Image processing
Digital image processing is the use of algorithms to make computers analyze the content of digital images.
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Are you using the latest version? Is the version currently in use as reported by npm ls sharp
the same as the latest version as reported by npm view sharp dist-tags.latest
?
Yes, using sharp v0.29.1
What are the steps to r
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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__
.
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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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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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System information (version)
Detailed description
When running cv2.seamlessClone() the error is a bit misleading when the incorrect image path is supplied. It doesn't make it obvious that the problem is in the path
Steps to reproduce