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import torch
import numpy as np
from einops import rearrange
from kornia.geometry.transform.crop2d import warp_affine
from utils.matlab_cp2tform import get_similarity_transform_for_cv2
from torchvision.transforms import Pad
REFERNCE_FACIAL_POINTS_RELATIVE = np.array([[38.29459953, 51.69630051],
[72.53179932, 51.50139999],
[56.02519989, 71.73660278],
[41.54930115, 92.3655014],
[70.72990036, 92.20410156]
]) / 112 # Original points are 112 * 96 added 8 to the x axis to make it 112 * 112
@torch.no_grad()
def detect_face(images: torch.Tensor, mtcnn: torch.nn.Module) -> torch.Tensor:
"""
Detect faces in the images using MTCNN. If no face is detected, use the whole image.
"""
images = rearrange(images, "b c h w -> b h w c")
if images.dtype != torch.uint8:
images = ((images * 0.5 + 0.5) * 255).type(torch.uint8) # Unnormalize
_, _, landmarks = mtcnn(images, landmarks=True)
return landmarks
def extract_faces_and_landmarks(images: torch.Tensor, output_size=112, mtcnn: torch.nn.Module = None, refernce_points=REFERNCE_FACIAL_POINTS_RELATIVE):
"""
detect faces in the images and crop them (in a differentiable way) to 112x112 using MTCNN.
"""
images = Pad(200)(images)
landmarks_batched = detect_face(images, mtcnn=mtcnn)
affine_transformations = []
invalid_indices = []
for i, landmarks in enumerate(landmarks_batched):
if landmarks is None:
invalid_indices.append(i)
affine_transformations.append(np.eye(2, 3).astype(np.float32))
else:
affine_transformations.append(get_similarity_transform_for_cv2(landmarks[0].astype(np.float32),
refernce_points.astype(np.float32) * output_size))
affine_transformations = torch.from_numpy(np.stack(affine_transformations).astype(np.float32)).to(device=images.device, dtype=torch.float32)
invalid_indices = torch.tensor(invalid_indices).to(device=images.device)
fp_images = images.to(torch.float32)
return warp_affine(fp_images, affine_transformations, dsize=(output_size, output_size)).to(dtype=images.dtype), invalid_indices |