Collection of metrics used to evaluate the state of a ESupervisedSegmenter tool. More...

#include <eVision/EasyDeepLearning/EasySegment/ESupervisedSegmenterMetrics.h>

Inheritance diagram for eVision::EasyDeepLearning::ESupervisedSegmenterMetrics:

Collaboration diagram for eVision::EasyDeepLearning::ESupervisedSegmenterMetrics:

Public Member Functions
	ESupervisedSegmenterMetrics ()
	Constructs an ESupervisedSegmenterMetrics object.
	ESupervisedSegmenterMetrics (const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics &other)
	Copy constructor of an ESupervisedSegmenterMetrics object.
virtual	~ESupervisedSegmenterMetrics () throw ()
eVision::EasyDeepLearning::ESupervisedSegmenterMetrics &	operator= (const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics &other)
	Assignment operator.
bool	operator== (const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics &other) const
	Equality operator.
virtual void	Load (const std::string &path)
	Loads an supervised segmentation metric. The given ESerializer must have been created for reading.
virtual void	Load (eVision::ESerializer *serializer)
	Loads an supervised segmentation metric. The given ESerializer must have been created for reading.
virtual void	Save (const std::string &path)
	Saves an supervised segmentation metric. The given ESerializer must have been created for writing.
virtual void	Save (eVision::ESerializer *serializer)
	Saves an supervised segmentation metric. The given ESerializer must have been created for writing.
bool	IsValid () const
	Indicates whether the object contains at least one result.
float	GetWeightedPixelAccuracy () const
	Weighted accuracy.
float	GetBalancedPixelAccuracy () const
	Balanced accuracy.
float	GetPixelAccuracy () const
	Accuracy.
float	GetPixelLabelAccuracy (const std::string &label) const
	Pixel label accuracy.
float	GetWeightedError () const
	Weighted error.
float	GetBalancedError () const
	Balanced error.
float	GetError () const
	Error.
float	GetLabelError (const std::string &label) const
	Label error.
uint64_t	GetPixelConfusion (const std::string &trueLabel, const std::string &predictedLabel) const
	Pixel-wise confusion between the given true and predicted labels.
float	GetNormalizedPixelConfusion (const std::string &trueLabel, const std::string &predictedLabel) const
	Pixel-wise normalized confusion between the given true and predicted labels.
float	GetWeightedIntersectionOverUnion () const
	Weighted Intersection over Union (IoU).
float	GetBalancedIntersectionOverUnion () const
	Balanced Intersection over Union (IoU).
float	GetIntersectionOverUnion (const std::string &label) const
	Intersection over union.
uint64_t	GetGroundtruthBlobConfusion (const std::string &groundtruthLabel, const std::string &predictedLabel) const
	Number of ground truth blobs of the given ground truth label that best match with blobs of the given predicted label.
uint64_t	GetGroundtruthBlobConfusion (int groundtruthLabelIndex, int predictedLabelIndex) const
	Number of ground truth blobs of the given ground truth label that best match with blobs of the given predicted label.
uint64_t	GetPredictedBlobConfusion (const std::string &groundtruthLabel, const std::string &predictedLabel) const
	Number of predicted blobs of the given predicted label that match to blobs of the given ground truth label.
uint64_t	GetPredictedBlobConfusion (int groundtruthLabelIndex, int predictedLabelIndex) const
	Number of predicted blobs of the given predicted label that match to blobs of the given ground truth label.
float	GetBlobDetectionAveragePrecision () const
	Average precision for blob detection.
float	GetBlobDetectionBestFScore () const
	Best F1-Score of the segmenter for blob detection.
float	GetBlobDetectionBestFScoreThreshold () const
	Classification threshold that achieves the best F1-Score for blob detection.
float	GetBlobDetectionRecall () const
	Recall for blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).
float	GetBlobDetectionPrecision () const
	Precision for blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).
float	GetBlobDetectionFScore () const
	F1-Score for defective blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).
int	GetNumLabels () const
	Number of labels recognized by the segmenter that produced the results aggregated in the metrics.
