1 <html><body> 2 <style> 3 4 body, h1, h2, h3, div, span, p, pre, a { 5 margin: 0; 6 padding: 0; 7 border: 0; 8 font-weight: inherit; 9 font-style: inherit; 10 font-size: 100%; 11 font-family: inherit; 12 vertical-align: baseline; 13 } 14 15 body { 16 font-size: 13px; 17 padding: 1em; 18 } 19 20 h1 { 21 font-size: 26px; 22 margin-bottom: 1em; 23 } 24 25 h2 { 26 font-size: 24px; 27 margin-bottom: 1em; 28 } 29 30 h3 { 31 font-size: 20px; 32 margin-bottom: 1em; 33 margin-top: 1em; 34 } 35 36 pre, code { 37 line-height: 1.5; 38 font-family: Monaco, 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Lucida Console', monospace; 39 } 40 41 pre { 42 margin-top: 0.5em; 43 } 44 45 h1, h2, h3, p { 46 font-family: Arial, sans serif; 47 } 48 49 h1, h2, h3 { 50 border-bottom: solid #CCC 1px; 51 } 52 53 .toc_element { 54 margin-top: 0.5em; 55 } 56 57 .firstline { 58 margin-left: 2 em; 59 } 60 61 .method { 62 margin-top: 1em; 63 border: solid 1px #CCC; 64 padding: 1em; 65 background: #EEE; 66 } 67 68 .details { 69 font-weight: bold; 70 font-size: 14px; 71 } 72 73 </style> 74 75 <h1><a href="vision_v1.html">Google Cloud Vision API</a> . <a href="vision_v1.images.html">images</a></h1> 76 <h2>Instance Methods</h2> 77 <p class="toc_element"> 78 <code><a href="#annotate">annotate(body, x__xgafv=None)</a></code></p> 79 <p class="firstline">Run image detection and annotation for a batch of images.</p> 80 <h3>Method Details</h3> 81 <div class="method"> 82 <code class="details" id="annotate">annotate(body, x__xgafv=None)</code> 83 <pre>Run image detection and annotation for a batch of images. 84 85 Args: 86 body: object, The request body. (required) 87 The object takes the form of: 88 89 { # Multiple image annotation requests are batched into a single service call. 90 "requests": [ # Individual image annotation requests for this batch. 91 { # Request for performing Google Cloud Vision API tasks over a user-provided 92 # image, with user-requested features. 93 "imageContext": { # Image context and/or feature-specific parameters. # Additional context that may accompany the image. 94 "latLongRect": { # Rectangle determined by min and max `LatLng` pairs. # lat/long rectangle that specifies the location of the image. 95 "minLatLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Min lat/long pair. 96 # of doubles representing degrees latitude and degrees longitude. Unless 97 # specified otherwise, this must conform to the 98 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 99 # standard</a>. Values must be within normalized ranges. 100 # 101 # Example of normalization code in Python: 102 # 103 # def NormalizeLongitude(longitude): 104 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 105 # q, r = divmod(longitude, 360.0) 106 # if r > 180.0 or (r == 180.0 and q <= -1.0): 107 # return r - 360.0 108 # return r 109 # 110 # def NormalizeLatLng(latitude, longitude): 111 # """Wraps decimal degrees latitude and longitude to 112 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 113 # r = latitude % 360.0 114 # if r <= 90.0: 115 # return r, NormalizeLongitude(longitude) 116 # elif r >= 270.0: 117 # return r - 360, NormalizeLongitude(longitude) 118 # else: 119 # return 180 - r, NormalizeLongitude(longitude + 180.0) 120 # 121 # assert 180.0 == NormalizeLongitude(180.0) 122 # assert -180.0 == NormalizeLongitude(-180.0) 123 # assert -179.0 == NormalizeLongitude(181.0) 124 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 125 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 126 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 127 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 128 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 129 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 130 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 131 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 132 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 133 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 134 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 135 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 136 }, 137 "maxLatLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Max lat/long pair. 138 # of doubles representing degrees latitude and degrees longitude. Unless 139 # specified otherwise, this must conform to the 140 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 141 # standard</a>. Values must be within normalized ranges. 142 # 143 # Example of normalization code in Python: 144 # 145 # def NormalizeLongitude(longitude): 146 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 147 # q, r = divmod(longitude, 360.0) 148 # if r > 180.0 or (r == 180.0 and q <= -1.0): 149 # return r - 360.0 150 # return r 151 # 152 # def NormalizeLatLng(latitude, longitude): 153 # """Wraps decimal degrees latitude and longitude to 154 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 155 # r = latitude % 360.0 156 # if r <= 90.0: 157 # return r, NormalizeLongitude(longitude) 158 # elif r >= 270.0: 159 # return r - 360, NormalizeLongitude(longitude) 160 # else: 161 # return 180 - r, NormalizeLongitude(longitude + 180.0) 162 # 163 # assert 180.0 == NormalizeLongitude(180.0) 164 # assert -180.0 == NormalizeLongitude(-180.0) 165 # assert -179.0 == NormalizeLongitude(181.0) 166 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 167 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 168 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 169 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 170 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 171 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 172 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 173 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 174 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 175 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 176 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 177 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 178 }, 179 }, 180 "languageHints": [ # List of languages to use for TEXT_DETECTION. In most cases, an empty value 181 # yields the best results since it enables automatic language detection. For 182 # languages based on the Latin alphabet, setting `language_hints` is not 183 # needed. In rare cases, when the language of the text in the image is known, 184 # setting a hint will help get better results (although it will be a 185 # significant hindrance if the hint is wrong). Text detection returns an 186 # error if one or more of the specified languages is not one of the 187 # [supported languages](/vision/docs/languages). 