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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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Check if test_random_gen_accumulative_additive_additive
still crashes in pytorch 1.10.1
Originally posted by @edgarriba in kornia/kornia#1611 (comment)
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Discussed in SixLabors/ImageSharp#2061
Originally posted by br3aker March 13, 2022
Current jpeg implementation lacks of COM marker support, does it need to support it? Should be easy to implement as this marker is just an array of bytes - itu spec leaves 'interpretation to the application', decoding API shoul
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Documentation/Tutorial: splitting clumped objects using distance transform and seeded watershed
Splitting clumped objects after binarization/thresholding (e.g. touching cells or grains) using distance transform and seeded watershed is a very common operation when using tradiitonal (non-machine learning) segmentation. I just noticed that there is no tutorial covering this use case in the tutorials gallery.
The "coin" example is not very useful in that regard as everything is well separated.
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🚨 🚨 Feature Request
- A new implementation (Improvement, Extension)
Is your feature request related to a problem?
Currently, if a user tries to access an index that is larger than the dataset length or tensor length, an internal error is thrown which is not easy to understand.
Description of the possible solution
We can catch the error and throw a more descriptive e
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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__
.