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Image processing
Digital image processing is the use of algorithms to make computers analyze the content of digital images.
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🚀 Feature
As reported by deepsource in here we abuse from using built-in input function in our functionality.
Motivation
We target to have a clean and healthy source code free of risk.
Pitch
Replace variable names whether it makes sense e.g. for image based functionality input -> image ; in l
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A follow up on SixLabors/ImageSharp#1378 (comment).
Currently 32 bit test execution is only done for .NET Framework, with dotnet xunit which is an obsolete tool today, we need to adapt dotnet test, and add 32 bit CI targets for both net5.0 and netcoreapp3.1. Opening an issue to remember and track this debt.
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Discussed in scikit-image/scikit-image#5938
Originally posted by stefanv July 11, 2017
RANSAC was implemented in scikit-image & scikit-learn around the same time, by the same @ahojnnes. In the mean time, some improvements have landed on either side.
We should compare the two implementations, and port fixe
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Enhancement
A discussion in #614 revealed a good place for improvement - we should ensure that input image is continuous upon start of the augmentation pipeline. This could be implemented by adding
image = np.ascontiguousarray(image)to image and mask targets.A proposed place to add this call - somewhere at the beginning of
A.Compose.__call__.