# Google Cloud Vision API . images

## Instance Methods

Run image detection and annotation for a batch of images.

### Method Details

`annotate(body, x__xgafv=None)`

    Run image detection and annotation for a batch of images.

    Args:
      body: object, The request body. (required)
        The object takes the form of:

    { # Multiple image annotation requests are batched into a single service call.
        "requests": [ # Individual image annotation requests for this batch.
          { # Request for performing Google Cloud Vision API tasks over a user-provided
              # image, with user-requested features.
            "imageContext": { # Image context. # Additional context that may accompany the image.
              "latLongRect": { # Rectangle determined by min and max LatLng pairs. # Lat/long rectangle that specifies the location of the image.
                "minLatLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Min lat/long pair.
                    # of doubles representing degrees latitude and degrees longitude. Unless
                    # specified otherwise, this must conform to the
                    # WGS84
                    # standard. Values must be within normalized ranges.
                    #
                    # Example of normalization code in Python:
                    #
                    #     def NormalizeLongitude(longitude):
                    #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                    #       q, r = divmod(longitude, 360.0)
                    #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                    #         return r - 360.0
                    #       return r
                    #
                    #     def NormalizeLatLng(latitude, longitude):
                    #       """Wraps decimal degrees latitude and longitude to
                    #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                    #       r = latitude % 360.0
                    #       if r <= 90.0:
                    #         return r, NormalizeLongitude(longitude)
                    #       elif r >= 270.0:
                    #         return r - 360, NormalizeLongitude(longitude)
                    #       else:
                    #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                    #
                    #     assert 180.0 == NormalizeLongitude(180.0)
                    #     assert -180.0 == NormalizeLongitude(-180.0)
                    #     assert -179.0 == NormalizeLongitude(181.0)
                    #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                    #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                    #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                    #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                    #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                    #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                    #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                    #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                    #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                    #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                  "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                  "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].
                },
                "maxLatLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Max lat/long pair.
                    # of doubles representing degrees latitude and degrees longitude. Unless
                    # specified otherwise, this must conform to the
                    # WGS84
                    # standard. Values must be within normalized ranges.
                    #
                    # Example of normalization code in Python:
                    #
                    #     def NormalizeLongitude(longitude):
                    #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                    #       q, r = divmod(longitude, 360.0)
                    #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                    #         return r - 360.0
                    #       return r
                    #
                    #     def NormalizeLatLng(latitude, longitude):
                    #       """Wraps decimal degrees latitude and longitude to
                    #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                    #       r = latitude % 360.0
                    #       if r <= 90.0:
                    #         return r, NormalizeLongitude(longitude)
                    #       elif r >= 270.0:
                    #         return r - 360, NormalizeLongitude(longitude)
                    #       else:
                    #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                    #
                    #     assert 180.0 == NormalizeLongitude(180.0)
                    #     assert -180.0 == NormalizeLongitude(-180.0)
                    #     assert -179.0 == NormalizeLongitude(181.0)
                    #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                    #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                    #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                    #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                    #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                    #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                    #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                    #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                    #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                    #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                  "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                  "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].
                },
              },
              "languageHints": [ # List of languages to use for TEXT_DETECTION. In most cases, an empty value
                  # yields the best results since it enables automatic language detection. For
                  # languages based on the Latin alphabet, setting `language_hints` is not
                  # needed. In rare cases, when the language of the text in the image is known,
                  # setting a hint will help get better results (although it will be a
                  # significant hindrance if the hint is wrong). Text detection returns an
                  # error if one or more of the specified languages is not one of the
                  # [supported
                  # languages](/translate/v2/translate-reference#supported_languages).
                "A String",
              ],
            },
            "image": { # Client image to perform Google Cloud Vision API tasks over. # The image to be processed.
              "content": "A String", # Image content, represented as a stream of bytes.
