Run image detection and annotation for a batch of images.
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 # will yield the best results as it will allow text detection to # automatically detect the text language. For languages based on the latin # alphabet a hint is not needed. In rare cases, when the language of # the text in the image is known in advance, setting this hint will help get # better results (although it will hurt a great deal if the hint is wrong). # Text detection will return an error if one or more of the languages # specified here are not supported. The exact list of supported languages are # specified here: # https://cloud.google.com/translate/v2/using_rest#language-params "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. "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 itslocale
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 optionalProperty
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 itslocale
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 optionalProperty
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 # thefd
(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 itslocale
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 optionalProperty
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 itslocale
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 optionalProperty
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 whenerror
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 @ype with type URL. }, ], }, }, ], }