188 "A String", 189 ], 190 "cropHintsParams": { # Parameters for crop hints annotation request. # Parameters for crop hints annotation request. 191 "aspectRatios": [ # Aspect ratios in floats, representing the ratio of the width to the height 192 # of the image. For example, if the desired aspect ratio is 4/3, the 193 # corresponding float value should be 1.33333. If not specified, the 194 # best possible crop is returned. The number of provided aspect ratios is 195 # limited to a maximum of 16; any aspect ratios provided after the 16th are 196 # ignored. 197 3.14, 198 ], 199 }, 200 }, 201 "image": { # Client image to perform Google Cloud Vision API tasks over. # The image to be processed. 202 "content": "A String", # Image content, represented as a stream of bytes. 203 # Note: as with all `bytes` fields, protobuffers use a pure binary 204 # representation, whereas JSON representations use base64. 205 "source": { # External image source (Google Cloud Storage image location). # Google Cloud Storage image location. If both `content` and `source` 206 # are provided for an image, `content` takes precedence and is 207 # used to perform the image annotation request. 208 "gcsImageUri": "A String", # NOTE: For new code `image_uri` below is preferred. 209 # Google Cloud Storage image URI, which must be in the following form: 210 # `gs://bucket_name/object_name` (for details, see 211 # [Google Cloud Storage Request 212 # URIs](https://cloud.google.com/storage/docs/reference-uris)). 213 # NOTE: Cloud Storage object versioning is not supported. 214 "imageUri": "A String", # Image URI which supports: 215 # 1) Google Cloud Storage image URI, which must be in the following form: 216 # `gs://bucket_name/object_name` (for details, see 217 # [Google Cloud Storage Request 218 # URIs](https://cloud.google.com/storage/docs/reference-uris)). 219 # NOTE: Cloud Storage object versioning is not supported. 220 # 2) Publicly accessible image HTTP/HTTPS URL. 221 # This is preferred over the legacy `gcs_image_uri` above. When both 222 # `gcs_image_uri` and `image_uri` are specified, `image_uri` takes 223 # precedence. 224 }, 225 }, 226 "features": [ # Requested features. 227 { # Users describe the type of Google Cloud Vision API tasks to perform over 228 # images by using *Feature*s. Each Feature indicates a type of image 229 # detection task to perform. Features encode the Cloud Vision API 230 # vertical to operate on and the number of top-scoring results to return. 231 "type": "A String", # The feature type. 232 "maxResults": 42, # Maximum number of results of this type. 233 }, 234 ], 235 }, 236 ], 237 } 238 239 x__xgafv: string, V1 error format. 240 Allowed values 241 1 - v1 error format 242 2 - v2 error format 243 244 Returns: 245 An object of the form: 246 247 { # Response to a batch image annotation request. 248 "responses": [ # Individual responses to image annotation requests within the batch. 249 { # Response to an image annotation request. 250 "safeSearchAnnotation": { # Set of features pertaining to the image, computed by computer vision # If present, safe-search annotation has completed successfully. 251 # methods over safe-search verticals (for example, adult, spoof, medical, 252 # violence). 253 "medical": "A String", # Likelihood that this is a medical image. 254 "spoof": "A String", # Spoof likelihood. The likelihood that an modification 255 # was made to the image's canonical version to make it appear 256 # funny or offensive. 257 "violence": "A String", # Violence likelihood. 258 "adult": "A String", # Represents the adult content likelihood for the image. 259 }, 260 "textAnnotations": [ # If present, text (OCR) detection has completed successfully. 261 { # Set of detected entity features. 262 "confidence": 3.14, # The accuracy of the entity detection in an image. 263 # For example, for an image in which the "Eiffel Tower" entity is detected, 264 # this field represents the confidence that there is a tower in the query 265 # image. Range [0, 1]. 266 "description": "A String", # Entity textual description, expressed in its `locale` language. 267 "locale": "A String", # The language code for the locale in which the entity textual 268 # `description` is expressed. 269 "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the 270 # image. For example, the relevancy of "tower" is likely higher to an image 271 # containing the detected "Eiffel Tower" than to an image containing a 272 # detected distant towering building, even though the confidence that 273 # there is a tower in each image may be the same. Range [0, 1]. 274 "mid": "A String", # Opaque entity ID. Some IDs may be available in 275 # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). 276 "locations": [ # The location information for the detected entity. Multiple 277 # `LocationInfo` elements can be present because one location may 278 # indicate the location of the scene in the image, and another location 279 # may indicate the location of the place where the image was taken. 280 # Location information is usually present for landmarks. 281 { # Detected entity location information. 282 "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates. 283 # of doubles representing degrees latitude and degrees longitude. Unless 284 # specified otherwise, this must conform to the 285 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 286 # standard</a>. Values must be within normalized ranges. 287 # 288 # Example of normalization code in Python: 289 # 290 # def NormalizeLongitude(longitude): 291 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 292 # q, r = divmod(longitude, 360.0) 293 # if r > 180.0 or (r == 180.0 and q <= -1.0): 294 # return r - 360.0 295 # return r 296 # 297 # def NormalizeLatLng(latitude, longitude): 298 # """Wraps decimal degrees latitude and longitude to 299 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 300 # r = latitude % 360.0 301 # if r <= 90.0: 302 # return r, NormalizeLongitude(longitude) 303 # elif r >= 270.0: 304 # return r - 360, NormalizeLongitude(longitude) 305 # else: 306 # return 180 - r, NormalizeLongitude(longitude + 180.0) 307 # 308 # assert 180.0 == NormalizeLongitude(180.0) 309 # assert -180.0 == NormalizeLongitude(-180.0) 310 # assert -179.0 == NormalizeLongitude(181.0) 311 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 312 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 313 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 314 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 315 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 316 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 317 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 318 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 319 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 320 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 321 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 322 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 323 }, 324 }, 325 ], 326 "score": 3.14, # Overall score of the result. Range [0, 1]. 