                  # Note: as with all `bytes` fields, protobuffers use a pure binary
                  # representation, whereas JSON representations use base64.
              "source": { # External image source (Google Cloud Storage image location). # Google Cloud Storage image location. If both 'content' and 'source'
                  # are filled for an image, 'content' takes precedence and it will be
                  # used for performing the image annotation request.
                "gcsImageUri": "A String", # Google Cloud Storage image URI. It must be in the following form:
                    # `gs://bucket_name/object_name`. For more
                    # details, please see: https://cloud.google.com/storage/docs/reference-uris.
                    # NOTE: Cloud Storage object versioning is not supported!
              },
            },
            "features": [ # Requested features.
              { # The Feature indicates what type of image detection task to perform.
                  # Users describe the type of Google Cloud Vision API tasks to perform over
                  # images by using Features. Features encode the Cloud Vision API
                  # vertical to operate on and the number of top-scoring results to return.
                "type": "A String", # The feature type.
                "maxResults": 42, # Maximum number of results of this type.
              },
            ],
          },
        ],
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # Response to a batch image annotation request.
        "responses": [ # Individual responses to image annotation requests within the batch.
          { # Response to an image annotation request.
            "safeSearchAnnotation": { # Set of features pertaining to the image, computed by various computer vision # If present, safe-search annotation completed successfully.
                # methods over safe-search verticals (for example, adult, spoof, medical,
                # violence).
              "medical": "A String", # Likelihood this is a medical image.
              "violence": "A String", # Violence likelihood.
              "spoof": "A String", # Spoof likelihood. The likelihood that an obvious modification
                  # was made to the image's canonical version to make it appear
                  # funny or offensive.
              "adult": "A String", # Represents the adult contents likelihood for the image.
            },
            "textAnnotations": [ # If present, text (OCR) detection completed successfully.
              { # Set of detected entity features.
                "confidence": 3.14, # The accuracy of the entity detection in an image.
                    # For example, for an image containing 'Eiffel Tower,' this field represents
                    # the confidence that there is a tower in the query image. Range [0, 1].
                "description": "A String", # Entity textual description, expressed in its locale language.
                "locale": "A String", # The language code for the locale in which the entity textual
                    # description (next field) is expressed.
                "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the
                    # image. For example, the relevancy of 'tower' to an image containing
                    # 'Eiffel Tower' is likely higher than an image containing a distant towering
                    # building, though the confidence that there is a tower may be the same.
                    # Range [0, 1].
                "mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).
                    # For more details on KG please see:
                    # https://developers.google.com/knowledge-graph/
                "locations": [ # The location information for the detected entity. Multiple
                    # LocationInfo elements can be present since one location may
                    # indicate the location of the scene in the query image, and another the
                    # location of the place where the query image was taken. Location information
                    # is usually present for landmarks.
                  { # Detected entity location information.
                    "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.
                        # of doubles representing degrees latitude and degrees longitude. Unless
                        # specified otherwise, this must conform to the
                        # WGS84
                        # standard. Values must be within normalized ranges.
                        #
                        # Example of normalization code in Python:
                        #
                        #     def NormalizeLongitude(longitude):
                        #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                        #       q, r = divmod(longitude, 360.0)
                        #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                        #         return r - 360.0
                        #       return r
                        #
                        #     def NormalizeLatLng(latitude, longitude):
                        #       """Wraps decimal degrees latitude and longitude to
                        #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                        #       r = latitude % 360.0
                        #       if r <= 90.0:
                        #         return r, NormalizeLongitude(longitude)
                        #       elif r >= 270.0:
                        #         return r - 360, NormalizeLongitude(longitude)
                        #       else:
                        #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                        #
                        #     assert 180.0 == NormalizeLongitude(180.0)
                        #     assert -180.0 == NormalizeLongitude(-180.0)
                        #     assert -179.0 == NormalizeLongitude(181.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                        #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                        #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                        #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                        #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                      "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                      "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].
                "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently
                    # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s
                    # are produced for the entire text detected in an image region, followed by
                    # `boundingPoly`s for each word within the detected text.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "properties": [ # Some entities can have additional optional Property fields.
                    # For example a different kind of score or string that qualifies the entity.
                  { # Arbitrary name/value pair.
