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classify

Classify image as normal or anomalous

Since R2022b

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    Syntax

    isAnomaly = classify(detector,I)
    isAnomaly = classify(detector,I,Name=Value)

    Description

    example

    isAnomaly = classify(detector,I) predicts whether test image I is anomalous or normal, given an anomaly detector with an anomaly threshold.

    Note

    This functionality requires Deep Learning Toolbox™ and the Computer Vision Toolbox™ Automated Visual Inspection Library. You can install the Computer Vision Toolbox Automated Visual Inspection Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

    isAnomaly = classify(detector,I,Name=Value) specifies options using one or more name-value arguments. For example, classify(detector,I,MiniBatchSize=32) limits the batch size to 32.

    Examples

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    This example uses:

    Open Live Script

    Load test images and corresponding labels, then create a datastore that reads the test data. The data set consists of grayscale images of handwritten digits 0–9.

    [Xtest,gtLabels] = digitTest4DArrayData;
dsTest = arrayDatastore(Xtest,IterationDimension=4);

    Load a pretrained FCDD anomaly detector. This detector has been trained to classify the digit 8 as normal and all other digits as anomalies. Therefore, specify the set of anomaly labels as the set of digits between 0 and 9, excluding 8.

    load("digit8AnomalyDetector.mat");
anomalyLabels = setdiff(string(0:9),"8");

    Classify each test image as normal or anomalous.

    predLabels = classify(detector,dsTest);

    Calculate metrics for the anomaly detection results.

    metrics = evaluateAnomalyDetection(predLabels,gtLabels,anomalyLabels)
    Evaluating anomaly detection results
------------------------------------
* Finalizing... Done.
* Data set metrics:

    GlobalAccuracy    MeanAccuracy    Precision    Recall    Specificity    F1Score    FalsePositiveRate    FalseNegativeRate
    ______________    ____________    _________    ______    ___________    _______    _________________    _________________

        0.7662           0.839         0.98971     0.748        0.93        0.85204          0.07                 0.252      

    metrics = 
  anomalyDetectionMetrics with properties:

              ConfusionMatrix: [2x2 table]
    NormalizedConfusionMatrix: [2x2 table]
               DataSetMetrics: [1x8 table]
                 ClassMetrics: [2x2 table]

    Display the confusion matrix.

    cm = metrics.ConfusionMatrix
    cm=2×2 table
               Normal    Anomaly
               ______    _______

    Normal       465        35  
    Anomaly     1134      3366

    Input Arguments

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    Anomaly detector, specified as an fcddAnomalyDetector object, a fastFlowAnomalyDetector object, or a patchCoreAnomalyDetector object. You must set the Threshold property of the detector before calling this function.

    Test image, specified in one of these formats:

    FormatSupported Detectors

    M-by-N-by-3 numeric array representing a truecolor image.

    		FCDD, FastFlow, PatchCore

    M-by-N-by-3-by-B numeric array representing a batch of B truecolor images.

    		FCDD, FastFlow, PatchCore

    Datastore that reads and returns truecolor images. The images must all have the same size.

    		FCDD, FastFlow, PatchCore

    Formatted dlarray (Deep Learning Toolbox) object with two spatial dimensions and one channel dimension. You can specify multiple test images by including a batch dimension.

    		FCDD, FastFlow

    FCDD anomaly detectors also support grayscale test images, with one color channel instead of three.

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: isAnomaly = classify(detector,I,MiniBatchSize=32) limits the batch size to 32

    Size of batches for calculating predictions, specified as a positive integer. Larger batch sizes lead to faster processing but take up more memory.

    Hardware resource on which to run the detector, specified as "auto", "gpu", or "cpu". The table shows the valid hardware resource values.

    Resource Action
    "auto"Use a GPU if it is available. Otherwise, use the CPU.
    "gpu"Use the GPU. To use a GPU, you must have Parallel Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU. If a suitable GPU is not available, the function returns an error. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).
    "cpu"Use the CPU.

    Output Arguments

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    Test image is anomaly, returned as an N-element logical vector, where N is the number of test images in I. The value true indicates an anomaly (positive detection). The value false indicates normality (negative detection).

    Data Types: logical

    Extended Capabilities

    Version History

    Introduced in R2022b

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    See Also

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