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import torch
# import torch.nn as nn
from scipy.io import loadmat
import numpy as np
import torch.nn.functional as F
# CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
def perspective_projection(focal, center):
# return p.T (N, 3) @ (3, 3)
return np.array([
focal, 0, center,
0, focal, center,
0, 0, 1
]).reshape([3, 3]).astype(np.float32).transpose()
class SH:
def __init__(self):
self.a = [np.pi, 2 * np.pi / np.sqrt(3.), 2 * np.pi / np.sqrt(8.)]
self.c = [1/np.sqrt(4 * np.pi), np.sqrt(3.) / np.sqrt(4 * np.pi), 3 * np.sqrt(5.) / np.sqrt(12 * np.pi)]
class BFM(torch.nn.Module):
# BFM 3D face model
def __init__(self,
recenter=True,
camera_distance=10.,
init_lit=np.array([0.8, 0, 0, 0, 0, 0, 0, 0, 0]),
focal=1015.,
image_size=224,
bfm_model_path='pretrained/BFM_model_front.mat'
):
super().__init__()
model = loadmat(bfm_model_path)
# self.bfm_uv = loadmat(os.path.join(CURRENT_PATH, 'BFM/BFM_UV.mat'))
# print(model.keys())
# mean face shape. [3*N,1]
# self.meanshape = torch.from_numpy(model['meanshape'])
self.register_buffer('meanshape', torch.from_numpy(model['meanshape']).float())
if recenter:
meanshape = self.meanshape.view(-1, 3)
meanshape = meanshape - torch.mean(meanshape, dim=0, keepdim=True)
self.meanshape = meanshape.view(-1, 1)
# identity basis. [3*N,80]
# self.idBase = torch.from_numpy(model['idBase'])
self.register_buffer('idBase', torch.from_numpy(model['idBase']).float())
# self.idBase = nn.Parameter(torch.from_numpy(model['idBase']).float())
# self.exBase = torch.from_numpy(model['exBase'].astype(
# np.float32)) # expression basis. [3*N,64]
self.register_buffer('exBase', torch.from_numpy(model['exBase']).float())
# self.exBase = nn.Parameter(torch.from_numpy(model['exBase']).float())
# mean face texture. [3*N,1] (0-255)
# self.meantex = torch.from_numpy(model['meantex'])
self.register_buffer('meantex', torch.from_numpy(model['meantex']).float())
# texture basis. [3*N,80]
# self.texBase = torch.from_numpy(model['texBase'])
self.register_buffer('texBase', torch.from_numpy(model['texBase']).float())
# self.texBase = nn.Parameter(torch.from_numpy(model['texBase']).float())
# triangle indices for each vertex that lies in. starts from 0. [N,8]
self.register_buffer('point_buf', torch.from_numpy(model['point_buf']).long()-1)
# self.point_buf = model['point_buf'].astype(np.int32)
# vertex indices in each triangle. starts from 0. [F,3]
self.register_buffer('face_buf', torch.from_numpy(model['tri']).long()-1)
# self.tri = model['tri'].astype(np.int32)
# vertex indices of 68 facial landmarks. starts from 0. [68]
self.register_buffer('keypoints', torch.from_numpy(model['keypoints']).long().view(68)-1)
