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_silhouettes(self, vertices, faces):
# fill back
# if self.fill_back:
# faces = torch.cat((faces, faces[:, :, list(reversed(list(range(faces.shape[-1]))))]), dim=1)
# 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':
vertices = nr.projection(vertices, self.P, self.dist_coeffs,
self.orig_size)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images_real = nr.rasterize_silhouettes(faces, self.image_size,
self.anti_aliasing).detach()
images_fake = self.forward_eff_render(faces, self.image_size,
self.sigma_val, self.sigma_num,
self.sigma_mul)
return images_fake, images_real
def render_depth(self, vertices, faces):
# fill back
if self.fill_back:
faces = torch.cat(
(faces, faces[:, :,
list(reversed(range(faces.shape[-1])))]),
dim=1).detach()
# 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':
vertices = nr.projection(vertices, self.P, self.dist_coeffs,
self.orig_size)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images = nr.rasterize_depth(faces, self.image_size, self.anti_aliasing)
return images
def project(self,
vertices,
K=None,
R=None,
t=None,
dist_coeffs=None,
orig_size=None):
# 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)
return vertices
def render_silhouettes(self, vertices, faces, K=None, R=None, t=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)
# 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)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images = nr.rasterize_silhouettes(faces, self.image_size, self.anti_aliasing)
return images
def look_at_verts(self, poses):
verts, joints, joints_36m = self.smpl_p.get_all_from_pose(poses)
verts_lookat = nr.look_at(verts, self.renderer.eye)
verts_proj = nr.perspective(verts_lookat)
verts_proj *= 1024/5
verts_proj += self.image_size/2
joints_lookat = nr.look_at(joints_36m, self.renderer.eye)
joints_proj = nr.perspective(joints_lookat)
joints_proj *= 1024/5
joints_proj += self.image_size/2
return verts_proj, joints_proj, verts, joints, joints_36m
def render(self, vertices, faces, textures):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
textures = cf.concat((textures, textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
# lighting
faces_lighting = neural_renderer.vertices_to_faces(vertices, faces)
textures = neural_renderer.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 = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye, self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices, angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
images = neural_renderer.rasterize(
faces, textures, self.image_size, self.anti_aliasing, self.near, self.far, self.rasterizer_eps,
self.background_color)
return images
def test_case1(self):
v_in = [1, 2, 10]
v_out = [np.sqrt(3) / 10, 2 * np.sqrt(3) / 10, 10]
vertices = np.array(v_in, 'float32')
vertices = vertices[None, None, :]
transformed = neural_renderer.perspective(vertices)
chainer.testing.assert_allclose(transformed.data.flatten(), np.array(v_out, 'float32'))
def render_normal(self, vertices, faces):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
# normal
faces_normal = nr.vertices_to_faces(vertices, faces)
(bs, nf) = faces_normal.shape[:2]
faces_normal = faces_normal.reshape((bs * nf, 3, 3))
v10 = faces_normal[:, 0] - faces_normal[:, 1]
v12 = faces_normal[:, 2] - faces_normal[:, 1]
normals = cf.normalize(nr.cross(v10, v12))
normals = normals.reshape((bs, nf, 3))
textures = normals[:, :, None, None, None, :]
textures = cf.tile(textures, (1, 1, 2, 2, 2, 1))
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = nr.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = nr.look(vertices, self.eye, self.camera_direction, self.up)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images = nr.rasterize(
faces, textures, self.image_size, self.anti_aliasing, self.near, self.far, self.rasterizer_eps,
self.background_color)
return images
def render_silhouettes(self, vertices, faces):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye,
self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices,
angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
# ==== TM changes ====
results_dict = neural_renderer.rasterize_silhouettes(
faces, self.image_size, self.anti_aliasing)
masks = results_dict['alpha']
face_index_map = results_dict['face_index_map']
weight_map = results_dict['weight_map']
sampling_weight_map = results_dict['sampling_weight_map']
# ==== Making another dictionary (just for clarity) ====
return_dict = dict()
return_dict['masks'] = masks
return_dict['face_index_map'] = face_index_map
return_dict['weight_map'] = weight_map
return_dict['sampling_weight_map'] = sampling_weight_map
return return_dict
def render(self, vertices, faces, textures):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
