.0)

762

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

763

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

764

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

765

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

766

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

767

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

768

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

769

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

770

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

771

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

772

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

773

"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].

778

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

779

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

780

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

781

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

782

"vertices": [ # The bounding polygon vertices.

783

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

784

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

785

"y": 42, # Y coordinate.

786

"x": 42, # X coordinate.

},

],

},

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

791

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

792

{ # Arbitrary name/value pair.

793

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

794

"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.

800

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

801

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

802

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

803

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

804

#

805

# - Simple to use and understand for most users

806

# - Flexible enough to meet unexpected needs

#

# # Overview

#

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

811

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

812

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

813

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

814

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

815

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

816

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

817

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

818

# 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

823

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

824

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

825

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

826

# 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

831

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

832

# consistent developer experience across different environments.

833

#

834

# Example uses of this error model include:

835

#

836

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

837

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

838

# errors.

839

#

840

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

841

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

842

#

843

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

844

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

845

# each error sub-response.

846

#

847

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

848

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

849

# represented directly using the `Status` message.

850

#

851

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

852

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

853

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

854

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

855

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

856

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

857

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

858

# common set of message types for APIs to use.

859

{

860

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

},

],

},

},

],

}</pre>

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