# self.keypoints = model['keypoints'].astype(np.int32)[0]
# print(self.keypoints)
# print('keypoints', self.keypoints)
# vertex indices for small face region to compute photometric error. starts from 0.
# self.front_mask = np.squeeze(model['frontmask2_idx']).astype(np.int64) - 1
self.register_buffer('front_mask', torch.from_numpy(np.squeeze(model['frontmask2_idx'])).long()-1)
# vertex indices for each face from small face region. starts from 0. [f,3]
# self.front_face_buf = model['tri_mask2'].astype(np.int64) - 1
self.register_buffer('front_face_buf', torch.from_numpy(np.squeeze(model['tri_mask2'])).long() - 1)
# vertex indices for pre-defined skin region to compute reflectance loss
# self.skin_mask = np.squeeze(model['skinmask'])
self.register_buffer('skin_mask', torch.from_numpy(np.squeeze(model['skinmask'])))
# keypoints_222 = []
# with open(os.path.join(CURRENT_PATH, 'BFM/D3DFR_222.txt'), 'r') as f:
# for line in f.readlines():
# idx = int(line.strip())
# keypoints_222.append(max(idx, 0))
# self.register_buffer('keypoints_222', torch.from_numpy(np.array(keypoints_222)).long())
# (1) right eye outer corner, (2) right eye inner corner, (3) left eye inner corner, (4) left eye outer corner,
# (5) nose bottom, (6) right mouth corner, (7) left mouth corner
self.register_buffer('keypoints_7', self.keypoints[[36, 39, 42, 45, 33, 48, 54]])
# self.persc_proj = torch.from_numpy(perspective_projection(focal, center)).float()
self.register_buffer('persc_proj', torch.from_numpy(perspective_projection(focal, image_size/2)))
self.camera_distance = camera_distance
self.image_size = image_size
self.SH = SH()
# self.init_lit = init_lit.reshape([1, 1, -1]).astype(np.float32)
self.register_buffer('init_lit', torch.from_numpy(init_lit.reshape([1, 1, -1]).astype(np.float32)))
# (1) right eye outer corner, (2) right eye inner corner, (3) left eye inner corner, (4) left eye outer corner,
# (5) nose bottom, (6) right mouth corner, (7) left mouth corner
# print(self.keypoints[[36, 39, 42, 45, 33, 48, 54]])
# Lm3D = loadmat(os.path.join(CURRENT_PATH, 'BFM/similarity_Lm3D_all.mat'))
# Lm3D = Lm3D['lm']
# # print(Lm3D)
#
# # calculate 5 facial landmarks using 68 landmarks
# lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
# Lm3D = np.stack([Lm3D[lm_idx[0], :], np.mean(Lm3D[lm_idx[[1, 2]], :], 0), np.mean(
# Lm3D[lm_idx[[3, 4]], :], 0), Lm3D[lm_idx[5], :], Lm3D[lm_idx[6], :]], axis=0)
# Lm3D = Lm3D[[1, 2, 0, 3, 4], :]
# self.Lm3D = Lm3D
# print(Lm3D.shape)
def split_coeff(self, coeff):
# input: coeff with shape [1,258]
id_coeff = coeff[:, 0:80] # identity(shape) coeff of dim 80
ex_coeff = coeff[:, 80:144] # expression coeff of dim 64
tex_coeff = coeff[:, 144:224] # texture(albedo) coeff of dim 80
gamma = coeff[:, 227:254] # lighting coeff for 3 channel SH function of dim 27
angles = coeff[:, 224:227] # ruler angles(x,y,z) for rotation of dim 3
translation = coeff[:, 254:257] # translation coeff of dim 3
return id_coeff, ex_coeff, tex_coeff, gamma, angles, translation
def split_coeff_orderly(self, coeff):
# input: coeff with shape [1,258]
id_coeff = coeff[:, 0:80] # identity(shape) coeff of dim 80
ex_coeff = coeff[:, 80:144] # expression coeff of dim 64
tex_coeff = coeff[:, 144:224] # texture(albedo) coeff of dim 80
angles = coeff[:, 224:227] # ruler angles(x,y,z) for rotation of dim 3
gamma = coeff[:, 227:254] # lighting coeff for 3 channel SH function of dim 27
translation = coeff[:, 254:257] # translation coeff of dim 3
return id_coeff, ex_coeff, tex_coeff, angles, gamma, translation
def compute_exp_deform(self, exp_coeff):
exp_part = torch.einsum('ij,aj->ai', self.exBase, exp_coeff)
return exp_part
def compute_id_deform(self, id_coeff):
id_part = torch.einsum('ij,aj->ai', self.idBase, id_coeff)
return id_part
def compute_shape_from_coeff(self, coeff):
id_coeff = coeff[:, 0:80]
ex_coeff = coeff[:, 80:144]
batch_size = coeff.shape[0]
id_part = torch.einsum('ij,aj->ai', self.idBase, id_coeff) #B, n
exp_part = torch.einsum('ij,aj->ai', self.exBase, ex_coeff) #B, n
face_shape = id_part + exp_part + self.meanshape.view(1, -1)
return face_shape.view(batch_size, -1, 3)
def compute_shape(self, id_coeff, exp_coeff):
"""
Return:
face_shape -- torch.tensor, size (B, N, 3)
Parameters:
id_coeff -- torch.tensor, size (B, 80), identity coeffs
id_relative_scale -- torch.tensor, size (B, 1), identity coeffs
exp_coeff -- torch.tensor, size (B, 64), expression coeffs
"""
batch_size = id_coeff.shape[0]
id_part = torch.einsum('ij,aj->ai', self.idBase, id_coeff) #B, n
exp_part = torch.einsum('ij,aj->ai', self.exBase, exp_coeff) #B, n
face_shape = id_part + exp_part + self.meanshape.view(1, -1)
return face_shape.view(batch_size, -1, 3)
def compute_texture(self, tex_coeff, normalize=True):
"""
Return:
face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
Parameters:
tex_coeff -- torch.tensor, size (B, 80)
"""
batch_size = tex_coeff.shape[0]
face_texture = torch.einsum('ij,aj->ai', self.texBase, tex_coeff) + self.meantex
if normalize:
face_texture = face_texture / 255.