textures = cf.concat(
(textures, textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
# lighting
faces_lighting = neural_renderer.vertices_to_faces(vertices, faces)
textures = neural_renderer.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 = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye,
self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices,
angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
images = neural_renderer.rasterize(faces, textures, self.image_size,
self.anti_aliasing, self.near,
self.far, self.rasterizer_eps,
self.background_color)
return images
def test_case1(self):
v_in = [1, 2, 10]
v_out = [np.sqrt(3) / 10, 2 * np.sqrt(3) / 10, 10]
vertices = np.array(v_in, 'float32')
vertices = vertices[None, None, :]
transformed = neural_renderer.perspective(vertices)
chainer.testing.assert_allclose(transformed.data.flatten(),
np.array(v_out, 'float32'))
def render(self, vertices, faces, textures):
# 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)
# 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':
vertices = nr.projection(vertices, self.P, self.dist_coeffs,
self.orig_size)
elif self.camera_mode == 'projection_by_params':
vertices = nr.projection_by_params(vertices, self.camera_f,
self.camera_c, self.camera_rt,
self.camera_t, self.dist_coeffs,
self.orig_size)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images = nr.rasterize(faces, textures, self.image_size,
self.anti_aliasing, self.near, self.far,
self.rasterizer_eps, self.background_color)
return images
def project_points(self,
vertices,
K=None,
R=None,
t=None,
dist_coeffs=None,
orig_size=None):
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)
vertices[:, :,
0] = (orig_size / 2) * vertices[:, :, 0] + (orig_size / 2)
vertices[:, :,
1] = orig_size - ((orig_size / 2) * vertices[:, :, 1] +
(orig_size / 2))
return vertices
def render_color(self, vertices, faces, textures):
# 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)
# 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':
vertices = nr.projection(vertices, self.P, self.dist_coeffs,
self.orig_size)
# rasterization
# faces_ = nr.vertices_to_faces(vertices, faces)
# images_fake, dis = self.forward_eff_render_shading(faces_, textures, self.image_size, self.sigma_val, self.sigma_num, self.sigma_mul, self.gamma_val, self.gamma_num, self.gamma_mul, self.near, self.far)
faces = torch.cat((faces, faces[:, :, [2, 1, 0]]), dim=1).contiguous()
textures = torch.cat((textures, textures), dim=1).contiguous()
faces = nr.vertices_to_faces(vertices, faces)
images_real = nr.rasterize_rgba(faces, textures, self.image_size,
self.anti_aliasing)
# import pdb;pdb.set_trace()
images_real = torch.cat(
[images_real['rgb'], images_real['alpha'].unsqueeze(1)],
dim=1) # .detach()
# import pdb;pdb.set_trace()
return images_real
def project_points_perspective(self, verts, cams, depth=False):
verts = self.proj_fn(verts, cams, offset_z=self.offset_z)
verts = neural_renderer.look_at(verts, self.renderer.eye)
if self.renderer.perspective:
verts = neural_renderer.perspective(
verts, angle=self.renderer.viewing_angle)
if depth:
return verts[:, :, :2], verts[:, :, 2]
else:
return verts[:, :, :2]
def render_silhouettes(self, vertices, faces):
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye, self.camera_direction)
if self.perspective:
vertices = neural_renderer.perspective(vertices)
faces = neural_renderer.vertices_to_faces(vertices, faces)
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1)
images = neural_renderer.rasterize_silhouettes(faces, self.image_size, self.anti_aliasing)
return images
def transform_vertices(self, vertices, lights=None):
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.viewpoints)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.viewpoints,
self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices,
angle=self.viewing_angle)
return vertices
def render(self, vertices, faces, textures):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
textures = cf.concat(
(textures, textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
# lighting
faces_lighting = neural_renderer.vertices_to_faces(vertices, faces)
textures = neural_renderer.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 = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye,
self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices,
angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
# ==== TM changes ====
results_dict = neural_renderer.rasterize(faces, textures,
self.image_size,
self.anti_aliasing, self.near,
self.far, self.rasterizer_eps,
self.background_color)
images = results_dict['rgb']
face_index_map = results_dict['face_index_map']
weight_map = results_dict['weight_map']
sampling_weight_map = results_dict['sampling_weight_map']
