Menentukan apakah suatu gambar anomali - Amazon Lookout for Vision

Pemberitahuan akhir dukungan: Pada 31 Oktober 2025, AWS akan menghentikan dukungan untuk Amazon Lookout for Vision. Setelah 31 Oktober 2025, Anda tidak akan lagi dapat mengakses konsol Lookout for Vision atau sumber daya Lookout for Vision. Untuk informasi lebih lanjut, kunjungi posting blog ini.

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Menentukan apakah suatu gambar anomali

Anda dapat menentukan apakah suatu gambar anomali dalam berbagai cara. Metode yang Anda pilih tergantung pada kasus penggunaan Anda dan jenis model Anda. Berikut ini adalah solusi potensial.

Klasifikasi

IsAnomalousmengklasifikasikan gambar sebagai anomali, gunakan Confidence bidang untuk membantu memutuskan apakah gambar tersebut benar-benar anomali. Nilai yang lebih tinggi menunjukkan kepercayaan yang lebih besar. Misalnya, Anda mungkin memutuskan produk rusak hanya jika kepercayaan diri lebih dari 80%. Anda dapat mengklasifikasikan gambar yang dianalisis berdasarkan model klasifikasi atau dengan model segmentasi gambar.

Python

Untuk contoh kode lengkap, lihat 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; }

Segmentasi

Jika model Anda adalah model segmentasi gambar, Anda dapat menggunakan informasi segmentasi untuk menentukan apakah gambar mengandung anomali. Anda juga dapat menggunakan model segmentasi gambar untuk mengklasifikasikan gambar. Misalnya kode yang mendapatkan dan menampilkan topeng gambar, lihat Menampilkan informasi klasifikasi dan segmentasi

Area anomali

Gunakan persentase cakupan (TotalPercentageArea) anomali pada gambar. Misalnya, Anda mungkin memutuskan produk rusak jika area anomali lebih besar dari 1% gambar.

Python

Untuk contoh kode lengkap, lihat 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; }

Jumlah jenis anomali

Gunakan hitungan berbagai jenis anomali (Name) yang ditemukan pada gambar. Misalnya, Anda mungkin memutuskan produk rusak jika ada lebih dari dua jenis anomali yang ada.

Python

Untuk contoh kode lengkap, lihat 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; }