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face_decoder.py
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import tensorflow as tf
import math as m
import numpy as np
from scipy.io import loadmat
import platform
is_windows = platform.system() == "Windows"
if not is_windows:
from renderer import mesh_renderer
###############################################################################################
# Reconstruct 3D face based on output coefficients and facemodel
###############################################################################################
# BFM 3D face model
class BFM():
def __init__(self,model_path = './BFM/BFM_model_front.mat'):
model = loadmat(model_path)
self.meanshape = tf.constant(model['meanshape']) # mean face shape. [3*N,1]
self.idBase = tf.constant(model['idBase']) # identity basis. [3*N,80]
self.exBase = tf.constant(model['exBase'].astype(np.float32)) # expression basis. [3*N,64]
self.meantex = tf.constant(model['meantex']) # mean face texture. [3*N,1] (0-255)
self.texBase = tf.constant(model['texBase']) # texture basis. [3*N,80]
self.point_buf = tf.constant(model['point_buf']) # face indices for each vertex that lies in. starts from 1. [N,8]
self.face_buf = tf.constant(model['tri']) # vertex indices for each face. starts from 1. [F,3]
self.front_mask_render = tf.squeeze(tf.constant(model['frontmask2_idx'])) # vertex indices for small face region to compute photometric error. starts from 1.
self.mask_face_buf = tf.constant(model['tri_mask2']) # vertex indices for each face from small face region. starts from 1. [f,3]
self.skin_mask = tf.squeeze(tf.constant(model['skinmask'])) # vertex indices for pre-defined skin region to compute reflectance loss
self.keypoints = tf.squeeze(tf.constant(model['keypoints'])) # vertex indices for 68 landmarks. starts from 1. [68,1]
# Analytic 3D face
class Face3D():
def __init__(self):
facemodel = BFM()
self.facemodel = facemodel
def get_pca_facemodel(self,coeff,opt):
#coeff: [batchsize,257] reconstruction coefficients
id_coeff,ex_coeff,tex_coeff,angles,translation,gamma,camera_scale,f_scale = self.Split_coeff(coeff)
# [batchsize,N,3] canonical face shape in BFM space
pca_facemodel = self.Shape_formation_block(id_coeff,ex_coeff,self.facemodel)
# [batchsize,N,3] vertex texture (in RGB order)
face_texture_linear = self.Texture_formation_block(tex_coeff,self.facemodel)
# [batchsize,3,3] rotation matrix for face shape
rotation = self.Compute_rotation_matrix(angles)
# [batchsize,N,3] vertex normal
face_norm_linear = self.Compute_norm(pca_facemodel,self.facemodel)
norm_r_linear = tf.matmul(face_norm_linear,rotation)
# do rigid transformation for face shape using predicted rotation and translation
face_shape_linear_t = self.Rigid_transform_block(pca_facemodel,rotation,translation)
# [batchsize,N,3] vertex color (in RGB order)
face_color_linear = self.Illumination_block(face_texture_linear, norm_r_linear, gamma)
render_imgs_linear,img_mask_linear,img_mask_crop_linear = self.Render_block(face_shape_linear_t,norm_r_linear,face_color_linear,camera_scale,f_scale,self.facemodel,opt.batch_size,opt.is_train)
self.pca_facemodel = pca_facemodel
self.face_norm_linear = face_norm_linear
self.face_shape_linear_t = face_shape_linear_t
self.render_imgs_linear = render_imgs_linear
self.img_mask_linear = img_mask_linear
self.img_mask_crop_linear = img_mask_crop_linear
return pca_facemodel
# analytic 3D face reconstructions with coefficients from R-Net
def Reconstruction_Block(self,coeff,opt,mesh):
#coeff: [batchsize,257] reconstruction coefficients
id_coeff,ex_coeff,tex_coeff,angles,translation,gamma,camera_scale,f_scale = self.Split_coeff(coeff)
