[ aws . rekognition ]
detect-labels¶
Description¶
Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.
For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
Optional Parameters
You can specify one or both of the GENERAL_LABELS and IMAGE_PROPERTIES feature types when calling the DetectLabels API. Including GENERAL_LABELS will ensure the response includes the labels detected in the input image, while including IMAGE_PROPERTIES will ensure the response includes information about the image quality and color.
When using GENERAL_LABELS and/or IMAGE_PROPERTIES you can provide filtering criteria to the Settings parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering see Detecting Labels in an Image .
When getting labels, you can specify MinConfidence to control the confidence threshold for the labels returned. The default is 55%. You can also add the MaxLabels parameter to limit the number of labels returned. The default and upper limit is 1000 labels. These arguments are only valid when supplying GENERAL_LABELS as a feature type.
Response Elements
For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:
- Name - The name of the detected label.
- Confidence - The level of confidence in the label assigned to a detected object.
- Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.
- Aliases - Possible Aliases for the label.
- Categories - The label categories that the detected label belongs to.
- BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.
The API returns the following information regarding the image, as part of the ImageProperties structure:
- Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.
- Dominant Color - An array of the dominant colors in the image.
- Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.
- Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.
The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:
{Name: lighthouse, Confidence: 98.4629}
{Name: rock,Confidence: 79.2097}
{Name: sea,Confidence: 75.061}
The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
{Name: flower,Confidence: 99.0562}
{Name: plant,Confidence: 99.0562}
{Name: tulip,Confidence: 99.0562}
In this example, the detection algorithm more precisely identifies the flower as a tulip.
Note
If the object detected is a person, the operation doesn’t provide the same facial details that the DetectFaces operation provides.This is a stateless API operation that doesn’t return any data.
This operation requires permissions to perform the rekognition:DetectLabels action.
See also: AWS API Documentation
Synopsis¶
detect-labels
[--image <value>]
[--max-labels <value>]
[--min-confidence <value>]
[--features <value>]
[--settings <value>]
[--image-bytes <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]
Options¶
--image (structure)
The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the
Bytesfield. For more information, see Images in the Amazon Rekognition developer guide.To specify a local file use
--image-bytesinstead.Bytes -> (blob)
Blob of image bytes up to 5 MBs. Note that the maximum image size you can pass toDetectCustomLabelsis 4MB.S3Object -> (structure)
Identifies an S3 object as the image source.
Bucket -> (string)
Name of the S3 bucket.Name -> (string)
S3 object key name.Version -> (string)
If the bucket is versioning enabled, you can specify the object version.
Shorthand Syntax:
Bytes=blob,S3Object={Bucket=string,Name=string,Version=string}
JSON Syntax:
{
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
}
--max-labels (integer)
Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.
--min-confidence (float)
Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn’t return any labels with confidence lower than this specified value.
If
MinConfidenceis not specified, the operation returns labels with a confidence values greater than or equal to 55 percent. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.
--features (list)
A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the label detection feature, while specifying IMAGE_PROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.
(string)
Syntax:
"string" "string" ...
Where valid values are:
GENERAL_LABELS
IMAGE_PROPERTIES
--settings (structure)
A list of the filters to be applied to returned detected labels and image properties. Specified filters can be inclusive, exclusive, or a combination of both. Filters can be used for individual labels or label categories. The exact label names or label categories must be supplied. For a full list of labels and label categories, see Detecting labels .
GeneralLabels -> (structure)
Contains the specified filters for GENERAL_LABELS.
LabelInclusionFilters -> (list)
The labels that should be included in the return from DetectLabels.
(string)
LabelExclusionFilters -> (list)
The labels that should be excluded from the return from DetectLabels.
(string)
LabelCategoryInclusionFilters -> (list)
The label categories that should be included in the return from DetectLabels.
(string)
LabelCategoryExclusionFilters -> (list)
The label categories that should be excluded from the return from DetectLabels.
(string)
ImageProperties -> (structure)
Contains the chosen number of maximum dominant colors in an image.
MaxDominantColors -> (integer)
The maximum number of dominant colors to return when detecting labels in an image. The default value is 10.
