# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import cv2
import numpy as np
import torch
import torch.nn.functional as F
import trimesh
from PIL import Image
from .camera_utils import (
transform_pos,
get_mv_matrix,
get_orthographic_projection_matrix,
get_perspective_projection_matrix,
)
from .mesh_processor import meshVerticeInpaint
from .mesh_utils import load_mesh, save_mesh
def stride_from_shape(shape):
stride = [1]
for x in reversed(shape[1:]):
stride.append(stride[-1] * x)
return list(reversed(stride))
def scatter_add_nd_with_count(input, count, indices, values, weights=None):
# input: [..., C], D dimension + C channel
# count: [..., 1], D dimension
# indices: [N, D], long
# values: [N, C]
D = indices.shape[-1]
C = input.shape[-1]
size = input.shape[:-1]
stride = stride_from_shape(size)
assert len(size) == D
input = input.view(-1, C) # [HW, C]
count = count.view(-1, 1)
flatten_indices = (indices * torch.tensor(stride,
dtype=torch.long, device=indices.device)).sum(-1) # [N]
if weights is None:
weights = torch.ones_like(values[..., :1])
input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
count.scatter_add_(0, flatten_indices.unsqueeze(1), weights)
return input.view(*size, C), count.view(*size, 1)
def linear_grid_put_2d(H, W, coords, values, return_count=False):
# coords: [N, 2], float in [0, 1]
# values: [N, C]
C = values.shape[-1]
indices = coords * torch.tensor(
[H - 1, W - 1], dtype=torch.float32, device=coords.device
)
indices_00 = indices.floor().long() # [N, 2]
indices_00[:, 0].clamp_(0, H - 2)
indices_00[:, 1].clamp_(0, W - 2)
indices_01 = indices_00 + torch.tensor(
[0, 1], dtype=torch.long, device=indices.device
)
indices_10 = indices_00 + torch.tensor(
[1, 0], dtype=torch.long, device=indices.device
)
indices_11 = indices_00 + torch.tensor(
[1, 1], dtype=torch.long, device=indices.device
)
h = indices[..., 0] - indices_00[..., 0].float()
w = indices[..., 1] - indices_00[..., 1].float()
w_00 = (1 - h) * (1 - w)
w_01 = (1 - h) * w
w_10 = h * (1 - w)
w_11 = h * w
result = torch.zeros(H, W, C, device=values.device,
dtype=values.dtype) # [H, W, C]
count = torch.zeros(H, W, 1, device=values.device,
dtype=values.dtype) # [H, W, 1]
weights = torch.ones_like(values[..., :1]) # [N, 1]
result, count = scatter_add_nd_with_count(
result, count, indices_00, values * w_00.unsqueeze(1), weights * w_00.unsqueeze(1))
result, count = scatter_add_nd_with_count(
result, count, indices_01, values * w_01.unsqueeze(1), weights * w_01.unsqueeze(1))
result, count = scatter_add_nd_with_count(
result, count, indices_10, values * w_10.unsqueeze(1), weights * w_10.unsqueeze(1))
result, count = scatter_add_nd_with_count(
result, count, indices_11, values * w_11.unsqueeze(1), weights * w_11.unsqueeze(1))
if return_count:
return result, count
mask = (count.squeeze(-1) > 0)
result[mask] = result[mask] / count[mask].repeat(1, C)
return result
class MeshRender():
def __init__(
self,
camera_distance=1.45, camera_type='orth',
default_resolution=1024, texture_size=1024,
use_antialias=True, max_mip_level=None, filter_mode='linear',
bake_mode='linear', raster_mode='cr', device='cuda'):
self.device = device
self.set_default_render_resolution(default_resolution)
self.set_default_texture_resolution(texture_size)
self.camera_distance = camera_distance
self.use_antialias = use_antialias
self.max_mip_level = max_mip_level
self.filter_mode = filter_mode
self.bake_angle_thres = 75
self.bake_unreliable_kernel_size = int(
(2 / 512) * max(self.default_resolution[0], self.default_resolution[1]))
self.bake_mode = bake_mode
self.raster_mode = raster_mode
if self.raster_mode == 'cr':
import custom_rasterizer as cr
self.raster = cr
else:
raise f'No raster named {self.raster_mode}'
if camera_type == 'orth':
self.ortho_scale = 1.2
self.camera_proj_mat = get_orthographic_projection_matrix(
left=-self.ortho_scale * 0.5, right=self.ortho_scale * 0.5,
bottom=-self.ortho_scale * 0.5, top=self.ortho_scale * 0.5,
