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bd096d2 d897172 bd096d2 72a62e3 1dbf56f bd096d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2023 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: mica@tue.mpg.de
import os
import pickle
from pixel3dmm import env_paths
import numpy as np
import torch
import torch.nn as nn
from trimesh import Trimesh
def to_tensor(array, dtype=torch.float32):
if 'torch.tensor' not in str(type(array)):
return torch.tensor(array, dtype=dtype)
def to_np(array, dtype=np.float32):
if 'scipy.sparse' in str(type(array)):
array = array.todense()
return np.array(array, dtype=dtype)
class Struct(object):
def __init__(self, **kwargs):
for key, val in kwargs.items():
setattr(self, key, val)
class Masking(nn.Module):
def __init__(self, config):
super(Masking, self).__init__()
ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')
with open(f'{ROOT_DIR}/data/FLAME2020/FLAME_masks/FLAME_masks.pkl', 'rb') as f:
ss = pickle.load(f, encoding='latin1')
self.masks = Struct(**ss)
with open(f'{env_paths.FLAME_ASSET}', 'rb') as f:
ss = pickle.load(f, encoding='latin1')
flame_model = Struct(**ss)
self.masked_faces = None
self.cfg = config.mask_weights
self.dtype = torch.float32
self.register_buffer('faces', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long))
self.register_buffer('vertices', to_tensor(to_np(flame_model.v_template), dtype=self.dtype))
self.neighbours = {}
for f in self.faces.numpy():
for v in f:
if str(v) not in self.neighbours:
self.neighbours[str(v)] = set()
for a in list(filter(lambda i: i != v, f)):
self.neighbours[str(v)].add(a)
def get_faces(self):
return self.faces
def get_mask_face(self):
return self.masks.face
def get_mask_eyes(self):
left = self.masks.left_eyeball
right = self.masks.right_eyeball
return np.unique(np.concatenate((left, right)))
def get_mask_forehead(self):
return self.masks.forehead
def get_mask_lips(self):
return self.masks.lips
def get_mask_eye_region(self):
return self.masks.eye_region
def get_mask_lr_eye_region(self):
left = self.masks.left_eye_region
right = self.masks.right_eye_region
return np.unique(np.concatenate((left, right, self.get_mask_eyes())))
def get_mask_nose(self):
return self.masks.nose
def get_mask_ears(self):
left = self.masks.left_ear
right = self.masks.right_ear
return np.unique(np.concatenate((left, right)))
def get_triangle_face_mask(self):
m = self.masks.face
return self.get_triangle_mask(m)
def get_triangle_eyes_mask(self):
m = self.get_mask_eyes()
return self.get_triangle_mask(m)
def get_triangle_whole_mask(self):
m = self.get_whole_mask()
return self.get_triangle_mask(m)
def get_triangle_mask(self, m):
f = self.faces.cpu().numpy()
selected = []
for i in range(f.shape[0]):
l = f[i]
valid = 0
for j in range(3):
if l[j] in m:
valid += 1
if valid == 3:
selected.append(i)
return np.unique(selected)
def make_soft(self, mask, value, degree=4):
soft = []
mask = set(mask)
for ring in range(degree):
soft_ring = []
for v in mask.copy():
for n in self.neighbours[str(v)]:
if n in mask:
continue
soft_ring.append(n)
mask.add(n)
soft.append((soft_ring, value / (ring + 2)))
return soft
def get_binary_triangle_mask(self):
mask = self.get_whole_mask()
faces = self.faces.cpu().numpy()
reduced_faces = []
for f in faces:
valid = 0
for v in f:
if v in mask:
valid += 1
reduced_faces.append(True if valid == 3 else False)
return reduced_faces
def get_masked_faces(self):
if self.masked_faces is None:
faces = self.faces.cpu().numpy()
vertices = self.vertices.cpu().numpy()
m = Trimesh(vertices=vertices, faces=faces, process=False)
m.update_faces(self.get_binary_triangle_mask())
self.masked_faces = torch.from_numpy(np.array(m.faces)).cuda().long()[None]
return self.masked_faces
def get_weights_per_triangle(self):
mask = torch.ones_like(self.get_faces()[None]).detach() * self.cfg.whole
mask[:, self.get_triangle_eyes_mask(), :] = self.cfg.eyes
mask[:, self.get_triangle_face_mask(), :] = self.cfg.face
return mask[:, :, 0:1]
def get_weights_per_vertex(self):
mask = torch.ones_like(self.vertices[None]).detach() * self.cfg.whole
mask[:, self.get_mask_eyes(), :] = self.cfg.eyes
mask[:, self.get_mask_ears(), :] = self.cfg.ears
mask[:, self.get_mask_face(), :] = self.cfg.face
return mask
def get_masked_mesh(self, vertices, triangle_mask):
if len(vertices.shape) == 2:
vertices = vertices[None]
B, N, V = vertices.shape
faces = self.faces.cpu().numpy()
masked_vertices = torch.empty(0, 0, 3).cuda()
masked_faces = torch.empty(0, 0, 3).cuda()
for i in range(B):
m = Trimesh(vertices=vertices[i].detach().cpu().numpy(), faces=faces, process=False)
m.update_faces(triangle_mask)
m.process()
f = torch.from_numpy(np.array(m.faces)).cuda()[None]
v = torch.from_numpy(np.array(m.vertices)).cuda()[None].float()
if masked_vertices.shape[1] != v.shape[1]:
masked_vertices = torch.empty(0, v.shape[1], 3).cuda()
if masked_faces.shape[1] != f.shape[1]:
masked_faces = torch.empty(0, f.shape[1], 3).cuda()
masked_vertices = torch.cat([masked_vertices, v])
masked_faces = torch.cat([masked_faces, f])
return masked_vertices, masked_faces
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