Adding Gaussian Smoothening for images #443
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| typename gil::image<gil::gray8_pixel_t>::view_t temp_view = view(temp_img); | ||
| gil::gray8_view_t dst_view(temp_view); | ||
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| gil::gaussian_filter(src_view, dst_view, 3, 1.0f); |
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In case of your this implementation, even Gaussian kernel generation requires additional test but as I have suggested the changes above it would not be required.
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I verified output of the Gaussian Kernel Generation code with cv's getGaussianKernel function and I find it very much same up to some extent (precision after 4 decimals)
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@laxsuryavanshi First, I'd like you to read two comments, #435 (comment) and #430 (comment), to learn more about the process of GSoC preparation and what to expect about your PR submitted as the competency test. You should also have a look at https://www.boost.org/community/gsoc.html |
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| #define BOOST_TEST_MODULE gil/test/core/image_processing/gaussian_filter | |||
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Description
Gaussian smoothening is the result of blurring an image with a Gaussian function.
The implementation here takes advantage of the separable property of the Gaussian filter by dividing the process into two passes.
Motivation
This PR is proposed as a solution to the competency test for GSoC 2020. I chose the project proposed in the ideas list "Image Processing Algorithms". If accepted, I could start implementing more algorithms like Wiener filter, Median filter, Histogram (even though proposed separately)
Reference
https://en.wikipedia.org/wiki/Gaussian_filter
http://rastergrid.com/blog/2010/09/efficient-gaussian-blur-with-linear-sampling/