File size: 6,485 Bytes
8d34f50 |
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 |
import torch
import torch.nn as nn
import render_util
import geo_transform
import numpy as np
def compute_tri_normal(geometry, tris):
geometry = geometry.permute(0, 2, 1)
tri_1 = tris[:, 0]
tri_2 = tris[:, 1]
tri_3 = tris[:, 2]
vert_1 = torch.index_select(geometry, 2, tri_1)
vert_2 = torch.index_select(geometry, 2, tri_2)
vert_3 = torch.index_select(geometry, 2, tri_3)
nnorm = torch.cross(vert_2-vert_1, vert_3-vert_1, 1)
normal = nn.functional.normalize(nnorm).permute(0, 2, 1)
return normal
class Compute_normal_base(torch.autograd.Function):
@staticmethod
def forward(ctx, normal):
normal_b, = render_util.normal_base_forward(normal)
ctx.save_for_backward(normal)
return normal_b
@staticmethod
def backward(ctx, grad_normal_b):
normal, = ctx.saved_tensors
grad_normal, = render_util.normal_base_backward(grad_normal_b, normal)
return grad_normal
class Normal_Base(torch.nn.Module):
def __init__(self):
super(Normal_Base, self).__init__()
def forward(self, normal):
return Compute_normal_base.apply(normal)
def preprocess_render(geometry, euler, trans, cam, tris, vert_tris, ori_img):
point_num = geometry.shape[1]
rott_geo = geo_transform.euler_trans_geo(geometry, euler, trans)
proj_geo = geo_transform.proj_geo(rott_geo, cam)
rot_tri_normal = compute_tri_normal(rott_geo, tris)
rot_vert_normal = torch.index_select(rot_tri_normal, 1, vert_tris)
is_visible = -torch.bmm(rot_vert_normal.reshape(-1, 1, 3),
nn.functional.normalize(rott_geo.reshape(-1, 3, 1))).reshape(-1, point_num)
is_visible[is_visible < 0.01] = -1
pixel_valid = torch.zeros((ori_img.shape[0], ori_img.shape[1]*ori_img.shape[2]),
dtype=torch.float32, device=ori_img.device)
return rott_geo, proj_geo, rot_tri_normal, is_visible, pixel_valid
class Render_Face(torch.autograd.Function):
@staticmethod
def forward(ctx, proj_geo, texture, nbl, ori_img, is_visible, tri_inds,
pixel_valid):
batch_size, h, w, _ = ori_img.shape
ori_img = ori_img.view(batch_size, -1, 3)
ori_size = torch.cat((torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*h,
torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*w),
dim=1).view(-1)
tri_index, tri_coord, render, real = render_util.render_face_forward(
proj_geo, ori_img, ori_size, texture, nbl, is_visible, tri_inds, pixel_valid)
ctx.save_for_backward(ori_img, ori_size, proj_geo, texture, nbl,
tri_inds, tri_index, tri_coord)
return render, real
@staticmethod
def backward(ctx, grad_render, grad_real):
ori_img, ori_size, proj_geo, texture, nbl, tri_inds, tri_index, tri_coord = \
ctx.saved_tensors
grad_proj_geo, grad_texture, grad_nbl = render_util.render_face_backward(
grad_render, grad_real, ori_img, ori_size, proj_geo, texture, nbl, tri_inds,
tri_index, tri_coord)
return grad_proj_geo, grad_texture, grad_nbl, None, None, None, None
class Render_RGB(nn.Module):
def __init__(self):
super(Render_RGB, self).__init__()
def forward(self, proj_geo, texture, nbl, ori_img, is_visible, tri_inds, pixel_valid):
return Render_Face.apply(proj_geo, texture, nbl, ori_img, is_visible,
tri_inds, pixel_valid)
def cal_land(proj_geo, is_visible, lands_info, land_num):
land_index, = render_util.update_contour(
lands_info, is_visible, land_num)
proj_land = torch.index_select(
proj_geo.reshape(-1, 3), 0, land_index)[:, :2].reshape(-1, land_num, 2)
return proj_land
class Render_Land(nn.Module):
def __init__(self):
super(Render_Land, self).__init__()
lands_info = np.loadtxt('../data/3DMM/lands_info.txt', dtype=np.int32)
self.lands_info = torch.as_tensor(lands_info).cuda()
tris = np.loadtxt('../data/3DMM/tris.txt', dtype=np.int64)
self.tris = torch.as_tensor(tris).cuda() - 1
vert_tris = np.loadtxt('../data/3DMM/vert_tris.txt', dtype=np.int64)
self.vert_tris = torch.as_tensor(vert_tris).cuda()
self.normal_baser = Normal_Base().cuda()
self.renderer = Render_RGB().cuda()
def render_mesh(self, geometry, euler, trans, cam, ori_img, light):
batch_size, h, w, _ = ori_img.shape
ori_img = ori_img.view(batch_size, -1, 3)
ori_size = torch.cat((torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*h,
torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*w),
dim=1).view(-1)
rott_geo, proj_geo, rot_tri_normal, _, _ = preprocess_render(
geometry, euler, trans, cam, self.tris, self.vert_tris, ori_img)
tri_nb = self.normal_baser(rot_tri_normal.contiguous())
nbl = torch.bmm(tri_nb, (light.reshape(-1, 9, 3))
[:, :, 0].unsqueeze(-1).repeat(1, 1, 3))
texture = torch.ones_like(geometry) * 200
render, = render_util.render_mesh(
proj_geo, ori_img, ori_size, texture, nbl, self.tris)
return render.view(batch_size, h, w, 3).byte()
def cal_loss_rgb(self, geometry, euler, trans, cam, ori_img, light, texture, lands):
rott_geo, proj_geo, rot_tri_normal, is_visible, pixel_valid = \
preprocess_render(geometry, euler, trans, cam,
self.tris, self.vert_tris, ori_img)
tri_nb = self.normal_baser(rot_tri_normal.contiguous())
nbl = torch.bmm(tri_nb, light.reshape(-1, 9, 3))
render, real = self.renderer(
proj_geo, texture, nbl, ori_img, is_visible, self.tris, pixel_valid)
proj_land = cal_land(proj_geo, is_visible,
self.lands_info, lands.shape[1])
col_minus = torch.norm((render-real).reshape(-1, 3),
dim=1).reshape(ori_img.shape[0], -1)
col_dis = torch.mean(col_minus*pixel_valid) / \
(torch.mean(pixel_valid)+0.00001)
land_dists = torch.norm(
(proj_land-lands).reshape(-1, 2), dim=1).reshape(ori_img.shape[0], -1)
lan_dis = torch.mean(land_dists)
return col_dis, lan_dis
|