Variance of Laplacian #4729
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Hi, I’m looking to implement a Variance of Laplacian calculation similar to OpenCV’s Laplacian function. My goal is to use it for blur detection as described here: https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv Previously, I solved this using SKIA by creating a matrix convolution filter with the Laplace kernel. However, SKIA is slow and memory-intensive compared to VIPS. I can convert the image to grayscale using I hope you can provide me some guidance here. The expected result is that an image like Kind regards, Stefan |
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Replies: 1 comment 3 replies
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Hi @StefanOltmann, there's an example here: https://github.com/libvips/pyvips?tab=readme-ov-file#example so eg.: mask = pyvips.Image.new_from_array([
[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1],
], scale=8)
image = image.conv(mask, precision='integer')You'll want a slightly different mask, of course. |
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Thank you. :) |
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Ah, wait, is this C++? Try: VImage mask = VImage::new_matrixv(3, 3,
-1.0, -1.0, -1.0, -1.0, 16.0, -1.0, -1.0, -1.0, -1.0);
mask.set("scale", 8);
image = image.conv(mask, VImage::option()->
set("precision", VIPS_PRECISION_INTEGER)); |
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I try to do it in Java/Kotlin, but this specific API seems to be missing. I just saw that someone else asked for that function, too.
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Hi @StefanOltmann, there's an example here:
https://github.com/libvips/pyvips?tab=readme-ov-file#example
so eg.:
You'll want a slightly different mask, of course.