Add benchmark suite for weiner and inverse functions #3316
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Waiting for the decision on #3319 (comment). |
emmanuelle
reviewed
Sep 5, 2018
| self.image = np.random.random((4000, 4000)) | ||
| self.image[:2000, :2000] += 1 | ||
| self.image[3000:, 3000] += 0.5 | ||
| self.image = np.random.random((600, 600)) |
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why did you decrease the size of the image? I think the idea of having a large enough image was to have an example close to demanding applications, which is more interesting for a benchmark than small images.
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Hey @abarya, I may be missing some context here but I guess this did not get enough attention by reviewers? Sorry for that. Did you close it because you are unable to continue or would you reopen if I were to commit some review time? Otherwise I might make a "Good first issue" out of this. 😉 |
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Description
The running time for both the functions is approximately 1.5s.
Checklist
[It's fine to submit PRs which are a work in progress! But before they are merged, all PRs should provide:]
./doc/examples(new features only)./benchmarks, if your changes aren't covered by anexisting benchmark
[For detailed information on these and other aspects see scikit-image contribution guidelines]
References
[If this is a bug-fix or enhancement, it closes issue # ]
[If this is a new feature, it implements the following paper: ]
For reviewers
(Don't remove the checklist below.)
later.
__init__.py.doc/release/release_dev.rst.@meeseeksdev backport to v0.14.x