画像が異常かどうかの判断 - Amazon Lookout for Vision

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画像が異常かどうかの判断

画像に異常があるかどうかは、さまざまな方法で判断できます。選択する方法は、ユースケースとモデルのタイプによって異なります。考えられる解決策は次のとおりです。

分類

IsAnomalous画像を異常なものとして分類します。Confidenceこのフィールドを使用して、画像が実際に異常かどうかを判断します。値が高いほど、信頼度が高いことを示します。たとえば、信頼度が 80% を超える場合にのみ、製品に欠陥があると判断する場合があります。分類モデルまたは画像セグメンテーションモデルによって分析された画像を分類できます。

Python

完全なコード例については、を参照してくださいGitHub

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 value between 0 and 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
Java V2
public static boolean rejectOnClassification(String image, DetectAnomalyResult prediction, float minConfidence) { /** * Rejects an image based on its anomaly classification and prediction * confidence * * @param image The file name of the analyzed image. * @param prediction The prediction for an image analyzed with * DetectAnomalies. * @param minConfidence The minimum acceptable confidence for the prediction * (0-1). * * @return boolean True if the image is anomalous, otherwise False. */ Boolean reject = false; logger.log(Level.INFO, "Checking classification for {0}", image); String[] logParameters = { prediction.confidence().toString(), String.valueOf(minConfidence) }; if (Boolean.TRUE.equals(prediction.isAnomalous()) && prediction.confidence() >= minConfidence) { logger.log(Level.INFO, "Rejected: Anomaly confidence {0} is greater than confidence limit {1}", logParameters); reject = true; } if (Boolean.FALSE.equals(reject)) logger.log(Level.INFO, ": No anomalies found."); return reject; }

セグメント化

モデルが画像セグメンテーションモデルの場合は、セグメンテーション情報を使用して画像に異常が含まれているかどうかを判断できます。画像セグメンテーションモデルを使用して画像を分類することもできます。イメージマスクを取得して表示するコードの例については、を参照してください。分類とセグメンテーション情報の表示

異常領域

画像の異常のカバー率 (TotalPercentageArea) を使用してください。たとえば、異常領域が画像の 1% より大きい場合に、商品に欠陥があると判断できます。

Python

完全なコード例については、を参照してくださいGitHub

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
Java V2
public static Boolean rejectOnCoverage(String image, DetectAnomalyResult prediction, float minConfidence, String anomalyType, float maxCoverage) { /** * Rejects an image based on a maximum allowable coverage area for an anomaly * type. * * @param image The file name of the analyzed image. * @param prediction The prediction for an image analyzed with * DetectAnomalies. * @param minConfidence The minimum acceptable confidence for the prediction * (0-1). * @param anomalyTypes The anomaly type to check. * @param maxCoverage The maximum allowable coverage area of the anomaly type. * (0-1). * * @return boolean True if the coverage area of the anomaly type exceeds the * maximum allowed, otherwise False. */ Boolean reject = false; logger.log(Level.INFO, "Checking coverage for {0}", image); if (Boolean.TRUE.equals(prediction.isAnomalous()) && prediction.confidence() >= minConfidence) { for (Anomaly anomaly : prediction.anomalies()) { if (Objects.equals(anomaly.name(), anomalyType) && anomaly.pixelAnomaly().totalPercentageArea() >= maxCoverage) { String[] logParameters = { prediction.confidence().toString(), String.valueOf(minConfidence), String.valueOf(anomaly.pixelAnomaly().totalPercentageArea()), String.valueOf(maxCoverage) }; logger.log(Level.INFO, "Rejected: Anomaly confidence {0} is greater than confidence limit {1} and " + "{2} anomaly type coverage is higher than coverage limit {3}\n", logParameters); reject = true; } } } if (Boolean.FALSE.equals(reject)) logger.log(Level.INFO, ": No anomalies found."); return reject; }

異常タイプの数

画像で見つかったさまざまな異常タイプ (Name) の数を使用してください。たとえば、2 種類以上の異常が存在する場合、ある製品に欠陥があると判断できます。

Python

完全なコード例については、を参照してくださいGitHub

def reject_on_anomaly_types(image, prediction, confidence_limit, anomaly_types_limit): """ Checks if the number of anomaly types is greater than 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 value between 0 and 1. :param anomaly_types_limit: The maximum number of allowable anomaly types (Integer). :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
Java V2
public static Boolean rejectOnAnomalyTypeCount(String image, DetectAnomalyResult prediction, float minConfidence, Integer maxAnomalyTypes) { /** * Rejects an image based on a maximum allowable number of anomaly types. * * @param image The file name of the analyzed image. * @param prediction The prediction for an image analyzed with * DetectAnomalies. * @param minConfidence The minimum acceptable confidence for the predictio * (0-1). * @param maxAnomalyTypes The maximum allowable number of anomaly types. * * @return boolean True if the image contains more than the maximum allowed * anomaly types, otherwise False. */ Boolean reject = false; logger.log(Level.INFO, "Checking coverage for {0}", image); Set<String> defectTypes = new HashSet<>(); if (Boolean.TRUE.equals(prediction.isAnomalous()) && prediction.confidence() >= minConfidence) { for (Anomaly anomaly : prediction.anomalies()) { defectTypes.add(anomaly.name()); } // Reduce defect types by one to account for 'background' anomaly type. if ((defectTypes.size() - 1) > maxAnomalyTypes) { String[] logParameters = { prediction.confidence().toString(), String.valueOf(minConfidence), String.valueOf(defectTypes.size()), String.valueOf(maxAnomalyTypes) }; logger.log(Level.INFO, "Rejected: Anomaly confidence {0} is >= minimum confidence {1} and " + "the number of anomaly types {2} > the allowable number of anomaly types {3}\n", logParameters); reject = true; } } if (Boolean.FALSE.equals(reject)) logger.log(Level.INFO, ": No anomalies found."); return reject; }