Shorthand Syntax:
GeneralLabels={LabelInclusionFilters=[string,string],LabelExclusionFilters=[string,string],LabelCategoryInclusionFilters=[string,string],LabelCategoryExclusionFilters=[string,string]},ImageProperties={MaxDominantColors=integer}
JSON Syntax:
{
"GeneralLabels": {
"LabelInclusionFilters": ["string", ...],
"LabelExclusionFilters": ["string", ...],
"LabelCategoryInclusionFilters": ["string", ...],
"LabelCategoryExclusionFilters": ["string", ...]
},
"ImageProperties": {
"MaxDominantColors": integer
}
}
--image-bytes (blob)
The content of the image to be uploaded. To specify the content of a local file use the fileb:// prefix. Example: fileb://image.png
--cli-input-json | --cli-input-yaml (string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.
--generate-cli-skeleton (string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.
Global Options¶
--debug (boolean)
Turn on debug logging.
--endpoint-url (string)
Override command’s default URL with the given URL.
--no-verify-ssl (boolean)
By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.
--no-paginate (boolean)
Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.
--output (string)
The formatting style for command output.
- json
- text
- table
- yaml
- yaml-stream
--query (string)
A JMESPath query to use in filtering the response data.
--profile (string)
Use a specific profile from your credential file.
--region (string)
The region to use. Overrides config/env settings.
--version (string)
Display the version of this tool.
--color (string)
Turn on/off color output.
- on
- off
- auto
--no-sign-request (boolean)
Do not sign requests. Credentials will not be loaded if this argument is provided.
--ca-bundle (string)
The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.
--cli-read-timeout (int)
The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.
--cli-connect-timeout (int)
The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.
--cli-binary-format (string)
The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.
- base64
- raw-in-base64-out
--no-cli-pager (boolean)
Disable cli pager for output.
--cli-auto-prompt (boolean)
Automatically prompt for CLI input parameters.
--no-cli-auto-prompt (boolean)
Disable automatically prompt for CLI input parameters.
Examples¶
Note
To use the following examples, you must have the AWS CLI installed and configured. See the Getting started guide in the AWS CLI User Guide for more information.
Unless otherwise stated, all examples have unix-like quotation rules. These examples will need to be adapted to your terminal’s quoting rules. See Using quotation marks with strings in the AWS CLI User Guide .
To detect a label in an image
The following detect-labels example detects scenes and objects in an image stored in an Amazon S3 bucket.
aws rekognition detect-labels \
--image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'
Output:
{
"Labels": [
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Automobile"
},
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Transportation"
}
],
"Name": "Vehicle"
},
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [],
"Name": "Transportation"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.10616336017847061,
"Top": 0.5039216876029968,
"Left": 0.0037978808395564556,
"Height": 0.18528179824352264
},
"Confidence": 99.15271759033203
},
{
"BoundingBox": {
"Width": 0.2429988533258438,
"Top": 0.5251884460449219,
"Left": 0.7309805154800415,
"Height": 0.21577216684818268
},
"Confidence": 99.1286392211914
},
{
"BoundingBox": {
"Width": 0.14233611524105072,
"Top": 0.5333095788955688,
"Left": 0.6494812965393066,
"Height": 0.15528248250484467
},
"Confidence": 98.48368072509766
},
{
"BoundingBox": {
"Width": 0.11086395382881165,
"Top": 0.5354844927787781,
"Left": 0.10355594009160995,
"Height": 0.10271988064050674
},
"Confidence": 96.45606231689453
},
{
"BoundingBox": {
"Width": 0.06254628300666809,
"Top": 0.5573825240135193,
"Left": 0.46083059906959534,
