Busca ejemplos de Lookout for Vision SDK usando Python (Boto3)

"""
This example shows how to list your Amazon Lookout for Vision projects.
If you haven't previously created a project in the current AWS Region,
the response is an empty list, however it confirms that you can call the
Lookout for Vision API.
"""
from botocore.exceptions import ClientError
import boto3


class Hello:
    """Hello class for Amazon Lookout for Vision"""

    @staticmethod
    def list_projects(lookoutvision_client):
        """
        Lists information about the projects that are in your AWS account
        and in the current AWS Region.

        : param lookoutvision_client: A Boto3 Lookout for Vision client.
        """
        try:
            response = lookoutvision_client.list_projects()
            for project in response["Projects"]:
                print("Project: " + project["ProjectName"])
                print("ARN: " + project["ProjectArn"])
                print()
            print("Done!")
        except ClientError as err:
            print(f"Couldn't list projects. \n{err}")
            raise


def main():
    session = boto3.Session(profile_name="lookoutvision-access")
    lookoutvision_client = session.client("lookoutvision")

    Hello.list_projects(lookoutvision_client)


if __name__ == "__main__":
    main()

class Datasets:

    @staticmethod
    def create_dataset(lookoutvision_client, project_name, manifest_file, dataset_type):
        """
        Creates a new Lookout for Vision dataset

        :param lookoutvision_client: A Lookout for Vision Boto3 client.
        :param project_name: The name of the project in which you want to
                             create a dataset.
        :param bucket: The bucket that contains the manifest file.
        :param manifest_file: The path and name of the manifest file.
        :param dataset_type: The type of the dataset (train or test).
        """
        try:
            bucket, key = manifest_file.replace("s3://", "").split("/", 1)
            logger.info("Creating %s dataset type...", dataset_type)
            dataset = {
                "GroundTruthManifest": {"S3Object": {"Bucket": bucket, "Key": key}}
            }
            response = lookoutvision_client.create_dataset(
                ProjectName=project_name,
                DatasetType=dataset_type,
                DatasetSource=dataset,
            )
            logger.info("Dataset Status: %s", response["DatasetMetadata"]["Status"])
            logger.info(
                "Dataset Status Message: %s",
                response["DatasetMetadata"]["StatusMessage"],
            )
            logger.info("Dataset Type: %s", response["DatasetMetadata"]["DatasetType"])

            # Wait until either created or failed.
            finished = False
            status = ""
            dataset_description = {}
            while finished is False:
                dataset_description = lookoutvision_client.describe_dataset(
                    ProjectName=project_name, DatasetType=dataset_type
                )
                status = dataset_description["DatasetDescription"]["Status"]

                if status == "CREATE_IN_PROGRESS":
                    logger.info("Dataset creation in progress...")
                    time.sleep(2)
                elif status == "CREATE_COMPLETE":
                    logger.info("Dataset created.")
                    finished = True
                else:
                    logger.info(
                        "Dataset creation failed: %s",
                        dataset_description["DatasetDescription"]["StatusMessage"],
                    )
                    finished = True

            if status != "CREATE_COMPLETE":
                message = dataset_description["DatasetDescription"]["StatusMessage"]
                logger.exception("Couldn't create dataset: %s", message)
                raise Exception(f"Couldn't create dataset: {message}")

        except ClientError:
            logger.exception("Service error: Couldn't create dataset.")
            raise

class Models:

