# ViDiELSegment

You can use the ViDi EL Segment tool for finding flaws or locating features. Use the Pen tool to mark the feature you want the ViDi EL Segment tool to find. You can create classes for the different features, so the ViDi EL Segment tool can search for several features at the same time. After training, the tool can predict the appearance of the features in the new images.

Note: When using high-resolution images in EL Segment tools, prediction algorithms may produce exceedingly small defect labels and could clutter the training due to the intricate detail. You may need to zoom in to see these labels. You can either partially erase the image or completely clear the entire prediction to manage the detailed predictions.

To add the ViDi EL Segment tool:

1. Drag and drop the ViDi EL Segment tool into the Spreadsheet.

   The property sheet pops up:

   Note: If you open the property sheet of an already trained ViDi EL Segment tool, the tool automatically shows predictions based on the training the tool received.

2. To set up the ROI, double-click the Region parameter and adjust the red rectangle.

3. Start labeling images.

   You can do the labeling in the following ways:

   - Use the Pen tool by holding down the left mouse button to color the areas you want to mark.

   - Use the Polygon tool by clicking around the edge of an area or feature to draw lines. Double-clicking closes up the lines and creates a colored polygon for you.

   If you do not like the label you created, you can use the Undo and Redo buttons, delete part of the markings with the Eraser tool, or all of them with the Clear button. The Clear button deletes every label in the image, regardless of the class.

   You can rename the class or give the class a new color by clicking on the Edit Class button.

4. Click Accept Labels to train the image.

   Note: When training a large number of images in a single session, make sure to save the job A job is a user-configured program in Spreadsheet consisting of functions and designed for specific applications. You can create and edit jobs using the Spreadsheet interface, and load them on a vision system for deployment in a live environment. intermittently to avoid losing progress.

5. Show the next image to the tool. At this point, the tool makes a prediction, meaning that the tool tries to find the patterns that you marked in the previous images.

6. Finetune your labeling if needed.

   For example, there can be false predictions in your image. You can delete these faults by using the Eraser tool before accepting the changes.

7. If needed, create more classes by clicking the Add Class button. Name your new class and pick a different color.

   Note: Label at least five images for each class. The more images you train, the higher the quality of your output is.

   You can view your trained images in the sample viewer. To reach the sample viewer window, click the Edit button on the right.

   In the sample viewer, you can correct and finetune the trained images by clicking on the Edit button. You can also delete them by clicking on the Delete button, or you can edit the classes.

   Click on the Edit Classes button to open the class editor where you can rename and delete classes, or change the class color:

8. Click OK, if you finished labeling. The output of the function Functions are tools that are available in Spreadsheet for processing and analyzing acquisitions or other results. You can add functions to your Spreadsheet job to create tool chains and produce results for specific applications. appears on the Spreadsheet.

Inputs

Setting | Description
Image | Reference to a cell containing an image structure. Default value: $A$0.
Fixture A fixture is a coordinate location on the image that keeps the tool in the place determined by the fixture. | Fixture origin (X, Y, Theta). Offsets from the image origin.
Region | Region of interest (X, Y, Width, Height, Angle).
Feature Size | The size of features to segment. The default setting is Medium(32). Smaller feature sizes capture finer details, while larger feature sizes improve processing and training speed. You can choose from the following sizes: Very Small(16) Small(32) Medium(64) Large(128) The numbers in the parentheses, when divided by 8, represent the factor by which each dimension of the image is downscaled before processing by the model. For example, if the feature size is 16, the image dimensions are divided by 2, since 16 / 8 = 2, resulting in a quarter of the original number of pixels. Since each dimension is halved, the total number of pixels is reduced by a factor of 4. Note: Using the Very Small(16) feature size with larger ROIs may reduce the maximum number of samples you can label.
Confidence Threshold | Specifies the minimum required confidence percentage when determining which pixel is assigned to which class.
Show | Select the type of graphic to overlay on the image.

Outputs

Output | Description
Class | The names of the classes you gave.
Output Mask | Image data structure.
DetectBlob | Blob A blob is a group of pixels on the image that form a distinct pattern, typically used to represent objects, features, or areas of interest in object detection and segmentation tasks. data structure.
Number Found | The number of the found blobs.