near=0.1, far=100
)
elif camera_type == 'perspective':
self.camera_proj_mat = get_perspective_projection_matrix(
49.13, self.default_resolution[1] / self.default_resolution[0],
0.01, 100.0
)
else:
raise f'No camera type {camera_type}'
def raster_rasterize(self, pos, tri, resolution, ranges=None, grad_db=True):
if self.raster_mode == 'cr':
rast_out_db = None
if pos.dim() == 2:
pos = pos.unsqueeze(0)
findices, barycentric = self.raster.rasterize(pos, tri, resolution)
rast_out = torch.cat((barycentric, findices.unsqueeze(-1)), dim=-1)
rast_out = rast_out.unsqueeze(0)
else:
raise f'No raster named {self.raster_mode}'
return rast_out, rast_out_db
def raster_interpolate(self, uv, rast_out, uv_idx, rast_db=None, diff_attrs=None):
if self.raster_mode == 'cr':
textd = None
barycentric = rast_out[0, ..., :-1]
findices = rast_out[0, ..., -1]
if uv.dim() == 2:
uv = uv.unsqueeze(0)
textc = self.raster.interpolate(uv, findices, barycentric, uv_idx)
else:
raise f'No raster named {self.raster_mode}'
return textc, textd
def raster_texture(self, tex, uv, uv_da=None, mip_level_bias=None, mip=None, filter_mode='auto',
boundary_mode='wrap', max_mip_level=None):
if self.raster_mode == 'cr':
raise f'Texture is not implemented in cr'
else:
raise f'No raster named {self.raster_mode}'
return color
def raster_antialias(self, color, rast, pos, tri, topology_hash=None, pos_gradient_boost=1.0):
if self.raster_mode == 'cr':
# Antialias has not been supported yet
color = color
else:
raise f'No raster named {self.raster_mode}'
return color
def load_mesh(
self,
mesh,
scale_factor=1.15,
auto_center=True,
):
vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data = load_mesh(mesh)
self.mesh_copy = mesh
self.set_mesh(vtx_pos, pos_idx,
vtx_uv=vtx_uv, uv_idx=uv_idx,
scale_factor=scale_factor, auto_center=auto_center
)
if texture_data is not None:
self.set_texture(texture_data)
def save_mesh(self):
texture_data = self.get_texture()
texture_data = Image.fromarray((texture_data * 255).astype(np.uint8))
return save_mesh(self.mesh_copy, texture_data)
def set_mesh(
self,
vtx_pos, pos_idx,
vtx_uv=None, uv_idx=None,
scale_factor=1.15, auto_center=True
):
self.vtx_pos = torch.from_numpy(vtx_pos).to(self.device).float()
self.pos_idx = torch.from_numpy(pos_idx).to(self.device).to(torch.int)
if (vtx_uv is not None) and (uv_idx is not None):
self.vtx_uv = torch.from_numpy(vtx_uv).to(self.device).float()
self.uv_idx = torch.from_numpy(uv_idx).to(self.device).to(torch.int)
else:
self.vtx_uv = None
self.uv_idx = None
self.vtx_pos[:, [0, 1]] = -self.vtx_pos[:, [0, 1]]
self.vtx_pos[:, [1, 2]] = self.vtx_pos[:, [2, 1]]
if (vtx_uv is not None) and (uv_idx is not None):
self.vtx_uv[:, 1] = 1.0 - self.vtx_uv[:, 1]
if auto_center:
max_bb = (self.vtx_pos - 0).max(0)[0]
min_bb = (self.vtx_pos - 0).min(0)[0]
center = (max_bb + min_bb) / 2
scale = torch.norm(self.vtx_pos - center, dim=1).max() * 2.0
self.vtx_pos = (self.vtx_pos - center) * \
(scale_factor / float(scale))
self.scale_factor = scale_factor
def set_texture(self, tex):
if isinstance(tex, np.ndarray):
tex = Image.fromarray((tex * 255).astype(np.uint8))
elif isinstance(tex, torch.Tensor):
tex = tex.cpu().numpy()
tex = Image.fromarray((tex * 255).astype(np.uint8))
tex = tex.resize(self.texture_size).convert('RGB')
tex = np.array(tex) / 255.0
self.tex = torch.from_numpy(tex).to(self.device)
self.tex = self.tex.float()
def set_default_render_resolution(self, default_resolution):
if isinstance(default_resolution, int):
default_resolution = (default_resolution, default_resolution)
self.default_resolution = default_resolution
def set_default_texture_resolution(self, texture_size):
if isinstance(texture_size, int):
texture_size = (texture_size, texture_size)
self.texture_size = texture_size
def get_mesh(self):
vtx_pos = self.vtx_pos.cpu().numpy()
pos_idx = self.pos_idx.cpu().numpy()
vtx_uv = self.vtx_uv.cpu().numpy()
uv_idx = self.uv_idx.cpu().numpy()