    @staticmethod
    def create_model(
        lookoutvision_client,
        project_name,
        training_results,
        tag_key=None,
        tag_key_value=None,
    ):
        """
        Creates a version of a Lookout for Vision model.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project in which you want to create a
                             model.
        :param training_results: The Amazon S3 location where training results are stored.
        :param tag_key: The key for a tag to add to the model.
        :param tag_key_value - A value associated with the tag_key.
        return: The model status and version.
        """
        try:
            logger.info("Training model...")
            output_bucket, output_folder = training_results.replace("s3://", "").split(
                "/", 1
            )
            output_config = {
                "S3Location": {"Bucket": output_bucket, "Prefix": output_folder}
            }
            tags = []
            if tag_key is not None:
                tags = [{"Key": tag_key, "Value": tag_key_value}]

            response = lookoutvision_client.create_model(
                ProjectName=project_name, OutputConfig=output_config, Tags=tags
            )

            logger.info("ARN: %s", response["ModelMetadata"]["ModelArn"])
            logger.info("Version: %s", response["ModelMetadata"]["ModelVersion"])
            logger.info("Started training...")

            print("Training started. Training might take several hours to complete.")

            # Wait until training completes.
            finished = False
            status = "UNKNOWN"
            while finished is False:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project_name,
                    ModelVersion=response["ModelMetadata"]["ModelVersion"],
                )
                status = model_description["ModelDescription"]["Status"]

                if status == "TRAINING":
                    logger.info("Model training in progress...")
                    time.sleep(600)
                    continue

                if status == "TRAINED":
                    logger.info("Model was successfully trained.")
                else:
                    logger.info(
                        "Model training failed: %s ",
                        model_description["ModelDescription"]["StatusMessage"],
                    )
                finished = True
        except ClientError:
            logger.exception("Couldn't train model.")
            raise
        else:
            return status, response["ModelMetadata"]["ModelVersion"]

class Projects:

    @staticmethod
    def create_project(lookoutvision_client, project_name):
        """
        Creates a new Lookout for Vision project.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param  project_name: The name for the new project.
        :return project_arn: The ARN of the new project.
        """
        try:
            logger.info("Creating project: %s", project_name)
            response = lookoutvision_client.create_project(ProjectName=project_name)
            project_arn = response["ProjectMetadata"]["ProjectArn"]
            logger.info("project ARN: %s", project_arn)
        except ClientError:
            logger.exception("Couldn't create project %s.", project_name)
            raise
        else:
            return project_arn

class Datasets:

    @staticmethod
    def delete_dataset(lookoutvision_client, project_name, dataset_type):
        """
        Deletes a Lookout for Vision dataset

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that contains the dataset that
                             you want to delete.
        :param dataset_type: The type (train or test) of the dataset that you
                             want to delete.
        """
        try:
            logger.info(
                "Deleting the %s dataset for project %s.", dataset_type, project_name
            )
            lookoutvision_client.delete_dataset(
                ProjectName=project_name, DatasetType=dataset_type
            )
            logger.info("Dataset deleted.")
        except ClientError:
            logger.exception("Service error: Couldn't delete dataset.")
            raise

class Models:

    @staticmethod
    def delete_model(lookoutvision_client, project_name, model_version):
        """
        Deletes a Lookout for Vision model. The model must first be stopped and can't
        be in training.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that contains the desired model.
        :param model_version: The version of the model that you want to delete.
        """
        try:
            logger.info("Deleting model: %s", model_version)
            lookoutvision_client.delete_model(
                ProjectName=project_name, ModelVersion=model_version
            )

            model_exists = True
            while model_exists:
                response = lookoutvision_client.list_models(ProjectName=project_name)

                model_exists = False
                for model in response["Models"]:
                    if model["ModelVersion"] == model_version:
                        model_exists = True

                if model_exists is False:
                    logger.info("Model deleted")
                else:
                    logger.info("Model is being deleted...")
                    time.sleep(2)

            logger.info("Deleted Model: %s", model_version)
        except ClientError:
            logger.exception("Couldn't delete model.")
            raise

class Projects:

    @staticmethod
    def delete_project(lookoutvision_client, project_name):
        """
        Deletes a Lookout for Vision Model

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that you want to delete.
        """
        try:
            logger.info("Deleting project: %s", project_name)
            response = lookoutvision_client.delete_project(ProjectName=project_name)
            logger.info("Deleted project ARN: %s ", response["ProjectArn"])
        except ClientError as err:
            logger.exception("Couldn't delete project %s.", project_name)
            raise

class Datasets:

