np.float
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- Dominant language
- Python
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Description
Traceback (most recent call last):
File "/host/home/yanai-lab/Sotsuken24/xiong-p/test/Practical-RIFE/inference_video.py", line 115, in
lastframe = next(videogen)
^^^^^^^^^^^^^^
File "/home/yanai-lab/xiong-p/miniconda3/envs/RIFE/lib/python3.11/site-packages/skvideo/io/io.py", line 251, in vreader
reader = FFmpegReader(fname, inputdict=inputdict, outputdict=outputdict, verbosity=verbosity)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yanai-lab/xiong-p/miniconda3/envs/RIFE/lib/python3.11/site-packages/skvideo/io/ffmpeg.py", line 103, in init
self.inputfps = np.float(parts[0])/np.float(parts[1])
^^^^^^^^
File "/home/yanai-lab/xiong-p/miniconda3/envs/RIFE/lib/python3.11/site-packages/numpy/init.py", line 324, in getattr
raise AttributeError(former_attrs[attr])
AttributeError: module 'numpy' has no attribute 'float'.
np.float was a deprecated alias for the builtin float. To avoid this error in existing code, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'cfloat'?
Contributor guide
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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 running inference_video.py and inspect the FFmpegReader code at skvideo/io/ffmpeg.py:103, where the traceback reports the failure. Trace whether that code is maintained by this project or supplied as a dependency, then verify that video inference completes without the reported NumPy attribute error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- computer-vision
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100