FFHQ Face Alignment
ffhq-align is a face alignment operation that allows gradient computation and runs on PyTorch GPU entirely.
This implementation is faster than original even running on CPU.
See original implementation which not support gradient computation and not run on GPU: ffhq-dataset
Install
run one of the following commands.
> pip install ffhq-align> git clone https://github.com/kynk94/ffhq-align
> cd ffhq-align
> python setup.py installExamples
usage: align.py [-h] --input INPUT [--output OUTPUT] [--resolution RESOLUTION] [--batch_size BATCH_SIZE]
Face Alignment
optional arguments:
-h, --help show this help message and exit
--input INPUT, -i INPUT
--output OUTPUT, -o OUTPUT
--resolution RESOLUTION, -r RESOLUTION
--batch_size BATCH_SIZE, -b BATCH_SIZE
If all input images resolution is the same, can set batch_size larger than 1.
--device DEVICE, -d DEVICE> python align.py -i samples -r 512 -b 1import torch
from PIL import Image
from ffhq_align import Aligner
aligner = Aligner(padding_mode="blur")
image = Image.open("samples/input.jpg")
aligned_image = aligner(image, resolution=512) # (1, 3, 512, 512)from typing import List, Optional
import numpy as np
import torch
from numpy import ndarray
from PIL import Image
from torchvision import transforms
from ffhq_align import LandmarkFA, image_align
landmark_model = LandmarkFA()
image = Image.open("samples/input.jpg")
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(0.5, 0.5)]
)
tensor = transform(image)[None] # (1, 3, H, W)
# landmark_model returns batch landmarks.
# if there is no faces, the value is None.
landmarks: List[Optional[ndarray]] = landmark_model(tensor)
if any(l is None for l in landmarks):
raise ValueError("Face not Found")
landmarks = torch.from_numpy(np.array(landmarks)) # (1, 68, 2)
aligned_image = image_align(tensor, landmarks, resolution=512) # (1, 3, 512, 512)