# 坐标变换的逆变换
vtx_pos[:, [1, 2]] = vtx_pos[:, [2, 1]]
vtx_pos[:, [0, 1]] = -vtx_pos[:, [0, 1]]
vtx_uv[:, 1] = 1.0 - vtx_uv[:, 1]
return vtx_pos, pos_idx, vtx_uv, uv_idx
def get_texture(self):
return self.tex.cpu().numpy()
def to(self, device):
self.device = device
for attr_name in dir(self):
attr_value = getattr(self, attr_name)
if isinstance(attr_value, torch.Tensor):
setattr(self, attr_name, attr_value.to(self.device))
def color_rgb_to_srgb(self, image):
if isinstance(image, Image.Image):
image_rgb = torch.tesnor(
np.array(image) /
255.0).float().to(
self.device)
elif isinstance(image, np.ndarray):
image_rgb = torch.tensor(image).float()
else:
image_rgb = image.to(self.device)
image_srgb = torch.where(
image_rgb <= 0.0031308,
12.92 * image_rgb,
1.055 * torch.pow(image_rgb, 1 / 2.4) - 0.055
)
if isinstance(image, Image.Image):
image_srgb = Image.fromarray(
(image_srgb.cpu().numpy() *
255).astype(
np.uint8))
elif isinstance(image, np.ndarray):
image_srgb = image_srgb.cpu().numpy()
else:
image_srgb = image_srgb.to(image.device)
return image_srgb
def _render(
self,
glctx,
mvp,
pos,
pos_idx,
uv,
uv_idx,
tex,
resolution,
max_mip_level,
keep_alpha,
filter_mode
):
pos_clip = transform_pos(mvp, pos)
if isinstance(resolution, (int, float)):
resolution = [resolution, resolution]
rast_out, rast_out_db = self.raster_rasterize(
glctx, pos_clip, pos_idx, resolution=resolution)
tex = tex.contiguous()
if filter_mode == 'linear-mipmap-linear':
texc, texd = self.raster_interpolate(
uv[None, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all')
color = self.raster_texture(
tex[None, ...], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level)
else:
texc, _ = self.raster_interpolate(uv[None, ...], rast_out, uv_idx)
color = self.raster_texture(tex[None, ...], texc, filter_mode=filter_mode)
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
color = color * visible_mask # Mask out background.
if self.use_antialias:
color = self.raster_antialias(color, rast_out, pos_clip, pos_idx)
if keep_alpha:
color = torch.cat([color, visible_mask], dim=-1)
return color[0, ...]
def render(
self,
elev,
azim,
camera_distance=None,
center=None,
resolution=None,
tex=None,
keep_alpha=True,
bgcolor=None,
filter_mode=None,
return_type='th'
):
proj = self.camera_proj_mat
r_mv = get_mv_matrix(
elev=elev,
azim=azim,
camera_distance=self.camera_distance if camera_distance is None else camera_distance,
center=center)
r_mvp = np.matmul(proj, r_mv).astype(np.float32)
if tex is not None:
if isinstance(tex, Image.Image):
tex = torch.tensor(np.array(tex) / 255.0)
elif isinstance(tex, np.ndarray):
tex = torch.tensor(tex)
if tex.dim() == 2:
tex = tex.unsqueeze(-1)
tex = tex.float().to(self.device)
image = self._render(r_mvp, self.vtx_pos, self.pos_idx, self.vtx_uv, self.uv_idx,
self.tex if tex is None else tex,
self.default_resolution if resolution is None else resolution,
self.max_mip_level, True, filter_mode if filter_mode else self.filter_mode)
mask = (image[..., [-1]] == 1).float()
if bgcolor is None:
bgcolor = [0 for _ in range(image.shape[-1] - 1)]
image = image * mask + (1 - mask) * \
torch.tensor(bgcolor + [0]).to(self.device)
if keep_alpha == False:
image = image[..., :-1]
if return_type == 'np':
image = image.cpu().numpy()
elif return_type == 'pl':
image = image.squeeze(-1).cpu().numpy() * 255
image = Image.fromarray(image.astype(np.uint8))
return image
def render_normal(
self,
elev,
azim,
camera_distance=None,
center=None,
resolution=None,
bg_color=[1, 1, 1],
use_abs_coor=False,
normalize_rgb=True,
return_type='th'
):
pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
if resolution is None:
resolution = self.default_resolution
if isinstance(resolution, (int, float)):
resolution = [resolution, resolution]
rast_out, rast_out_db = self.raster_rasterize(
pos_clip, self.pos_idx, resolution=resolution)
if use_abs_coor:
mesh_triangles = self.vtx_pos[self.pos_idx[:, :3], :]
else:
pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
mesh_triangles = pos_camera[self.pos_idx[:, :3], :]
face_normals = F.normalize(
torch.cross(mesh_triangles[:,
1,
:] - mesh_triangles[:,
0,
:],
mesh_triangles[:,
2,
:] - mesh_triangles[:,
0,
:],
dim=-1),
dim=-1)
vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0],
faces=self.pos_idx.cpu(),
face_normals=face_normals.cpu(), )
vertex_normals = torch.from_numpy(
vertex_normals).float().to(self.device).contiguous()
# Interpolate normal values across the rasterized pixels
normal, _ = self.raster_interpolate(
vertex_normals[None, ...], rast_out, self.pos_idx)
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
normal = normal * visible_mask + \
torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
visible_mask) # Mask out background.
if normalize_rgb:
normal = (normal + 1) * 0.5
if self.use_antialias:
normal = self.raster_antialias(normal, rast_out, pos_clip, self.pos_idx)
image = normal[0, ...]
if return_type == 'np':
image = image.cpu().numpy()
elif return_type == 'pl':
image = image.cpu().numpy() * 255
image = Image.fromarray(image.astype(np.uint8))
return image
def convert_normal_map(self, image):
# blue is front, red is left, green is top
if isinstance(image, Image.Image):
image = np.array(image)
mask = (image == [255, 255, 255]).all(axis=-1)
image = (image / 255.0) * 2.0 - 1.0
image[..., [1]] = -image[..., [1]]
image[..., [1, 2]] = image[..., [2, 1]]
image[..., [0]] = -image[..., [0]]
image = (image + 1.0) * 0.5
image = (image * 255).astype(np.uint8)
image[mask] = [127, 127, 255]
return Image.fromarray(image)
def get_pos_from_mvp(self, elev, azim, camera_distance, center):
proj = self.camera_proj_mat
r_mv = get_mv_matrix(
elev=elev,
azim=azim,
camera_distance=self.camera_distance if camera_distance is None else camera_distance,
center=center)
pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True)
pos_clip = transform_pos(proj, pos_camera)
return pos_camera, pos_clip
def render_depth(
self,
elev,
azim,
camera_distance=None,
center=None,
resolution=None,
return_type='th'
):
pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
if resolution is None:
resolution = self.default_resolution
if isinstance(resolution, (int, float)):
resolution = [resolution, resolution]
rast_out, rast_out_db = self.raster_rasterize(
pos_clip, self.pos_idx, resolution=resolution)
pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous()
# Interpolate depth values across the rasterized pixels
depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx)
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
depth_max, depth_min = depth[visible_mask >
0].max(), depth[visible_mask > 0].min()
depth = (depth - depth_min) / (depth_max - depth_min)
depth = depth * visible_mask # Mask out background.
if self.use_antialias:
depth = self.raster_antialias(depth, rast_out, pos_clip, self.pos_idx)
image = depth[0, ...]
if return_type == 'np':
image = image.cpu().numpy()
elif return_type == 'pl':
image = image.squeeze(-1).cpu().numpy() * 255
image = Image.fromarray(image.astype(np.uint8))
return image
def render_position(self, elev, azim, camera_distance=None, center=None,
resolution=None, bg_color=[1, 1, 1], return_type='th'):
pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
if resolution is None:
resolution = self.default_resolution
if isinstance(resolution, (int, float)):
resolution = [resolution, resolution]
rast_out, rast_out_db = self.raster_rasterize(
pos_clip, self.pos_idx, resolution=resolution)
tex_position = 0.5 - self.vtx_pos[:, :3] / self.scale_factor
tex_position = tex_position.contiguous()
# Interpolate depth values across the rasterized pixels
position, _ = self.raster_interpolate(
tex_position[None, ...], rast_out, self.pos_idx)
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
position = position * visible_mask + \
torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
visible_mask) # Mask out background.