327 "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced 328 # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s 329 # are produced for the entire text detected in an image region, followed by 330 # `boundingPoly`s for each word within the detected text. 331 "vertices": [ # The bounding polygon vertices. 332 { # A vertex represents a 2D point in the image. 333 # NOTE: the vertex coordinates are in the same scale as the original image. 334 "y": 42, # Y coordinate. 335 "x": 42, # X coordinate. 336 }, 337 ], 338 }, 339 "properties": [ # Some entities may have optional user-supplied `Property` (name/value) 340 # fields, such a score or string that qualifies the entity. 341 { # A `Property` consists of a user-supplied name/value pair. 342 "uint64Value": "A String", # Value of numeric properties. 343 "name": "A String", # Name of the property. 344 "value": "A String", # Value of the property. 345 }, 346 ], 347 }, 348 ], 349 "webDetection": { # Relevant information for the image from the Internet. # If present, web detection has completed successfully. 350 "webEntities": [ # Deduced entities from similar images on the Internet. 351 { # Entity deduced from similar images on the Internet. 352 "entityId": "A String", # Opaque entity ID. 353 "score": 3.14, # Overall relevancy score for the entity. 354 # Not normalized and not comparable across different image queries. 355 "description": "A String", # Canonical description of the entity, in English. 356 }, 357 ], 358 "pagesWithMatchingImages": [ # Web pages containing the matching images from the Internet. 359 { # Metadata for web pages. 360 "url": "A String", # The result web page URL. 361 "score": 3.14, # Overall relevancy score for the web page. 362 # Not normalized and not comparable across different image queries. 363 }, 364 ], 365 "visuallySimilarImages": [ # The visually similar image results. 366 { # Metadata for online images. 367 "url": "A String", # The result image URL. 368 "score": 3.14, # Overall relevancy score for the image. 369 # Not normalized and not comparable across different image queries. 370 }, 371 ], 372 "partialMatchingImages": [ # Partial matching images from the Internet. 373 # Those images are similar enough to share some key-point features. For 374 # example an original image will likely have partial matching for its crops. 375 { # Metadata for online images. 376 "url": "A String", # The result image URL. 377 "score": 3.14, # Overall relevancy score for the image. 378 # Not normalized and not comparable across different image queries. 379 }, 380 ], 381 "fullMatchingImages": [ # Fully matching images from the Internet. 382 # Can include resized copies of the query image. 383 { # Metadata for online images. 384 "url": "A String", # The result image URL. 385 "score": 3.14, # Overall relevancy score for the image. 386 # Not normalized and not comparable across different image queries. 387 }, 388 ], 389 }, 390 "fullTextAnnotation": { # TextAnnotation contains a structured representation of OCR extracted text. # If present, text (OCR) detection or document (OCR) text detection has 391 # completed successfully. 392 # This annotation provides the structural hierarchy for the OCR detected 393 # text. 394 # The hierarchy of an OCR extracted text structure is like this: 395 # TextAnnotation -> Page -> Block -> Paragraph -> Word -> Symbol 396 # Each structural component, starting from Page, may further have their own 397 # properties. Properties describe detected languages, breaks etc.. Please 398 # refer to the google.cloud.vision.v1.TextAnnotation.TextProperty message 399 # definition below for more detail. 400 "text": "A String", # UTF-8 text detected on the pages. 401 "pages": [ # List of pages detected by OCR. 402 { # Detected page from OCR. 403 "width": 42, # Page width in pixels. 404 "property": { # Additional information detected on the structural component. # Additional information detected on the page. 405 "detectedBreak": { # Detected start or end of a structural component. # Detected start or end of a text segment. 406 "isPrefix": True or False, # True if break prepends the element. 407 "type": "A String", # Detected break type. 408 }, 409 "detectedLanguages": [ # A list of detected languages together with confidence. 410 { # Detected language for a structural component. 411 "languageCode": "A String", # The BCP-47 language code, such as "en-US" or "sr-Latn". For more 412 # information, see 413 # http://www.unicode.org/reports/tr35/#Unicode_locale_identifier. 414 "confidence": 3.14, # Confidence of detected language. Range [0, 1]. 415 }, 416 ], 417 }, 418 "blocks": [ # List of blocks of text, images etc on this page. 419 { # Logical element on the page. 420 "boundingBox": { # A bounding polygon for the detected image annotation. # The bounding box for the block. 421 # The vertices are in the order of top-left, top-right, bottom-right, 422 # bottom-left. When a rotation of the bounding box is detected the rotation 423 # is represented as around the top-left corner as defined when the text is 424 # read in the 'natural' orientation. 425 # For example: 426 # * when the text is horizontal it might look like: 427 # 0----1 428 # | | 429 # 3----2 430 # * when it's rotated 180 degrees around the top-left corner it becomes: 431 # 2----3 432 # | | 433 # 1----0 434 # and the vertice order will still be (0, 1, 2, 3). 435 "vertices": [ # The bounding polygon vertices. 436 { # A vertex represents a 2D point in the image. 437 # NOTE: the vertex coordinates are in the same scale as the original image. 438 "y": 42, # Y coordinate. 439 "x": 42, # X coordinate. 440 }, 441 ], 442 }, 443 "blockType": "A String", # Detected block type (text, image etc) for this block. 444 "property": { # Additional information detected on the structural component. # Additional information detected for the block. 445 "detectedBreak": { # Detected start or end of a structural component. # Detected start or end of a text segment. 446 "isPrefix": True or False, # True if break prepends the element. 447 "type": "A String", # Detected break type. 448 }, 449 "detectedLanguages": [ # A list of detected languages together with confidence. 