# [batchsize,N,3] canonical face shape in BFM space
# if opt.pretrain_stage:
# face_shape_linear_ = mesh
# face_shape = self.Shape_formation_block(id_coeff,ex_coeff,self.facemodel)
# else:
# face_shape_linear_ = self.Shape_formation_block(id_coeff,ex_coeff,self.facemodel)
face_shape = mesh
# [batchsize,N,3] vertex texture (in RGB order)
face_texture = self.Texture_formation_block(tex_coeff,self.facemodel)
# [batchsize,3,3] rotation matrix for face shape
rotation = self.Compute_rotation_matrix(angles)
# [batchsize,N,3] vertex normal
face_norm = self.Compute_norm(face_shape,self.facemodel)
# face_norm_linear = self.Compute_norm(face_shape_linear_,self.facemodel)
norm_r = tf.matmul(face_norm,rotation)
# do rigid transformation for face shape using predicted rotation and translation
face_shape_t = self.Rigid_transform_block(face_shape,rotation,translation)
# face_shape_linear = self.Rigid_transform_block(face_shape_linear_,rotation,translation)
# compute 2d landmark projections
# landmark_p: [batchsize,68,2]
face_landmark_t = self.Compute_landmark(face_shape_t,self.facemodel)
landmark_p = self.Projection_block(face_landmark_t,camera_scale,f_scale)
# [batchsize,N,3] vertex color (in RGB order)
face_color = self.Illumination_block(face_texture, norm_r, gamma)
# reconstruction images and region masks for computing photometric loss
render_imgs,img_mask,img_mask_crop = self.Render_block(face_shape_t,norm_r,face_color,camera_scale,f_scale,self.facemodel,opt.batch_size,opt.is_train)
self.id_coeff = id_coeff
self.ex_coeff = ex_coeff
self.tex_coeff = tex_coeff
self.f_scale = f_scale
self.gamma = gamma
self.face_shape = face_shape
self.face_shape_t = face_shape_t
self.face_texture = face_texture
self.face_color = face_color
self.landmark_p = landmark_p
self.render_imgs = render_imgs
self.img_mask = img_mask
self.img_mask_crop = img_mask_crop
self.norm_r = norm_r
self.camera_scale = camera_scale
self.translation = translation
self.rotation = rotation
self.angles = angles
self.camera_scale = camera_scale
self.f_scale = f_scale
# self.face_shape_linear = face_shape_linear
# self.face_norm_linear = face_norm_linear
self.face_norm = face_norm
#----------------------------------------------------------------------------------------------
def Split_coeff(self,coeff):
id_coeff = coeff[:,:80]
ex_coeff = coeff[:,80:144]
tex_coeff = coeff[:,144:224]
angles = coeff[:,224:227]
gamma = coeff[:,227:254]
translation = coeff[:,254:257]
camera_scale = tf.ones([tf.shape(coeff)[0],1])
f_scale = tf.ones([tf.shape(coeff)[0],1])
return id_coeff,ex_coeff,tex_coeff,angles,translation,gamma,camera_scale,f_scale
def Shape_formation_block(self,id_coeff,ex_coeff,facemodel):
face_shape = tf.einsum('ij,aj->ai',facemodel.idBase,id_coeff) + \
tf.einsum('ij,aj->ai',facemodel.exBase,ex_coeff) + facemodel.meanshape
# reshape face shape to [batchsize,N,3]
face_shape = tf.reshape(face_shape,[tf.shape(face_shape)[0],-1,3])
# re-centering the face shape with mean shape
face_shape = face_shape - tf.reshape(tf.reduce_mean(tf.reshape(facemodel.meanshape,[-1,3]),0),[1,1,3])
return face_shape
def Compute_norm(self,face_shape,facemodel):
shape = face_shape
face_id = facemodel.face_buf
point_id = facemodel.point_buf
# face_id and point_id index starts from 1
face_id = tf.cast(face_id - 1,tf.int32)
point_id = tf.cast(point_id - 1,tf.int32)
#compute normal for each face
v1 = tf.gather(shape,face_id[:,0], axis = 1)
v2 = tf.gather(shape,face_id[:,1], axis = 1)
v3 = tf.gather(shape,face_id[:,2], axis = 1)
e1 = v1 - v2
e2 = v2 - v3
face_norm = tf.cross(e1,e2)
face_norm = tf.nn.l2_normalize(face_norm, dim = 2) # normalized face_norm first
face_norm = tf.concat([face_norm,tf.zeros([tf.shape(face_shape)[0],1,3])], axis = 1)
#compute normal for each vertex using one-ring neighborhood
v_norm = tf.reduce_sum(tf.gather(face_norm, point_id, axis = 1), axis = 2)
v_norm = tf.nn.l2_normalize(v_norm, dim = 2)
return v_norm
