Using numba my code runs faster by about 20 times 😲

• im2dhisteq

  4 24 8.8 Python

  This module attempts to enhance contrast of a given image by equalizing its two dimensional histogram.

I recently added im2dhisteq to my repository and because it was very slow, I searched for a new way to make my code run faster, as I was already using numpy built-in functions and there were no (at least easy) other way to optimize the code using just numpy. I recently found out about Numba, which is advertised to run my codes 1000 times faster, but as I later found out the degree of that is actually very dependant on your code. After reading through their website and through a long series of trials and erros I learned how to write a code that is Numba-friendly and is satisfyingly faster than my base code. website of Numba: Numba Proof: My code: https://github.com/Mamdasn/im2dhisteq In my repository, in addition to the Numba-flavord versions, I released the Numba-less versions, which are accessible here: im2dhisteq and imhist. You can check them out and compare them to come to a base understanding of how Numba-friendly codes looks like.

• im2dhist

  1 6 4.8 Python

  This small piece of code is intended to help researchers, especially in field of image processing, to easily calculate two dimensional histogram of a given image.

I recently added im2dhisteq to my repository and because it was very slow, I searched for a new way to make my code run faster, as I was already using numpy built-in functions and there were no (at least easy) other way to optimize the code using just numpy. I recently found out about Numba, which is advertised to run my codes 1000 times faster, but as I later found out the degree of that is actually very dependant on your code. After reading through their website and through a long series of trials and erros I learned how to write a code that is Numba-friendly and is satisfyingly faster than my base code. website of Numba: Numba Proof: My code: https://github.com/Mamdasn/im2dhisteq In my repository, in addition to the Numba-flavord versions, I released the Numba-less versions, which are accessible here: im2dhisteq and imhist. You can check them out and compare them to come to a base understanding of how Numba-friendly codes looks like.

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• Numba

  124 9,471 9.9 Python

  NumPy aware dynamic Python compiler using LLVM

  

I recently added im2dhisteq to my repository and because it was very slow, I searched for a new way to make my code run faster, as I was already using numpy built-in functions and there were no (at least easy) other way to optimize the code using just numpy. I recently found out about Numba, which is advertised to run my codes 1000 times faster, but as I later found out the degree of that is actually very dependant on your code. After reading through their website and through a long series of trials and erros I learned how to write a code that is Numba-friendly and is satisfyingly faster than my base code. website of Numba: Numba Proof: My code: https://github.com/Mamdasn/im2dhisteq In my repository, in addition to the Numba-flavord versions, I released the Numba-less versions, which are accessible here: im2dhisteq and imhist. You can check them out and compare them to come to a base understanding of how Numba-friendly codes looks like.

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