判斷影像是否異常

您可以透過多種方式判斷影像是否異常。您選擇的方法取決於您的使用案例和模型的類型。以下是潛在的解決方案。

分類

IsAnomalous將影像分類為異常，請使用Confidence欄位，以協助決定影像是否實際上是異常的。較高的值表示信心越大。例如，只有當信心超過 80％ 時，您可能才會決定某個產品有缺陷。您可以分類按分類模型或圖像分割模型分析的圖像進行分類。

Python

如需完整的程式碼範例，請參閱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;

    }

分段

如果您的模型是影像分割模型，您可以使用分割資訊來判斷影像是否包含異常。您也可以使用影像分割模型來分類影像。如需取得並顯示影像遮色片的範例程式碼，請參閱顯示分類和區段資訊

異常區域

使用百分比覆蓋範圍 (TotalPercentageArea) 影像上的異常狀況。例如，如果某個異常區域大於影像的 1%，您可能會決定某個產品有瑕疵。

Python

如需完整的程式碼範例，請參閱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;
    }

異常類型數

使用不同異常類型的計數 (Name）在圖像上找到。例如，如果存在兩種以上的異常類型，您可能會決定某個產品有缺陷。

Python

如需完整的程式碼範例，請參閱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;
    }