Use DetectAnomalies com um AWS SDK ou CLI

class Inference:
    """
    Shows how to detect anomalies in an image using a trained Lookout for Vision model.
    """

    @staticmethod
    def detect_anomalies(lookoutvision_client, project_name, model_version, photo):
        """
        Calls DetectAnomalies using the supplied project, model version, and image.
        :param lookoutvision_client: A Lookout for Vision Boto3 client.
        :param project: The project that contains the model that you want to use.
        :param model_version: The version of the model that you want to use.
        :param photo: The photo that you want to analyze.
        :return: The DetectAnomalyResult object that contains the analysis results.
        """

        image_type = imghdr.what(photo)
        if image_type == "jpeg":
            content_type = "image/jpeg"
        elif image_type == "png":
            content_type = "image/png"
        else:
            logger.info("Image type not valid for %s", photo)
            raise ValueError(
                f"File format not valid. Supply a jpeg or png format file: {photo}"
            )

        # Get images bytes for call to detect_anomalies.
        with open(photo, "rb") as image:
            response = lookoutvision_client.detect_anomalies(
                ProjectName=project_name,
                ContentType=content_type,
                Body=image.read(),
                ModelVersion=model_version,
            )

        return response["DetectAnomalyResult"]

    @staticmethod
    def download_from_s3(s3_resource, photo):
        """
        Downloads an image from an S3 bucket.

        :param s3_resource: A Boto3 Amazon S3 resource.
        :param photo: The Amazon S3 path of a photo to download.
        return: The local path to the downloaded file.
        """
        try:
            bucket, key = photo.replace("s3://", "").split("/", 1)
            local_file = os.path.basename(photo)
        except ValueError:
            logger.exception("Couldn't get S3 info for %s", photo)
            raise

        try:
            logger.info("Downloading %s", photo)
            s3_resource.Bucket(bucket).download_file(key, local_file)
        except ClientError:
            logger.exception("Couldn't download %s from S3.", photo)
            raise

        return local_file

    @staticmethod
    def reject_on_classification(image, prediction, confidence_limit):
        """
        Returns True if the anomaly confidence is greater than or equal to
        the supplied confidence limit.
        :param image: The name of the image file that was analyzed.
        :param prediction: The DetectAnomalyResult object returned from DetectAnomalies.
        :param confidence_limit: The minimum acceptable confidence (float 0 - 1).
        :return: True if the error condition indicates an anomaly, otherwise False.
        """

        reject = False

        logger.info("Checking classification for %s", image)

        if prediction["IsAnomalous"] and prediction["Confidence"] >= confidence_limit:
            reject = True
            reject_info = (
                f"Rejected: Anomaly confidence ({prediction['Confidence']:.2%}) is greater"
                f" than limit ({confidence_limit:.2%})"
            )
            logger.info("%s", reject_info)

        if not reject:
            logger.info("No anomalies found.")
        return reject

    @staticmethod
    def reject_on_anomaly_types(
        image, prediction, confidence_limit, anomaly_types_limit
    ):
        """
        Checks if the number of anomaly types is greater than the anomaly types
        limit and if the prediction confidence is greater than the confidence limit.
        :param image: The name of the image file that was analyzed.
        :param prediction: The DetectAnomalyResult object returned from DetectAnomalies.
        :param confidence: The minimum acceptable confidence (float 0 - 1).
        :param anomaly_types_limit: The maximum number of allowable anomaly types (int).
        :return: True if the error condition indicates an anomaly, otherwise False.
        """

        logger.info("Checking number of anomaly types for %s", image)

        reject = False

        if prediction["IsAnomalous"] and prediction["Confidence"] >= confidence_limit:
            anomaly_types = {
                anomaly["Name"]
                for anomaly in prediction["Anomalies"]
                if anomaly["Name"] != "background"
            }

            if len(anomaly_types) > anomaly_types_limit:
                reject = True
                reject_info = (
                    f"Rejected: Anomaly confidence ({prediction['Confidence']:.2%}) "
                    f"is greater than limit ({confidence_limit:.2%}) and "
                    f"the number of anomaly types ({len(anomaly_types)-1}) is "
                    f"greater than the limit ({anomaly_types_limit})"
                )

                logger.info("%s", reject_info)

        if not reject:
            logger.info("No anomalies found.")
        return reject