450 { # Detected language for a structural component. 451 "languageCode": "A String", # The BCP-47 language code, such as "en-US" or "sr-Latn". For more 452 # information, see 453 # http://www.unicode.org/reports/tr35/#Unicode_locale_identifier. 454 "confidence": 3.14, # Confidence of detected language. Range [0, 1]. 455 }, 456 ], 457 }, 458 "paragraphs": [ # List of paragraphs in this block (if this blocks is of type text). 459 { # Structural unit of text representing a number of words in certain order. 460 "boundingBox": { # A bounding polygon for the detected image annotation. # The bounding box for the paragraph. 461 # The vertices are in the order of top-left, top-right, bottom-right, 462 # bottom-left. When a rotation of the bounding box is detected the rotation 463 # is represented as around the top-left corner as defined when the text is 464 # read in the 'natural' orientation. 465 # For example: 466 # * when the text is horizontal it might look like: 467 # 0----1 468 # | | 469 # 3----2 470 # * when it's rotated 180 degrees around the top-left corner it becomes: 471 # 2----3 472 # | | 473 # 1----0 474 # and the vertice order will still be (0, 1, 2, 3). 475 "vertices": [ # The bounding polygon vertices. 476 { # A vertex represents a 2D point in the image. 477 # NOTE: the vertex coordinates are in the same scale as the original image. 478 "y": 42, # Y coordinate. 479 "x": 42, # X coordinate. 480 }, 481 ], 482 }, 483 "property": { # Additional information detected on the structural component. # Additional information detected for the paragraph. 484 "detectedBreak": { # Detected start or end of a structural component. # Detected start or end of a text segment. 485 "isPrefix": True or False, # True if break prepends the element. 486 "type": "A String", # Detected break type. 487 }, 488 "detectedLanguages": [ # A list of detected languages together with confidence. 489 { # Detected language for a structural component. 490 "languageCode": "A String", # The BCP-47 language code, such as "en-US" or "sr-Latn". For more 491 # information, see 492 # http://www.unicode.org/reports/tr35/#Unicode_locale_identifier. 493 "confidence": 3.14, # Confidence of detected language. Range [0, 1]. 494 }, 495 ], 496 }, 497 "words": [ # List of words in this paragraph. 498 { # A word representation. 499 "boundingBox": { # A bounding polygon for the detected image annotation. # The bounding box for the word. 500 # The vertices are in the order of top-left, top-right, bottom-right, 501 # bottom-left. When a rotation of the bounding box is detected the rotation 502 # is represented as around the top-left corner as defined when the text is 503 # read in the 'natural' orientation. 504 # For example: 505 # * when the text is horizontal it might look like: 506 # 0----1 507 # | | 508 # 3----2 509 # * when it's rotated 180 degrees around the top-left corner it becomes: 510 # 2----3 511 # | | 512 # 1----0 513 # and the vertice order will still be (0, 1, 2, 3). 514 "vertices": [ # The bounding polygon vertices. 515 { # A vertex represents a 2D point in the image. 516 # NOTE: the vertex coordinates are in the same scale as the original image. 517 "y": 42, # Y coordinate. 518 "x": 42, # X coordinate. 519 }, 520 ], 521 }, 522 "symbols": [ # List of symbols in the word. 523 # The order of the symbols follows the natural reading order. 524 { # A single symbol representation. 525 "boundingBox": { # A bounding polygon for the detected image annotation. # The bounding box for the symbol. 526 # The vertices are in the order of top-left, top-right, bottom-right, 527 # bottom-left. When a rotation of the bounding box is detected the rotation 528 # is represented as around the top-left corner as defined when the text is 529 # read in the 'natural' orientation. 530 # For example: 531 # * when the text is horizontal it might look like: 532 # 0----1 533 # | | 534 # 3----2 535 # * when it's rotated 180 degrees around the top-left corner it becomes: 536 # 2----3 537 # | | 538 # 1----0 539 # and the vertice order will still be (0, 1, 2, 3). 540 "vertices": [ # The bounding polygon vertices. 541 { # A vertex represents a 2D point in the image. 542 # NOTE: the vertex coordinates are in the same scale as the original image. 543 "y": 42, # Y coordinate. 544 "x": 42, # X coordinate. 545 }, 546 ], 547 }, 548 "text": "A String", # The actual UTF-8 representation of the symbol. 549 "property": { # Additional information detected on the structural component. # Additional information detected for the symbol. 550 "detectedBreak": { # Detected start or end of a structural component. # Detected start or end of a text segment. 551 "isPrefix": True or False, # True if break prepends the element. 552 "type": "A String", # Detected break type. 553 }, 554 "detectedLanguages": [ # A list of detected languages together with confidence. 555 { # Detected language for a structural component. 556 "languageCode": "A String", # The BCP-47 language code, such as "en-US" or "sr-Latn". For more 557 # information, see 558 # http://www.unicode.org/reports/tr35/#Unicode_locale_identifier. 559 "confidence": 3.14, # Confidence of detected language. Range [0, 1]. 560 }, 561 ], 562 }, 563 }, 564 ], 565 "property": { # Additional information detected on the structural component. # Additional information detected for the word. 566 "detectedBreak": { # Detected start or end of a structural component. # Detected start or end of a text segment. 567 "isPrefix": True or False, # True if break prepends the element. 568 "type": "A String", # Detected break type. 569 }, 570 "detectedLanguages": [ # A list of detected languages together with confidence. 571 { # Detected language for a structural component. 572 "languageCode": "A String", # The BCP-47 language code, such as "en-US" or "sr-Latn". For more 573 # information, see 574 # http://www.unicode.org/reports/tr35/#Unicode_locale_identifier. 575 "confidence": 3.14, # Confidence of detected language. Range [0, 1]. 576 }, 577 ], 578 }, 579 }, 580 ], 581 }, 582 ], 583 }, 584 ], 585 "height": 42, # Page height in pixels. 586 }, 587 ], 588 }, 589 "labelAnnotations": [ # If present, label detection has completed successfully. 590 { # Set of detected entity features. 591 "confidence": 3.14, # The accuracy of the entity detection in an image. 592 # For example, for an image in which the "Eiffel Tower" entity is detected, 593 # this field represents the confidence that there is a tower in the query 594 # image. Range [0, 1]. 595 "description": "A String", # Entity textual description, expressed in its `locale` language. 