    @staticmethod
    def describe_dataset(lookoutvision_client, project_name, dataset_type):
        """
        Gets information about a Lookout for Vision dataset.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that contains the dataset that
                             you want to describe.
        :param dataset_type: The type (train or test) of the dataset that you want
                             to describe.
        """
        try:
            response = lookoutvision_client.describe_dataset(
                ProjectName=project_name, DatasetType=dataset_type
            )
            print(f"Name: {response['DatasetDescription']['ProjectName']}")
            print(f"Type: {response['DatasetDescription']['DatasetType']}")
            print(f"Status: {response['DatasetDescription']['Status']}")
            print(f"Message: {response['DatasetDescription']['StatusMessage']}")
            print(f"Images: {response['DatasetDescription']['ImageStats']['Total']}")
            print(f"Labeled: {response['DatasetDescription']['ImageStats']['Labeled']}")
            print(f"Normal: {response['DatasetDescription']['ImageStats']['Normal']}")
            print(f"Anomaly: {response['DatasetDescription']['ImageStats']['Anomaly']}")
        except ClientError:
            logger.exception("Service error: problem listing datasets.")
            raise
        print("Done.")

class Models:

    @staticmethod
    def describe_model(lookoutvision_client, project_name, model_version):
        """
        Shows the performance metrics for a trained model.

        :param lookoutvision_client: A Boto3 Amazon Lookout for Vision client.
        :param project_name: The name of the project that contains the desired model.
        :param model_version: The version of the model.
        """
        response = lookoutvision_client.describe_model(
            ProjectName=project_name, ModelVersion=model_version
        )
        model_description = response["ModelDescription"]
        print(f"\tModel version: {model_description['ModelVersion']}")
        print(f"\tARN: {model_description['ModelArn']}")
        if "Description" in model_description:
            print(f"\tDescription: {model_description['Description']}")
        print(f"\tStatus: {model_description['Status']}")
        print(f"\tMessage: {model_description['StatusMessage']}")
        print(f"\tCreated: {str(model_description['CreationTimestamp'])}")

        if model_description["Status"] in ("TRAINED", "HOSTED"):
            training_start = model_description["CreationTimestamp"]
            training_end = model_description["EvaluationEndTimestamp"]
            duration = training_end - training_start
            print(f"\tTraining duration: {duration}")

            print("\n\tPerformance metrics\n\t-------------------")
            print(f"\tRecall: {model_description['Performance']['Recall']}")
            print(f"\tPrecision: {model_description['Performance']['Precision']}")
            print(f"\tF1: {model_description['Performance']['F1Score']}")

            training_output_bucket = model_description["OutputConfig"]["S3Location"][
                "Bucket"
            ]
            prefix = model_description["OutputConfig"]["S3Location"]["Prefix"]
            print(f"\tTraining output: s3://{training_output_bucket}/{prefix}")

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()

class Models:

    @staticmethod
    def describe_models(lookoutvision_client, project_name):
        """
        Gets information about all models in a Lookout for Vision project.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that you want to use.
        """
        try:
            response = lookoutvision_client.list_models(ProjectName=project_name)
            print("Project: " + project_name)
            for model in response["Models"]:
                Models.describe_model(
                    lookoutvision_client, project_name, model["ModelVersion"]
                )
                print()
            print("Done...")
        except ClientError:
            logger.exception("Couldn't list models.")
            raise

class Projects:

