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| <h1><a href="vision_v1.html">Google Cloud Vision API</a> . <a href="vision_v1.images.html">images</a></h1> |
| <h2>Instance Methods</h2> |
| <p class="toc_element"> |
| <code><a href="#annotate">annotate(body, x__xgafv=None)</a></code></p> |
| <p class="firstline">Run image detection and annotation for a batch of images.</p> |
| <h3>Method Details</h3> |
| <div class="method"> |
| <code class="details" id="annotate">annotate(body, x__xgafv=None)</code> |
| <pre>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 and/or feature-specific parameters. # 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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](/vision/docs/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 provided for an image, `content` takes precedence and is |
| # used to perform the image annotation request. |
| "gcsImageUri": "A String", # Google Cloud Storage image URI, which must be in the following form: |
| # `gs://bucket_name/object_name` (for details, see |
| # [Google Cloud Storage Request URIs](https://cloud.google.com/storage/docs/reference-uris)). |
| # NOTE: Cloud Storage object versioning is not supported. |
| }, |
| }, |
| "features": [ # Requested features. |
| { # Users describe the type of Google Cloud Vision API tasks to perform over |
| # images by using *Feature*s. Each Feature indicates a type of image |
| # detection task to perform. 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": { # If present, safe-search annotation has completed successfully. |
| "medical": "A String", # Likelihood that this is a medical image. |
| "violence": "A String", # Violence likelihood. |
| "spoof": "A String", # Spoof likelihood. The likelihood that an modification |
| # was made to the image's canonical version to make it appear |
| # funny or offensive. |
| "adult": "A String", # Represents the adult content likelihood for the image. |
| }, |
| "textAnnotations": [ # If present, text (OCR) detection has completed successfully. |
| { # Set of detected entity features. |
| "confidence": 3.14, # The accuracy of the entity detection in an image. |
| # For example, for an image in which the "Eiffel Tower" entity is detected, |
| # 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` is expressed. |
| "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the |
| # image. For example, the relevancy of "tower" is likely higher to an image |
| # containing the detected "Eiffel Tower" than to an image containing a |
| # detected distant towering building, even though the confidence that |
| # there is a tower in each image may be the same. Range [0, 1]. |
| "mid": "A String", # Opaque entity ID. Some IDs may be available in |
| # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). |
| "locations": [ # The location information for the detected entity. Multiple |
| # `LocationInfo` elements can be present because one location may |
| # indicate the location of the scene in the image, and another location |
| # may indicate the location of the place where the 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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. Currently not produced |
| # 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 may have optional user-supplied `Property` (name/value) |
| # fields, such a score or string that qualifies the entity. |
| { # A `Property` consists of a user-supplied name/value pair. |
| "name": "A String", # Name of the property. |
| "value": "A String", # Value of the property. |
| }, |
| ], |
| }, |
| ], |
| "labelAnnotations": [ # If present, label detection has completed successfully. |
| { # Set of detected entity features. |
| "confidence": 3.14, # The accuracy of the entity detection in an image. |
| # For example, for an image in which the "Eiffel Tower" entity is detected, |
| # 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` is expressed. |
| "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the |
| # image. For example, the relevancy of "tower" is likely higher to an image |
| # containing the detected "Eiffel Tower" than to an image containing a |
| # detected distant towering building, even though the confidence that |
| # there is a tower in each image may be the same. Range [0, 1]. |
| "mid": "A String", # Opaque entity ID. Some IDs may be available in |
| # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). |
| "locations": [ # The location information for the detected entity. Multiple |
| # `LocationInfo` elements can be present because one location may |
| # indicate the location of the scene in the image, and another location |
| # may indicate the location of the place where the 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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. Currently not produced |
| # 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 may have optional user-supplied `Property` (name/value) |
| # fields, such a score or string that qualifies the entity. |
| { # A `Property` consists of a user-supplied name/value pair. |
| "name": "A String", # Name of the property. |
| "value": "A String", # Value of the property. |
| }, |
| ], |
| }, |
| ], |
| "imagePropertiesAnnotation": { # Stores image properties, such as 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 the fraction of |
| # the image that 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, # 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 has completed successfully. |
| { # A face annotation object contains the results of face detection. |
| "panAngle": 3.14, # Yaw angle, which 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 |
| # if 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, which 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, which 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. # The `fd_bounding_poly` bounding polygon is tighter than the |
| # `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 <code>fd</code> (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 has completed successfully. |
| { # Set of detected entity features. |
| "confidence": 3.14, # The accuracy of the entity detection in an image. |
| # For example, for an image in which the "Eiffel Tower" entity is detected, |
| # 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` is expressed. |
| "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the |
| # image. For example, the relevancy of "tower" is likely higher to an image |
| # containing the detected "Eiffel Tower" than to an image containing a |
| # detected distant towering building, even though the confidence that |
| # there is a tower in each image may be the same. Range [0, 1]. |
| "mid": "A String", # Opaque entity ID. Some IDs may be available in |
| # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). |
| "locations": [ # The location information for the detected entity. Multiple |
| # `LocationInfo` elements can be present because one location may |
| # indicate the location of the scene in the image, and another location |
| # may indicate the location of the place where the 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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. Currently not produced |
| # 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 may have optional user-supplied `Property` (name/value) |
| # fields, such a score or string that qualifies the entity. |
| { # A `Property` consists of a user-supplied name/value pair. |
| "name": "A String", # Name of the property. |
| "value": "A String", # Value of the property. |
| }, |
| ], |
| }, |
| ], |
| "landmarkAnnotations": [ # If present, landmark detection has completed successfully. |
| { # Set of detected entity features. |
| "confidence": 3.14, # The accuracy of the entity detection in an image. |
| # For example, for an image in which the "Eiffel Tower" entity is detected, |
| # 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` is expressed. |
| "topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the |
| # image. For example, the relevancy of "tower" is likely higher to an image |
| # containing the detected "Eiffel Tower" than to an image containing a |
| # detected distant towering building, even though the confidence that |
| # there is a tower in each image may be the same. Range [0, 1]. |
| "mid": "A String", # Opaque entity ID. Some IDs may be available in |
| # [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). |
| "locations": [ # The location information for the detected entity. Multiple |
| # `LocationInfo` elements can be present because one location may |
| # indicate the location of the scene in the image, and another location |
| # may indicate the location of the place where the 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 |
| # <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84 |
| # standard</a>. 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. Currently not produced |
| # 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 may have optional user-supplied `Property` (name/value) |
| # fields, such a score or string that qualifies the entity. |
| { # A `Property` consists of a user-supplied 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 image annotations are guaranteed to be |
| # correct, even when `error` is set. |
| # 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. |
| }, |
| ], |
| }, |
| }, |
| ], |
| }</pre> |
| </div> |
| |
| </body></html> |