                    "name": "A String", # Name of the property.
                    "value": "A String", # Value of the property.
                  },
                ],
              },
            ],
            "labelAnnotations": [ # If present, label detection completed successfully.
              { # Set of detected entity features.
                "confidence": 3.14, # The accuracy of the entity detection in an image.
                    # For example, for an image containing 'Eiffel Tower,' this field represents
                    # the confidence that there is a tower in the query image. Range [0, 1].
                "description": "A String", # Entity textual description, expressed in its locale language.
                "locale": "A String", # The language code for the locale in which the entity textual
                    # description (next field) is expressed.
                "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the
                    # image. For example, the relevancy of 'tower' to an image containing
                    # 'Eiffel Tower' is likely higher than an image containing a distant towering
                    # building, though the confidence that there is a tower may be the same.
                    # Range [0, 1].
                "mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).
                    # For more details on KG please see:
                    # https://developers.google.com/knowledge-graph/
                "locations": [ # The location information for the detected entity. Multiple
                    # LocationInfo elements can be present since one location may
                    # indicate the location of the scene in the query image, and another the
                    # location of the place where the query image was taken. Location information
                    # is usually present for landmarks.
                  { # Detected entity location information.
                    "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.
                        # of doubles representing degrees latitude and degrees longitude. Unless
                        # specified otherwise, this must conform to the
                        # WGS84
                        # standard. Values must be within normalized ranges.
                        #
                        # Example of normalization code in Python:
                        #
                        #     def NormalizeLongitude(longitude):
                        #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                        #       q, r = divmod(longitude, 360.0)
                        #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                        #         return r - 360.0
                        #       return r
                        #
                        #     def NormalizeLatLng(latitude, longitude):
                        #       """Wraps decimal degrees latitude and longitude to
                        #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                        #       r = latitude % 360.0
                        #       if r <= 90.0:
                        #         return r, NormalizeLongitude(longitude)
                        #       elif r >= 270.0:
                        #         return r - 360, NormalizeLongitude(longitude)
                        #       else:
                        #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                        #
                        #     assert 180.0 == NormalizeLongitude(180.0)
                        #     assert -180.0 == NormalizeLongitude(-180.0)
                        #     assert -179.0 == NormalizeLongitude(181.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                        #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                        #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                        #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                        #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                      "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                      "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].
                "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently
                    # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s
                    # are produced for the entire text detected in an image region, followed by
                    # `boundingPoly`s for each word within the detected text.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "properties": [ # Some entities can have additional optional Property fields.
                    # For example a different kind of score or string that qualifies the entity.
                  { # Arbitrary name/value pair.
                    "name": "A String", # Name of the property.
                    "value": "A String", # Value of the property.
                  },
                ],
              },
            ],
            "imagePropertiesAnnotation": { # Stores image properties (e.g. dominant colors). # If present, image properties were extracted successfully.
              "dominantColors": { # Set of dominant colors and their corresponding scores. # If present, dominant colors completed successfully.
                "colors": [ # RGB color values, with their score and pixel fraction.
                  { # Color information consists of RGB channels, score and fraction of
                      # image the color occupies in the image.
                    "color": { # Represents a color in the RGBA color space. This representation is designed # RGB components of the color.