    @staticmethod
    def list_projects(lookoutvision_client):
        """
        Lists information about the projects that are in in your AWS account
        and in the current AWS Region.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        """
        try:
            response = lookoutvision_client.list_projects()
            for project in response["Projects"]:
                print("Project: " + project["ProjectName"])
                print("\tARN: " + project["ProjectArn"])
                print("\tCreated: " + str(["CreationTimestamp"]))
                print("Datasets")
                project_description = lookoutvision_client.describe_project(
                    ProjectName=project["ProjectName"]
                )
                if not project_description["ProjectDescription"]["Datasets"]:
                    print("\tNo datasets")
                else:
                    for dataset in project_description["ProjectDescription"][
                        "Datasets"
                    ]:
                        print(f"\ttype: {dataset['DatasetType']}")
                        print(f"\tStatus: {dataset['StatusMessage']}")

                print("Models")
                response_models = lookoutvision_client.list_models(
                    ProjectName=project["ProjectName"]
                )
                if not response_models["Models"]:
                    print("\tNo models")
                else:
                    for model in response_models["Models"]:
                        Models.describe_model(
                            lookoutvision_client,
                            project["ProjectName"],
                            model["ModelVersion"],
                        )

                print("------------------------------------------------------------\n")
            print("Done!")
        except ClientError:
            logger.exception("Problem listing projects.")
            raise

class Hosting:

    @staticmethod
    def start_model(
        lookoutvision_client, project_name, model_version, min_inference_units
    ):
        """
        Starts the hosting of a Lookout for Vision model.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name:  The name of the project that contains the version of the
                              model that you want to start hosting.
        :param model_version: The version of the model that you want to start hosting.
        :param min_inference_units: The number of inference units to use for hosting.
        """
        try:
            logger.info(
                "Starting model version %s for project %s", model_version, project_name
            )
            lookoutvision_client.start_model(
                ProjectName=project_name,
                ModelVersion=model_version,
                MinInferenceUnits=min_inference_units,
            )
            print("Starting hosting...")

            status = ""
            finished = False

            # Wait until hosted or failed.
            while finished is False:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project_name, ModelVersion=model_version
                )
                status = model_description["ModelDescription"]["Status"]

                if status == "STARTING_HOSTING":
                    logger.info("Host starting in progress...")
                    time.sleep(10)
                    continue

                if status == "HOSTED":
                    logger.info("Model is hosted and ready for use.")
                    finished = True
                    continue

                logger.info("Model hosting failed and the model can't be used.")
                finished = True

            if status != "HOSTED":
                logger.error("Error hosting model: %s", status)
                raise Exception(f"Error hosting model: {status}")
        except ClientError:
            logger.exception("Couldn't host model.")
            raise

class Hosting:

    @staticmethod
    def stop_model(lookoutvision_client, project_name, model_version):
        """
        Stops a running Lookout for Vision Model.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project that contains the version of
                             the model that you want to stop hosting.
        :param model_version:  The version of the model that you want to stop hosting.
        """
        try:
            logger.info("Stopping model version %s for %s", model_version, project_name)
            response = lookoutvision_client.stop_model(
                ProjectName=project_name, ModelVersion=model_version
            )
            logger.info("Stopping hosting...")

            status = response["Status"]
            finished = False

            # Wait until stopped or failed.
            while finished is False:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project_name, ModelVersion=model_version
                )
                status = model_description["ModelDescription"]["Status"]

                if status == "STOPPING_HOSTING":
                    logger.info("Host stopping in progress...")
                    time.sleep(10)
                    continue

                if status == "TRAINED":
                    logger.info("Model is no longer hosted.")
                    finished = True
                    continue

                logger.info("Failed to stop model: %s ", status)
                finished = True

            if status != "TRAINED":
                logger.error("Error stopping model: %s", status)
                raise Exception(f"Error stopping model: {status}")
        except ClientError:
            logger.exception("Couldn't stop hosting model.")
            raise

class Datasets:

    @staticmethod
    def create_manifest_file_s3(s3_resource, image_s3_path, manifest_s3_path):
        """
        Creates a manifest file and uploads to Amazon S3.

