google_api_vision v0.0.1 API Reference
Modules
API calls for all endpoints tagged Images
Handle Tesla connections for GoogleApi.Vision.V1
Helper functions for deserializing responses into models
Request for performing Google Cloud Vision API tasks over a user-provided image, with user-requested features
Response to an image annotation request
Multiple image annotation requests are batched into a single service call
Response to a batch image annotation request
Logical element on the page
A bounding polygon for the detected image annotation
Represents a color in the RGBA color space. This representation is designed 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(rgbcolor) { 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 = rgbcolor.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(''); }; //
Color information consists of RGB channels, score, and the fraction of the image that the color occupies in the image
Single crop hint that is used to generate a new crop when serving an image
Set of crop hints that are used to generate new crops when serving images
Parameters for crop hints annotation request
Detected start or end of a structural component
Detected language for a structural component
Set of dominant colors and their corresponding scores
Set of detected entity features
A face annotation object contains the results of face detection
Users describe the type of Google Cloud Vision API tasks to perform over images by using Features. 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
Client image to perform Google Cloud Vision API tasks over
Image context and/or feature-specific parameters
Stores image properties, such as dominant colors
External image source (Google Cloud Storage image location)
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`
An object representing a latitude/longitude pair. This is expressed as a 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)
Rectangle determined by min and max `LatLng` pairs
Detected entity location information
Detected page from OCR
Structural unit of text representing a number of words in certain order
A 3D position in the image, used primarily for Face detection landmarks. A valid Position must have both x and y coordinates. The position coordinates are in the same scale as the original image
A `Property` consists of a user-supplied name/value pair
Set of features pertaining to the image, computed by computer vision methods over safe-search verticals (for example, adult, spoof, medical, violence)
The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by 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` that 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. - 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
A single symbol representation
TextAnnotation contains a structured representation of OCR extracted text. The hierarchy of an OCR extracted text structure is like this: TextAnnotation -> Page -> Block -> Paragraph -> Word -> Symbol Each structural component, starting from Page, may further have their own properties. Properties describe detected languages, breaks etc.. Please refer to the google.cloud.vision.v1.TextAnnotation.TextProperty message definition below for more detail
Additional information detected on the structural component
A vertex represents a 2D point in the image. NOTE: the vertex coordinates are in the same scale as the original image
Relevant information for the image from the Internet
Entity deduced from similar images on the Internet
Metadata for online images
Metadata for web pages
A word representation
Helper functions for building Tesla requests