                        # for simplicity of conversion to/from color representations in various
                        # languages over compactness; for example, the fields of this representation
                        # can be trivially provided to the constructor of "java.awt.Color" in Java; it
                        # can also be trivially provided to UIColor's "+colorWithRed:green:blue:alpha"
                        # method in iOS; and, with just a little work, it can be easily formatted into
                        # a CSS "rgba()" string in JavaScript, as well. Here are some examples:
                        #
                        # Example (Java):
                        #
                        #      import com.google.type.Color;
                        #
                        #      // ...
                        #      public static java.awt.Color fromProto(Color protocolor) {
                        #        float alpha = protocolor.hasAlpha()
                        #            ? protocolor.getAlpha().getValue()
                        #            : 1.0;
                        #
                        #        return new java.awt.Color(
                        #            protocolor.getRed(),
                        #            protocolor.getGreen(),
                        #            protocolor.getBlue(),
                        #            alpha);
                        #      }
                        #
                        #      public static Color toProto(java.awt.Color color) {
                        #        float red = (float) color.getRed();
                        #        float green = (float) color.getGreen();
                        #        float blue = (float) color.getBlue();
                        #        float denominator = 255.0;
                        #        Color.Builder resultBuilder =
                        #            Color
                        #                .newBuilder()
                        #                .setRed(red / denominator)
                        #                .setGreen(green / denominator)
                        #                .setBlue(blue / denominator);
                        #        int alpha = color.getAlpha();
                        #        if (alpha != 255) {
                        #          result.setAlpha(
                        #              FloatValue
                        #                  .newBuilder()
                        #                  .setValue(((float) alpha) / denominator)
                        #                  .build());
                        #        }
                        #        return resultBuilder.build();
                        #      }
                        #      // ...
                        #
                        # Example (iOS / Obj-C):
                        #
                        #      // ...
                        #      static UIColor* fromProto(Color* protocolor) {
                        #         float red = [protocolor red];
                        #         float green = [protocolor green];
                        #         float blue = [protocolor blue];
                        #         FloatValue* alpha_wrapper = [protocolor alpha];
                        #         float alpha = 1.0;
                        #         if (alpha_wrapper != nil) {
                        #           alpha = [alpha_wrapper value];
                        #         }
                        #         return [UIColor colorWithRed:red green:green blue:blue alpha:alpha];
                        #      }
                        #
                        #      static Color* toProto(UIColor* color) {
                        #          CGFloat red, green, blue, alpha;
                        #          if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) {
                        #            return nil;
                        #          }
                        #          Color* result = [Color alloc] init];
                        #          [result setRed:red];
                        #          [result setGreen:green];
                        #          [result setBlue:blue];
                        #          if (alpha <= 0.9999) {
                        #            [result setAlpha:floatWrapperWithValue(alpha)];
                        #          }
                        #          [result autorelease];
                        #          return result;
                        #     }
                        #     // ...
                        #
                        #  Example (JavaScript):
                        #
                        #     // ...
                        #
                        #     var protoToCssColor = function(rgb_color) {
                        #        var redFrac = rgb_color.red || 0.0;
                        #        var greenFrac = rgb_color.green || 0.0;
                        #        var blueFrac = rgb_color.blue || 0.0;
                        #        var red = Math.floor(redFrac * 255);
                        #        var green = Math.floor(greenFrac * 255);
                        #        var blue = Math.floor(blueFrac * 255);
                        #
                        #        if (!('alpha' in rgb_color)) {
                        #           return rgbToCssColor_(red, green, blue);
                        #        }
                        #
                        #        var alphaFrac = rgb_color.alpha.value || 0.0;
                        #        var rgbParams = [red, green, blue].join(',');
                        #        return ['rgba(', rgbParams, ',', alphaFrac, ')'].join('');
                        #     };
                        #
                        #     var rgbToCssColor_ = function(red, green, blue) {
                        #       var rgbNumber = new Number((red << 16) | (green << 8) | blue);
                        #       var hexString = rgbNumber.toString(16);
                        #       var missingZeros = 6 - hexString.length;
                        #       var resultBuilder = ['#'];
                        #       for (var i = 0; i < missingZeros; i++) {
                        #          resultBuilder.push('0');
                        #       }
                        #       resultBuilder.push(hexString);
                        #       return resultBuilder.join('');
                        #     };
                        #
                        #     // ...
                      "blue": 3.14, # The amount of blue in the color as a value in the interval [0, 1].