        :param s3_resource: A Boto3 Amazon S3 resource.
        :param image_s3_path: The Amazon S3 path to the images referenced by the
                              manifest file. The images must be in an Amazon S3 bucket
                              with the following folder structure.
                                s3://amzn-s3-demo-bucket/<train or test>/
                                    normal/
                                    anomaly/
                              Place normal images in the normal folder and anomalous
                              images in the anomaly folder.
        :param manifest_s3_path: The Amazon S3 location in which to store the created
                                 manifest file.
        """
        output_manifest_file = "temp.manifest"
        try:
            # Current date and time in manifest file format.
            dttm = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")

            # Get bucket and folder from image and manifest file paths.
            bucket, prefix = image_s3_path.replace("s3://", "").split("/", 1)
            if prefix[-1] != "/":
                prefix += "/"
            manifest_bucket, manifest_prefix = manifest_s3_path.replace(
                "s3://", ""
            ).split("/", 1)

            with open(output_manifest_file, "w") as mfile:
                logger.info("Creating manifest file")
                src_bucket = s3_resource.Bucket(bucket)

                # Create JSON lines for anomalous images.
                for obj in src_bucket.objects.filter(
                    Prefix=prefix + "anomaly/", Delimiter="/"
                ):
                    image_path = f"s3://{src_bucket.name}/{obj.key}"
                    manifest = Datasets.create_json_line(image_path, "anomaly", dttm)
                    mfile.write(json.dumps(manifest) + "\n")

                # Create json lines for normal images.
                for obj in src_bucket.objects.filter(
                    Prefix=prefix + "normal/", Delimiter="/"
                ):
                    image_path = f"s3://{src_bucket.name}/{obj.key}"
                    manifest = Datasets.create_json_line(image_path, "normal", dttm)
                    mfile.write(json.dumps(manifest) + "\n")

            logger.info("Uploading manifest file to %s", manifest_s3_path)
            s3_resource.Bucket(manifest_bucket).upload_file(
                output_manifest_file, manifest_prefix
            )
        except ClientError:
            logger.exception("Error uploading manifest.")
            raise
        except Exception:
            logger.exception("Error uploading manifest.")
            raise
        else:
            logger.info("Completed manifest file creation and upload.")
        finally:
            try:
                os.remove(output_manifest_file)
            except FileNotFoundError:
                pass

    @staticmethod
    def create_json_line(image, class_name, dttm):
        """
        Creates a single JSON line for an image.

        :param image: The S3 location for the image.
        :param class_name: The class of the image (normal or anomaly)
        :param dttm: The date and time that the JSON is created.
        """

        label = 0
        if class_name == "normal":
            label = 0
        elif class_name == "anomaly":
            label = 1
        else:
            logger.error("Unexpected label value: %s for %s", label, image)
            raise Exception(f"Unexpected label value: {label} for {image}")

        manifest = {
            "source-ref": image,
            "anomaly-label": label,
            "anomaly-label-metadata": {
                "confidence": 1,
                "job-name": "labeling-job/anomaly-label",
                "class-name": class_name,
                "human-annotated": "yes",
                "creation-date": dttm,
                "type": "groundtruth/image-classification",
            },
        }
        return manifest

Crea y, opcionalmente, inicia un modelo de Amazon Lookout for Vision mediante argumentos de línea de comandos. El código de ejemplo crea un proyecto, un conjunto de datos de entrenamiento, un conjunto de datos de prueba opcional y un modelo nuevos. Una vez finalizado el entrenamiento con el modelo, puede usar un script proporcionado para probar el modelo con una imagen.

Este ejemplo requiere un conjunto de imágenes para entrenar su modelo. Puede encontrar ejemplos de imágenes de placas de circuito GitHub que puede utilizar para entrenamiento y pruebas. Para obtener información sobre cómo copiar estas imágenes en un bucket de Amazon Simple Storage Service (Amazon S3), consulte Preparar imágenes de ejemplo.

Para ver el código fuente completo y las instrucciones sobre cómo configurarlo y ejecutarlo, consulta el ejemplo completo en GitHub.

