fn detect_labels(
        &self,
        input: DetectLabelsRequest,
    ) -> RusotoFuture<DetectLabelsResponse, DetectLabelsError> {
        let mut request = SignedRequest::new("POST", "rekognition", &self.region, "/");

        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "RekognitionService.DetectLabels");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));

        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;

                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }

                    serde_json::from_str::<DetectLabelsResponse>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(DetectLabelsError::from_response(response))),
                )
            }
        })
    }
/// <p>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. </p> <p>For an example, see Analyzing Images Stored in an Amazon S3 Bucket in the Amazon Rekognition Developer Guide.</p> <note> <p> <code>DetectLabels</code> does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see StartLabelDetection in the Amazon Rekognition Developer Guide.</p> </note> <p>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 Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. </p> <p> 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. </p> <p> <code>{Name: lighthouse, Confidence: 98.4629}</code> </p> <p> <code>{Name: rock,Confidence: 79.2097}</code> </p> <p> <code> {Name: sea,Confidence: 75.061}</code> </p> <p>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. </p> <p> <code>{Name: flower,Confidence: 99.0562}</code> </p> <p> <code>{Name: plant,Confidence: 99.0562}</code> </p> <p> <code>{Name: tulip,Confidence: 99.0562}</code> </p> <p>In this example, the detection algorithm more precisely identifies the flower as a tulip.</p> <p>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. </p> <note> <p>If the object detected is a person, the operation doesn't provide the same facial details that the <a>DetectFaces</a> operation provides.</p> </note> <p>This is a stateless API operation. That is, the operation does not persist any data.</p> <p>This operation requires permissions to perform the <code>rekognition:DetectLabels</code> action. </p>
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.   DetectLabels does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see StartLabelDetection 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 Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   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.
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 . DetectLabels does not support the detection of activities . However activity detection is supported for label detection in videos . For more information see StartLabelDetection 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 Amazon CLI to call Amazon Rekognition operations passing image bytes is not supported . The image must be either a PNG or JPEG formatted file . 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 .
fn detect_labels(
        &self,
        input: DetectLabelsRequest,
    ) -> RusotoFuture<DetectLabelsResponse, DetectLabelsError> {
        let mut request = SignedRequest::new("POST", "rekognition", &self.region, "/");
        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "RekognitionService.DetectLabels");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));
        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;
                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }
                    serde_json::from_str::<DetectLabelsResponse>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(DetectLabelsError::from_response(response))),
                )
            }
        })
    }
fn detect_labels ( & self , input : DetectLabelsRequest , ) -> RusotoFuture < DetectLabelsResponse , DetectLabelsError > { let mut request = SignedRequest :: new ( " " , " " , & self . region , " " ) ; request . set_content_type ( " " . to_owned ( ) ) ; request . add_header ( " " , " " ) ; let encoded = serde_json :: to_string ( & input ) . unwrap ( ) ; request . set_payload ( Some ( encoded . into_bytes ( ) ) ) ; self . client . sign_and_dispatch ( request , | response | { if response . status . is_success ( ) { Box :: new ( response . buffer ( ) . from_err ( ) . map ( | response | { let mut body = response . body ; if body . is_empty ( ) || body == b" " { body = b" " . to_vec ( ) ; } serde_json :: from_str :: < DetectLabelsResponse > ( String :: from_utf8_lossy ( body . as_ref ( ) ) . as_ref ( ) , ) . unwrap ( ) } ) ) } else { Box :: new ( response . buffer ( ) . from_err ( ) . and_then ( | response | Err ( DetectLabelsError :: from_response ( response ) ) ) , ) } } ) }
<p>Detects instances of real-world entities within an image (JPEG or PNG) provided as input.
<p > Detects instances of real - world entities within an image ( JPEG or PNG ) provided as input .
&self
input
DetectLabelsRequest
&self
input
DetectLabelsRequest