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Cut lets you crop the image manually from all four sides by adjusting the sliders on the right side of the image or by drawing a rectangle around the area you want to crop out and then pressing the Crop button whicg is located under the slider.
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 button, 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 and prevents detection outside of ROI. |
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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. Or, you can use this function to just rotate images to a position that makes it easier to makeannotations. |
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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 documentation. Blur 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, the higher the Threshold value, the larger the edge must be for it to be shown on the processed image. Details in the OpenCV documentation.
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Canny edge detector can be useful for higlighting structural defects, while getting rid of the noise caused by different colors. |
Remove background
Remove background function can help you with focusing only on the main object. Offers two types of background removal (histogram and canny), and each can be useful for different kinds of images. The Canny background removal uses uses either canny edge detector, dilation, and erosion or OpenCV histogram.
Canny Edge Histogram
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When removing background using the Canny Edge detector, you can set the blur size (it blurs the image based on the data of pixels in an NxN rectangle, you set the N in the “Blur” field). Threshold is for setting the size of the edges that will be kept. The rest are settings for Dilataion and Erosion.
Histogram removes background based on the frequency of colors of pixels. The pixel values with frequency between Extreme 1 and Extreme 2 will be kept.
The Background Color table can be used to choose which color to set the removed part of the image to.
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Remove background is useful when the background is distracting and you want the AI model to focus only on a particular object on the image. |
Unifier
Unifies the position and angle of objects or just puts the angle of the object into Context (if this option is ticked). Remove background that needs to be used before Unifier and have the same background color selected.
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Although this seems very useful for unifying the images, it takes a long time and thus it’s not recommended for projects that are required to run within a strict time limit. |
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Contrast equalisation
Equalises the contrast using the CLAHE algorithm, which works with a histogram of a pixel and its surroundings (depending on the kernel size). The clip limit determines the value at which the histogram is clipped. Details in the OpenCV documentation for CLAHE and histogram equalisation.
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This function lets you adjust the gamma value.
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Very useful when the input image is too bright or too dark. |
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Camera calibration
This function can be used to remove some camera-related distortions of the image.
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