"Height": 0.053911514580249786
},
"Confidence": 93.65448760986328
},
{
"BoundingBox": {
"Width": 0.10105438530445099,
"Top": 0.534368634223938,
"Left": 0.5743985772132874,
"Height": 0.12226245552301407
},
"Confidence": 93.06217193603516
},
{
"BoundingBox": {
"Width": 0.056389667093753815,
"Top": 0.5235804319381714,
"Left": 0.9427769780158997,
"Height": 0.17163699865341187
},
"Confidence": 92.6864013671875
},
{
"BoundingBox": {
"Width": 0.06003860384225845,
"Top": 0.5441341400146484,
"Left": 0.22409997880458832,
"Height": 0.06737709045410156
},
"Confidence": 90.4227066040039
},
{
"BoundingBox": {
"Width": 0.02848697081208229,
"Top": 0.5107086896896362,
"Left": 0,
"Height": 0.19150497019290924
},
"Confidence": 86.65286254882812
},
{
"BoundingBox": {
"Width": 0.04067881405353546,
"Top": 0.5566273927688599,
"Left": 0.316415935754776,
"Height": 0.03428703173995018
},
"Confidence": 85.36471557617188
},
{
"BoundingBox": {
"Width": 0.043411049991846085,
"Top": 0.5394920110702515,
"Left": 0.18293385207653046,
"Height": 0.0893595889210701
},
"Confidence": 82.21705627441406
},
{
"BoundingBox": {
"Width": 0.031183116137981415,
"Top": 0.5579366683959961,
"Left": 0.2853088080883026,
"Height": 0.03989990055561066
},
"Confidence": 81.0157470703125
},
{
"BoundingBox": {
"Width": 0.031113790348172188,
"Top": 0.5504819750785828,
"Left": 0.2580395042896271,
"Height": 0.056484755128622055
},
"Confidence": 56.13441467285156
},
{
"BoundingBox": {
"Width": 0.08586374670267105,
"Top": 0.5438792705535889,
"Left": 0.5128012895584106,
"Height": 0.08550430089235306
},
"Confidence": 52.37760925292969
}
],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Car"
},
{
"Instances": [],
"Confidence": 98.9914321899414,
"Parents": [],
"Name": "Human"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.19360728561878204,
"Top": 0.35072067379951477,
"Left": 0.43734854459762573,
"Height": 0.2742200493812561
},
"Confidence": 98.9914321899414
},
{
"BoundingBox": {
"Width": 0.03801717236638069,
"Top": 0.5010883808135986,
"Left": 0.9155802130699158,
"Height": 0.06597328186035156
},
"Confidence": 85.02790832519531
}
],
"Confidence": 98.9914321899414,
"Parents": [],
"Name": "Person"
},
{
"Instances": [],
"Confidence": 93.24951934814453,
"Parents": [],
"Name": "Machine"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.03561960905790329,
"Top": 0.6468243598937988,
"Left": 0.7850857377052307,
"Height": 0.08878646790981293
},
"Confidence": 93.24951934814453
},
{
"BoundingBox": {
"Width": 0.02217046171426773,
"Top": 0.6149078607559204,
"Left": 0.04757237061858177,
"Height": 0.07136218994855881
},
"Confidence": 91.5025863647461
},
{
"BoundingBox": {
"Width": 0.016197510063648224,
"Top": 0.6274210214614868,
"Left": 0.6472989320755005,
"Height": 0.04955997318029404
},
"Confidence": 85.14686584472656
},
{
"BoundingBox": {
"Width": 0.020207518711686134,
"Top": 0.6348286867141724,
"Left": 0.7295016646385193,
"Height": 0.07059963047504425
},
"Confidence": 83.34547424316406
},
{
"BoundingBox": {
"Width": 0.020280985161662102,
"Top": 0.6171894669532776,
"Left": 0.08744934946298599,
"Height": 0.05297485366463661
},
"Confidence": 79.9981460571289
},
{
"BoundingBox": {
"Width": 0.018318990245461464,
"Top": 0.623889148235321,
"Left": 0.6836880445480347,
"Height": 0.06730121374130249
},
"Confidence": 78.87144470214844
},
{
"BoundingBox": {
"Width": 0.021310249343514442,
"Top": 0.6167286038398743,
"Left": 0.004064912907779217,
"Height": 0.08317798376083374
},
"Confidence": 75.89361572265625
},
{
"BoundingBox": {
"Width": 0.03604431077837944,
"Top": 0.7030032277107239,
"Left": 0.9254803657531738,
"Height": 0.04569442570209503
},
"Confidence": 64.402587890625
},
{
"BoundingBox": {
"Width": 0.009834849275648594,
"Top": 0.5821820497512817,
"Left": 0.28094568848609924,
"Height": 0.01964157074689865
},
"Confidence": 62.79907989501953
},
{
"BoundingBox": {
"Width": 0.01475677452981472,
"Top": 0.6137543320655823,
"Left": 0.5950819253921509,