def Texture_formation_block(self,tex_coeff,facemodel):
face_texture = tf.einsum('ij,aj->ai',facemodel.texBase,tex_coeff) + facemodel.meantex
# reshape face texture to [batchsize,N,3], note that texture is in RGB order
face_texture = tf.reshape(face_texture,[tf.shape(face_texture)[0],-1,3])
return face_texture
def Compute_rotation_matrix(self,angles):
n_data = tf.shape(angles)[0]
# compute rotation matrix for X-axis, Y-axis, Z-axis respectively
rotation_X = tf.concat([tf.ones([n_data,1]),
tf.zeros([n_data,3]),
tf.reshape(tf.cos(angles[:,0]),[n_data,1]),
-tf.reshape(tf.sin(angles[:,0]),[n_data,1]),
tf.zeros([n_data,1]),
tf.reshape(tf.sin(angles[:,0]),[n_data,1]),
tf.reshape(tf.cos(angles[:,0]),[n_data,1])],
axis = 1
)
rotation_Y = tf.concat([tf.reshape(tf.cos(angles[:,1]),[n_data,1]),
tf.zeros([n_data,1]),
tf.reshape(tf.sin(angles[:,1]),[n_data,1]),
tf.zeros([n_data,1]),
tf.ones([n_data,1]),
tf.zeros([n_data,1]),
-tf.reshape(tf.sin(angles[:,1]),[n_data,1]),
tf.zeros([n_data,1]),
tf.reshape(tf.cos(angles[:,1]),[n_data,1])],
axis = 1
)
rotation_Z = tf.concat([tf.reshape(tf.cos(angles[:,2]),[n_data,1]),
-tf.reshape(tf.sin(angles[:,2]),[n_data,1]),
tf.zeros([n_data,1]),
tf.reshape(tf.sin(angles[:,2]),[n_data,1]),
tf.reshape(tf.cos(angles[:,2]),[n_data,1]),
tf.zeros([n_data,3]),
tf.ones([n_data,1])],
axis = 1
)
rotation_X = tf.reshape(rotation_X,[n_data,3,3])
rotation_Y = tf.reshape(rotation_Y,[n_data,3,3])
rotation_Z = tf.reshape(rotation_Z,[n_data,3,3])
# R = RzRyRx
rotation = tf.matmul(tf.matmul(rotation_Z,rotation_Y),rotation_X)
rotation = tf.transpose(rotation, perm = [0,2,1])
return rotation
def Projection_block(self,face_shape,camera_scale,f_scale):
# pre-defined camera focal for pespective projection
focal = tf.constant(1015.0)
focal = focal*f_scale
focal = tf.reshape(focal,[-1,1])
batchsize = tf.shape(focal)[0]
# define camera position
camera_pos = tf.reshape(tf.constant([0.0,0.0,10.0]),[1,1,3])*tf.reshape(camera_scale,[-1,1,1])
reverse_z = tf.tile(tf.reshape(tf.constant([1.0,0,0,0,1,0,0,0,-1.0]),[1,3,3]),[tf.shape(face_shape)[0],1,1])
# compute projection matrix
p_matrix = tf.concat([focal,tf.zeros([batchsize,1]),112.*tf.ones([batchsize,1]),tf.zeros([batchsize,1]),focal,112.*tf.ones([batchsize,1]),tf.zeros([batchsize,2]),tf.ones([batchsize,1])],axis = 1)
p_matrix = tf.reshape(p_matrix,[-1,3,3])
# convert z in world space to the distance to camera
face_shape = tf.matmul(face_shape,reverse_z) + camera_pos
aug_projection = tf.matmul(face_shape,tf.transpose(p_matrix,[0,2,1]))
# [batchsize, N,2] 2d face projection
face_projection = aug_projection[:,:,0:2]/tf.reshape(aug_projection[:,:,2],[tf.shape(face_shape)[0],tf.shape(aug_projection)[1],1])
return face_projection
def Compute_landmark(self,face_shape,facemodel):
# compute 3D landmark postitions with pre-computed 3D face shape
keypoints_idx = facemodel.keypoints
keypoints_idx = tf.cast(keypoints_idx - 1,tf.int32)
face_landmark = tf.gather(face_shape,keypoints_idx,axis = 1)
return face_landmark
def Illumination_block(self,face_texture,norm_r,gamma):
n_data = tf.shape(gamma)[0]
n_point = tf.shape(norm_r)[1]
gamma = tf.reshape(gamma,[n_data,3,9])
# set initial lighting with an ambient lighting
init_lit = tf.constant([0.8,0,0,0,0,0,0,0,0])
gamma = gamma + tf.reshape(init_lit,[1,1,9])
# compute vertex color using SH function approximation
a0 = m.pi
a1 = 2*m.pi/tf.sqrt(3.0)
a2 = 2*m.pi/tf.sqrt(8.0)
c0 = 1/tf.sqrt(4*m.pi)
c1 = tf.sqrt(3.0)/tf.sqrt(4*m.pi)
c2 = 3*tf.sqrt(5.0)/tf.sqrt(12*m.pi)
Y = tf.concat([tf.tile(tf.reshape(a0*c0,[1,1,1]),[n_data,n_point,1]),
tf.expand_dims(-a1*c1*norm_r[:,:,1],2),
tf.expand_dims(a1*c1*norm_r[:,:,2],2),
tf.expand_dims(-a1*c1*norm_r[:,:,0],2),
tf.expand_dims(a2*c2*norm_r[:,:,0]*norm_r[:,:,1],2),
tf.expand_dims(-a2*c2*norm_r[:,:,1]*norm_r[:,:,2],2),
