detect_faces ¶

def detect_faces(
    self,
    image: "capo_rekognition.types.image.Image",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    attributes: Optional["capo_rekognition.types.attributes.Attributes"] = None,
) -> "capo_rekognition.types.detect_faces_response.DetectFacesResponse":
    """<p>Detects faces within an image that is provided as input.</p> <p> <code>DetectFaces</code> detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), pose, presence of facial occlusion, and so on.</p> <p>The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence. </p> <p>You pass the input image either 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. </p> <note> <p>This is a stateless API operation. That is, the operation does not persist any data.</p> </note> <p>This operation requires permissions to perform the <code>rekognition:DetectFaces</code> action. </p>

    Args:
        image: <p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>
        attributes: <p>An array of facial attributes you want to be returned. A <code>DEFAULT</code> subset of facial attributes - <code>BoundingBox</code>, <code>Confidence</code>, <code>Pose</code>, <code>Quality</code>, and <code>Landmarks</code> - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using [<code>"DEFAULT", "FACE_OCCLUDED"</code>] or just [<code>"FACE_OCCLUDED"</code>]. You can request for all facial attributes by using [<code>"ALL"]</code>. Requesting more attributes may increase response time.</p> <p>If you provide both, <code>["ALL", "DEFAULT"]</code>, the service uses a logical "AND" operator to determine which attributes to return (in this case, all attributes). </p> <p>Note that while the FaceOccluded and EyeDirection attributes are supported when using <code>DetectFaces</code>, they aren't supported when analyzing videos with <code>StartFaceDetection</code> and <code>GetFaceDetection</code>.</p>

    Raises:
        capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
        capo_rekognition.errors.image_too_large_exception.ImageTooLargeException: <p>The input image size exceeds the allowed limit. If you are calling DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. </p>
        capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</p>
        capo_rekognition.errors.invalid_image_format_exception.InvalidImageFormatException: <p>The provided image format is not supported. </p>
        capo_rekognition.errors.invalid_parameter_exception.InvalidParameterException: <p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>
        capo_rekognition.errors.invalid_s3_object_exception.InvalidS3ObjectException: <p>Amazon Rekognition is unable to access the S3 object specified in the request.</p>
        capo_rekognition.errors.provisioned_throughput_exceeded_exception.ProvisionedThroughputExceededException: <p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>
        capo_rekognition.errors.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
        capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To detect faces in an image
        This operation detects faces in an image stored in an AWS S3 bucket.

        >>> client.detect_faces(image={'S3Object': {'Bucket': 'mybucket', 'Name': 'myphoto'}})
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.detect_faces_request.DetectFacesRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.detect_faces_response.DetectFacesResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.detect_faces

        output, http_response = (
            capo_rekognition._operations.rekognition_service.detect_faces.detect_faces(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DetectFaces")
    )
    input_: capo_rekognition.types.detect_faces_request.DetectFacesRequest = {
        "image": image
    }
    if attributes is not None:
        input_["attributes"] = attributes

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output