Comments (2)
And for fined face mask,there is a kind of algorithm names image matting, repo like global matting may helps but it need tri-map mask(a rough mask which can be generated by your morphology cv algorithm). Thank you for your sharing!
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@shartoo the main reason to use dlib here is just because NVIDIA used it in training their pre-trained models in the first place, and provided their implementation of aligning images, so we can align them in a consistent fashion. But really any detector that provides the same basic landmarks should be fine, and would allow you to more or less replicate their results.
Also, it doesn't matter so much if you're training your own models, in that case you can process your data in a consistent fashion so that the encoded images are treated in the same way. I'm glad you found the masking helpful, grabcut was designed for it, really. If you'd like to provide another compatible method for aligning images and/or generating masks, well, patches are welcome!
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Related Issues (20)
- Trianing error about launch failed
- Error running encode_images.py on Colab HOT 2
- Error while running encode_images.py #41
- google drive exceeded encode_images.py
- Two bugs found HOT 2
- AttributeError: module 'tensorflow' has no attribute 'Dimension' HOT 2
- Face Alignment results in exception error
- Compatibility with pretrained stylegan2 model? HOT 7
- train encoder directly HOT 1
- requirements.txt empty and compatibility issues HOT 1
- assert img.shape == self.shape Assertion Error HOT 6
- How to interpolate a one region of two faces? HOT 1
- Why can't we find the vector Z and have to use W ? HOT 2
- runing StyleGAN_Encoder sehape error HOT 2
- Weird error, how do I fix it???'
- Why requirments.txt is empty HOT 1
- Getting error while run the 'encode_images.py' file
- 'latent_training_data' is no longer available HOT 1
- PreTrained Model HOT 1
- tf compatibility issue
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