tf.expand_dims(a2*c2*0.5/tf.sqrt(3.0)*(3*tf.square(norm_r[:,:,2])-1),2),
tf.expand_dims(-a2*c2*norm_r[:,:,0]*norm_r[:,:,2],2),
tf.expand_dims(a2*c2*0.5*(tf.square(norm_r[:,:,0])-tf.square(norm_r[:,:,1])),2)],axis = 2)
color_r = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,0,:],2)),axis = 2)
color_g = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,1,:],2)),axis = 2)
color_b = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,2,:],2)),axis = 2)
#[batchsize,N,3] vertex color in RGB order
face_color = tf.stack([color_r*face_texture[:,:,0],color_g*face_texture[:,:,1],color_b*face_texture[:,:,2]],axis = 2)
return face_color
def Rigid_transform_block(self,face_shape,rotation,translation):
# do rigid transformation for 3D face shape
face_shape_r = tf.matmul(face_shape,rotation)
face_shape_t = face_shape_r + tf.reshape(translation,[tf.shape(face_shape)[0],1,3])
return face_shape_t
def Render_block(self,face_shape,face_norm,face_color,camera_scale,f_scale,facemodel,batchsize,is_train=True):
if is_train and is_windows:
raise ValueError('Not support training with Windows environment.')
if is_windows:
return [],[],[]
# render reconstruction images
n_vex = int(facemodel.idBase.shape[0].value/3)
fov_y = 2*tf.atan(112./(1015.*f_scale))*180./m.pi
fov_y = tf.reshape(fov_y,[batchsize])
# full face region
face_shape = tf.reshape(face_shape,[batchsize,n_vex,3])
face_norm = tf.reshape(face_norm,[batchsize,n_vex,3])
face_color = tf.reshape(face_color,[batchsize,n_vex,3])
# pre-defined cropped face region
mask_face_shape = tf.gather(face_shape,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)
mask_face_norm = tf.gather(face_norm,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)
mask_face_color = tf.gather(face_color,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)
# setting cammera settings
camera_position = tf.constant([[0,0,10.0]])*tf.reshape(camera_scale,[-1,1])
camera_lookat = tf.constant([0,0,0.0])
camera_up = tf.constant([0,1.0,0])
# setting light source position(intensities are set to 0 because we have computed the vertex color)
light_positions = tf.tile(tf.reshape(tf.constant([0,0,1e5]),[1,1,3]),[batchsize,1,1])
light_intensities = tf.tile(tf.reshape(tf.constant([0.0,0.0,0.0]),[1,1,3]),[batchsize,1,1])
ambient_color = tf.tile(tf.reshape(tf.constant([1.0,1,1]),[1,3]),[batchsize,1])
#using tf_mesh_renderer for rasterization (https://github.com/google/tf_mesh_renderer)
# img: [batchsize,224,224,3] images in RGB order (0-255)
# mask:[batchsize,224,224,1] transparency for img ({0,1} value)
img_rgba = mesh_renderer.mesh_renderer(face_shape,
tf.cast(facemodel.face_buf-1,tf.int32),
face_norm,
face_color,
camera_position = camera_position,
camera_lookat = camera_lookat,
camera_up = camera_up,
light_positions = light_positions,
light_intensities = light_intensities,
image_width = 224,
image_height = 224,
fov_y = fov_y,
near_clip = 0.01,
far_clip = 50.0,
ambient_color = ambient_color)
img = img_rgba[:,:,:,:3]
mask = img_rgba[:,:,:,3:]
img = tf.cast(img[:,:,:,::-1],tf.float32) #transfer RGB to BGR
mask = tf.cast(mask,tf.float32) # full face region
if is_train:
# compute mask for small face region
img_crop_rgba = mesh_renderer.mesh_renderer(mask_face_shape,
tf.cast(facemodel.mask_face_buf-1,tf.int32),
mask_face_norm,
mask_face_color,
camera_position = camera_position,
camera_lookat = camera_lookat,
camera_up = camera_up,
light_positions = light_positions,
light_intensities = light_intensities,
image_width = 224,
image_height = 224,
fov_y = fov_y,
near_clip = 0.01,
far_clip = 50.0,
ambient_color = ambient_color)
mask_f = img_crop_rgba[:,:,:,3:]
mask_f = tf.cast(mask_f,tf.float32) # small face region
return img,mask,mask_f
img_rgba = tf.cast(tf.clip_by_value(img_rgba,0,255),tf.float32)
return img_rgba,mask,mask