Evaluation

Automatically decides whether the image is OK or NOK. This information is represented by a green or red stripe in the corner of each image in the image preview.

Evaluation can be used in the following modules:

Anomaly Detector

Classifier

Detector

Surface Detection

Measure

OCR

If one of the modules in flow uses evaluation, this evaluation will affect next modules. In case the next module uses evaluation as well it will overwrite the results of previous evaluation.

When you use parallelism, a boolean operator AND is used among the branches. That means if one of the branches has a NOK result, the overall result will be NOK as well.

Anomaly Detector

Evaluation considers the amount of anomalies and compares it with a threshold value. More information on calculating the threshold can be found here. If the values are bigger than the threshold, image is evaluated as NOK, otherwise it is considered OK.

Evaluation

To activate this function, you need to tick the ‘Evaluation’ button above the generated sensitivity graph. When processing is active, it evaluates image as valid (green stripe) or invalid (red stripe).

Threshold

Value which determines the amount of anomalies that are tolerated.

Detector, Classifier, Surface Detector, Measure, OCR

Images are evaluated based on set rules. If image follows all of the rules it is considered OK, otherwise it is NOK.

If there are multiple classes, the rules can be set for a specific class or one of Any/Every/Together.

• Any - OK if the rule is true for at least one of the classes (e.g. rule “Any Count = 10” is true if Class1 count is 10 and Class2 count is 4)

• Every - OK if the rule is true for each of the classes found in image (e.g. rule “Every Count = 10” is true if Class1 count is 10 and Class2 count is 10)

• Together - OK if the rule is true for all classes present in the image combined (e.g. rule “Together Count = 10” is true if Class1 count is 6 and Class2 count is 4)