return face_texture.view(batch_size, -1, 3)
def compute_norm(self, face_shape):
"""
Return:
vertex_norm -- torch.tensor, size (B, N, 3)
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
v1 = face_shape[:, self.face_buf[:, 0]]
v2 = face_shape[:, self.face_buf[:, 1]]
v3 = face_shape[:, self.face_buf[:, 2]]
e1 = v1 - v2
e2 = v2 - v3
face_norm = torch.cross(e1, e2, dim=-1)
face_norm = F.normalize(face_norm, dim=-1, p=2)
face_norm = torch.cat([face_norm, torch.zeros(face_norm.shape[0], 1, 3).to(self.meanshape)], dim=1)
vertex_norm = torch.sum(face_norm[:, self.point_buf], dim=2)
vertex_norm = F.normalize(vertex_norm, dim=-1, p=2)
return vertex_norm
def compute_color(self, face_texture, face_norm, gamma):
"""
Return:
face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
Parameters:
face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
face_norm -- torch.tensor, size (B, N, 3), rotated face normal
gamma -- torch.tensor, size (B, 27), SH coeffs
"""
batch_size = gamma.shape[0]
v_num = face_texture.shape[1]
a, c = self.SH.a, self.SH.c
gamma = gamma.reshape([batch_size, 3, 9])
gamma = gamma + self.init_lit
gamma = gamma.permute(0, 2, 1)
Y = torch.cat([
a[0] * c[0] * torch.ones_like(face_norm[..., :1]).to(self.meanshape),
-a[1] * c[1] * face_norm[..., 1:2],
a[1] * c[1] * face_norm[..., 2:],
-a[1] * c[1] * face_norm[..., :1],
a[2] * c[2] * face_norm[..., :1] * face_norm[..., 1:2],
-a[2] * c[2] * face_norm[..., 1:2] * face_norm[..., 2:],
0.5 * a[2] * c[2] / np.sqrt(3.) * (3 * face_norm[..., 2:] ** 2 - 1),
-a[2] * c[2] * face_norm[..., :1] * face_norm[..., 2:],
0.5 * a[2] * c[2] * (face_norm[..., :1] ** 2 - face_norm[..., 1:2] ** 2)
], dim=-1)
r = Y @ gamma[..., :1]
g = Y @ gamma[..., 1:2]
b = Y @ gamma[..., 2:]
face_color = torch.cat([r, g, b], dim=-1) * face_texture
return face_color
def compute_rotation(self, angles):
"""
Return:
rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
Parameters:
angles -- torch.tensor, size (B, 3), radian
"""
batch_size = angles.shape[0]
ones = torch.ones([batch_size, 1]).to(self.meanshape)
zeros = torch.zeros([batch_size, 1]).to(self.meanshape)
x, y, z = angles[:, :1], angles[:, 1:2], angles[:, 2:],
rot_x = torch.cat([
ones, zeros, zeros,
zeros, torch.cos(x), -torch.sin(x),
zeros, torch.sin(x), torch.cos(x)
], dim=1).reshape([batch_size, 3, 3])
rot_y = torch.cat([
torch.cos(y), zeros, torch.sin(y),
zeros, ones, zeros,
-torch.sin(y), zeros, torch.cos(y)
], dim=1).reshape([batch_size, 3, 3])
rot_z = torch.cat([
torch.cos(z), -torch.sin(z), zeros,
torch.sin(z), torch.cos(z), zeros,
zeros, zeros, ones
], dim=1).reshape([batch_size, 3, 3])
rot = rot_z @ rot_y @ rot_x
return rot.permute(0, 2, 1)
def to_camera(self, face_shape):
face_shape[..., -1] = self.camera_distance - face_shape[..., -1]
return face_shape
def to_image(self, face_shape):
"""
Return:
face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
# to image_plane
face_proj = face_shape @ self.persc_proj
# print(face_proj.shape)
face_proj = face_proj[..., :2] / face_proj[..., 2:]
return face_proj
def rotate(self, face_shape, rot):
"""
Return:
face_shape -- torch.tensor, size (B, N, 3) pts @ rot + trans