596 "locale": "A String", # The language code for the locale in which the entity textual 597 # `description` is expressed. 598 "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the 599 # image. For example, the relevancy of "tower" is likely higher to an image 600 # containing the detected "Eiffel Tower" than to an image containing a 601 # detected distant towering building, even though the confidence that 602 # there is a tower in each image may be the same. Range [0, 1]. 603 "mid": "A String", # Opaque entity ID. Some IDs may be available in 604 # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). 605 "locations": [ # The location information for the detected entity. Multiple 606 # `LocationInfo` elements can be present because one location may 607 # indicate the location of the scene in the image, and another location 608 # may indicate the location of the place where the image was taken. 609 # Location information is usually present for landmarks. 610 { # Detected entity location information. 611 "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates. 612 # of doubles representing degrees latitude and degrees longitude. Unless 613 # specified otherwise, this must conform to the 614 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 615 # standard</a>. Values must be within normalized ranges. 616 # 617 # Example of normalization code in Python: 618 # 619 # def NormalizeLongitude(longitude): 620 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 621 # q, r = divmod(longitude, 360.0) 622 # if r > 180.0 or (r == 180.0 and q <= -1.0): 623 # return r - 360.0 624 # return r 625 # 626 # def NormalizeLatLng(latitude, longitude): 627 # """Wraps decimal degrees latitude and longitude to 628 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 629 # r = latitude % 360.0 630 # if r <= 90.0: 631 # return r, NormalizeLongitude(longitude) 632 # elif r >= 270.0: 633 # return r - 360, NormalizeLongitude(longitude) 634 # else: 635 # return 180 - r, NormalizeLongitude(longitude + 180.0) 636 # 637 # assert 180.0 == NormalizeLongitude(180.0) 638 # assert -180.0 == NormalizeLongitude(-180.0) 639 # assert -179.0 == NormalizeLongitude(181.0) 640 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 641 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 642 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 643 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 644 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 645 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 646 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 647 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 648 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 649 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 650 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 651 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 652 }, 653 }, 654 ], 655 "score": 3.14, # Overall score of the result. Range [0, 1]. 656 "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced 657 # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s 658 # are produced for the entire text detected in an image region, followed by 659 # `boundingPoly`s for each word within the detected text. 660 "vertices": [ # The bounding polygon vertices. 661 { # A vertex represents a 2D point in the image. 662 # NOTE: the vertex coordinates are in the same scale as the original image. 663 "y": 42, # Y coordinate. 664 "x": 42, # X coordinate. 665 }, 666 ], 667 }, 668 "properties": [ # Some entities may have optional user-supplied `Property` (name/value) 669 # fields, such a score or string that qualifies the entity. 670 { # A `Property` consists of a user-supplied name/value pair. 671 "uint64Value": "A String", # Value of numeric properties. 672 "name": "A String", # Name of the property. 673 "value": "A String", # Value of the property. 674 }, 675 ], 676 }, 677 ], 678 "imagePropertiesAnnotation": { # Stores image properties, such as dominant colors. # If present, image properties were extracted successfully. 679 "dominantColors": { # Set of dominant colors and their corresponding scores. # If present, dominant colors completed successfully. 680 "colors": [ # RGB color values with their score and pixel fraction. 681 { # Color information consists of RGB channels, score, and the fraction of 682 # the image that the color occupies in the image. 683 "color": { # Represents a color in the RGBA color space. This representation is designed # RGB components of the color. 684 # for simplicity of conversion to/from color representations in various 685 # languages over compactness; for example, the fields of this representation 686 # can be trivially provided to the constructor of "java.awt.Color" in Java; it 687 # can also be trivially provided to UIColor's "+colorWithRed:green:blue:alpha" 688 # method in iOS; and, with just a little work, it can be easily formatted into 689 # a CSS "rgba()" string in JavaScript, as well. Here are some examples: 690 # 691 # Example (Java): 692 # 693 # import com.google.type.Color; 694 # 695 # // ... 696 # public static java.awt.Color fromProto(Color protocolor) { 697 # float alpha = protocolor.hasAlpha() 698 # ? protocolor.getAlpha().getValue() 699 # : 1.0; 700 # 701 # return new java.awt.Color( 702 # protocolor.getRed(), 703 # protocolor.getGreen(), 704 # protocolor.getBlue(), 705 # alpha); 706 # } 707 # 708 # public static Color toProto(java.awt.Color color) { 709 # float red = (float) color.getRed(); 710 # float green = (float) color.getGreen(); 711 # float blue = (float) color.getBlue(); 712 # float denominator = 255.0; 713 # Color.Builder resultBuilder = 714 # Color 715 # .newBuilder() 716 # .setRed(red / denominator) 717 # .setGreen(green / denominator) 718 # .setBlue(blue / denominator); 719 # int alpha = color.getAlpha(); 720 # if (alpha != 255) { 721 # result.setAlpha( 722 # FloatValue 723 # .newBuilder() 724 # .setValue(((float) alpha) / denominator) 725 # .build()); 726 # } 727 # return resultBuilder.build(); 728 # } 729 # // ... 730 # 731 # Example (iOS / Obj-C): 732 # 733 # // ... 734 # static UIColor* fromProto(Color* protocolor) { 735 # float red = [protocolor red]; 736 # float green = [protocolor green]; 737 # float blue = [protocolor blue]; 738 # FloatValue* alpha_wrapper = [protocolor alpha]; 739 # float alpha = 1.0; 740 # if (alpha_wrapper != nil) { 741 # alpha = [alpha_wrapper value]; 742 # } 743 # return [UIColor colorWithRed:red green:green blue:blue alpha:alpha]; 744 # } 745 # 746 # static Color* toProto(UIColor* color) { 747 # CGFloat red, green, blue, alpha; 748 # if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) { 749 # return nil; 750 # } 751 # Color* result = [Color alloc] init]; 752 # [result setRed:red]; 753 # [result setGreen:green]; 754 # [result setBlue:blue]; 755 # if (alpha <= 0.9999) { 756 # [result setAlpha:floatWrapperWithValue(alpha)]; 757 # } 758 # [result autorelease]; 759 # return result; 760 # } 761 # // ... 