"""
Purpose

Shows how to export the datasets (manifest files and images)
from an Amazon Lookout for Vision project to a new Amazon 
S3 location.
"""

import argparse
import json
import logging

import boto3
from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)


def copy_file(s3_resource, source_file, destination_file):
    """
    Copies a file from a source Amazon S3 folder to a destination
    Amazon S3 folder.
    The destination can be in a different S3 bucket.
    :param s3: An Amazon S3 Boto3 resource.
    :param source_file: The Amazon S3 path to the source file.
    :param destination_file: The destination Amazon S3 path for
    the copy operation.
    """

    source_bucket, source_key = source_file.replace("s3://", "").split("/", 1)
    destination_bucket, destination_key = destination_file.replace("s3://", "").split(
        "/", 1
    )

    try:
        bucket = s3_resource.Bucket(destination_bucket)
        dest_object = bucket.Object(destination_key)
        dest_object.copy_from(CopySource={"Bucket": source_bucket, "Key": source_key})
        dest_object.wait_until_exists()
        logger.info("Copied %s to %s", source_file, destination_file)
    except ClientError as error:
        if error.response["Error"]["Code"] == "404":
            error_message = (
                f"Failed to copy {source_file} to "
                f"{destination_file}. : {error.response['Error']['Message']}"
            )
            logger.warning(error_message)
            error.response["Error"]["Message"] = error_message
        raise


def upload_manifest_file(s3_resource, manifest_file, destination):
    """
    Uploads a manifest file to a destination Amazon S3 folder.
    :param s3: An Amazon S3 Boto3 resource.
    :param manifest_file: The manifest file that you want to upload.
    :destination: The Amazon S3 folder location to upload the manifest
    file to.
    """

    destination_bucket, destination_key = destination.replace("s3://", "").split("/", 1)

    bucket = s3_resource.Bucket(destination_bucket)

    put_data = open(manifest_file, "rb")
    obj = bucket.Object(destination_key + manifest_file)

    try:
        obj.put(Body=put_data)
        obj.wait_until_exists()
        logger.info("Put manifest file '%s' to bucket '%s'.", obj.key, obj.bucket_name)
    except ClientError:
        logger.exception(
            "Couldn't put manifest file '%s' to bucket '%s'.", obj.key, obj.bucket_name
        )
        raise
    finally:
        if getattr(put_data, "close", None):
            put_data.close()


def get_dataset_types(lookoutvision_client, project):
    """
    Determines the types of the datasets (train or test) in an
    Amazon Lookout for Vision project.
    :param lookoutvision_client: A Lookout for Vision Boto3 client.
    :param project: The Lookout for Vision project that you want to check.
    :return: The dataset types in the project.
    """

    try:
        response = lookoutvision_client.describe_project(ProjectName=project)

        datasets = []

        for dataset in response["ProjectDescription"]["Datasets"]:
            if dataset["Status"] in ("CREATE_COMPLETE", "UPDATE_COMPLETE"):
                datasets.append(dataset["DatasetType"])
        return datasets

    except lookoutvision_client.exceptions.ResourceNotFoundException:
        logger.exception("Project %s not found.", project)
        raise


def process_json_line(s3_resource, entry, dataset_type, destination):
    """
    Creates a JSON line for a new manifest file, copies image and mask to
    destination.
    :param s3_resource: An Amazon S3 Boto3 resource.
    :param entry: A JSON line from the manifest file.
    :param dataset_type: The type (train or test) of the dataset that
    you want to create the manifest file for.
    :param destination: The destination Amazon S3 folder for the manifest
    file and dataset images.
    :return: A JSON line with details for the destination location.
    """
    entry_json = json.loads(entry)

    print(f"source: {entry_json['source-ref']}")

    # Use existing folder paths to ensure console added image names don't clash.
    bucket, key = entry_json["source-ref"].replace("s3://", "").split("/", 1)
    logger.info("Source location: %s/%s", bucket, key)

    destination_image_location = destination + dataset_type + "/images/" + key

    copy_file(s3_resource, entry_json["source-ref"], destination_image_location)