# ==== Making another dictionary (just for clarity) ====
return_dict = dict()
return_dict['images'] = images
return_dict['face_index_map'] = face_index_map
return_dict['weight_map'] = weight_map
return_dict['sampling_weight_map'] = sampling_weight_map
return return_dict
def forward(self, vertices, faces, textures=None, mode=None):
'''
Implementation of forward rendering method
The old API is preserved for back-compatibility with the Chainer implementation
'''
_textures = textures
if mode not in [None, 'silhouettes', 'depth']:
raise ValueError("mode should be one of None, 'silhouettes' or 'depth'")
# fill back
if self.fill_back:
faces = torch.cat((faces, faces[:, :, list(reversed(range(faces.shape[-1])))]), dim=1).detach()
if _textures is not None:
_textures = torch.cat((_textures, textures.permute((0, 1, 4, 3, 2, 5))), dim=1)
if textures is not None:
# 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)
# projection
vertices = nr.projection(vertices, self.camera)
if self.camera.perspective:
vertices = nr.perspective(vertices, angle=self.camera.viewing_angle)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
if mode is None:
images = nr.rasterize(
faces, _textures, self.camera.image_size, self.anti_aliasing, self.camera.near, self.camera.far, self.rasterizer_eps,
self.background_color)
elif mode == 'silhouettes':
images = nr.rasterize_silhouettes(faces, self.camera.image_size, self.anti_aliasing)
elif mode == 'depth':
images = nr.rasterize_depth(faces, self.camera.image_size, self.anti_aliasing)
return images
def render(self, vertices, faces, textures):
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye, self.camera_direction)
if self.perspective:
vertices = neural_renderer.perspective(vertices)
faces = neural_renderer.vertices_to_faces(vertices, faces)
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1)
textures = cf.concat((textures, textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
textures = neural_renderer.lighting(
faces, textures, self.light_intensity_ambient, self.light_intensity_directional)
images = neural_renderer.rasterize(
faces, textures, self.image_size, self.anti_aliasing, self.near, self.far, self.rasterizer_eps,
self.background_color)
return images
def render_depth(self, vertices, faces):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye, self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices, angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
images = neural_renderer.rasterize_depth(faces, self.image_size, self.anti_aliasing)
return images
def render_silhouettes(self, vertices, faces):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = neural_renderer.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = neural_renderer.look(vertices, self.eye, self.camera_direction)
# perspective transformation
if self.perspective:
vertices = neural_renderer.perspective(vertices, angle=self.viewing_angle)
# rasterization
faces = neural_renderer.vertices_to_faces(vertices, faces)
images = rasterize_silhouettes(faces, self.image_size, self.anti_aliasing)
return images
def render_rgb(self,
vertices,
faces,
textures,
K=None,
R=None,
t=None,
dist_coeffs=None,
orig_size=None,
lighting=True):
# 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
if lighting and textures.size()[-1] == 3: # 3 channels (RGB)
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)
# 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)
if isinstance(self.background_color, (tuple, list)):
background = self.background_color
else:
assert (isinstance(self.background_color, (float, int)))
background = [
self.background_color for _ in range(textures.size()[-1])
]
images = nr.rasterize(faces, textures, self.image_size,
self.anti_aliasing, self.near, self.far,
self.rasterizer_eps, background)
return images
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']
def render(
self,
vertices,
faces,
textures,
K=None,
R=None,
t=None,
dist_coeffs=None,
orig_size=None,
detach_renders=False,
):
# 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
if not self.no_light:
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)
# 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":
vertices = self.project(vertices,
K=K,
R=R,
t=t,
dist_coeffs=dist_coeffs,
orig_size=orig_size)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
if detach_renders:
faces = faces.detach()
out = rasterize.rasterize_rgbad(
faces,
textures,
self.image_size,
self.anti_aliasing,
self.near,
self.far,
self.rasterizer_eps,
self.background_color,
)
return out
def test_case1(self):
vertices = torch.from_numpy(np.array([1,2,10], np.float32))
v_out = np.array([np.sqrt(3) / 10, 2 * np.sqrt(3) / 10, 10], np.float32)
vertices = vertices[None, None, :]
transformer = nr.perspective(vertices)
assert(np.allclose(transformer.data.squeeze().numpy(), v_out))