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
rot -- torch.tensor, size (B, 3, 3)
"""
return face_shape @ rot
def get_landmarks7(self, face_proj):
"""
Return:
face_lms -- torch.tensor, size (B, 68, 2)
Parameters:
face_proj -- torch.tensor, size (B, N, 2)
"""
return face_proj[:, self.keypoints_7, :]
def get_landmarks68(self, face_proj):
"""
Return:
face_lms -- torch.tensor, size (B, 68, 2)
Parameters:
face_proj -- torch.tensor, size (B, N, 2)
"""
return face_proj[:, self.keypoints, :]
def get_landmarks222(self, face_proj):
"""
Return:
face_lms -- torch.tensor, size (B, 68, 2)
Parameters:
face_proj -- torch.tensor, size (B, N, 2)
"""
return face_proj[:, self.keypoints_222, :]
def compute_for_render(self, coeffs):
"""
Return:
face_vertex -- torch.tensor, size (B, N, 3), in camera coordinate
face_color -- torch.tensor, size (B, N, 3), in RGB order
landmark -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
Parameters:
coeffs -- torch.tensor, size (B, 258)
"""
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeffs)
# id_relative_scale = id_relative_scale.clamp(0.9,1.1)
face_shape = self.compute_shape(id_coeff, ex_coeff)
# face_shape_noexp = self.compute_shape(id_coeff, torch.zeros_like(ex_coeff))
# print(face_shape.size())
rotation = self.compute_rotation(angles)
# print('rotation')
face_shape_rotated = self.rotate(face_shape, rotation)
face_shape_transformed = face_shape_rotated + translation.unsqueeze(1)
face_vertex = self.to_camera(face_shape_transformed)
face_proj = self.to_image(face_vertex)
# face_shape_transformed_noexp = self.transform(face_shape_noexp, rotation, translation, scale_xyz)
# face_vertex_noexp = self.to_camera(face_shape_transformed_noexp)
landmark68 = self.get_landmarks68(face_proj)
# landmark_face = face_proj[:,self.front_mask[::32], :]
landmark68[:, :, 1] = self.image_size - 1 - landmark68[:, :, 1]
face_texture = self.compute_texture(tex_coeff)
face_norm_roted = self.compute_norm(face_shape_rotated)
# face_norm_roted = face_norm @ rotation
face_color = self.compute_color(face_texture, face_norm_roted, gamma)
# face_norm_noexp = self.compute_norm(face_shape_noexp)
# face_norm_noexp_roted = face_norm_noexp @ rotation
# face_color_noexp = self.compute_color(face_texture, face_norm_noexp_roted, gamma)
return face_shape, face_vertex, face_color, face_texture, landmark68
def get_lm68(self, coeffs):
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeffs)
ex_coeff = torch.zeros_like(ex_coeff)
# id_relative_scale = id_relative_scale.clamp(0.9,1.1)
face_shape = self.compute_shape(id_coeff, ex_coeff)
# face_shape_noexp = self.compute_shape(id_coeff, torch.zeros_like(ex_coeff))
# print(face_shape.size())
rotation = self.compute_rotation(angles)
# print('rotation')
face_shape_rotated = self.rotate(face_shape, rotation)
face_shape_transformed = face_shape_rotated + translation.unsqueeze(1)
face_vertex = self.to_camera(face_shape_transformed)
face_proj = self.to_image(face_vertex)
landmark68 = self.get_landmarks68(face_proj)
# landmark_face = face_proj[:,self.front_mask[::32], :]
landmark68[:, :, 1] = self.image_size - 1 - landmark68[:, :, 1]
return landmark68, ex_coeff
def get_coeffs(self, coeffs):
