Does af.regions work on 3D image input?
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- computer-vision
Research direction
Start at the af.regions entry point and compare its documented 2D input description with the underlying ArrayFire af_regions behavior for 3D arrays. Verify the behavior with a minimal 3D input, then update the documentation to state the supported dimensionality and expected connectivity semantics.
Written by the indexing model from the issue text.
Description
From the documentation it appears as though the method is designed to work in 2D only:
`def regions(image, conn = CONNECTIVITY.FOUR, out_type = Dtype.f32):
"""
Find the connected components in the image.
Parameters
----------
image : af.Array
- A 2 D arrayfire array representing an image.
conn : optional: af.CONNECTIVITY. default: af.CONNECTIVITY.FOUR.
- Specifies the connectivity of the pixels.
out_type : optional: af.Dtype. default: af.Dtype.f32.
- Specifies the type for the output.
Returns
---------
output : af.Array
- An array where each pixel is labeled with its component number.
"""
output = Array()
safe_call(backend.get().af_regions(c_pointer(output.arr), image.arr,
conn.value, out_type.value))
return output`
- Dominant language
- Python
- Stars
- 422
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
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