std::string	GetLabel (int labelIdx) const
	Label recognized by the segmenter that produced the results aggregated in the metrics.
	ESupervisedSegmenterMetrics (CppWrapperInternal::InternalConstructor)
Public Member Functions inherited from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics
	EDeepLearningDefectDetectionMetrics ()
	Constructs an empty EDeepLearningDefectDetectionMetrics object.
	EDeepLearningDefectDetectionMetrics (const eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &other)
	Copy constructor.
virtual	~EDeepLearningDefectDetectionMetrics () throw ()
eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &	operator= (const eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &other)
	Copy operator.
bool	IsDefectDetectionMetricsValid () const
	Whether the defect detection metrics are valid or not. Defect detection metrics are completely valid when the metrics has results for at least one good and one defective images. Some metrics might be valid with only defective or good results.
int	GetNumDefectiveSample () const
	Number of defective sample added to the metric.
int	GetNumGoodSample () const
	Number of good sample added to the metric.
float	GetAccuracy (int index) const
	The accuracy of the segmenter.
float	GetAccuracy () const
	The accuracy of the segmenter.
float	GetBestAccuracy () const
	Best achievable accuracy.
float	GetBestBalancedAccuracy () const
	Best achievable balanced accuracy.
float	GetBestWeightedAccuracy (float goodWeight, float badWeight) const
	Best achievable weighted accuracy.
float	GetBestAccuracyClassificationThreshold () const
	Classification threshold giving the best achievable accuracy (see EDeepLearningDefectDetectionMetrics::GetBestAccuracy).
float	GetBestBalancedAccuracyClassificationThreshold () const
	Classification threshold giving the best achievable balanced accuracy (see EDeepLearningDefectDetectionMetrics::GetBestBalancedAccuracy).
float	GetBestWeightedAccuracyClassificationThreshold (float goodWeight, float badWeight) const
	Classification threshold giving the best achievable weighted accuracy (see EDeepLearningDefectDetectionMetrics::GetBestWeightedAccuracy).
uint32_t	GetConfusion (eVision::EasyDeepLearning::EConfusionMatrixElement element, int index) const
	Confusion value of one label with another.
uint32_t	GetConfusion (eVision::EasyDeepLearning::EConfusionMatrixElement element) const
	Confusion value of one label with another.
float	GetAreaUnderROCCurve () const
	The area under ROC curve (AUC) of the classifier (see EDeepLearningDefectDetectionMetrics::GetROCPoint). It's value is between 0 and 1.
float	GetAveragePrecision () const
	Average precision.
float	GetBestFScore () const
	Best F1-Score.
float	GetBestFScoreThreshold () const
	Classification threshold that yields the best F1-Score.
float	GetRecall (int index) const
	Recall for the current threshold.
float	GetRecall () const
	Recall for the current threshold.
float	GetPrecision (int index) const
	Precision for the current threshold.
float	GetPrecision () const
	Precision for the current threshold.
float	GetFScore (int index) const
	The F1-Score for the current threshold.
float	GetFScore () const
	The F1-Score for the current threshold.
int	GetPrecisionRecallCurveIndex () const
	Index in the precision/recall curve for the current EDeepLearningDefectDetectionMetrics::GetClassificationThreshold.
int	GetNumPrecisionRecallCurvePoint () const
	Number of point in the precision/recall curve.
eVision::EasyDeepLearning::EROCPoint	GetPrecisionRecallCurvePoint (int index) const
	Get a point on the precision/recall curve.
eVision::EasyDeepLearning::EROCPoint	GetPrecisionRecallCurvePoint () const
	Get a point on the precision/recall curve.
int	GetNumberOfClassifiers () const
	Number of different possible classifiers.
eVision::EasyDeepLearning::EROCPoint	GetROCPoint (int index) const
	ROC (Receiver Operating Characteristic) point.
eVision::EasyDeepLearning::EROCPoint	GetROCPoint () const
	ROC (Receiver Operating Characteristic) point.
float	GetClassificationThreshold () const
	Classification threshold.
void	SetClassificationThreshold (float threshold)
	Classification threshold.
	EDeepLearningDefectDetectionMetrics (CppWrapperInternal::InternalConstructor)

