Container for the parameters to the DetectLabels operation. Detects instances of real-world labels 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 get-started-exercise-detect-labels.

For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response will include all three labels, one for each object.

{Name: lighthouse, Confidence: 98.4629}

{Name: rock,Confidence: 79.2097}

{Name: sea,Confidence: 75.061}

In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. 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.

You can provide the input image as an S3 object or as base64-encoded bytes. In response, the API returns an array of labels. In addition, the response also includes the orientation correction. Optionally, you can specify MinConfidence to control the confidence threshold for the labels returned. The default is 50%. You can also add the MaxLabels parameter to limit the number of labels returned.

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 is, the operation does not persist any data.

This operation requires permissions to perform the rekognition:DetectLabels action.

Inheritance: AmazonRekognitionRequest
Ejemplo n.º 1
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        /// <summary>
        /// Detects instances of real-world labels 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 <a>get-started-exercise-detect-labels</a>.
        /// 
        ///  
        /// <para>
        ///  For each object, scene, and concept the API returns one or more labels. Each label
        /// provides the object name, and the level of confidence that the image contains the
        /// object. For example, suppose the input image has a lighthouse, the sea, and a rock.
        /// The response will include all three labels, one for each object. 
        /// </para>
        ///  
        /// <para>
        ///  <code>{Name: lighthouse, Confidence: 98.4629}</code> 
        /// </para>
        ///  
        /// <para>
        ///  <code>{Name: rock,Confidence: 79.2097}</code> 
        /// </para>
        ///  
        /// <para>
        ///  <code> {Name: sea,Confidence: 75.061}</code> 
        /// </para>
        ///  
        /// <para>
        ///  In the preceding example, the operation returns one label for each of the three objects.
        /// The operation can also return multiple labels for the same object in the image. For
        /// example, if the input image shows a flower (for example, a tulip), the operation might
        /// return the following three labels. 
        /// </para>
        ///  
        /// <para>
        ///  <code>{Name: flower,Confidence: 99.0562}</code> 
        /// </para>
        ///  
        /// <para>
        ///  <code>{Name: plant,Confidence: 99.0562}</code> 
        /// </para>
        ///  
        /// <para>
        ///  <code>{Name: tulip,Confidence: 99.0562}</code> 
        /// </para>
        ///  
        /// <para>
        /// In this example, the detection algorithm more precisely identifies the flower as a
        /// tulip.
        /// </para>
        ///  
        /// <para>
        ///  You can provide the input image as an S3 object or as base64-encoded bytes. In response,
        /// the API returns an array of labels. In addition, the response also includes the orientation
        /// correction. Optionally, you can specify <code>MinConfidence</code> to control the
        /// confidence threshold for the labels returned. The default is 50%. You can also add
        /// the <code>MaxLabels</code> parameter to limit the number of labels returned. 
        /// </para>
        ///  <note> 
        /// <para>
        /// If the object detected is a person, the operation doesn't provide the same facial
        /// details that the <a>DetectFaces</a> operation provides.
        /// </para>
        ///  </note> 
        /// <para>
        /// This is a stateless API operation. That is, the operation does not persist any data.
        /// </para>
        ///  
        /// <para>
        /// This operation requires permissions to perform the <code>rekognition:DetectLabels</code>
        /// action. 
        /// </para>
        /// </summary>
        /// <param name="request">Container for the necessary parameters to execute the DetectLabels service method.</param>
        /// 
        /// <returns>The response from the DetectLabels service method, as returned by Rekognition.</returns>
        /// <exception cref="Amazon.Rekognition.Model.AccessDeniedException">
        /// You are not authorized to perform the action.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.ImageTooLargeException">
        /// The input image size exceeds the allowed limit. For more information, see <a>limits</a>.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.InternalServerErrorException">
        /// Amazon Rekognition experienced a service issue. Try your call again.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.InvalidImageFormatException">
        /// The provided image format is not supported.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.InvalidParameterException">
        /// Input parameter violated a constraint. Validate your parameter before calling the
        /// API again.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.InvalidS3ObjectException">
        /// Amazon Rekognition is unable to access the S3 object specified in the request.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.ProvisionedThroughputExceededException">
        /// The number of requests exceeded your throughput limit. If you want to increase this
        /// limit, contact Amazon Rekognition.
        /// </exception>
        /// <exception cref="Amazon.Rekognition.Model.ThrottlingException">
        /// Amazon Rekognition is temporarily unable to process the request. Try your call again.
        /// </exception>
        public DetectLabelsResponse DetectLabels(DetectLabelsRequest request)
        {
            var marshaller = new DetectLabelsRequestMarshaller();
            var unmarshaller = DetectLabelsResponseUnmarshaller.Instance;

            return Invoke<DetectLabelsRequest,DetectLabelsResponse>(request, marshaller, unmarshaller);
        }
Ejemplo n.º 2
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        /// <summary>
        /// Initiates the asynchronous execution of the DetectLabels operation.
        /// </summary>
        /// 
        /// <param name="request">Container for the necessary parameters to execute the DetectLabels operation on AmazonRekognitionClient.</param>
        /// <param name="callback">An AsyncCallback delegate that is invoked when the operation completes.</param>
        /// <param name="state">A user-defined state object that is passed to the callback procedure. Retrieve this object from within the callback
        ///          procedure using the AsyncState property.</param>
        /// 
        /// <returns>An IAsyncResult that can be used to poll or wait for results, or both; this value is also needed when invoking EndDetectLabels
        ///         operation.</returns>
        public IAsyncResult BeginDetectLabels(DetectLabelsRequest request, AsyncCallback callback, object state)
        {
            var marshaller = new DetectLabelsRequestMarshaller();
            var unmarshaller = DetectLabelsResponseUnmarshaller.Instance;

            return BeginInvoke<DetectLabelsRequest>(request, marshaller, unmarshaller,
                callback, state);
        }
Ejemplo n.º 3
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        /// <summary>
        /// Initiates the asynchronous execution of the DetectLabels operation.
        /// </summary>
        /// 
        /// <param name="request">Container for the necessary parameters to execute the DetectLabels operation.</param>
        /// <param name="cancellationToken">
        ///     A cancellation token that can be used by other objects or threads to receive notice of cancellation.
        /// </param>
        /// <returns>The task object representing the asynchronous operation.</returns>
        public Task<DetectLabelsResponse> DetectLabelsAsync(DetectLabelsRequest request, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
        {
            var marshaller = new DetectLabelsRequestMarshaller();
            var unmarshaller = DetectLabelsResponseUnmarshaller.Instance;

            return InvokeAsync<DetectLabelsRequest,DetectLabelsResponse>(request, marshaller, 
                unmarshaller, cancellationToken);
        }