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeffs)
return id_coeff, ex_coeff, tex_coeff, gamma, angles, translation
def get_vertex(self, coeffs):
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeffs)
# id_relative_scale = id_relative_scale.clamp(0.9,1.1)
face_shape = self.compute_shape(id_coeff, ex_coeff)
# face_shape_noexp = self.compute_shape(id_coeff, torch.zeros_like(ex_coeff))
# print(face_shape.size())
rotation = self.compute_rotation(angles)
# print('rotation')
face_shape_rotated = self.rotate(face_shape, rotation)
face_shape_transformed = face_shape_rotated + translation.unsqueeze(1)
face_vertex = self.to_camera(face_shape_transformed)
face_proj = self.to_image(face_vertex)
return face_proj
def forward(self, coeffs):
face_shape, face_vertex, face_color, face_texture, landmark68 = self.compute_for_render(coeffs)
return face_shape, face_vertex, face_color, face_texture, landmark68
def save_obj(self, coeff, obj_name):
# The image size is 224 * 224
# face reconstruction with coeff and BFM model
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeff)
# compute face shape
face_shape = self.compute_shape(id_coeff, ex_coeff).cpu().detach().numpy()[0]
face_tri = self.face_buf.cpu().numpy()
with open(obj_name, 'w') as fobj:
for i in range(face_shape.shape[0]):
fobj.write(
'v ' + str(face_shape[i][0]) + ' ' + str(face_shape[i][1]) + ' ' + str(face_shape[i][2]) + '\n')
# start from 1
for i in range(face_tri.shape[0]):
fobj.write('f ' + str(face_tri[i][0] + 1) + ' ' + str(face_tri[i][1] + 1) + ' ' + str(
face_tri[i][2] + 1) + '\n')
# lm7 = face_shape[[2215, 5828, 10455, 14066, 8204, 5522, 10795], :]
# with open(obj_name[:-3]+'txt', 'w') as f:
# for point in lm7:
# f.write('{} {} {}\n'.format(point[0], point[1], point[2]))
def save_neutral_obj(self, coeff, obj_name):
# The image size is 224 * 224
# face reconstruction with coeff and BFM model
id_coeff, ex_coeff, tex_coeff, gamma, angles, translation = self.split_coeff(coeff)
# compute face shape
face_shape = self.compute_shape(id_coeff, ex_coeff*0).cpu().numpy()[0]
face_tri = self.face_buf.cpu().numpy()
with open(obj_name, 'w') as fobj:
for i in range(face_shape.shape[0]):
fobj.write(
'v ' + str(face_shape[i][0]) + ' ' + str(face_shape[i][1]) + ' ' + str(face_shape[i][2]) + '\n')
# start from 1
for i in range(face_tri.shape[0]):
fobj.write('f ' + str(face_tri[i][0] + 1) + ' ' + str(face_tri[i][1] + 1) + ' ' + str(
face_tri[i][2] + 1) + '\n')
# lm7 = face_shape[[2215, 5828, 10455, 14066, 8204, 5522, 10795], :]
# with open(obj_name[:-3]+'txt', 'w') as f:
# for point in lm7:
# f.write('{} {} {}\n'.format(point[0], point[1], point[2]))
# def clip(self, g_ratio=0.1, t_ratio=0.1):
# self.idBase.data = torch.minimum(torch.maximum(self.idBase_org * (1 - g_ratio), self.idBase.data), self.idBase_org * (1 + g_ratio))
# self.exBase.data = self.exBase_org #torch.minimum(torch.maximum(self.exBase_org * (1 - 0.001), self.exBase.data), self.exBase_org * (1 + 0.001))
# self.texBase.data = torch.minimum(torch.maximum(self.texBase_org * (1 - t_ratio), self.texBase.data), self.texBase_org * (1 + t_ratio))
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