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import torch
import numpy as np
from skimage import transform as trans
from kornia.geometry import warp_affine
import torch.nn.functional as F
def extract_3p_flame(lm):
p0 = lm[36:42].mean(0)
p1 = lm[42:48].mean(0)
p2 = lm[60:68].mean(0)
lm3p = np.stack([p0, p1, p2], axis=0) # (3,2)
return lm3p
def estimate_norm_pdfgc(lm_70p, H, reverse_y=True):
# modified from https://github.com/deepinsight/insightface/blob/c61d3cd208a603dfa4a338bd743b320ce3e94730/recognition/common/face_align.py#L68
"""
Return:
trans_m --numpy.array (2, 3)
Parameters:
lm --numpy.array (70, 2), y direction is opposite to v direction
H --int/float , image height
"""
lm = extract_3p_flame(lm_70p)
if reverse_y:
lm[:, -1] = H - 1 - lm[:, -1]
tform = trans.SimilarityTransform()
src = np.array([[87, 59], [137, 59], [112, 120]], dtype=np.float32) # in size of 224
tform.estimate(lm, src)
M = tform.params
if np.linalg.det(M) == 0:
M = np.eye(3)
return M[0:2, :]
def estimate_norm_torch_pdfgc(lm_70p, H, reverse_y=True):
lm_70p_ = lm_70p.detach().cpu().numpy()
M = []
for i in range(lm_70p_.shape[0]):
M.append(estimate_norm_pdfgc(lm_70p_[i], H, reverse_y=reverse_y))
M = torch.tensor(np.array(M), dtype=torch.float32).to(lm_70p.device)
return M
def get_motion_feature(pd_fgc, imgs, lmks, crop_size=224, crop_len=16, reverse_y=False):
trans_m = estimate_norm_torch_pdfgc(lmks, imgs.shape[-1], reverse_y=reverse_y)
imgs_warp = warp_affine(imgs, trans_m, dsize=(224, 224))
imgs_warp = imgs_warp[:, :, :crop_size - crop_len * 2, crop_len:crop_size - crop_len]
imgs_warp = torch.clamp(F.interpolate(imgs_warp, size=[crop_size, crop_size], mode='bilinear'), -1, 1)
out = pd_fgc(imgs_warp)
motions = torch.cat([out[1], out[2], out[3]], dim=-1)
return motions |