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This module serves to detect a specific surface and it can also classify the types of surface, which are to be detected, into different classes like the Classifier.
Training
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Annotations
Surfaces can be marked in two ways - by brush painting in the image or by making polygons, those ways can also be combined. Each surface class has one color assigned. For quick color switching, you can press a numeric key indicating the number of the class that is given in square brackets.
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Basic painting into the image by a brush of selected size. To change the brush size or switch between paint- and erase- mode, you can either use the sliders at the top, or you can use shortcuts, which can be found in the Help section under the ‘?’ symbol. Currently, you can press or hold the 'k' and 'l' keys to decrease resp. increase the brush size, and holding 'e' temporarily switches the paint brush to eraser.
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Polygons
When you choose the polygon mode, you left-click into the image to define vertices of the polygon, which are being connected with a line in the order you put them in. You can move existing vertices by clicking and dragging them, or use double-click to delete themalso hold the left button while moving your mouse to draw a curve. When you're satisfied with the shape, use rightleft-click on the initial point marked with a circle, which joins the last vertex with the first one and fills the polygon with chosen color. Polygons work in the erase-mode as well.
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Include
It is used to include the selected image for training if it does not contain any defects. The picture does not fall into one of the classes which were created, its whole surface is marked as defect-free.
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The model is optimized for specific GPU, so if you transfer the project to a PC with a different type of GPU, the optimisation needs to be done again for the new graphics card. |
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The TRT optimisation is available only for models with one class at the moment. |
This type does not require large datasets, however, performance on highly variable patterns may be more suitable for the Precise option.
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The detection result is a set of heatmaps (one for each class). When validating, heatmaps are plotted in the image for better illustration. In the heatmaps, Hetmaps for the whole image are added to 'heatmaps' in Context, all the rectangles surrounding the searched-for areas surrounded by rectangles are added to the context to ‘detectedRectangles’. Each rectangle has a class assigned, depending on which heatmap it was found in.
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