Table Of Contents

Classify Color Advanced (G Dataflow)

Last Modified: October 26, 2017

Classifies the image sample located in the given ROI and returns advanced information, such as the sample results.

connector_pane_image
datatype_icon

ROI descriptor

Region of interest specifying the location of the sample in the image.

The ROI must be one or more closed contours. If ROI descriptor is empty or not connected, the entire image is considered to be the region. For best performance, use only one rectangle or one rotated rectangle per sample.

datatype_icon

Global Rectangle

Coordinates of the bounding rectangle.

datatype_icon

Contours

Individual shapes that define an ROI.

datatype_icon

ID

Object specifying if contour is the external or internal edge of an ROI.

datatype_icon

Type

Shape type of the contour.

datatype_icon

Coordinates

Relative position of the contour.

datatype_icon

classifier session in

Reference to the classifier session on which the node operates.

datatype_icon

image in

Reference to the source image.

datatype_icon

error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

datatype_icon

class results

Array with one element for every class in the classifier session.

datatype_icon

class

One of the classes in classifier session in.

datatype_icon

distance

Distance from the closest sample in class to the input sample when performing nearest neighbor and k-nearest neighbor classification.

distance is the distance between the input sample and the center of each class when performing minimum mean distance classification.

datatype_icon

classifier session out

Reference to the classifier session the node creates.

datatype_icon

image out

Reference to the source image.

datatype_icon

class

Class into which the classifier session categorizes the input sample.

datatype_icon

error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.
datatype_icon

scores

Estimations of how well the classifier session classified the input.

The score can vary from 0 to 1000, where 1000 represents the best possible score.

datatype_icon

classification score

Score that indicates how much better the assigned class represents the input sample than other classes represent the input.

datatype_icon

identification score

Score that indicates the similarity of the input and the assigned class.

Use identification score only when you cannot reach a decision about the class of a sample using classification score alone.

datatype_icon

sample results

Array with information about the closest samples.

If the nearest neighbor engine is used, the information for the 10 closest samples is returned. If the k nearest neighbor engine is used, the information for k closest samples is returned. The array size is NULL for the minimum mean distance engine. Every sample in the array contains what class the sample belongs to, the distance of the class to each other class, and the index of the sample.

datatype_icon

class

Class into which the classifier session categorizes the input sample.

datatype_icon

distance

Distance from the closest sample in class to the input sample when performing nearest neighbor and k-nearest neighbor classification.

datatype_icon

index

Location of the sample in the classifier session among the entire trained sample.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application


Recently Viewed Topics