762 # 763 # Example (JavaScript): 764 # 765 # // ... 766 # 767 # var protoToCssColor = function(rgb_color) { 768 # var redFrac = rgb_color.red || 0.0; 769 # var greenFrac = rgb_color.green || 0.0; 770 # var blueFrac = rgb_color.blue || 0.0; 771 # var red = Math.floor(redFrac * 255); 772 # var green = Math.floor(greenFrac * 255); 773 # var blue = Math.floor(blueFrac * 255); 774 # 775 # if (!('alpha' in rgb_color)) { 776 # return rgbToCssColor_(red, green, blue); 777 # } 778 # 779 # var alphaFrac = rgb_color.alpha.value || 0.0; 780 # var rgbParams = [red, green, blue].join(','); 781 # return ['rgba(', rgbParams, ',', alphaFrac, ')'].join(''); 782 # }; 783 # 784 # var rgbToCssColor_ = function(red, green, blue) { 785 # var rgbNumber = new Number((red << 16) | (green << 8) | blue); 786 # var hexString = rgbNumber.toString(16); 787 # var missingZeros = 6 - hexString.length; 788 # var resultBuilder = ['#']; 789 # for (var i = 0; i < missingZeros; i++) { 790 # resultBuilder.push('0'); 791 # } 792 # resultBuilder.push(hexString); 793 # return resultBuilder.join(''); 794 # }; 795 # 796 # // ... 797 "blue": 3.14, # The amount of blue in the color as a value in the interval [0, 1]. 798 "alpha": 3.14, # The fraction of this color that should be applied to the pixel. That is, 799 # the final pixel color is defined by the equation: 800 # 801 # pixel color = alpha * (this color) + (1.0 - alpha) * (background color) 802 # 803 # This means that a value of 1.0 corresponds to a solid color, whereas 804 # a value of 0.0 corresponds to a completely transparent color. This 805 # uses a wrapper message rather than a simple float scalar so that it is 806 # possible to distinguish between a default value and the value being unset. 807 # If omitted, this color object is to be rendered as a solid color 808 # (as if the alpha value had been explicitly given with a value of 1.0). 809 "green": 3.14, # The amount of green in the color as a value in the interval [0, 1]. 810 "red": 3.14, # The amount of red in the color as a value in the interval [0, 1]. 811 }, 812 "pixelFraction": 3.14, # The fraction of pixels the color occupies in the image. 813 # Value in range [0, 1]. 814 "score": 3.14, # Image-specific score for this color. Value in range [0, 1]. 815 }, 816 ], 817 }, 818 }, 819 "faceAnnotations": [ # If present, face detection has completed successfully. 820 { # A face annotation object contains the results of face detection. 821 "sorrowLikelihood": "A String", # Sorrow likelihood. 822 "landmarkingConfidence": 3.14, # Face landmarking confidence. Range [0, 1]. 823 "underExposedLikelihood": "A String", # Under-exposed likelihood. 824 "detectionConfidence": 3.14, # Detection confidence. Range [0, 1]. 825 "joyLikelihood": "A String", # Joy likelihood. 826 "landmarks": [ # Detected face landmarks. 827 { # A face-specific landmark (for example, a face feature). 828 # Landmark positions may fall outside the bounds of the image 829 # if the face is near one or more edges of the image. 830 # Therefore it is NOT guaranteed that `0 <= x < width` or 831 # `0 <= y < height`. 832 "position": { # A 3D position in the image, used primarily for Face detection landmarks. # Face landmark position. 833 # A valid Position must have both x and y coordinates. 834 # The position coordinates are in the same scale as the original image. 835 "y": 3.14, # Y coordinate. 836 "x": 3.14, # X coordinate. 837 "z": 3.14, # Z coordinate (or depth). 838 }, 839 "type": "A String", # Face landmark type. 840 }, 841 ], 842 "surpriseLikelihood": "A String", # Surprise likelihood. 843 "blurredLikelihood": "A String", # Blurred likelihood. 844 "tiltAngle": 3.14, # Pitch angle, which indicates the upwards/downwards angle that the face is 845 # pointing relative to the image's horizontal plane. Range [-180,180]. 846 "angerLikelihood": "A String", # Anger likelihood. 847 "boundingPoly": { # A bounding polygon for the detected image annotation. # The bounding polygon around the face. The coordinates of the bounding box 848 # are in the original image's scale, as returned in `ImageParams`. 849 # The bounding box is computed to "frame" the face in accordance with human 850 # expectations. It is based on the landmarker results. 851 # Note that one or more x and/or y coordinates may not be generated in the 852 # `BoundingPoly` (the polygon will be unbounded) if only a partial face 853 # appears in the image to be annotated. 854 "vertices": [ # The bounding polygon vertices. 855 { # A vertex represents a 2D point in the image. 856 # NOTE: the vertex coordinates are in the same scale as the original image. 857 "y": 42, # Y coordinate. 858 "x": 42, # X coordinate. 859 }, 860 ], 861 }, 862 "rollAngle": 3.14, # Roll angle, which indicates the amount of clockwise/anti-clockwise rotation 863 # of the face relative to the image vertical about the axis perpendicular to 864 # the face. Range [-180,180]. 865 "panAngle": 3.14, # Yaw angle, which indicates the leftward/rightward angle that the face is 866 # pointing relative to the vertical plane perpendicular to the image. Range 867 # [-180,180]. 868 "headwearLikelihood": "A String", # Headwear likelihood. 869 "fdBoundingPoly": { # A bounding polygon for the detected image annotation. # The `fd_bounding_poly` bounding polygon is tighter than the 870 # `boundingPoly`, and encloses only the skin part of the face. Typically, it 871 # is used to eliminate the face from any image analysis that detects the 872 # "amount of skin" visible in an image. It is not based on the 873 # landmarker results, only on the initial face detection, hence 874 # the <code>fd</code> (face detection) prefix. 875 "vertices": [ # The bounding polygon vertices. 876 { # A vertex represents a 2D point in the image. 877 # NOTE: the vertex coordinates are in the same scale as the original image. 878 "y": 42, # Y coordinate. 879 "x": 42, # X coordinate. 880 }, 881 ], 882 }, 883 }, 884 ], 885 "logoAnnotations": [ # If present, logo detection has completed successfully. 886 { # Set of detected entity features. 887 "confidence": 3.14, # The accuracy of the entity detection in an image. 