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extra alpha mask #1

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z0yuu opened this issue Apr 30, 2018 · 9 comments
Open

extra alpha mask #1

z0yuu opened this issue Apr 30, 2018 · 9 comments

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@z0yuu
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z0yuu commented Apr 30, 2018

May I ask How to get the extra alpha mask ?

@highland0971
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The same question with @z0yuu , where can we download the extra alpha mask to reproduce the model ? Thanks.

@GuideWsp
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We have make alpha by ourselves, but we can't get similar result. If possible, please give some advise.

@zhujun001
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you could run scripts/prepare_matting_dataset.py before scripts/prepare_seg_tri_dataset.py. prepare_matting_dataset.py will generate a pesudo alpha mask by gaussian blur

@leaf918
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leaf918 commented Oct 30, 2018

you could run scripts/prepare_matting_dataset.py before scripts/prepare_seg_tri_dataset.py. prepare_matting_dataset.py will generate a pesudo alpha mask by gaussian blur

Wonderful!!

@mansentan
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you could run scripts/prepare_matting_dataset.py before scripts/prepare_seg_tri_dataset.py. prepare_matting_dataset.py will generate a pesudo alpha mask by gaussian blur

@zhujun001, I'm getting following error when I run scripts/prepare_matting_dataset.py, do you have idea to it?

[2019/03/24 11:07:11.723] INFO (1666) __main__ : Compute look up table for weights Traceback (most recent call last): File "scripts/prepare_matting_dataset.py", line 146, in <module> main() File "scripts/prepare_matting_dataset.py", line 135, in main weight_lut = AlphaWeightLut(names, config.img_alpha_dir) File "scripts/prepare_matting_dataset.py", line 72, in __init__ self.distrib_lut = -np.log(sum_distrib / np.sum(sum_distrib)) TypeError: unsupported operand type(s) for /: 'NoneType' and 'NoneType'

@wkunique
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try
python scripts/prepare_matting_dataset.py --pseudo_alpha

@mansentan
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thanks @wkunique, you are saving my life.

@Maru-chan511
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@zhujun001 Thanks for a million!

@nicho2605
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nicho2605 commented May 13, 2021

try
python scripts/prepare_matting_dataset.py --pseudo_alpha

i still having nonetype error after using --psuedo_alpha
anyone can help ?

currently using numpy 1.13.3

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9 participants