"Height": 0.039063986390829086
},
"Confidence": 59.40483474731445
}
],
"Confidence": 93.24951934814453,
"Parents": [
{
"Name": "Machine"
}
],
"Name": "Wheel"
},
{
"Instances": [],
"Confidence": 92.61514282226562,
"Parents": [],
"Name": "Road"
},
{
"Instances": [],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Sport"
},
{
"Instances": [],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Sports"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.12326609343290329,
"Top": 0.6332163214683533,
"Left": 0.44815489649772644,
"Height": 0.058117982000112534
},
"Confidence": 92.37877655029297
}
],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
},
{
"Name": "Sport"
}
],
"Name": "Skateboard"
},
{
"Instances": [],
"Confidence": 90.62931060791016,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Pedestrian"
},
{
"Instances": [],
"Confidence": 88.81334686279297,
"Parents": [],
"Name": "Asphalt"
},
{
"Instances": [],
"Confidence": 88.81334686279297,
"Parents": [],
"Name": "Tarmac"
},
{
"Instances": [],
"Confidence": 88.23201751708984,
"Parents": [],
"Name": "Path"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [],
"Name": "Urban"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "Town"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [],
"Name": "Building"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "City"
},
{
"Instances": [],
"Confidence": 78.37934875488281,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Parking Lot"
},
{
"Instances": [],
"Confidence": 78.37934875488281,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Parking"
},
{
"Instances": [],
"Confidence": 74.37590026855469,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
},
{
"Name": "City"
}
],
"Name": "Downtown"
},
{
"Instances": [],
"Confidence": 69.84622955322266,
"Parents": [
{
"Name": "Road"
}
],
"Name": "Intersection"
},
{
"Instances": [],
"Confidence": 57.68518829345703,
"Parents": [
{
"Name": "Sports Car"
},
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Coupe"
},
{
"Instances": [],
"Confidence": 57.68518829345703,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Sports Car"
},
{
"Instances": [],
"Confidence": 56.59492111206055,
"Parents": [
{
"Name": "Path"
}
],
"Name": "Sidewalk"
},
{
"Instances": [],
"Confidence": 56.59492111206055,
"Parents": [
{
"Name": "Path"
}
],
"Name": "Pavement"
},
{
"Instances": [],
"Confidence": 55.58770751953125,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "Neighborhood"
}
],
"LabelModelVersion": "2.0"
}
For more information, see Detecting Labels in an Image in the Amazon Rekognition Developer Guide.
Output¶
Labels -> (list)
An array of labels for the real-world objects detected.
(structure)
Structure containing details about the detected label, including the name, detected instances, parent labels, and level of confidence.
Name -> (string)
The name (label) of the object or scene.Confidence -> (float)
Level of confidence.Instances -> (list)
If
Labelrepresents an object,Instancescontains the bounding boxes for each instance of the detected object. Bounding boxes are returned for common object labels such as people, cars, furniture, apparel or pets.(structure)
An instance of a label returned by Amazon Rekognition Image ( DetectLabels ) or by Amazon Rekognition Video ( GetLabelDetection ).
BoundingBox -> (structure)
The position of the label instance on the image.
Width -> (float)
Width of the bounding box as a ratio of the overall image width.Height -> (float)
Height of the bounding box as a ratio of the overall image height.Left -> (float)
Left coordinate of the bounding box as a ratio of overall image width.Top -> (float)
Top coordinate of the bounding box as a ratio of overall image height.Confidence -> (float)
The confidence that Amazon Rekognition has in the accuracy of the bounding box.DominantColors -> (list)
The dominant colors found in an individual instance of a label.
(structure)
A description of the dominant colors in an image.