888 # For example, for an image in which the "Eiffel Tower" entity is detected, 889 # this field represents the confidence that there is a tower in the query 890 # image. Range [0, 1]. 891 "description": "A String", # Entity textual description, expressed in its `locale` language. 892 "locale": "A String", # The language code for the locale in which the entity textual 893 # `description` is expressed. 894 "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the 895 # image. For example, the relevancy of "tower" is likely higher to an image 896 # containing the detected "Eiffel Tower" than to an image containing a 897 # detected distant towering building, even though the confidence that 898 # there is a tower in each image may be the same. Range [0, 1]. 899 "mid": "A String", # Opaque entity ID. Some IDs may be available in 900 # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). 901 "locations": [ # The location information for the detected entity. Multiple 902 # `LocationInfo` elements can be present because one location may 903 # indicate the location of the scene in the image, and another location 904 # may indicate the location of the place where the image was taken. 905 # Location information is usually present for landmarks. 906 { # Detected entity location information. 907 "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates. 908 # of doubles representing degrees latitude and degrees longitude. Unless 909 # specified otherwise, this must conform to the 910 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 911 # standard</a>. Values must be within normalized ranges. 912 # 913 # Example of normalization code in Python: 914 # 915 # def NormalizeLongitude(longitude): 916 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 917 # q, r = divmod(longitude, 360.0) 918 # if r > 180.0 or (r == 180.0 and q <= -1.0): 919 # return r - 360.0 920 # return r 921 # 922 # def NormalizeLatLng(latitude, longitude): 923 # """Wraps decimal degrees latitude and longitude to 924 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 925 # r = latitude % 360.0 926 # if r <= 90.0: 927 # return r, NormalizeLongitude(longitude) 928 # elif r >= 270.0: 929 # return r - 360, NormalizeLongitude(longitude) 930 # else: 931 # return 180 - r, NormalizeLongitude(longitude + 180.0) 932 # 933 # assert 180.0 == NormalizeLongitude(180.0) 934 # assert -180.0 == NormalizeLongitude(-180.0) 935 # assert -179.0 == NormalizeLongitude(181.0) 936 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 937 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 938 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 939 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 940 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 941 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 942 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 943 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 944 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 945 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 946 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 947 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 948 }, 949 }, 950 ], 951 "score": 3.14, # Overall score of the result. Range [0, 1]. 952 "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced 953 # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s 954 # are produced for the entire text detected in an image region, followed by 955 # `boundingPoly`s for each word within the detected text. 956 "vertices": [ # The bounding polygon vertices. 957 { # A vertex represents a 2D point in the image. 958 # NOTE: the vertex coordinates are in the same scale as the original image. 959 "y": 42, # Y coordinate. 960 "x": 42, # X coordinate. 961 }, 962 ], 963 }, 964 "properties": [ # Some entities may have optional user-supplied `Property` (name/value) 965 # fields, such a score or string that qualifies the entity. 966 { # A `Property` consists of a user-supplied name/value pair. 967 "uint64Value": "A String", # Value of numeric properties. 968 "name": "A String", # Name of the property. 969 "value": "A String", # Value of the property. 970 }, 971 ], 972 }, 973 ], 974 "landmarkAnnotations": [ # If present, landmark detection has completed successfully. 975 { # Set of detected entity features. 976 "confidence": 3.14, # The accuracy of the entity detection in an image. 977 # For example, for an image in which the "Eiffel Tower" entity is detected, 978 # this field represents the confidence that there is a tower in the query 979 # image. Range [0, 1]. 980 "description": "A String", # Entity textual description, expressed in its `locale` language. 981 "locale": "A String", # The language code for the locale in which the entity textual 982 # `description` is expressed. 983 "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the 984 # image. For example, the relevancy of "tower" is likely higher to an image 985 # containing the detected "Eiffel Tower" than to an image containing a 986 # detected distant towering building, even though the confidence that 987 # there is a tower in each image may be the same. Range [0, 1]. 988 "mid": "A String", # Opaque entity ID. Some IDs may be available in 989 # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). 990 "locations": [ # The location information for the detected entity. Multiple 991 # `LocationInfo` elements can be present because one location may 992 # indicate the location of the scene in the image, and another location 993 # may indicate the location of the place where the image was taken. 994 # Location information is usually present for landmarks. 995 { # Detected entity location information. 996 "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates. 997 # of doubles representing degrees latitude and degrees longitude. Unless 998 # specified otherwise, this must conform to the 999 # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 1000 # standard</a>. Values must be within normalized ranges. 1001 # 1002 # Example of normalization code in Python: 1003 # 1004 # def NormalizeLongitude(longitude): 1005 # """Wraps decimal degrees longitude to [-180.0, 180.0].""" 1006 # q, r = divmod(longitude, 360.0) 1007 # if r > 180.0 or (r == 180.0 and q <= -1.0): 1008 # return r - 360.0 1009 # return r 1010 # 1011 # def NormalizeLatLng(latitude, longitude): 1012 # """Wraps decimal degrees latitude and longitude to 1013 # [-90.0, 90.0] and [-180.0, 180.0], respectively.""" 