                      "alpha": 3.14, # The fraction of this color that should be applied to the pixel. That is,
                          # the final pixel color is defined by the equation:
                          #
                          #   pixel color = alpha * (this color) + (1.0 - alpha) * (background color)
                          #
                          # This means that a value of 1.0 corresponds to a solid color, whereas
                          # a value of 0.0 corresponds to a completely transparent color. This
                          # uses a wrapper message rather than a simple float scalar so that it is
                          # possible to distinguish between a default value and the value being unset.
                          # If omitted, this color object is to be rendered as a solid color
                          # (as if the alpha value had been explicitly given with a value of 1.0).
                      "green": 3.14, # The amount of green in the color as a value in the interval [0, 1].
                      "red": 3.14, # The amount of red in the color as a value in the interval [0, 1].
                    },
                    "pixelFraction": 3.14, # Stores the fraction of pixels the color occupies in the image.
                        # Value in range [0, 1].
                    "score": 3.14, # Image-specific score for this color. Value in range [0, 1].
                  },
                ],
              },
            },
            "faceAnnotations": [ # If present, face detection completed successfully.
              { # A face annotation object contains the results of face detection.
                "panAngle": 3.14, # Yaw angle. Indicates the leftward/rightward angle that the face is
                    # pointing, relative to the vertical plane perpendicular to the image. Range
                    # [-180,180].
                "sorrowLikelihood": "A String", # Sorrow likelihood.
                "underExposedLikelihood": "A String", # Under-exposed likelihood.
                "detectionConfidence": 3.14, # Detection confidence. Range [0, 1].
                "joyLikelihood": "A String", # Joy likelihood.
                "landmarks": [ # Detected face landmarks.
                  { # A face-specific landmark (for example, a face feature).
                      # Landmark positions may fall outside the bounds of the image
                      # when the face is near one or more edges of the image.
                      # Therefore it is NOT guaranteed that 0 <= x < width or 0 <= y < height.
                    "position": { # A 3D position in the image, used primarily for Face detection landmarks. # Face landmark position.
                        # A valid Position must have both x and y coordinates.
                        # The position coordinates are in the same scale as the original image.
                      "y": 3.14, # Y coordinate.
                      "x": 3.14, # X coordinate.
                      "z": 3.14, # Z coordinate (or depth).
                    },
                    "type": "A String", # Face landmark type.
                  },
                ],
                "surpriseLikelihood": "A String", # Surprise likelihood.
                "blurredLikelihood": "A String", # Blurred likelihood.
                "tiltAngle": 3.14, # Pitch angle. Indicates the upwards/downwards angle that the face is
                    # pointing
                    # relative to the image's horizontal plane. Range [-180,180].
                "angerLikelihood": "A String", # Anger likelihood.
                "boundingPoly": { # A bounding polygon for the detected image annotation. # The bounding polygon around the face. The coordinates of the bounding box
                    # are in the original image's scale, as returned in ImageParams.
                    # The bounding box is computed to "frame" the face in accordance with human
                    # expectations. It is based on the landmarker results.
                    # Note that one or more x and/or y coordinates may not be generated in the
                    # BoundingPoly (the polygon will be unbounded) if only a partial face appears in
                    # the image to be annotated.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "rollAngle": 3.14, # Roll angle. Indicates the amount of clockwise/anti-clockwise rotation of
                    # the
                    # face relative to the image vertical, about the axis perpendicular to the
                    # face. Range [-180,180].
                "headwearLikelihood": "A String", # Headwear likelihood.
                "fdBoundingPoly": { # A bounding polygon for the detected image annotation. # This bounding polygon is tighter than the previous
                    # boundingPoly, and
                    # encloses only the skin part of the face. Typically, it is used to
                    # eliminate the face from any image analysis that detects the
                    # "amount of skin" visible in an image. It is not based on the
                    # landmarker results, only on the initial face detection, hence
                    # the fd (face detection) prefix.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "landmarkingConfidence": 3.14, # Face landmarking confidence. Range [0, 1].
              },
            ],
            "logoAnnotations": [ # If present, logo detection completed successfully.
              { # Set of detected entity features.
                "confidence": 3.14, # The accuracy of the entity detection in an image.
                    # For example, for an image containing 'Eiffel Tower,' this field represents
                    # the confidence that there is a tower in the query image. Range [0, 1].
                "description": "A String", # Entity textual description, expressed in its locale language.