    @staticmethod
    def reject_on_coverage(
        image, prediction, confidence_limit, anomaly_label, coverage_limit
    ):
        """
        Checks if the coverage area of an anomaly is greater than the coverage limit and if
        the prediction confidence is greater than the confidence limit.
        :param image: The name of the image file that was analyzed.
        :param prediction: The DetectAnomalyResult object returned from DetectAnomalies.
        :param confidence_limit: The minimum acceptable confidence (float 0-1).
        :anomaly_label: The anomaly label for the type of anomaly that you want to check.
        :coverage_limit: The maximum acceptable percentage coverage of an anomaly (float 0-1).
        :return: True if the error condition indicates an anomaly, otherwise False.
        """

        reject = False

        logger.info("Checking coverage for %s", image)

        if prediction["IsAnomalous"] and prediction["Confidence"] >= confidence_limit:
            for anomaly in prediction["Anomalies"]:
                if anomaly["Name"] == anomaly_label and anomaly["PixelAnomaly"][
                    "TotalPercentageArea"
                ] > (coverage_limit):
                    reject = True
                    reject_info = (
                        f"Rejected: Anomaly confidence ({prediction['Confidence']:.2%}) "
                        f"is greater than limit ({confidence_limit:.2%}) and {anomaly['Name']} "
                        f"coverage ({anomaly['PixelAnomaly']['TotalPercentageArea']:.2%}) "
                        f"is greater than limit ({coverage_limit:.2%})"
                    )

                    logger.info("%s", reject_info)

        if not reject:
            logger.info("No anomalies found.")

        return reject

    @staticmethod
    def analyze_image(lookoutvision_client, image, config):
        """
        Analyzes an image with an Amazon Lookout for Vision model. Also
        runs a series of checks to determine if the contents of an image
        should be rejected.
        :param lookoutvision_client: A Lookout for Vision Boto3 client.
        param image: A local image that you want to analyze.
        param config: Configuration information for the model and reject
        limits.
        """

        project = config["project"]
        model_version = config["model_version"]
        confidence_limit = config["confidence_limit"]
        coverage_limit = config["coverage_limit"]
        anomaly_types_limit = config["anomaly_types_limit"]
        anomaly_label = config["anomaly_label"]

        # Get analysis results.
        print(f"Analyzing {image}.")

        prediction = Inference.detect_anomalies(
            lookoutvision_client, project, model_version, image
        )

        anomalies = []

        reject = Inference.reject_on_classification(image, prediction, confidence_limit)

        if reject:
            anomalies.append("Classification: An anomaly was found.")

        reject = Inference.reject_on_coverage(
            image, prediction, confidence_limit, anomaly_label, coverage_limit
        )

        if reject:
            anomalies.append("Coverage: Anomaly coverage too high.")

        reject = Inference.reject_on_anomaly_types(
            image, prediction, confidence_limit, anomaly_types_limit
        )

        if reject:
            anomalies.append("Anomaly type count: Too many anomaly types found.")
            print()

        if len(anomalies) > 0:
            print(f"Anomalies found in {image}")
            for anomaly in anomalies:
                print(f"{anomaly}")
        else:
            print(f"No anomalies found in {image}")


def main():
    """
    Detects anomalies in an image file.
    """
    try:
        logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

        parser = argparse.ArgumentParser(
            description="Find anomalies with Amazon Lookout for Vision."
        )
        parser.add_argument(
            "image",
            help="The file that you want to analyze. Supply a local file path or a "
            "path to an S3 object.",
        )
        parser.add_argument(
            "config",
            help=(
                "The configuration JSON file to use. "
                "See https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/"
                "python/example_code/lookoutvision/README.md"
            ),
        )

        args = parser.parse_args()

        session = boto3.Session(profile_name="lookoutvision-access")

        lookoutvision_client = session.client("lookoutvision")
        s3_resource = session.resource("s3")

        # Get configuration information.
        with open(args.config, encoding="utf-8") as config_file:
            config = json.load(config_file)

        # Download image if located in S3 bucket.
        if args.image.startswith("s3://"):
            image = Inference.download_from_s3(s3_resource, args.image)
        else:
            image = args.image

        Inference.analyze_image(lookoutvision_client, image, config)

        # Delete image, if downloaded from S3 bucket.
        if args.image.startswith("s3://"):
            os.remove(image)

    except ClientError as err:
        print(f"Service error: {err.response['Error']['Message']}")
    except FileNotFoundError as err:
        print(f"The supplied file couldn't be found: {err.filename}.")
    except ValueError as err:
        print(f"A value error occurred: {err}.")
    else:
        print("\nSuccessfully completed analysis.")


if __name__ == "__main__":
    main()