1014 # r = latitude % 360.0 1015 # if r <= 90.0: 1016 # return r, NormalizeLongitude(longitude) 1017 # elif r >= 270.0: 1018 # return r - 360, NormalizeLongitude(longitude) 1019 # else: 1020 # return 180 - r, NormalizeLongitude(longitude + 180.0) 1021 # 1022 # assert 180.0 == NormalizeLongitude(180.0) 1023 # assert -180.0 == NormalizeLongitude(-180.0) 1024 # assert -179.0 == NormalizeLongitude(181.0) 1025 # assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0) 1026 # assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0) 1027 # assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0) 1028 # assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0) 1029 # assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0) 1030 # assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0) 1031 # assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0) 1032 # assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0) 1033 # assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0) 1034 # assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0) 1035 "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0]. 1036 "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0]. 1037 }, 1038 }, 1039 ], 1040 "score": 3.14, # Overall score of the result. Range [0, 1]. 1041 "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced 1042 # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s 1043 # are produced for the entire text detected in an image region, followed by 1044 # `boundingPoly`s for each word within the detected text. 1045 "vertices": [ # The bounding polygon vertices. 1046 { # A vertex represents a 2D point in the image. 1047 # NOTE: the vertex coordinates are in the same scale as the original image. 1048 "y": 42, # Y coordinate. 1049 "x": 42, # X coordinate. 1050 }, 1051 ], 1052 }, 1053 "properties": [ # Some entities may have optional user-supplied `Property` (name/value) 1054 # fields, such a score or string that qualifies the entity. 1055 { # A `Property` consists of a user-supplied name/value pair. 1056 "uint64Value": "A String", # Value of numeric properties. 1057 "name": "A String", # Name of the property. 1058 "value": "A String", # Value of the property. 1059 }, 1060 ], 1061 }, 1062 ], 1063 "error": { # The `Status` type defines a logical error model that is suitable for different # If set, represents the error message for the operation. 1064 # Note that filled-in image annotations are guaranteed to be 1065 # correct, even when `error` is set. 1066 # programming environments, including REST APIs and RPC APIs. It is used by 1067 # [gRPC](https://github.com/grpc). The error model is designed to be: 1068 # 1069 # - Simple to use and understand for most users 1070 # - Flexible enough to meet unexpected needs 1071 # 1072 # # Overview 1073 # 1074 # The `Status` message contains three pieces of data: error code, error message, 1075 # and error details. The error code should be an enum value of 1076 # google.rpc.Code, but it may accept additional error codes if needed. The 1077 # error message should be a developer-facing English message that helps 1078 # developers *understand* and *resolve* the error. If a localized user-facing 1079 # error message is needed, put the localized message in the error details or 1080 # localize it in the client. The optional error details may contain arbitrary 1081 # information about the error. There is a predefined set of error detail types 1082 # in the package `google.rpc` that can be used for common error conditions. 1083 # 1084 # # Language mapping 1085 # 1086 # The `Status` message is the logical representation of the error model, but it 1087 # is not necessarily the actual wire format. When the `Status` message is 1088 # exposed in different client libraries and different wire protocols, it can be 1089 # mapped differently. For example, it will likely be mapped to some exceptions 1090 # in Java, but more likely mapped to some error codes in C. 1091 # 1092 # # Other uses 1093 # 1094 # The error model and the `Status` message can be used in a variety of 1095 # environments, either with or without APIs, to provide a 1096 # consistent developer experience across different environments. 1097 # 1098 # Example uses of this error model include: 1099 # 1100 # - Partial errors. If a service needs to return partial errors to the client, 1101 # it may embed the `Status` in the normal response to indicate the partial 1102 # errors. 1103 # 1104 # - Workflow errors. A typical workflow has multiple steps. Each step may 1105 # have a `Status` message for error reporting. 1106 # 1107 # - Batch operations. If a client uses batch request and batch response, the 1108 # `Status` message should be used directly inside batch response, one for 1109 # each error sub-response. 1110 # 1111 # - Asynchronous operations. If an API call embeds asynchronous operation 1112 # results in its response, the status of those operations should be 1113 # represented directly using the `Status` message. 1114 # 1115 # - Logging. If some API errors are stored in logs, the message `Status` could 1116 # be used directly after any stripping needed for security/privacy reasons. 1117 "message": "A String", # A developer-facing error message, which should be in English. Any 1118 # user-facing error message should be localized and sent in the 1119 # google.rpc.Status.details field, or localized by the client. 1120 "code": 42, # The status code, which should be an enum value of google.rpc.Code. 1121 "details": [ # A list of messages that carry the error details. There will be a 1122 # common set of message types for APIs to use. 1123 { 1124 "a_key": "", # Properties of the object. Contains field @type with type URL. 1125 }, 1126 ], 1127 }, 1128 "cropHintsAnnotation": { # Set of crop hints that are used to generate new crops when serving images. # If present, crop hints have completed successfully. 1129 "cropHints": [ # Crop hint results. 1130 { # Single crop hint that is used to generate a new crop when serving an image. 1131 "confidence": 3.14, # Confidence of this being a salient region. Range [0, 1]. 1132 "boundingPoly": { # A bounding polygon for the detected image annotation. # The bounding polygon for the crop region. The coordinates of the bounding 1133 # box are in the original image's scale, as returned in `ImageParams`. 1134 "vertices": [ # The bounding polygon vertices. 1135 { # A vertex represents a 2D point in the image. 1136 # NOTE: the vertex coordinates are in the same scale as the original image. 1137 "y": 42, # Y coordinate. 1138 "x": 42, # X coordinate. 1139 }, 1140 ], 1141 }, 1142 "importanceFraction": 3.14, # Fraction of importance of this salient region with respect to the original 1143 # image. 1144 }, 1145 ], 1146 }, 1147 }, 1148 ], 1149 }</pre> 1150 </div> 1151 1152 </body></html>
