Efficiently convolve with 2D Gaussian (non-diagonal covariance matrix)

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    After looking at the SciPy docs and NumPy, I haven't found a way to efficiently convolve an image with a 2D Gaussian N(μ, Σ). Here, Σ is not diagonal, which means that the filter will not be separable.

    I have looked at scipy.ndimage.filters.gaussian_filter, scipy.signal.gaussian and scipy.signal.general_gaussian, but none of them seem to support it.

    Then I think the only way would be to create a the window (kernel) for my filter and call scipy.signal.convolve2d. However, in my use-case, I will need to generate many of those Gaussians, all with different Σ.

    What would be the most efficient way to proceed?

    asked 26 secs ago

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  • نویسنده : استخدام کار بازدید : 49 تاريخ : جمعه 29 ارديبهشت 1396 ساعت: 22:00
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