results are strange while testing on whole img about gfpgan HOT 7 CLOSED

1. If no face is detected, it will restore it.
2. As the current model also enhances colors, when putting faces back to the whole image, the results are not good. We will release a model w/o changing colors.

from gfpgan.

By the way, I have i small question about the Channel-Split SFT, which is how the F_prior is splited in chanels? How many channel F_prior have? Thanks!

Half the original channels~

from gfpgan.

I have updated the model without colorization. Colab demo: https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo

For your problem, I have not encountered this issue on my side.

from gfpgan.

1. If no face is detected, it will restore it.
2. As the current model also enhances colors, when putting faces back to the whole image, the results are not good. We will release a model w/o changing colors.

But I use default test img in original project, it will be strange if no face is detected. results are as follows:

from gfpgan.

By the way, I have i small question about the Channel-Split SFT, which is how the F_prior is splited in chanels? How many channel F_prior have? Thanks!

from gfpgan.

1. If no face is detected, it will restore it.
2. As the current model also enhances colors, when putting faces back to the whole image, the results are not good. We will release a model w/o changing colors.

But I use default test img in original project, it will be strange if no face is detected. results are as follows:

Did you use the colab demo?
It seems that there must be something wrong with face detection.~

from gfpgan.

1. If no face is detected, it will restore it.
2. As the current model also enhances colors, when putting faces back to the whole image, the results are not good. We will release a model w/o changing colors.

But I use default test img in original project, it will be strange if no face is detected. results are as follows:

Did you use the colab demo?
It seems that there must be something wrong with face detection.~

No, I tested on local side. After I recheck the crop region and found that for every input the detection region is always the left up 512*512 block. I tested on both moblienet and resnet detection model.
Is there something wrong with facexlib? I tested both v0.1.3.1 and v0.1

from gfpgan.