F_VN_TrainImageColorExp

Syntax

Definition:

FUNCTION F_VN_TrainImageColorExp : HRESULT
VAR_INPUT
    ipSrcImage       : ITcVnImage;
    ipColorModel     : Reference To  ITcVnColorModel;
    nDifferentColors : UDINT;
    eMethod          : ETcVnColorTrainingMethod;
    ipMask           : ITcVnImage;
    nSkipPixels      : UDINT;
    hrPrev           : HRESULT;
END_VAR

Inputs

Name	Type	Description
ipSrcImage	ITcVnImage	Source image (3 channels (RGB) of type USINT)
ipColorModel	Reference To ITcVnColorModel	Returns the color model
nDifferentColors	UDINT	Number of different colors to distinguish
eMethod	ETcVnColorTrainingMethod	Color training method
ipMask	ITcVnImage	Optional image mask (1 channel of type USINT, set to 0 if not required)
nSkipPixels	UDINT	Number of pixels to skip between each evaluated color sample (to achieve a better performance). 0 takes every pixel into account and tends to be more accurate.
hrPrev	HRESULT	HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

Return value

HRESULT

Further information

The function F_VN_TrainImageColorExp is the expert version of F_VN_TrainImageColor. It contains further parameters. If the trained color model is to be saved to disk, the F_VN_TrainImageColorExp_ITcVnMlModel with the type ITcVnMlModel must be used.

Parameter

Reference image

The reference image ipSrcImage must have 3 channels in RGB format and the element type USINT (8-bit).

Color model

The parameter ipColorModel returns the trained color model. This is an interface pointer of the type ITcVnColorModel.

Number of colors to be distinguished

The number nDifferentColors defines the number of colors to be trained. Note that the image background, for example, can also have its own color.

Expert parameters

Additional parameters can be found in another expert version F_VN_TrainImageColorExp2.

Training methods

When selecting the training method, the LAB and RGB color spaces are available.

Mask

The mask ipMask defines which pixels of the input image ipSrcImage are taken into account in the calculation. It is described by a 1-channel image with the element type USINT, which has the same size as the input image ipSrcImage. All pixels in the mask image with a value <> 0 are taken into account in the calculation of the cluster.

Pixel gap

For performance optimization, a number of pixels can be skipped so that fewer pixels need to be considered when dividing into the individual clusters.

Application

VAR
    ipColorModel    :   ITcVnColorModel;
END_VAR

hr := F_VN_TrainImageColor(
            ipSrcImage      :=  ipImageRef,
            ipColorModel    :=  ipColorModel,
            nDifferentColors:=  5, // 4 colors + 1 background = 5
            eMethod         :=  TCVN_CTM_LAB,
            ipMask          :=  0,
            nSkipPixels     :=  3,
            hrPrev          :=  hr);

Related functions

There are three ways to pass the reference color to the corresponding F_VN_ReferenceColorSimilarity function:

As a trained color model of the type ITcVnColorModel
As a trained color model of the type ITcVnMlModel
Directly with values in a TcVnVector3_LREAL

For the color models there are corresponding functions for training and for all three data types one function each for execution:

F_VN_TrainImageColor for the training of a color model
F_VN_ReferenceColorSimilarity for segmentation by means of a color model

Required License

TC3 Vision Base

System Requirements

Development environment	Target platform	PLC libraries to include
TwinCAT V3.1.4024.54 or later	PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU	Tc3_Vision