Additional Inherited Members
Public Attributes inherited from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

Collection of metrics used to evaluate the state of a ESupervisedSegmenter tool.

A metric is a value summarizing the quality of a collection of supervised segmentation results (see ESupervisedSegmenterResult) with respect to their ground truth. The ESupervisedSegmenterMetrics contains three types of metrics:

pixel based metrics that are related to the quality of the segmentation masks
blob based metrics that are related to the ability of the supervised segmentation tool to detect foreground blobs
defect detection metrics that are related to the ability of the supervised segmentation tool to differentiate between images that contains foreground pixels (defective images) and images that are entirely background (good images).

The pixel metrics are the error (see ESupervisedSegmenterMetrics::GetError), the pixel accuracy (see ESupervisedSegmenterMetrics::GetPixelAccuracy), the pixel confusion (see ESupervisedSegmenterMetrics::GetPixelConfusion), the pixel per-label accuracy (see ESupervisedSegmenterMetrics::GetPixelLabelAccuracy) and the intersection over union (see ESupervisedSegmenterMetrics::GetIntersectionOverUnion).

The blob based metrics are two confusion matrices (ESupervisedSegmenterMetrics::GetGroundtruthBlobConfusion and ESupervisedSegmenterMetrics::GetPredictedBlobConfusion), and various defect detection metrics (ESupervisedSegmenterMetrics::GetBlobDetectionRecall, ESupervisedSegmenterMetrics::GetBlobDetectionPrecision, and ESupervisedSegmenterMetrics::GetBlobDetectionFScore). Note that the metrics for defective blob detection are different from the metrics for defective image detection because, in the case of blob detection, we have no "good" ground truth results.

See EDeepLearningDefectDetectionMetrics for a description of the defect detection metrics. These metrics are available when EDeepLearningDefectDetectionMetrics::IsDefectDetectionMetricsValid is true, i.e. when both images that are entirely background and images with non-background pixels have been added to the metrics.

Most metrics depends upon the EDeepLearningDefectDetectionMetrics::GetClassificationThreshold.

License(s): BasicTypes

Constructor & Destructor Documentation

◆ ESupervisedSegmenterMetrics() [1/3]

eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::ESupervisedSegmenterMetrics ( )

Constructs an ESupervisedSegmenterMetrics object.

◆ ESupervisedSegmenterMetrics() [2/3]

eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::ESupervisedSegmenterMetrics ( const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics & other )

Copy constructor of an ESupervisedSegmenterMetrics object.

Parameters

other Reference to the ESupervisedSegmenterMetrics object that should be copied

◆ ~ESupervisedSegmenterMetrics()

eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::~ESupervisedSegmenterMetrics	(		)
throw	(	)

virtual

◆ ESupervisedSegmenterMetrics() [3/3]

eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::ESupervisedSegmenterMetrics ( CppWrapperInternal::InternalConstructor )

Member Function Documentation

◆ GetBalancedError()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBalancedError ( ) const

Balanced error.

The balanced error is the weighted error (ESupervisedSegmenterMetrics::GetWeightedError) where each label is given an equal weight.

The error is also called the cross-entropy loss.

◆ GetBalancedIntersectionOverUnion()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBalancedIntersectionOverUnion ( ) const

Balanced Intersection over Union (IoU).

The balanced intersection over union is the weighted intersection over union (ESupervisedSegmenterMetrics::GetWeightedIntersectionOverUnion) where each label is given equal weight.