    # Update JSON for writing.
    entry_json["source-ref"] = destination_image_location

    if "anomaly-mask-ref" in entry_json:
        source_anomaly_ref = entry_json["anomaly-mask-ref"]
        mask_bucket, mask_key = source_anomaly_ref.replace("s3://", "").split("/", 1)

        destination_mask_location = destination + dataset_type + "/masks/" + mask_key
        entry_json["anomaly-mask-ref"] = destination_mask_location

        copy_file(s3_resource, source_anomaly_ref, entry_json["anomaly-mask-ref"])

    return entry_json


def write_manifest_file(
    lookoutvision_client, s3_resource, project, dataset_type, destination
):
    """
    Creates a manifest file for a dataset. Copies the manifest file and
    dataset images (and masks, if present) to the specified Amazon S3 destination.
    :param lookoutvision_client: A Lookout for Vision Boto3 client.
    :param project: The Lookout for Vision project that you want to use.
    :param dataset_type: The type (train or test) of the dataset that
    you want to create the manifest file for.
    :param destination: The destination Amazon S3 folder for the manifest file
    and dataset images.
    """

    try:
        # Create a reusable Paginator
        paginator = lookoutvision_client.get_paginator("list_dataset_entries")

        # Create a PageIterator from the Paginator
        page_iterator = paginator.paginate(
            ProjectName=project,
            DatasetType=dataset_type,
            PaginationConfig={"PageSize": 100},
        )

        output_manifest_file = dataset_type + ".manifest"

        # Create manifest file then upload to Amazon S3 with images.
        with open(output_manifest_file, "w", encoding="utf-8") as manifest_file:
            for page in page_iterator:
                for entry in page["DatasetEntries"]:
                    try:
                        entry_json = process_json_line(
                            s3_resource, entry, dataset_type, destination
                        )

                        manifest_file.write(json.dumps(entry_json) + "\n")

                    except ClientError as error:
                        if error.response["Error"]["Code"] == "404":
                            print(error.response["Error"]["Message"])
                            print(f"Excluded JSON line: {entry}")
                        else:
                            raise
        upload_manifest_file(
            s3_resource, output_manifest_file, destination + "datasets/"
        )

    except ClientError:
        logger.exception("Problem getting dataset_entries")
        raise


def export_datasets(lookoutvision_client, s3_resource, project, destination):
    """
    Exports the datasets from an Amazon Lookout for Vision project to a specified
    Amazon S3 destination.
    :param project: The Lookout for Vision project that you want to use.
    :param destination: The destination Amazon S3 folder for the exported datasets.
    """
    # Add trailing backslash, if missing.
    destination = destination if destination[-1] == "/" else destination + "/"

    print(f"Exporting project {project} datasets to {destination}.")

    # Get each dataset and export to destination.

    dataset_types = get_dataset_types(lookoutvision_client, project)
    for dataset in dataset_types:
        logger.info("Copying %s dataset to %s.", dataset, destination)

        write_manifest_file(
            lookoutvision_client, s3_resource, project, dataset, destination
        )

    print("Exported dataset locations")
    for dataset in dataset_types:
        print(f"   {dataset}: {destination}datasets/{dataset}.manifest")

    print("Done.")


def add_arguments(parser):
    """
    Adds command line arguments to the parser.
    :param parser: The command line parser.
    """

    parser.add_argument("project", help="The project that contains the dataset.")
    parser.add_argument("destination", help="The destination Amazon S3 folder.")


def main():
    """
    Exports the datasets from an Amazon Lookout for Vision project to a
    destination Amazon S3 location.
    """
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
    parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
    add_arguments(parser)

    args = parser.parse_args()

    try:
        session = boto3.Session(profile_name="lookoutvision-access")
        lookoutvision_client = session.client("lookoutvision")
        s3_resource = session.resource("s3")

        export_datasets(
            lookoutvision_client, s3_resource, args.project, args.destination
        )
    except ClientError as err:
        logger.exception(err)
        print(f"Failed: {format(err)}")


if __name__ == "__main__":
    main()

import logging
import argparse
import boto3

from botocore.exceptions import ClientError


logger = logging.getLogger(__name__)


def find_tag(tags, key, value):
    """
    Finds a tag in the supplied list of tags.