Red -> (integer)
The Red RGB value for a dominant color.Blue -> (integer)
The Blue RGB value for a dominant color.Green -> (integer)
The Green RGB value for a dominant color.HexCode -> (string)
The Hex code equivalent of the RGB values for a dominant color.CSSColor -> (string)
The CSS color name of a dominant color.SimplifiedColor -> (string)
One of 12 simplified color names applied to a dominant color.PixelPercent -> (float)
The percentage of image pixels that have a given dominant color.Parents -> (list)
The parent labels for a label. The response includes all ancestor labels.
(structure)
A parent label for a label. A label can have 0, 1, or more parents.
Name -> (string)
The name of the parent label.Aliases -> (list)
A list of potential aliases for a given label.
(structure)
A potential alias of for a given label.
Name -> (string)
The name of an alias for a given label.Categories -> (list)
A list of the categories associated with a given label.
(structure)
The category that applies to a given label.
Name -> (string)
The name of a category that applies to a given label.
OrientationCorrection -> (string)
The value of
OrientationCorrectionis always null.If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.
LabelModelVersion -> (string)
Version number of the label detection model that was used to detect labels.
ImageProperties -> (structure)
Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.
Quality -> (structure)
Information about the quality of the image foreground as defined by brightness, sharpness, and contrast. The higher the value the greater the brightness, sharpness, and contrast respectively.
Brightness -> (float)
The brightness of an image provided for label detection.Sharpness -> (float)
The sharpness of an image provided for label detection.Contrast -> (float)
The contrast of an image provided for label detection.DominantColors -> (list)
Information about the dominant colors found in an image, described with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
(structure)
A description of the dominant colors in an image.
Red -> (integer)
The Red RGB value for a dominant color.Blue -> (integer)
The Blue RGB value for a dominant color.Green -> (integer)
The Green RGB value for a dominant color.HexCode -> (string)
The Hex code equivalent of the RGB values for a dominant color.CSSColor -> (string)
The CSS color name of a dominant color.SimplifiedColor -> (string)
One of 12 simplified color names applied to a dominant color.PixelPercent -> (float)
The percentage of image pixels that have a given dominant color.Foreground -> (structure)
Information about the properties of an image’s foreground, including the foreground’s quality and dominant colors, including the quality and dominant colors of the image.
Quality -> (structure)
The quality of the image foreground as defined by brightness and sharpness.
Brightness -> (float)
The brightness of an image provided for label detection.Sharpness -> (float)
The sharpness of an image provided for label detection.Contrast -> (float)
The contrast of an image provided for label detection.DominantColors -> (list)
The dominant colors found in the foreground of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
(structure)
A description of the dominant colors in an image.
Red -> (integer)
The Red RGB value for a dominant color.Blue -> (integer)
The Blue RGB value for a dominant color.Green -> (integer)
The Green RGB value for a dominant color.HexCode -> (string)
The Hex code equivalent of the RGB values for a dominant color.CSSColor -> (string)
The CSS color name of a dominant color.SimplifiedColor -> (string)
One of 12 simplified color names applied to a dominant color.PixelPercent -> (float)
The percentage of image pixels that have a given dominant color.Background -> (structure)
Information about the properties of an image’s background, including the background’s quality and dominant colors, including the quality and dominant colors of the image.
Quality -> (structure)
The quality of the image background as defined by brightness and sharpness.
Brightness -> (float)
The brightness of an image provided for label detection.Sharpness -> (float)
The sharpness of an image provided for label detection.Contrast -> (float)
The contrast of an image provided for label detection.DominantColors -> (list)
The dominant colors found in the background of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
(structure)
A description of the dominant colors in an image.
Red -> (integer)
The Red RGB value for a dominant color.Blue -> (integer)
The Blue RGB value for a dominant color.Green -> (integer)
The Green RGB value for a dominant color.HexCode -> (string)
The Hex code equivalent of the RGB values for a dominant color.CSSColor -> (string)
The CSS color name of a dominant color.SimplifiedColor -> (string)
One of 12 simplified color names applied to a dominant color.PixelPercent -> (float)
The percentage of image pixels that have a given dominant color.