◆ GetBalancedPixelAccuracy()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBalancedPixelAccuracy ( ) const

Balanced accuracy.

The balanced accuracy is the weighted accuracy (ESupervisedSegmenterMetrics::GetWeightedPixelAccuracy) where each label is given an equal weight.

◆ GetBlobDetectionAveragePrecision()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionAveragePrecision ( ) const

Average precision for blob detection.

Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label. The average precision for blob detection is the average of the precision (see ESupervisedSegmenterMetrics::GetBlobDetectionPrecision) over all the possible classification threshold values. As such, the average precision does not depend on a specific classification threshold.

◆ GetBlobDetectionBestFScore()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionBestFScore ( ) const

Best F1-Score of the segmenter for blob detection.

See ESupervisedSegmenterMetrics::GetBlobDetectionBestFScoreThreshold for the corresponding threshold.

See EDeepLearningDefectDetectionMetrics::GetFScore for a definition of the F1-Score. Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label.

◆ GetBlobDetectionBestFScoreThreshold()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionBestFScoreThreshold ( ) const

Classification threshold that achieves the best F1-Score for blob detection.

See ESupervisedSegmenterMetrics::GetBlobDetectionBestFScore for the corresponding F1-Score.

See EDeepLearningDefectDetectionMetrics::GetFScore for a definition of the F1-Score. Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label.

◆ GetBlobDetectionFScore()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionFScore ( ) const

F1-Score for defective blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).

See ESupervisedSegmenterMetrics::GetBlobDetectionBestFScore for the corresponding F1-Score.

See EDeepLearningDefectDetectionMetrics::GetFScore for a definition of the F1-Score. Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label.

◆ GetBlobDetectionPrecision()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionPrecision ( ) const

Precision for blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).

The precision is the proportion of detected defective blobs that match ground truth defective blobs.

Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label.

◆ GetBlobDetectionRecall()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetBlobDetectionRecall ( ) const

Recall for blob detection given the current classification threshold (see EDeepLearningDefectDetectionMetrics::GetClassificationThreshold).

The recall is the proportion of ground truth defective blobs that are matched to predicted defective blobs.

Blob detection is the ability of the segmenter to correctly detect foreground ground truth blobs, regardless of their specific label.

◆ GetError()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetError ( ) const

Error.

The error is also called the cross-entropy loss.

◆ GetGroundtruthBlobConfusion() [1/2]

uint64_t eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetGroundtruthBlobConfusion	(	const std::string &	groundtruthLabel,
		const std::string &	predictedLabel ) const

Number of ground truth blobs of the given ground truth label that best match with blobs of the given predicted label.

See ESupervisedSegmenterMetrics::GetPredictedBlobConfusion for the number of predicted blobs that match to these ground truth blobs.

Parameters

groundtruthLabel	Ground truth label
predictedLabel	Predicted label

A ground truth blob is matched to the subset of predicted blobs from the same predicted label that has the best intersection over union with the ground truth blob. Otherwise, the ground truth blob is matched to background.

◆ GetGroundtruthBlobConfusion() [2/2]

uint64_t eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetGroundtruthBlobConfusion	(	int	groundtruthLabelIndex,
		int	predictedLabelIndex ) const

Number of ground truth blobs of the given ground truth label that best match with blobs of the given predicted label.

See ESupervisedSegmenterMetrics::GetPredictedBlobConfusion for the number of predicted blobs that match to these ground truth blobs.

Parameters

groundtruthLabelIndex	Ground truth label index
predictedLabelIndex	Predicted label index

A ground truth blob is matched to the subset of predicted blobs from the same predicted label that has the best intersection over union with the ground truth blob. Otherwise, the ground truth blob is matched to background.

◆ GetIntersectionOverUnion()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetIntersectionOverUnion ( const std::string & label ) const

Intersection over union.

