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Implement Sobel and Scharr operators #392
This commit adds Sobel and Scharr
I agree that it's better to (create and) use dedicated types for job than trying to (bend to) re-use existing types where they don't necessarily fit, neither conceptually nor physically.
The output of Harris and Hessian seem to be differ from before the migration to
AFAIS, current failures are in the kernel tests, see https://dev.azure.com/boostorg/gil/_build/results?buildId=701&view=logs&jobId=2517ed61-6924-508d-087f-7c02f775cbba
This commit adds Sobel and Scharr operators with support for 0th and 1st degrees with other degrees planned for later
Generate Harris entries now uses signed image view. The Harris corner detector example now uses the Scharr filter generator and convolve_2d to reduce amount of code needed.
The Hessian example now uses signed image views and uses newly added kernel generators to compute gradients
In Harris and Hessian tests, unsigned pixel values was used to construct signed image, which was causing appveyor to error out.
This commit makes all kernel generator functions to return kernel_2d and adapts dependant threshold function to use the new interface