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Cut lets you crop the image manually from all four sides or by drawing a rectangle around the area you want to crop out and then pressing the Crop button.
The slidebars on the right side of the image let you choose from which side of the image and how many pixels to cut.
If you use the crop function, the slidebars will adjust automatically.
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This is useful when the area on which the detection is required is only on a portion of the image. Cutting the image can shorten training and detection times. |
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The same cut will apply to all images, so make sure that the are for detection is in the same portion of the image among all the images. |
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Scale
Scale lets you adjust the resolution of the image.
The value slider specifies the factor by which the image size is adjusted. For example, if you change the value to 0.5 just like on the second image, each side of the image will be halved.
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This can be useful when the defects on the image are alrge and clearly visible and thus the resolution of the image doesn’t need to be so high. Reducing the resolution can shorten inference and training times. |
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Rotation
You can rotate all images anti-clockwise by a specified angle.
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This can be useful when the models are already trained and implemented on the production line, but a camera on the line was rotated. So in order to avoid the need to retrain the models, you can just rotate the images back to the position that was used for training. |
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Background normalization
Background normalization serves for unifying colors. It evaluates each pixel and based on how many surrounding pixels are of a similar colour it shifts that pixel closer to gray. The value specifies the size of the are in which it takes the surrounding pixels into consideration, meaning the lower the value, the more gray/shifted will the resulting image be.
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This is useful if the things you want to detect take up only a small portion of the image, but are of very different color than their surroundings. |
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Blur
This function blurs the picture and reduces noise. Details in the OpenCV documentationBlur blurres the image by averaging the value of a pixel depending on the values of the surrounding pixels. The value specifies the area in which the surrounding pixels are use to calculate the new pixel value. For example, with value set to 100, it would calculate a value of a pixel based on it’s surrounding pixels in a 100 by 100 rectange around that pixel.
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Blur can be useful in situations when your trained models are detecting small defects that you don’t want them to detect. This can make blur especially useful when using Anomaly Detector. |
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Canny Edge Detector
Canny edge detector focuses only on the edges of the objects. You can set the Threshold value to highlight the edges. Details in the OpenCV documentation.
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