Submitted by Tekno-12345 t3_11qanvr in deeplearning
HarissaForte t1_jc9s369 wrote
Reply to comment by Tekno-12345 in Using GANs to generate defective data by Tekno-12345
For example you use a bubble mask to "cut" out a bubble patch from an image. Cutting can be a mix of rectangular selection + transparency where the mask equals zero.
Then you do some random change (flip, aspect ratio…) to this patch.
Then you take a defect free image, and you randomly choose a location where to paste this defect using the bottle mask. You can paste by simply over-writing on the image, or you can do a linear interpolation between the patch image and the defect-free image so it's smoother.
I assume you have very similar images, since they're from a standardized inspection process. Also the bubbles are a very simple pattern. So this could work quite well.
Tekno-12345 OP t1_jc9zmeh wrote
Ow I see,
I have already done something similar but the results were not convincing.
Maybe I'll try it again using the masks.
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