                "locale": "A String", # The language code for the locale in which the entity textual
                    # description (next field) is expressed.
                "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the
                    # image. For example, the relevancy of 'tower' to an image containing
                    # 'Eiffel Tower' is likely higher than an image containing a distant towering
                    # building, though the confidence that there is a tower may be the same.
                    # Range [0, 1].
                "mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).
                    # For more details on KG please see:
                    # https://developers.google.com/knowledge-graph/
                "locations": [ # The location information for the detected entity. Multiple
                    # LocationInfo elements can be present since one location may
                    # indicate the location of the scene in the query image, and another the
                    # location of the place where the query image was taken. Location information
                    # is usually present for landmarks.
                  { # Detected entity location information.
                    "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.
                        # of doubles representing degrees latitude and degrees longitude. Unless
                        # specified otherwise, this must conform to the
                        # WGS84
                        # standard. Values must be within normalized ranges.
                        #
                        # Example of normalization code in Python:
                        #
                        #     def NormalizeLongitude(longitude):
                        #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                        #       q, r = divmod(longitude, 360.0)
                        #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                        #         return r - 360.0
                        #       return r
                        #
                        #     def NormalizeLatLng(latitude, longitude):
                        #       """Wraps decimal degrees latitude and longitude to
                        #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                        #       r = latitude % 360.0
                        #       if r <= 90.0:
                        #         return r, NormalizeLongitude(longitude)
                        #       elif r >= 270.0:
                        #         return r - 360, NormalizeLongitude(longitude)
                        #       else:
                        #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                        #
                        #     assert 180.0 == NormalizeLongitude(180.0)
                        #     assert -180.0 == NormalizeLongitude(-180.0)
                        #     assert -179.0 == NormalizeLongitude(181.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                        #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                        #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                        #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                        #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                      "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                      "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].
                "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently
                    # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s
                    # are produced for the entire text detected in an image region, followed by
                    # `boundingPoly`s for each word within the detected text.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "properties": [ # Some entities can have additional optional Property fields.
                    # For example a different kind of score or string that qualifies the entity.
                  { # Arbitrary name/value pair.
                    "name": "A String", # Name of the property.
                    "value": "A String", # Value of the property.
                  },
                ],
              },
            ],
            "landmarkAnnotations": [ # If present, landmark detection completed successfully.
              { # Set of detected entity features.
                "confidence": 3.14, # The accuracy of the entity detection in an image.
                    # For example, for an image containing 'Eiffel Tower,' this field represents
                    # the confidence that there is a tower in the query image. Range [0, 1].
                "description": "A String", # Entity textual description, expressed in its locale language.
                "locale": "A String", # The language code for the locale in which the entity textual
                    # description (next field) is expressed.
                "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the
                    # image. For example, the relevancy of 'tower' to an image containing
                    # 'Eiffel Tower' is likely higher than an image containing a distant towering
                    # building, though the confidence that there is a tower may be the same.
                    # Range [0, 1].
                "mid": "A String", # Opaque entity ID. Some IDs might be available in Knowledge Graph(KG).
                    # For more details on KG please see:
                    # https://developers.google.com/knowledge-graph/
                "locations": [ # The location information for the detected entity. Multiple
                    # LocationInfo elements can be present since one location may
                    # indicate the location of the scene in the query image, and another the
                    # location of the place where the query image was taken. Location information
                    # is usually present for landmarks.
                  { # Detected entity location information.
                    "latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # Lat - long location coordinates.