if self.use_antialias:
position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx)
image = position[0, ...]
if return_type == 'np':
image = image.cpu().numpy()
elif return_type == 'pl':
image = image.squeeze(-1).cpu().numpy() * 255
image = Image.fromarray(image.astype(np.uint8))
return image
def render_uvpos(self, return_type='th'):
image = self.uv_feature_map(self.vtx_pos * 0.5 + 0.5)
if return_type == 'np':
image = image.cpu().numpy()
elif return_type == 'pl':
image = image.cpu().numpy() * 255
image = Image.fromarray(image.astype(np.uint8))
return image
def uv_feature_map(self, vert_feat, bg=None):
vtx_uv = self.vtx_uv * 2 - 1.0
vtx_uv = torch.cat(
[vtx_uv, torch.zeros_like(self.vtx_uv)], dim=1).unsqueeze(0)
vtx_uv[..., -1] = 1
uv_idx = self.uv_idx
rast_out, rast_out_db = self.raster_rasterize(
vtx_uv, uv_idx, resolution=self.texture_size)
feat_map, _ = self.raster_interpolate(vert_feat[None, ...], rast_out, uv_idx)
feat_map = feat_map[0, ...]
if bg is not None:
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...]
feat_map[visible_mask == 0] = bg
return feat_map
def render_sketch_from_geometry(self, normal_image, depth_image):
normal_image_np = normal_image.cpu().numpy()
depth_image_np = depth_image.cpu().numpy()
normal_image_np = (normal_image_np * 255).astype(np.uint8)
depth_image_np = (depth_image_np * 255).astype(np.uint8)
normal_image_np = cv2.cvtColor(normal_image_np, cv2.COLOR_RGB2GRAY)
normal_edges = cv2.Canny(normal_image_np, 80, 150)
depth_edges = cv2.Canny(depth_image_np, 30, 80)
combined_edges = np.maximum(normal_edges, depth_edges)
sketch_image = torch.from_numpy(combined_edges).to(
normal_image.device).float() / 255.0
sketch_image = sketch_image.unsqueeze(-1)
return sketch_image
def render_sketch_from_depth(self, depth_image):
depth_image_np = depth_image.cpu().numpy()
depth_image_np = (depth_image_np * 255).astype(np.uint8)
depth_edges = cv2.Canny(depth_image_np, 30, 80)
combined_edges = depth_edges
sketch_image = torch.from_numpy(combined_edges).to(
depth_image.device).float() / 255.0
sketch_image = sketch_image.unsqueeze(-1)
return sketch_image
def back_project(self, image, elev, azim,
camera_distance=None, center=None, method=None):
if isinstance(image, Image.Image):
image = torch.tensor(np.array(image) / 255.0)
elif isinstance(image, np.ndarray):
image = torch.tensor(image)
if image.dim() == 2:
image = image.unsqueeze(-1)
image = image.float().to(self.device)
resolution = image.shape[:2]
channel = image.shape[-1]
texture = torch.zeros(self.texture_size + (channel,)).to(self.device)
cos_map = torch.zeros(self.texture_size + (1,)).to(self.device)
proj = self.camera_proj_mat
r_mv = get_mv_matrix(
elev=elev,
azim=azim,
camera_distance=self.camera_distance if camera_distance is None else camera_distance,
center=center)
pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True)
pos_clip = transform_pos(proj, pos_camera)
pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
v0 = pos_camera[self.pos_idx[:, 0], :]
v1 = pos_camera[self.pos_idx[:, 1], :]
v2 = pos_camera[self.pos_idx[:, 2], :]
face_normals = F.normalize(
torch.cross(
v1 - v0,
v2 - v0,
dim=-1),
dim=-1)
vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0],
faces=self.pos_idx.cpu(),
face_normals=face_normals.cpu(), )
vertex_normals = torch.from_numpy(
vertex_normals).float().to(self.device).contiguous()
tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous()
rast_out, rast_out_db = self.raster_rasterize(
pos_clip, self.pos_idx, resolution=resolution)
visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...]
normal, _ = self.raster_interpolate(
vertex_normals[None, ...], rast_out, self.pos_idx)
normal = normal[0, ...]
uv, _ = self.raster_interpolate(self.vtx_uv[None, ...], rast_out, self.uv_idx)
depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx)
depth = depth[0, ...]