The intersection over union if the ratio between the number of correctly classified pixels from the given label to the total number of pixels that belongs to that label or are predicted as being from that label.

Assuming that the given label is the positive class, the intersection over union is expressed as IOU = TP / (TP + FP + FN) where TP is the number of true positive, FP the number of false positive (pixels that belongs to label but are not predicted as label) and FN the number of false negative (pixels predicted as label but that do not belong to label).

Parameters

label Label

The intersection over union is also called the Jaccard index.

◆ GetLabel()

std::string eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetLabel ( int labelIdx ) const

Label recognized by the segmenter that produced the results aggregated in the metrics.

Parameters

labelIdx Index of the label between 0 and ESupervisedSegmenterMetrics::GetNumLabels - 1.

◆ GetLabelError()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetLabelError ( const std::string & label ) const

Label error.

Parameters

label Label

◆ GetNormalizedPixelConfusion()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetNormalizedPixelConfusion	(	const std::string &	trueLabel,
		const std::string &	predictedLabel ) const

Pixel-wise normalized confusion between the given true and predicted labels.

The normalized confusion is the ratio of the number of pixels belonging to the 'trueLabel' that are classified as 'predictedLabel' to the total number of pixels belonging to the 'trueLabel'.

Parameters

trueLabel	True label
predictedLabel	Predicted label

◆ GetNumLabels()

int eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetNumLabels ( ) const

Number of labels recognized by the segmenter that produced the results aggregated in the metrics.

◆ GetPixelAccuracy()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetPixelAccuracy ( ) const

Accuracy.

The accuracy is the ratio of the number of correctly classified pixels to the total number of pixels.

The accuracy will be skewed towards the most represented labels in the dataset. For example, if your dataset has 1% of defective pixels, the accuracy will mostly reflect the results on the 99% of good pixels and will say nothing about the capability of the model to detect the defect labels.

◆ GetPixelConfusion()

uint64_t eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetPixelConfusion	(	const std::string &	trueLabel,
		const std::string &	predictedLabel ) const

Pixel-wise confusion between the given true and predicted labels.

The confusion is the number of pixels belonging to the 'trueLabel' that are classified as 'predictedLabel'.

Parameters

trueLabel	True label
predictedLabel	Predicted label

◆ GetPixelLabelAccuracy()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetPixelLabelAccuracy ( const std::string & label ) const

Pixel label accuracy.

The label accuracy is the ratio of the number of correctly classified pixels of the given class to the total number of ground truth pixels from that class. If there are no ground truth pixels from the requested label, the method returns '-1'.

Parameters

label Label

◆ GetPredictedBlobConfusion() [1/2]

uint64_t eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetPredictedBlobConfusion	(	const std::string &	groundtruthLabel,
		const std::string &	predictedLabel ) const

Number of predicted blobs of the given predicted label that match to blobs of the given ground truth label.

See ESupervisedSegmenterMetrics::GetGroundtruthBlobConfusion for the number of ground truth blobs that match to these predicted blobs.

Parameters

groundtruthLabel	Ground truth label
predictedLabel	Predicted label

A predicted blob may be counted several times in the confusion matrix for predicted blobs:

Once if the more than 50% of the blob intersects ground truth background.
Once for each non-background label the predicted blob has an intersection with.

◆ GetPredictedBlobConfusion() [2/2]

uint64_t eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetPredictedBlobConfusion	(	int	groundtruthLabelIndex,
		int	predictedLabelIndex ) const

Number of predicted blobs of the given predicted label that match to blobs of the given ground truth label.

See ESupervisedSegmenterMetrics::GetGroundtruthBlobConfusion for the number of ground truth blobs that match to these predicted blobs.

Parameters

groundtruthLabelIndex	Ground truth label index
predictedLabelIndex	Predicted label index

A predicted blob may be counted several times in the confusion matrix for predicted blobs:

Once if the more than 50% of the blob intersects ground truth background.
Once for each non-background label the predicted blob has an intersection with.