                        # of doubles representing degrees latitude and degrees longitude. Unless
                        # specified otherwise, this must conform to the
                        # WGS84
                        # standard. Values must be within normalized ranges.
                        #
                        # Example of normalization code in Python:
                        #
                        #     def NormalizeLongitude(longitude):
                        #       """Wraps decimal degrees longitude to [-180.0, 180.0]."""
                        #       q, r = divmod(longitude, 360.0)
                        #       if r > 180.0 or (r == 180.0 and q <= -1.0):
                        #         return r - 360.0
                        #       return r
                        #
                        #     def NormalizeLatLng(latitude, longitude):
                        #       """Wraps decimal degrees latitude and longitude to
                        #       [-90.0, 90.0] and [-180.0, 180.0], respectively."""
                        #       r = latitude % 360.0
                        #       if r <= 90.0:
                        #         return r, NormalizeLongitude(longitude)
                        #       elif r >= 270.0:
                        #         return r - 360, NormalizeLongitude(longitude)
                        #       else:
                        #         return 180 - r, NormalizeLongitude(longitude + 180.0)
                        #
                        #     assert 180.0 == NormalizeLongitude(180.0)
                        #     assert -180.0 == NormalizeLongitude(-180.0)
                        #     assert -179.0 == NormalizeLongitude(181.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)
                        #     assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)
                        #     assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)
                        #     assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)
                        #     assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)
                        #     assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)
                        #     assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)
                        #     assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)
                      "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
                      "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].
                "boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Not filled currently
                    # for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s
                    # are produced for the entire text detected in an image region, followed by
                    # `boundingPoly`s for each word within the detected text.
                  "vertices": [ # The bounding polygon vertices.
                    { # A vertex represents a 2D point in the image.
                        # NOTE: the vertex coordinates are in the same scale as the original image.
                      "y": 42, # Y coordinate.
                      "x": 42, # X coordinate.
                    },
                  ],
                },
                "properties": [ # Some entities can have additional optional Property fields.
                    # For example a different kind of score or string that qualifies the entity.
                  { # Arbitrary name/value pair.
                    "name": "A String", # Name of the property.
                    "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.
                # Note that filled-in mage annotations are guaranteed to be
                # correct, even when error is non-empty.
                # programming environments, including REST APIs and RPC APIs. It is used by
                # [gRPC](https://github.com/grpc). The error model is designed to be:
                #
                # - Simple to use and understand for most users
                # - Flexible enough to meet unexpected needs
                #
                # # Overview
                #
                # The `Status` message contains three pieces of data: error code, error message,
                # and error details. The error code should be an enum value of
                # google.rpc.Code, but it may accept additional error codes if needed.  The
                # error message should be a developer-facing English message that helps
                # developers *understand* and *resolve* the error. If a localized user-facing
                # error message is needed, put the localized message in the error details or
                # localize it in the client. The optional error details may contain arbitrary
                # information about the error. There is a predefined set of error detail types
                # 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
                # is not necessarily the actual wire format. When the `Status` message is
                # exposed in different client libraries and different wire protocols, it can be
                # mapped differently. For example, it will likely be mapped to some exceptions
                # 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
                # environments, either with or without APIs, to provide a
                # consistent developer experience across different environments.
                #
                # Example uses of this error model include:
                #
                # - Partial errors. If a service needs to return partial errors to the client,
                #     it may embed the `Status` in the normal response to indicate the partial
                #     errors.
                #
                # - Workflow errors. A typical workflow has multiple steps. Each step may
                #     have a `Status` message for error reporting purpose.
                #
                # - Batch operations. If a client uses batch request and batch response, the
                #     `Status` message should be used directly inside batch response, one for
                #     each error sub-response.
                #
                # - Asynchronous operations. If an API call embeds asynchronous operation
                #     results in its response, the status of those operations should be
                #     represented directly using the `Status` message.
                #
                # - Logging. If some API errors are stored in logs, the message `Status` could
                #     be used directly after any stripping needed for security/privacy reasons.
              "message": "A String", # A developer-facing error message, which should be in English. Any
                  # user-facing error message should be localized and sent in the
                  # google.rpc.Status.details field, or localized by the client.
              "code": 42, # The status code, which should be an enum value of google.rpc.Code.
              "details": [ # A list of messages that carry the error details.  There will be a
                  # common set of message types for APIs to use.
                {
                  "a_key": "", # Properties of the object. Contains field @type with type URL.
                },
              ],
            },
          },
        ],
      }