    :param tags: A list of tags associated with a Lookout for Vision model.
    :param key: The tag to search for.
    :param value: The tag key value to search for.
    :return: True if the tag value exists, otherwise False.
    """

    found = False
    for tag in tags:
        if key == tag["Key"]:
            logger.info("\t\tMatch found for tag: %s value: %s.", key, value)
            found = True
            break
    return found


def find_tag_in_projects(lookoutvision_client, key, value):
    """
    Finds Lookout for Vision models tagged with the supplied key and value.

    :param lookoutvision_client: A Boto3 Lookout for Vision client.
    :param key: The tag key to find.
    :param value: The value of the tag that you want to find.
    return: A list of matching model versions (and model projects) that were found.
    """
    try:
        found_tags = []
        found = False
        projects = lookoutvision_client.list_projects()
        # Iterate through each project and models within a project.
        for project in projects["Projects"]:
            logger.info("Searching project: %s ...", project["ProjectName"])

            response_models = lookoutvision_client.list_models(
                ProjectName=project["ProjectName"]
            )
            for model in response_models["Models"]:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project["ProjectName"],
                    ModelVersion=model["ModelVersion"],
                )
                tags = lookoutvision_client.list_tags_for_resource(
                    ResourceArn=model_description["ModelDescription"]["ModelArn"]
                )

                logger.info(
                    "\tSearching model: %s for tag: %s value: %s.",
                    model_description["ModelDescription"]["ModelArn"],
                    key,
                    value,
                )
                if find_tag(tags["Tags"], key, value) is True:
                    found = True
                    logger.info(
                        "\t\tMATCH: Project: %s: model version %s",
                        project["ProjectName"],
                        model_description["ModelDescription"]["ModelVersion"],
                    )
                    found_tags.append(
                        {
                            "Project": project["ProjectName"],
                            "ModelVersion": model_description["ModelDescription"][
                                "ModelVersion"
                            ],
                        }
                    )
        if found is False:
            logger.info("No match for tag %s with value %s.", key, value)
    except ClientError:
        logger.exception("Problem finding tags.")
        raise
    else:
        return found_tags


def main():
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
    parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
    parser.add_argument("tag", help="The tag that you want to find.")
    parser.add_argument("value", help="The tag value that you want to find.")
    args = parser.parse_args()
    key = args.tag
    value = args.value

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

    lookoutvision_client = session.client("lookoutvision")

    print(f"Searching your models for tag: {key} with value: {value}.")
    tagged_models = find_tag_in_projects(lookoutvision_client, key, value)

    print("Matched models\n--------------")
    if len(tagged_models) > 0:
        for model in tagged_models:
            print(f"Project: {model['Project']}. model version:{model['ModelVersion']}")
    else:
        print("No matches found.")


if __name__ == "__main__":
    main()

class Hosting:

    @staticmethod
    def list_hosted(lookoutvision_client):
        """
        Displays a list of models in your account that are currently hosted.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        """
        try:
            response = lookoutvision_client.list_projects()
            hosted = 0
            print("Hosted models\n-------------")

            for project in response["Projects"]:
                response_models = lookoutvision_client.list_models(
                    ProjectName=project["ProjectName"]
                )

                for model in response_models["Models"]:
                    model_description = lookoutvision_client.describe_model(
                        ProjectName=project["ProjectName"],
                        ModelVersion=model["ModelVersion"],
                    )

                    if model_description["ModelDescription"]["Status"] == "HOSTED":
                        print(
                            f"Project: {project['ProjectName']} Model version: "
                            f"{model['ModelVersion']}"
                        )
                        hosted += 1
            print(f"{hosted} model(s) hosted")
        except ClientError:
            logger.exception("Problem listing hosted models.")
            raise