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Determinar si una imagen es anómala
Puede determinar si una imagen es anómala de diversas formas. El método elegido depende del caso de uso y el tipo de modelo. Las siguientes son posibles soluciones.
Clasificación
IsAnomalous clasifica una imagen como anómala; utilice el campo Confidence para decidir si la imagen es realmente anómala. Un valor más alto indica una confianza más alta. Por ejemplo, puedes decidir que un producto es defectuoso solo si la confianza es superior al 80%. Puede clasificar las imágenes analizadas mediante modelos de clasificación o mediante modelos de segmentación de imágenes.
- Python
-
Consulte el ejemplo completo en 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;
}
Segmentación
Si su modelo es un modelo de segmentación de imágenes, puede utilizar la información de segmentación para determinar si una imagen contiene anomalías. También puede utilizar un modelo de segmentación de imágenes para clasificar las imágenes. Para ver un ejemplo de código que obtiene y muestra máscaras de imágenes, consulte Mostrar información de clasificación y segmentación
Área de anomalía
Utilice el porcentaje de cobertura (TotalPercentageArea) de una anomalía en la imagen. Por ejemplo, puede decidir que un producto es defectuoso si el área de la anomalía es superior al 1% de la imagen.
- Python
-
Consulte el ejemplo completo en 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;
}
Número de tipos de anomalías
Utilice un recuento de los diferentes tipos de anomalías (Name) que se encuentran en la imagen. Por ejemplo, puedes decidir que un producto es defectuoso si hay más de dos tipos de anomalías presentes.
- Python
-
Consulte el ejemplo completo en 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;
}
