Closed bbertolucci closed 4 years ago
Hello! Happy to hear you're interested. The process is actually quite complicated, and I think is one of the drawbacks to SwapNet (took quite a lot of effort to even get this repository up).
First of all, you need trained SwapNet models: Warp and Texture stage. If you see this thread, I'm currently preoccupied and the earliest I can personally train these is late December. If you have your own compute power, I suggest training on the Deep Fashion dataset yourself.
Second, SwapNet requires preprocessing the input into a body segmentation and cloth segmentation before it is sent to the Warp and Texture models. I forked two other repositories to allow this to happen, this for body, and this for cloth.
In my opinion, this complexity makes SwapNet impractical for real use. I am planning to do research in virtual try-on myself to address these issues. Hoping to get somewhere with that in ~4 months.
Hi, Thank you for your quick answer ! I am currently training Deep Fashion dataset myself. Ok I understand we need body segmentation and cloth segmentation, I will try to do it. And I suppose I also need to calculate normalization statistics ? So I understand the 3 files:
python inference.py --checkpoint checkpoints/deep_fashion \ --cloth_dir [SOURCE] --texture_dir [SOURCE] --body_dir [TARGET]
Where SOURCE contains the clothing you want to transfer, and TARGET contains the person to place clothing on.
But it doesn't answer my question : where to find the output ? ~ The body segmented photo with the new clothes.
I think we can use it for real. It's using computation for training but once it's done it should be quick to give a result.
Also how do we resume an aborted training epoch ?
Oh, looks like I forgot to mention that option in the readme. Set --results_dir
to choose where to output to. By default it goes to results/
. See the various test/inference options here: https://github.com/andrewjong/SwapNet/blob/master/options/test_options.py
For normalization statistics, you can run the tool I made here: https://github.com/andrewjong/SwapNet/blob/master/util/calculate_imagedir_stats.py
Oh.. Sorry I didn't see it ! Thank you.
I will continue to follow your work with attention, it's a really good project and you did it very well !
Thank you for the kind words :)
this thread helps! Thanks!
Hello, can you please guide me on how to test the output what type of files must be exactly placed? inference.py: error: unrecognized arguments: --cloth_dir cloth/images1.jpg I am facing the above error I used command as python inference.py --checkpoint checkpoints/deep_fashion \ --cloth_dir cloth/images1.jpg --texture_dir txture/images.jpg --body_dir body/images2.jpg I used images please help me out I am a beginner
I am sorry I am still a beginner in DL, and I am not sure how to test it. By testing I mean : 1) I have a first original image with someone wearing clothes, 2) I have an image with the isolate cloth I want to swap 3) I have a destination image where someone is wearing another cloth. 1 should correspond to the cloth folder 2 should correspond to the texture folder 3 should correspond to the body folder
But with this what command should I do ? And where to find the result image ? ( 3) cloth should be swap by 2) ) Also 1) 2) 3) are jpeg image with original picture and not segmented one ?
Thank you for your splendid work !