def render(self,
vertices,
faces,
textures,
K=None,
R=None,
t=None,
at=None,
up=None,
dist_coeffs=None,
orig_size=None):
# fill back
if self.fill_back:
faces = torch.cat(
(faces, faces[:, :,
list(reversed(range(faces.shape[-1])))]),
dim=1).detach()
textures = torch.cat((textures, textures.permute(
(0, 1, 4, 3, 2, 5))),
dim=1)
# lighting
faces_lighting = nr.vertices_to_faces(vertices, faces)
textures = nr.lighting(faces_lighting, textures,
self.light_intensity_ambient,
self.light_intensity_directional,
self.light_color_ambient,
self.light_color_directional,
self.light_direction)
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = nr.look_at(vertices, self.eye, up=up)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
elif self.camera_mode == 'look':
vertices = nr.look(vertices, self.eye, self.camera_direction)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
elif self.camera_mode == 'projection':
if K is None:
K = self.K
if R is None:
R = self.R
if t is None:
t = self.t
if dist_coeffs is None:
dist_coeffs = self.dist_coeffs
if orig_size is None:
orig_size = self.orig_size
vertices = nr.projection(vertices, K, R, t, dist_coeffs, orig_size)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
out = nr.rasterize_rgbad(faces, textures, self.image_size,
self.anti_aliasing, self.near, self.far,
self.rasterizer_eps, self.background_color)
return out['rgb'], out['depth'], out['alpha']
def render_fov(self, mesh, R, t, background_color=[0, 0, 0]):
vertices, faces, textures = mesh
if isinstance(t, np.ndarray):
t = torch.cuda.FloatTensor(t)
if isinstance(R, np.ndarray):
R = torch.cuda.FloatTensor(R)
t = t.view(-1, 1, 3)
R = R.view(-1, 3, 3)
assert len(vertices.shape) == 3
batch_size = max(t.shape[0], vertices.shape[0])
# expand dimension
vertices = vertices.expand(batch_size, *vertices.shape[1:])
faces = faces.expand(batch_size, *faces.shape[1:])
textures = textures.expand(batch_size, *textures.shape[1:])
# fill back
if self.fill_back:
faces = torch.cat(
(faces, faces[:, :,
list(reversed(range(faces.shape[-1])))]),
dim=1).detach()
textures = torch.cat((textures, textures.permute(
(0, 1, 4, 3, 2, 5))),
dim=1)
# lighting
faces_lighting = nr.vertices_to_faces(vertices, faces)
textures = nr.lighting(faces_lighting, textures,
self.light_intensity_ambient,
self.light_intensity_directional,
self.light_color_ambient,
self.light_color_directional,
self.light_direction)
# viewpoint transformation
vertices = nr.projection_fov(vertices, self.fov, R, t)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
out = nr.rasterize_rgbad(faces,
textures,
self.image_size,
self.anti_aliasing,
self.near,
self.far,
self.rasterizer_eps,
background_color=background_color)
return out['rgb'], out['depth'], out['alpha']
def render(self,
vertices,
faces,
textures,
K=None,
R=None,
t=None,
dist_coeffs=None,
orig_size=None):
# fill back
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = nr.look_at(vertices, self.eye)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
elif self.camera_mode == 'look':
vertices = nr.look(vertices, self.eye, self.camera_direction)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
elif self.camera_mode == 'projection':
if K is None:
K = self.K
if R is None:
R = self.R
if t is None:
t = self.t
if dist_coeffs is None:
dist_coeffs = self.dist_coeffs
if orig_size is None:
orig_size = self.orig_size
vertices = nr.projection(vertices, K, R, t, dist_coeffs, orig_size)
elif self.camera_mode == 'weak_projection':
if K is None:
K = self.K
if R is None:
R = self.R
if t is None:
t = self.t
vertices = weak_projection(vertices, K, R, t, orig_size)
# 正则化一下，让脸颊到鼻尖的向量和z轴负方向夹角小于90度 (a,b,c)*(0,0,-1)>0 -> c<0
# 另外将z值全部归到正轴上去
from MorphableModelFitting.models.face_model import FaceModelBFM
front_idx, back_idx_1, back_idx_2 = FaceModelBFM().keypoints[
30], FaceModelBFM().keypoints[0], FaceModelBFM().keypoints[16]
for i in range(len(vertices)):
back_z = (vertices[i, back_idx_1, 2] +
vertices[i, back_idx_2, 2]) / 2
if (vertices[i, front_idx, 2] - back_z) > 0:
vertices[i, :, 2] = -vertices[i, :, 2]
vertices[:, :, 2] = vertices[:, :, 2] - torch.min(
vertices[:, :, 2], dim=1)[0].unsqueeze(1) + 1
# lighting
if self.light_mode == "parallel":
textures = texture_from_point2faces(faces, textures) / 255
if self.fill_back:
faces = torch.cat(
(faces, faces[:, :,
list(reversed(range(faces.shape[-1])))]),
dim=1).detach()
textures = torch.cat(
(textures, textures.permute((0, 1, 4, 3, 2, 5))), dim=1)
faces_lighting = nr.vertices_to_faces(vertices, faces)
textures = nr.lighting(faces_lighting, textures,
self.light_intensity_ambient,
self.light_intensity_directional,
self.light_color_ambient,
self.light_color_directional,
self.light_direction)
elif self.light_mode == "SH":
point_buf = torch.from_numpy(self.facemodel.point_buf).long() - 1
point_norm = compute_point_norm(
vertices, faces[0][:,
list(reversed(range(faces.shape[-1])))],
point_buf)
# texture = texture_from_point2faces(triangles, texture).reshape((batch, -1, 3))
textures, _ = Illumination_SH(textures, point_norm, self.SH_coeff)
textures = texture_from_point2faces(faces, textures) / 255
if self.fill_back:
faces = torch.cat(
(faces, faces[:, :,
list(reversed(range(faces.shape[-1])))]),
dim=1).detach()
textures = torch.cat(
(textures, textures.permute((0, 1, 4, 3, 2, 5))), dim=1)
else:
return None
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
out = nr.rasterize_rgbad(faces, textures, self.image_size,
self.anti_aliasing, self.near, self.far,
self.rasterizer_eps, self.background_color)
return out['rgb'], out['depth'], out['alpha']