AWS SDK またはコマンドラインツールDetectAnomaliesで を使用する - AWS SDK コード例

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AWS SDK またはコマンドラインツールDetectAnomaliesで を使用する

次の例は、DetectAnomalies を使用する方法を説明しています。

詳細については、「画像内の異常を検出する」を参照してください。

Python
SDK for Python (Boto3)
注記

の詳細については、「」を参照してください GitHub。AWS コード例リポジトリ で全く同じ例を見つけて、設定と実行の方法を確認してください。

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()
  • API の詳細については、DetectAnomaliesAWS 「 SDK for Python (Boto3) API リファレンス」の「」を参照してください。