◆ GetWeightedError()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetWeightedError ( ) const

Weighted error.

The weighted error is the weighted average of each label error (ESupervisedSegmenterMetrics::GetLabelError) with respect to the dataset label weights.

The error is also called the cross-entropy loss.

◆ GetWeightedIntersectionOverUnion()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetWeightedIntersectionOverUnion ( ) const

Weighted Intersection over Union (IoU).

The weighted intersection over union is the weighted averaged of each label intersection over union (see ESupervisedSegmenterMetrics::GetIntersectionOverUnion) with respect to the dataset label weights.

◆ GetWeightedPixelAccuracy()

float eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::GetWeightedPixelAccuracy ( ) const

Weighted accuracy.

The weighted accuracy is the weighted average of each label accuracy (ESupervisedSegmenterMetrics::GetPixelLabelAccuracy) with respect to the dataset label weights.

◆ IsValid()

bool eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::IsValid ( ) const

Indicates whether the object contains at least one result.

◆ Load() [1/2]

void eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::Load ( const std::string & path )

virtual

Loads an supervised segmentation metric. The given ESerializer must have been created for reading.

Parameters

path	The file path.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ Load() [2/2]

void eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::Load ( eVision::ESerializer * serializer )

virtual

Loads an supervised segmentation metric. The given ESerializer must have been created for reading.

Parameters

serializer The serializer.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ operator=()

eVision::EasyDeepLearning::ESupervisedSegmenterMetrics & eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::operator= ( const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics & other )

Assignment operator.

Parameters

other Reference to the ESupervisedSegmenterMetrics object used for the assignment

◆ operator==()

bool eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::operator== ( const eVision::EasyDeepLearning::ESupervisedSegmenterMetrics & other ) const

Equality operator.

Parameters

other Reference to the ESupervisedSegmenterMetrics object

◆ Save() [1/2]

void eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::Save ( const std::string & path )

virtual

Saves an supervised segmentation metric. The given ESerializer must have been created for writing.

Parameters

path	The file path.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ Save() [2/2]

void eVision::EasyDeepLearning::ESupervisedSegmenterMetrics::Save ( eVision::ESerializer * serializer )

virtual

Saves an supervised segmentation metric. The given ESerializer must have been created for writing.

Parameters

serializer The serializer.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ ESupervisedSegmenterMetrics() [1/3]

◆ ESupervisedSegmenterMetrics() [2/3]

◆ ~ESupervisedSegmenterMetrics()

◆ ESupervisedSegmenterMetrics() [3/3]

Member Function Documentation

◆ GetBalancedError()

◆ GetBalancedIntersectionOverUnion()

◆ GetBalancedPixelAccuracy()

◆ GetBlobDetectionAveragePrecision()

◆ GetBlobDetectionBestFScore()

◆ GetBlobDetectionBestFScoreThreshold()

◆ GetBlobDetectionFScore()

◆ GetBlobDetectionPrecision()

◆ GetBlobDetectionRecall()

◆ GetError()

◆ GetGroundtruthBlobConfusion() [1/2]

◆ GetGroundtruthBlobConfusion() [2/2]

◆ GetIntersectionOverUnion()

◆ GetLabel()

◆ GetLabelError()

◆ GetNormalizedPixelConfusion()

◆ GetNumLabels()

◆ GetPixelAccuracy()

◆ GetPixelConfusion()

◆ GetPixelLabelAccuracy()

◆ GetPredictedBlobConfusion() [1/2]

◆ GetPredictedBlobConfusion() [2/2]

◆ GetWeightedError()

◆ GetWeightedIntersectionOverUnion()

◆ GetWeightedPixelAccuracy()

◆ IsValid()

◆ Load() [1/2]

◆ Load() [2/2]

◆ operator=()

◆ operator==()

◆ Save() [1/2]

◆ Save() [2/2]