depth_max, depth_min = depth[visible_mask >
0].max(), depth[visible_mask > 0].min()
depth_normalized = (depth - depth_min) / (depth_max - depth_min)
depth_image = depth_normalized * visible_mask # Mask out background.
sketch_image = self.render_sketch_from_depth(depth_image)
lookat = torch.tensor([[0, 0, -1]], device=self.device)
cos_image = torch.nn.functional.cosine_similarity(
lookat, normal.view(-1, 3))
cos_image = cos_image.view(normal.shape[0], normal.shape[1], 1)
cos_thres = np.cos(self.bake_angle_thres / 180 * np.pi)
cos_image[cos_image < cos_thres] = 0
# shrink
kernel_size = self.bake_unreliable_kernel_size * 2 + 1
kernel = torch.ones(
(1, 1, kernel_size, kernel_size), dtype=torch.float32).to(
sketch_image.device)
visible_mask = visible_mask.permute(2, 0, 1).unsqueeze(0).float()
visible_mask = F.conv2d(
1.0 - visible_mask,
kernel,
padding=kernel_size // 2)
visible_mask = 1.0 - (visible_mask > 0).float() # 二值化
visible_mask = visible_mask.squeeze(0).permute(1, 2, 0)
sketch_image = sketch_image.permute(2, 0, 1).unsqueeze(0)
sketch_image = F.conv2d(sketch_image, kernel, padding=kernel_size // 2)
sketch_image = (sketch_image > 0).float() # 二值化
sketch_image = sketch_image.squeeze(0).permute(1, 2, 0)
visible_mask = visible_mask * (sketch_image < 0.5)
cos_image[visible_mask == 0] = 0
method = self.bake_mode if method is None else method
if method == 'linear':
proj_mask = (visible_mask != 0).view(-1)
uv = uv.squeeze(0).contiguous().view(-1, 2)[proj_mask]
image = image.squeeze(0).contiguous().view(-1, channel)[proj_mask]
cos_image = cos_image.contiguous().view(-1, 1)[proj_mask]
sketch_image = sketch_image.contiguous().view(-1, 1)[proj_mask]
texture = linear_grid_put_2d(
self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], image)
cos_map = linear_grid_put_2d(
self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], cos_image)
boundary_map = linear_grid_put_2d(
self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], sketch_image)
else:
raise f'No bake mode {method}'
return texture, cos_map, boundary_map
def bake_texture(self, colors, elevs, azims,
camera_distance=None, center=None, exp=6, weights=None):
for i in range(len(colors)):
if isinstance(colors[i], Image.Image):
colors[i] = torch.tensor(
np.array(
colors[i]) / 255.0,
device=self.device).float()
if weights is None:
weights = [1.0 for _ in range(colors)]
textures = []
cos_maps = []
for color, elev, azim, weight in zip(colors, elevs, azims, weights):
texture, cos_map, _ = self.back_project(
color, elev, azim, camera_distance, center)
cos_map = weight * (cos_map ** exp)
textures.append(texture)
cos_maps.append(cos_map)
texture_merge, trust_map_merge = self.fast_bake_texture(
textures, cos_maps)
return texture_merge, trust_map_merge
@torch.no_grad()
def fast_bake_texture(self, textures, cos_maps):
channel = textures[0].shape[-1]
texture_merge = torch.zeros(
self.texture_size + (channel,)).to(self.device)
trust_map_merge = torch.zeros(self.texture_size + (1,)).to(self.device)
for texture, cos_map in zip(textures, cos_maps):
view_sum = (cos_map > 0).sum()
painted_sum = ((cos_map > 0) * (trust_map_merge > 0)).sum()
if painted_sum / view_sum > 0.99:
continue
texture_merge += texture * cos_map
trust_map_merge += cos_map
texture_merge = texture_merge / torch.clamp(trust_map_merge, min=1E-8)
return texture_merge, trust_map_merge > 1E-8
def uv_inpaint(self, texture, mask):
if isinstance(texture, torch.Tensor):
texture_np = texture.cpu().numpy()
elif isinstance(texture, np.ndarray):
texture_np = texture
elif isinstance(texture, Image.Image):
texture_np = np.array(texture) / 255.0
vtx_pos, pos_idx, vtx_uv, uv_idx = self.get_mesh()
texture_np, mask = meshVerticeInpaint(
texture_np, mask, vtx_pos, vtx_uv, pos_idx, uv_idx)
texture_np = cv2.inpaint(
(texture_np *
255).astype(
np.uint8),
255 -
mask,
3,
cv2.INPAINT_NS)
return texture_np