JuliaImages / JuliaImages/ImageCore.jl
support conversions from/to other frameworks
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- Dominant language
- Julia
- Stars
- 28
- Forks
- 21
- PR merge metrics
- No merged PRs in 30d
Description
We do have colorview/channelview/rawview to build these functionalities, but from a user's perspective, it would be much much easier to understand and use things like:
to_matlab/from_matlabto_torch/from_torch
Because other framework doesn't always contain colorspace information on their numerical array, assumptions must be made. For instance:
- for MATLAB we can assume that 3D numerical array with size
(m, n, 3)is an RGB image of size(m, n). - for deep learning frameworks like pytorch, we can assume that 4D tensor with size
(w, h, 3)is an RGB image of size(m, n).
These utilities do not need to cover every use case and do not need to provide 100% consistency; they're meant to help users to migrate the old codes from other frameworks.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing colorview, channelview, and rawview utilities in ImageCore.jl. Determine the supported array shapes and how color-space information is represented before defining the proposed to_matlab/from_matlab and to_torch/from_torch interfaces. Done means documented conversions for the stated MATLAB and deep-learning cases, with tests covering the assumed shapes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, matlab, pytorch
- Domain
- computer-vision
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100